diff --git a/authors.html b/authors.html
index a682320b..a0593f03 100644
--- a/authors.html
+++ b/authors.html
@@ -74,6 +74,10 @@ <h2>Authors</h2>
           <p><strong>Mario Krapp</strong>. Contributor. 
           </p>
         </li>
+        <li>
+          <p><strong>Andrea V. Pozzi</strong>. Contributor. 
+          </p>
+        </li>
         <li>
           <p><strong>Andrea Manica</strong>. Author, maintainer. 
           </p>
diff --git a/pkgdown.yml b/pkgdown.yml
index 138f416a..5e2e38c6 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   a1_available_datasets: a1_available_datasets.html
   a2_custom_datasets: a2_custom_datasets.html
   a3_pastclim_present_and_future: a3_pastclim_present_and_future.html
-last_built: 2024-06-19T07:35Z
+last_built: 2024-06-19T09:02Z
 urls:
   reference: https://evolecolgroup.github.io/pastclim/reference
   article: https://evolecolgroup.github.io/pastclim/articles
diff --git a/reference/pastclim-package.html b/reference/pastclim-package.html
index bbfdaef2..5230a734 100644
--- a/reference/pastclim-package.html
+++ b/reference/pastclim-package.html
@@ -82,6 +82,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </ul><p>Other contributors:</p><ul><li><p>Emily Y. Hallet [contributor]</p></li>
 <li><p>Robert Beyer [contributor]</p></li>
 <li><p>Mario Krapp [contributor]</p></li>
+<li><p>Andrea V. Pozzi [contributor]</p></li>
 </ul></div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/search.json b/search.json
index 5e4477c9..c3f3265d 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://evolecolgroup.github.io/pastclim/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://www.contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to pastclim","title":"Contributing to pastclim","text":"document outlines contribute development pastclim. package maintained voluntary basis, help always appreciated.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"the-basic-process-of-contributing","dir":"","previous_headings":"","what":"The basic process of contributing","title":"Contributing to pastclim","text":"Development work pastclim occurs dev branch. , want propose changes, work dev. Start forking project onto github repository, make changes directly fork (either dev branch, make custom branch). updating documentation checking tests pass (see ), start Pull Request. proposed changes reviewed, might asked fix/improve code. can iterative process, requiring rounds revision depending complexity code. Functions documented using roxygen. changes affects documentation , rebuild . root directory package, simply run: implemented new functionality, patched bug, consider whether add appropriate unit test. pastclim uses testthat framework unit tests. make sure tests work : Finally, submit push request, check changes don’t break build. can check, also builds vignette runs tests.: Make sure resolved warnings notes raised devtools::check()! followed 3 steps, ready make Pull Request. changes go automatic continuous integration, check impact changes multiple platforms. everything goes well, see green tick submission.","code":"devtools::document() devtools::test() devtools::check()"},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to pastclim","text":"spot typos, spelling mistakes, grammatical errors documentation, fix directly file describes function. .R file R directory, .Rd file man directory. .Rd files automatically generated roxygen2 edited hand. recommend study first roxygen2 comments work.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"functional-changes","dir":"","previous_headings":"","what":"Functional changes","title":"Contributing to pastclim","text":"want make change impacts functioning pastclim, ’s good idea first file issue explaining mind. change meant fix bug, add minimal reprex. good reprex also perfect starting point writing unit test, accompany functional change code. Unit tests also essential fixing bugs, can demonstrate fix work, prevent future changes undoing work. unit testing, use testthat; find tests tests, file dedicated function, following convention test_my_function.R naming files. creating tests, try make use built-datasets, rather adding data files package. Ideally, body Pull Request include phrase Fixes #issue-number, issue_number number Github. way, Pull Request automatically linked issue, issue closed Pull Request merged . user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html. continuous integration checks Pull Request reduce test coverage.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Functional changes","what":"Code style","title":"Contributing to pastclim","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. Lots commenting code helps mantainability; , doubt, always add explanation new code.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to pastclim","text":"Please note tidyverse project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC BY 4.0","title":"CC BY 4.0","text":"Attribution 4.0 International ======================================================================= Creative Commons Corporation (“Creative Commons”) law firm provide legal services legal advice. Distribution Creative Commons public licenses create lawyer-client relationship. Creative Commons makes licenses related information available “-” basis. 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Creative Commons may contacted creativecommons.org.","code":"Considerations for licensors: Our public licenses are  intended for use by those authorized to give the public  permission to use material in ways otherwise restricted by  copyright and certain other rights. Our licenses are  irrevocable. Licensors should read and understand the terms  and conditions of the license they choose before applying it.  Licensors should also secure all rights necessary before  applying our licenses so that the public can reuse the  material as expected. Licensors should clearly mark any  material not subject to the license. This includes other CC-  licensed material, or material used under an exception or  limitation to copyright. More considerations for licensors: wiki.creativecommons.org/Considerations_for_licensors   Considerations for the public: By using one of our public  licenses, a licensor grants the public permission to use the  licensed material under specified terms and conditions. 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More considerations  for the public:  wiki.creativecommons.org/Considerations_for_licensees"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"install-the-library","dir":"Articles","previous_headings":"","what":"Install the library","title":"pastclim overview","text":"pastclim CRAN, easiest way install : want latest development version, can get GitHub. install GitHub, need use devtools; haven’t done already, install CRAN install.packages(\"devtools\"). Also, note dev version pastclim tracks changes dev version terra, need upgrade : dedicated website, can find Articles giving step--step overview package, cheatsheet. also version site updated dev version (top left, version number red, format x.x.x.9xxx, indicating development version). want build vignette directly R installing pastclim GitHub, can : read directly R : Depending operating system use, might need additional packages build vignette.","code":"install.packages(\"pastclim\") install.packages(\"terra\", repos = \"https://rspatial.r-universe.dev\") devtools::install_github(\"EvolEcolGroup/pastclim\", ref = \"dev\") devtools::install_github(\"EvolEcolGroup/pastclim\", ref = \"dev\", build_vignettes = TRUE) vignette(\"pastclim_overview\", package = \"pastclim\")"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"download-the-data","dir":"Articles","previous_headings":"","what":"Download the data","title":"pastclim overview","text":"need download climatic reconstructions able real work pastclim. Pastclim currently includes data Beyer et al 2020 (Beyer2020, reconstruction climate based HadCM3 model last 120k years), Krapp et al 2021 (Krapp2021, covers last 800k years statistical emulator HadCM3), Barreto et al 2023 (Barreto2023), covering last 5M years using PALEO-PGEM emulator), CHELSA-TraCE21k, covering last 21k years high spatial temporal resolution (CHELSA_trace21k_0.5m_vsi), HYDE3.3 database land use reconstructions last 10k years (HYDE_3.3_baseline) paleoclim dataset, selected time steps last 120k years various resolutions (paleoclim_RESm), WorldClim CHELSA data (WorldClim_2.1_ CHELSA_2.1_, present, future projections number models emission scenarios). information datasets can found , using help page given dataset. detailed instructions use WorldClim CHELSA datasets present future reconstructions can found article also instructions build use custom datasets, need familiarity handling netcdf files. list datasets available can obtained typing: Please aware using dataset made available pastclim require cite pastclim well original publication presenting dataset. reference cite pastclim can obtained typing reference associated dataset choice (case “Beyer2020”) displayed together general information dataset command: datasets available pastclim, functions help download data choose variables. start pastclim first time, need set path reconstructions stored using set_data_path. default, package data path used: Press 1 happy offered choices, pastclim remember data path future sessions. Note data path look different example, depends user name operating system. prefer using custom path (e.g. “~/my_reconstructions”), can set : package includes small dataset, Example, use vignette suitable running real analyses; real datasets large (100s Mb Gb), need specify want download (see ). Let us start inspecting Example dataset. can get list variables available dataset : available time steps can obtained : can also query resolution dataset: , *Example” dataset resolution 1x1 degree. Beyer2020 Krapp2021, can get list available variables dataset : Note , default, annual variables shown. see available monthly variables, simply use: monthly variables, months coded “_xx” end variable names; e.g. “temperature_02” mean monthly temperature February. thorough description variable (including units) can obtained : able get available time steps download dataset. pastclim offers interface download necessary files data path. inspect datasets variables already downloaded data path, can use: Let’s now download bio01 bio05 Beyer2020 dataset (operation might take several minutes, datasets large; R pause download complete): Note multiple variables can packed together single file, get_downloaded_datasets() might list variables ones chose download (depends dataset). upgrading pastclim, new version various datasets might become available. make previously downloaded datasets obsolete, might suddenly told pastclim variables re-downloaded. can lead accumulation old datasets data path. function clean_data_path() can used delete old files longer needed.","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_available_datasets() #> Barreto2023, Beyer2020, CHELSA_trace21k_1.0_0.5m_vsi, Example, HYDE_3.3_baseline, Krapp2021, paleoclim_1.0_10m, paleoclim_1.0_2.5m, paleoclim_1.0_5m #> for present day reconstructions, use \"WorldClim_2.1_RESm\" or \"CHELSA_2.4_RESm\" where RES is an available resolution. #> for future predictions, use \"WorldClim_2.1_GCM_SSP_RESm\" or \"CHELSA_2.1_GCM_SSP_RESm\", where GCM is the GCM model, SSP is the Shared Socio-economic Pathways scenario. #> use help(\"WorldClim_2.1\") or help(\"CHELSA_2.1\") for a list of available options citation(\"pastclim\") #> To cite pastclim in publications use: #>  #>   Leonardi M, Hallet EY, Beyer R, Krapp M, Manica A (2023). \"pastclim #>   1.2: an R package to easily access and use paleoclimatic #>   reconstructions.\" _Ecography_, *2023*, e06481. doi:10.1111/ecog.06481 #>   <https://doi.org/10.1111/ecog.06481>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Article{pastclim-article, #>     title = {pastclim 1.2: an R package to easily access and use paleoclimatic reconstructions}, #>     author = {Michela Leonardi and Emily Y. Hallet and Robert Beyer and Mario Krapp and Andrea Manica}, #>     journal = {Ecography}, #>     year = {2023}, #>     volume = {2023}, #>     pages = {e06481}, #>     publisher = {Wiley}, #>     doi = {10.1111/ecog.06481}, #>   } help(\"Beyer2020\") #> Documentation for the Beyer2020 dataset #>  #> Description: #>  #>      This dataset covers the last 120k years, at intervals of 1/2 k #>      years, and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial #>      climate, bioclimate and vegetation for the last 120,000 years. Sci #>      Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 #>      <https://doi.org/10.1038/s41597-020-0552-1> #>  #>      The version included in 'pastclim' has the ice sheets masked, as #>      well as internal seas (Black and Caspian Sea) removed. The latter #>      are based on: #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4278> #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4282> #>  #>      As there is no reconstruction of their depth through time, modern #>      outlines were used for all time steps. #>  #>      Also, for bio15, the coefficient of variation was computed after #>      adding one to monthly estimates, and it was multiplied by 100 #>      following <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/deposit/7062281> #>  #>      v1.0.0 Remove ice sheets and internal seas, and use correct #>      formula for bio15. Files can be downloaded from: #>      doi:10.6084/m9.figshare.19723405.v1 #>      <https://doi.org/10.6084/m9.figshare.19723405.v1> library(pastclim) set_data_path() #> The data_path will be set to /home/andrea/.local/share/R/pastclim. #> A copy of the Example dataset will be copied there. #> This path will be saved by pastclim for future use. #> Proceed?  #>  #> 1: Yes #> 2: No set_data_path(path_to_nc = \"~/my_reconstructions\") get_vars_for_dataset(dataset = \"Example\") #> [1] \"bio01\" \"bio10\" \"bio12\" \"biome\" get_time_bp_steps(dataset = \"Example\") #> [1] -20000 -15000 -10000  -5000      0 get_resolution(dataset = \"Example\") #> [1] 1 1 get_vars_for_dataset(dataset = \"Beyer2020\") #>  [1] \"bio01\"    \"bio04\"    \"bio05\"    \"bio06\"    \"bio07\"    \"bio08\"    #>  [7] \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    \"bio13\"    \"bio14\"    #> [13] \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    \"bio19\"    \"npp\"      #> [19] \"lai\"      \"biome\"    \"altitude\" \"rugosity\" get_vars_for_dataset(dataset = \"Krapp2021\") #>  [1] \"bio01\"    \"bio04\"    \"bio05\"    \"bio06\"    \"bio07\"    \"bio08\"    #>  [7] \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    \"bio13\"    \"bio14\"    #> [13] \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    \"bio19\"    \"npp\"      #> [19] \"biome\"    \"lai\"      \"altitude\" \"rugosity\" get_vars_for_dataset(dataset = \"Beyer2020\", annual = FALSE, monthly = TRUE) #>  [1] \"temperature_01\"       \"temperature_02\"       \"temperature_03\"       #>  [4] \"temperature_04\"       \"temperature_05\"       \"temperature_06\"       #>  [7] \"temperature_07\"       \"temperature_08\"       \"temperature_09\"       #> [10] \"temperature_10\"       \"temperature_11\"       \"temperature_12\"       #> [13] \"precipitation_01\"     \"precipitation_02\"     \"precipitation_03\"     #> [16] \"precipitation_04\"     \"precipitation_05\"     \"precipitation_06\"     #> [19] \"precipitation_07\"     \"precipitation_08\"     \"precipitation_09\"     #> [22] \"precipitation_10\"     \"precipitation_11\"     \"precipitation_12\"     #> [25] \"cloudiness_01\"        \"cloudiness_02\"        \"cloudiness_03\"        #> [28] \"cloudiness_04\"        \"cloudiness_05\"        \"cloudiness_06\"        #> [31] \"cloudiness_07\"        \"cloudiness_08\"        \"cloudiness_09\"        #> [34] \"cloudiness_10\"        \"cloudiness_11\"        \"cloudiness_12\"        #> [37] \"relative_humidity_01\" \"relative_humidity_02\" \"relative_humidity_03\" #> [40] \"relative_humidity_04\" \"relative_humidity_05\" \"relative_humidity_06\" #> [43] \"relative_humidity_07\" \"relative_humidity_08\" \"relative_humidity_09\" #> [46] \"relative_humidity_10\" \"relative_humidity_11\" \"relative_humidity_12\" #> [49] \"wind_speed_01\"        \"wind_speed_02\"        \"wind_speed_03\"        #> [52] \"wind_speed_04\"        \"wind_speed_05\"        \"wind_speed_06\"        #> [55] \"wind_speed_07\"        \"wind_speed_08\"        \"wind_speed_09\"        #> [58] \"wind_speed_10\"        \"wind_speed_11\"        \"wind_speed_12\"        #> [61] \"mo_npp_01\"            \"mo_npp_02\"            \"mo_npp_03\"            #> [64] \"mo_npp_04\"            \"mo_npp_05\"            \"mo_npp_06\"            #> [67] \"mo_npp_07\"            \"mo_npp_08\"            \"mo_npp_09\"            #> [70] \"mo_npp_10\"            \"mo_npp_11\"            \"mo_npp_12\" get_vars_for_dataset(dataset = \"Example\", details = TRUE) #>   variable                           long_name           units #> 1    bio01             annual mean temperature degrees Celsius #> 2    bio10 mean temperature of warmest quarter degrees Celsius #> 3    bio12                annual precipitation     mm per year #> 4    biome                 biome (from BIOME4) get_downloaded_datasets() #> $Example #> [1] \"bio01\" \"bio10\" \"bio12\" \"biome\" download_dataset(dataset = \"Beyer2020\", bio_variables = c(\"bio01\", \"bio05\"))"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"get-climate-for-locations","dir":"Articles","previous_headings":"","what":"Get climate for locations","title":"pastclim overview","text":"Often want get climate specific locations. can using function location_slice. function, get slices climate times relevant locations interest. Let us consider five possible locations interest: Iho Eleru (Late Stone Age inland site Nigeria), La Riera (Late Palaeolithic coastal site Spain), Chalki (Mesolithic site Greek island), Oronsay (Mesolithic site Scottish Hebrides), Atlantis (fabled submersed city mentioned Plato). site date (realistic, made ) interested associating climatic reconstructions. extract climatic conditions bio01 bio12: pastclim finds closest time step (slice) available given date, outputs slice used column time_bp_slice (Example dataset use vignette temporal resolution 5k years). Note Chalki Atlantis available (get NA) appropriate time steps. occurs location, reconstructions, either water ice, pastclim can return estimate. instances, due discretisation space raster. can interpolate climate among nearest neighbours, thus using climate reconstructions neighbouring pixels location just one land pixels: Chalki, can see problem indeed , since small island, well represented reconstructions (bear mind Example dataset coarse spatial resolution), can reconstruct appropriate climate interpolating. Atlantis, hand, middle ocean, information climate might became submerged (assuming ever existed…). Note nn_interpol = TRUE default function. Sometimes, want get time series climatic reconstructions, thus allowing us see climate changed time: resulting dataframe can subsetted get time series location (small Example dataset contains 5 time slices): Also note locations, climate can available certain time steps, depending sea level ice sheet extent. case Oronsay: can quickly plot bio01 time locations:  expected, don’t data Atlantis (always underwater), also fail retrieve data Chalki. location_series interpolate nearest neighbours default (, differs location_slice behaviour). rationale behaviour interested whether locations might end underwater, want grab climate estimates submerged. However, cases (Chalki) might necessary allow interpolation. Pretty labels environmental variables can generated var_labels:  Note climatic reconstructions extracted Example dataset, coarse, used base real inference environmental conditions. note also higher resolution always better. important consider appropriate spatial scale relevant question hand. Sometimes, might necessary downscale simulations (see section end vignette), cases might want get estimates cover area around specific location (e.g. comparing proxies capture climatology broad area, certain sediment cores capture pollen broader region). location_slice location_series can provide mean estimates areas around location coordinates setting buffer parameter (see help pages functions details).","code":"locations <- data.frame(   name = c(\"Iho Eleru\", \"La Riera\", \"Chalki\", \"Oronsay\", \"Atlantis\"),   longitude = c(5, -4, 27, -6, -24), latitude = c(7, 44, 36, 56, 31),   time_bp = c(-11200, -18738, -10227, -10200, -11600) ) locations #>        name longitude latitude time_bp #> 1 Iho Eleru         5        7  -11200 #> 2  La Riera        -4       44  -18738 #> 3    Chalki        27       36  -10227 #> 4   Oronsay        -6       56  -10200 #> 5  Atlantis       -24       31  -11600 location_slice(   x = locations, bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\", nn_interpol = FALSE ) #>        name longitude latitude time_bp time_bp_slice     bio01    bio12 #> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.595 #> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.570 #> 3    Chalki        27       36  -10227        -10000        NA       NA #> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.824 #> 5  Atlantis       -24       31  -11600        -10000        NA       NA location_slice(   x = locations, bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\", nn_interpol = TRUE ) #>        name longitude latitude time_bp time_bp_slice     bio01     bio12 #> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.5950 #> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.5703 #> 3    Chalki        27       36  -10227        -10000 17.432425  723.1012 #> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.8245 #> 5  Atlantis       -24       31  -11600        -10000        NA        NA locations_ts <- location_series(   x = locations,   bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\" ) subset(locations_ts, name == \"Iho Eleru\") #>          name longitude latitude time_bp    bio01    bio12 #> 1   Iho Eleru         5        7  -20000 22.55133 1577.238 #> 1.1 Iho Eleru         5        7  -15000 23.27008 1850.715 #> 1.2 Iho Eleru         5        7  -10000 25.34670 2204.595 #> 1.3 Iho Eleru         5        7   -5000 25.65009 2109.735 #> 1.4 Iho Eleru         5        7       0 26.77033 1840.845 subset(locations_ts, name == \"Oronsay\") #>        name longitude latitude time_bp    bio01    bio12 #> 4   Oronsay        -6       56  -20000       NA       NA #> 4.1 Oronsay        -6       56  -15000       NA       NA #> 4.2 Oronsay        -6       56  -10000 6.937467 1362.824 #> 4.3 Oronsay        -6       56   -5000 8.167976 1462.253 #> 4.4 Oronsay        -6       56       0 8.185000 1434.490 library(ggplot2) ggplot(data = locations_ts, aes(x = time_bp, y = bio01, group = name)) +   geom_line(aes(col = name)) +   geom_point(aes(col = name)) #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_line()`). #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_point()`). library(ggplot2) ggplot(data = locations_ts, aes(x = time_bp, y = bio01, group = name)) +   geom_line(aes(col = name)) +   geom_point(aes(col = name)) +   labs(     y = var_labels(\"bio01\", dataset = \"Example\", abbreviated = TRUE),     x = \"time BP (yr)\"   ) #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_line()`). #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"get-climate-for-a-region","dir":"Articles","previous_headings":"","what":"Get climate for a region","title":"pastclim overview","text":"Instead focussing specific locations, might want look whole region. given time step, can extract slice climate returns raster (technically SpatRaster object defined terra library, meaning can perform standard terra raster operations object). interact SpatRaster objects, need library terra loaded (otherwise might get errors correct method found, e.g. plotting). pastclim automatically loads terra, able work terra objects without problem: plot three variables (layers raster):  can add informative labels var_labels:  possible also load time series rasters function region_series. case, function returns SpatRasterDataset, variable sub-dataset: sub-dataset SpatRaster, time steps layers: Note terra stores dates years AD, BP. can inspect times years BP : can plot time series given variable (relabel plots use years bp):  plot climate variables given time step, can slice time series:  Instead giving minimum maximum time step, can also provide specific time steps region_series. Note pastclim function get vector time steps given MIS dataset. example, MIS 1, get: can use:","code":"climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_20k #> class       : SpatRaster  #> dimensions  : 150, 360, 3  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> sources     : example_climate_v1.3.0.nc:BIO1   #>               example_climate_v1.3.0.nc:BIO10   #>               example_climate_v1.3.0.nc:BIO12   #> varnames    : bio01 (annual mean temperature)  #>               bio10 (mean temperature of warmest quarter)  #>               bio12 (annual precipitation)  #> names       :           bio01,           bio10,       bio12  #> unit        : degrees Celsius, degrees Celsius, mm per year  #> time (years): -18050 terra::plot(climate_20k) terra::plot(climate_20k,   main = var_labels(climate_20k, dataset = \"Example\", abbreviated = TRUE) ) climate_region <- region_series(   time_bp = list(min = -15000, max = 0),   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_region #> class       : SpatRasterDataset  #> subdatasets : 3  #> dimensions  : 150, 360 (nrow, ncol) #> nlyr        : 4, 4, 4  #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source(s)   : example_climate_v1.3.0.nc  #> names       : bio01, bio10, bio12 climate_region$bio01 #> class       : SpatRaster  #> dimensions  : 150, 360, 4  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source      : example_climate_v1.3.0.nc:BIO1  #> varname     : bio01 (annual mean temperature)  #> names       :    bio01_-15000,    bio01_-10000,     bio01_-5000,         bio01_0  #> unit        : degrees Celsius, degrees Celsius, degrees Celsius, degrees Celsius  #> time (years): -13050 to 1950 time_bp(climate_region) #> [1] -15000 -10000  -5000      0 terra::plot(climate_region$bio01, main = time_bp(climate_region)) slice_10k <- slice_region_series(climate_region, time_bp = -10000) terra::plot(slice_10k) mis1_steps <- get_mis_time_steps(mis = 1, dataset = \"Example\") mis1_steps #> [1] -10000  -5000      0 climate_mis1 <- region_series(   time_bp = mis1_steps,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_mis1 #> class       : SpatRasterDataset  #> subdatasets : 3  #> dimensions  : 150, 360 (nrow, ncol) #> nlyr        : 3, 3, 3  #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source(s)   : example_climate_v1.3.0.nc  #> names       : bio01, bio10, bio12"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"cropping","dir":"Articles","previous_headings":"","what":"Cropping","title":"pastclim overview","text":"Often want focus given region. number preset rectangular extents pastclim: can get corners European extent: can extract climate Europe setting ext region_slice:  can see plot, cutting Europe using rectangular shape keeps portion Northern Africa map. pastclim includes number pre-generated masks main continental masses, stored dataset region_outline sf::sfc object. can get list : can use function crop within region_slice keep area within desired outline.  can combine multiple regions together. example, can crop Africa Eurasia unioning two individual outlines:  Note outlines cross antimeridian split multiple polygons (can used without projecting rasters). Eurasia, eastern end Siberia left hand side plot. continent_outlines_union provides outlines single polygons (case want use projection). can also use custom outline (.e. polygon, coded terra::vect object) mask limit area covered raster. Note need reuse first vertex last vertex, close polygon:  region_series takes ext crop options region_slice limit extent climatic reconstructions.","code":"names(region_extent) #> [1] \"Africa\"    \"America\"   \"Asia\"      \"Europe\"    \"Eurasia\"   \"N_America\" #> [7] \"Oceania\"   \"S_America\" region_extent$Europe #> [1] -15  70  33  75 europe_climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   ext = region_extent$Europe ) terra::plot(europe_climate_20k) names(region_outline) #> [1] \"Africa\"    \"Eurasia\"   \"N_America\" \"Oceania\"   \"S_America\" \"Europe\" europe_climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = region_outline$Europe ) terra::plot(europe_climate_20k) library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE afr_eurasia <- sf::st_union(region_outline$Africa, region_outline$Eurasia) climate_20k_afr_eurasia <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = afr_eurasia ) terra::plot(climate_20k_afr_eurasia) custom_vec <- terra::vect(\"POLYGON ((0 70, 25 70, 50 80, 170 80, 170 10,                               119 2.4, 119 0.8, 116 -7.6, 114 -12, 100 -40,                               -25 -40, -25 64, 0 70))\") climate_20k_custom <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = custom_vec ) terra::plot(climate_20k_custom)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"working-with-biomes-and-ice-sheets","dir":"Articles","previous_headings":"","what":"Working with biomes and ice sheets","title":"pastclim overview","text":"Beyer2020 Krapp2021 datasets include categorical variable detailing extension biomes. can get biome 20k years ago plot :  Note legend massive. plotting multiple time slices, best use legned=FALSE plotting statement avoid legend. need plot extent specific biome, example desert, can simply set levels NA:  climate reconstructions show areas permanent ice. Ice sheets stored class 28 “biome” variable. can retrieve directly ice land (biome categories) masks :  can also add ice sheets plots climatic variables. First, need turn ice mask polygons: can add polygons layer (.e. variable) climate slice following code (note , add polygons every panel figure, need create function used argument fun within plot):  cases, multiple time points variable want see ice sheets change:  Note add ice sheets instance, build function takes single parameter index image (.e. 1 4 plot ) use subset list ice outlines. Sometimes interesting measure distance coastline (e.g. modelling species rely brackish water, determine distance marine resources archaeology). pastclim, can use use distance_from_sea, accounts sea level change based landmask:","code":"get_biome_classes(\"Example\") #>    id                           category #> 1   0                       Water bodies #> 2   1          Tropical evergreen forest #> 3   2     Tropical semi-deciduous forest #> 4   3 Tropical deciduous forest/woodland #> 5   4         Temperate deciduous forest #> 6   5           Temperate conifer forest #> 7   6                  Warm mixed forest #> 8   7                  Cool mixed forest #> 9   8                Cool conifer forest #> 10  9                  Cold mixed forest #> 11 10      Evegreen taiga/montane forest #> 12 11     Deciduous taiga/montane forest #> 13 12                   Tropical savanna #> 14 13      Tropical xerophytic shrubland #> 15 14     Temperate xerophytic shrubland #> 16 15     Temperate sclerophyll woodland #> 17 16      Temperate broadleaved savanna #> 18 17              Open conifer woodland #> 19 18                    Boreal parkland #> 20 19                 Tropical grassland #> 21 20                Temperate grassland #> 22 21                             Desert #> 23 22                      Steppe tundra #> 24 23                       Shrub tundra #> 25 24                 Dwarf shrub tundra #> 26 25             Prostrate shrub tundra #> 27 26    Cushion forb lichen moss tundra #> 28 27                             Barren #> 29 28                           Land ice biome_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"biome\"),   dataset = \"Example\" ) plot(biome_20k) biome_20k$desert <- biome_20k$biome biome_20k$desert[biome_20k$desert != 21] <- NA terra::plot(biome_20k$desert) ice_mask <- get_ice_mask(-20000, dataset = \"Example\") land_mask <- get_land_mask(-20000, dataset = \"Example\") terra::plot(c(ice_mask, land_mask)) ice_mask_vect <- as.polygons(ice_mask) plot(climate_20k,   fun = function() polys(ice_mask_vect, col = \"gray\", lwd = 0.5) ) europe_climate <- region_series(   time_bp = c(-20000, -15000, -10000, 0),   bio_variables = c(\"bio01\"),   dataset = \"Example\",   ext = region_extent$Europe ) ice_masks <- get_ice_mask(c(-20000, -15000, -10000, 0),   dataset = \"Example\" ) ice_poly_list <- lapply(ice_masks, as.polygons) plot(europe_climate$bio01,   main = time_bp(europe_climate),   fun = function(i) {     polys(ice_poly_list[[i]],       col = \"gray\",       lwd = 0.5     )   } ) distances_sea <- distance_from_sea(time_bp = c(-20000, 0), dataset = \"Example\") #> |---------|---------|---------|---------|=========================================                                          |---------|---------|---------|---------|========================================= distances_sea_australia <- crop(distances_sea, terra::ext(100, 170, -60, 20)) plot(distances_sea_australia, main = time_bp(distances_sea_australia))"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"adding-locations-to-region-plots","dir":"Articles","previous_headings":"","what":"Adding locations to region plots","title":"pastclim overview","text":"plot locations region plots, first need create SpatVector object dataframe metadata, specifying names columns x y coordinates: can add climate slice following code (note , add points every panel figure, need create function used argument fun within plot):  points within extent region plotted (, case, European locations). can combine ice sheets locations single plot:","code":"locations_vect <- vect(locations, geom = c(\"longitude\", \"latitude\")) locations_vect #>  class       : SpatVector  #>  geometry    : points  #>  dimensions  : 5, 2  (geometries, attributes) #>  extent      : -24, 27, 7, 56  (xmin, xmax, ymin, ymax) #>  coord. ref. :   #>  names       :      name    time_bp #>  type        :     <chr>      <num> #>  values      : Iho Eleru  -1.12e+04 #>                 La Riera -1.874e+04 #>                   Chalki -1.023e+04 plot(europe_climate_20k,   fun = function() points(locations_vect, col = \"red\", cex = 2) ) plot(europe_climate_20k,   fun = function() {     polys(ice_mask_vect, col = \"gray\", lwd = 0.5)     points(locations_vect, col = \"red\", cex = 2)   } )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"set-the-samples-within-the-background","dir":"Articles","previous_headings":"","what":"Set the samples within the background","title":"pastclim overview","text":"many studies, want set environmental conditions given set location within background time period. Let us start visualising background time step interest PCA:  can now get climatic conditions locations time step compute PCA scores based axes defined background: now can plot points top background  want pool background multiple time steps, can simple use region_series get series, transform data frame df_from_region_series.","code":"bio_vars <- c(\"bio01\", \"bio10\", \"bio12\") climate_10k <- region_slice(-10000,   bio_variables = bio_vars,   dataset = \"Example\" ) climate_values_10k <- df_from_region_slice(climate_10k) climate_10k_pca <- prcomp(climate_values_10k[, bio_vars],   scale = TRUE, center = TRUE ) plot(climate_10k_pca$x[, 2] ~ climate_10k_pca$x[, 1],   pch = 20, col = \"lightgray\",   xlab = \"PC1\", ylab = \"PC2\" ) locations_10k <- data.frame(   longitude = c(0, 90, 20, 5), latitude = c(20, 45, 50, 47),   time_bp = c(-9932, -9753, -10084, -10249) ) climate_loc_10k <- location_slice(   x = locations_10k[, c(\"longitude\", \"latitude\")],   time_bp = locations_10k$time_bp, bio_variables = bio_vars,   dataset = \"Example\" ) locations_10k_pca_scores <- predict(climate_10k_pca,   newdata = climate_loc_10k[, bio_vars] ) plot(climate_10k_pca$x[, 2] ~ climate_10k_pca$x[, 1],   pch = 20, col = \"lightgray\",   xlab = \"PC1\", ylab = \"PC2\" ) points(locations_10k_pca_scores, pch = 20, col = \"red\")"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"random-sampling-of-background","dir":"Articles","previous_headings":"","what":"Random sampling of background","title":"pastclim overview","text":"instances (e.g. underlying raster large handle), might desirable sample background instead using values. interested single time step, can simply generate raster time slice interest, use sample_region_slice: samples multiple time steps, need sample background proportionally number points time step. , example, wanted 30 samples 20k years ago 50 samples 10k years ago: use data build PCA.","code":"climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\"),   dataset = \"Example\" ) this_sample <- sample_region_slice(climate_20k, size = 100) head(this_sample) #>    cell     x     y     bio01    bio10 #> 1 30435  14.5   5.5  19.20609 21.04144 #> 2 11098 117.5  59.5 -17.02355 10.63760 #> 3 46402 141.5 -38.5  11.20755 14.61647 #> 4 28719  98.5  10.5  23.08009 25.09301 #> 5 32526 -54.5  -0.5  21.08426 22.37266 #> 6 21694 -86.5  29.5  11.34134 22.99179 climate_ts <- region_series(   time_bp = c(-20000, -10000),   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   ext = terra::ext(region_extent$Europe) ) sampled_climate <- sample_region_series(climate_ts, size = c(3, 5)) sampled_climate #>          cell    x    y     bio01     bio10     bio12 time_bp #> -20000.1 3010 19.5 39.5 11.405086 19.345711 1000.7158  -20000 #> -20000.2 3367 36.5 35.5 13.070941 21.583271  658.2184  -20000 #> -20000.3 2729 -6.5 42.5  1.555535  8.271003 1393.2020  -20000 #> -10000.1 3274 28.5 36.5 17.501736 28.305464  973.9351  -10000 #> -10000.2 2697 46.5 43.5  9.789731 25.253517  433.5954  -10000 #> -10000.3 1652 21.5 55.5  5.449332 16.197599  750.3496  -10000 #> -10000.4  723 27.5 66.5 -7.382193  2.715217  322.7987  -10000 #> -10000.5 3461 45.5 34.5 20.095482 35.738621  223.0041  -10000"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"downscaling","dir":"Articles","previous_headings":"","what":"Downscaling","title":"pastclim overview","text":"pastclim contain built-code change spatial resolution climatic reconstructions, possible downscale data using relevant function terra package. first need extract region time choice, case Europe 10,000 years ago  can downscale using disagg() function terra package, requiring aggregation factor expressed number cells direction (horizontally, vertically, , needed, layers). example used 25 horizontally vertically, using bilinear interpolation.  Note , whilst smoothed climate, land mask changed, thus still blocky edges.","code":"europe_10k <- region_slice(   dataset = \"Example\",   bio_variables = c(\"bio01\"),   time_bp = -10000, ext = region_extent$Europe ) terra::plot(europe_10k) europe_ds <- terra::disagg(europe_10k, fact = 25, method = \"bilinear\") terra::plot(europe_ds)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a1_available_datasets.html","id":"overview-of-datasets-available-in-pastclim","dir":"Articles","previous_headings":"","what":"Overview of datasets available in pastclim","title":"available datasets","text":"number datasets available pastclim. possible use custom datasets long properly formatted (look article format custom datasets interested). possible get list available datasets : comprehensive list can obtained : dataset, can get detailed information using help function: provide full documentation dataset (sorted alphabetical order):","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_available_datasets() #> Barreto2023, Beyer2020, CHELSA_trace21k_1.0_0.5m_vsi, Example, HYDE_3.3_baseline, Krapp2021, paleoclim_1.0_10m, paleoclim_1.0_2.5m, paleoclim_1.0_5m #> for present day reconstructions, use \"WorldClim_2.1_RESm\" or \"CHELSA_2.4_RESm\" where RES is an available resolution. #> for future predictions, use \"WorldClim_2.1_GCM_SSP_RESm\" or \"CHELSA_2.1_GCM_SSP_RESm\", where GCM is the GCM model, SSP is the Shared Socio-economic Pathways scenario. #> use help(\"WorldClim_2.1\") or help(\"CHELSA_2.1\") for a list of available options list_available_datasets() #>   [1] \"Barreto2023\"                               #>   [2] \"Beyer2020\"                                 #>   [3] \"CHELSA_2.1_0.5m\"                           #>   [4] \"CHELSA_2.1_0.5m_vsi\"                       #>   [5] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m\"          #>   [6] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi\"      #>   [7] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m\"          #>   [8] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m_vsi\"      #>   [9] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m\"          #>  [10] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m_vsi\"      #>  [11] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m\"       #>  [12] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m_vsi\"   #>  [13] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m\"       #>  [14] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m_vsi\"   #>  [15] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m\"       #>  [16] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m_vsi\"   #>  [17] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"      #>  [18] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m_vsi\"  #>  [19] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"      #>  [20] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m_vsi\"  #>  [21] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"      #>  [22] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m_vsi\"  #>  [23] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m\"         #>  [24] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m_vsi\"     #>  [25] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m\"         #>  [26] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m_vsi\"     #>  [27] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m\"         #>  [28] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m_vsi\"     #>  [29] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m\"        #>  [30] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m_vsi\"    #>  [31] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m\"        #>  [32] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_vsi\"    #>  [33] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\"        #>  [34] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\"    #>  [35] \"CHELSA_trace21k_1.0_0.5m_vsi\"              #>  [36] \"Example\"                                   #>  [37] \"HYDE_3.3_baseline\"                         #>  [38] \"Krapp2021\"                                 #>  [39] \"paleoclim_1.0_10m\"                         #>  [40] \"paleoclim_1.0_2.5m\"                        #>  [41] \"paleoclim_1.0_5m\"                          #>  [42] \"WorldClim_2.1_0.5m\"                        #>  [43] \"WorldClim_2.1_10m\"                         #>  [44] \"WorldClim_2.1_2.5m\"                        #>  [45] \"WorldClim_2.1_5m\"                          #>  [46] \"WorldClim_2.1_ACCESS-CM2_ssp126_0.5m\"      #>  [47] \"WorldClim_2.1_ACCESS-CM2_ssp126_10m\"       #>  [48] \"WorldClim_2.1_ACCESS-CM2_ssp126_2.5m\"      #>  [49] \"WorldClim_2.1_ACCESS-CM2_ssp126_5m\"        #>  [50] \"WorldClim_2.1_ACCESS-CM2_ssp245_0.5m\"      #>  [51] \"WorldClim_2.1_ACCESS-CM2_ssp245_10m\"       #>  [52] \"WorldClim_2.1_ACCESS-CM2_ssp245_2.5m\"      #>  [53] \"WorldClim_2.1_ACCESS-CM2_ssp245_5m\"        #>  [54] \"WorldClim_2.1_ACCESS-CM2_ssp370_0.5m\"      #>  [55] \"WorldClim_2.1_ACCESS-CM2_ssp370_10m\"       #>  [56] \"WorldClim_2.1_ACCESS-CM2_ssp370_2.5m\"      #>  [57] \"WorldClim_2.1_ACCESS-CM2_ssp370_5m\"        #>  [58] \"WorldClim_2.1_ACCESS-CM2_ssp585_0.5m\"      #>  [59] \"WorldClim_2.1_ACCESS-CM2_ssp585_10m\"       #>  [60] \"WorldClim_2.1_ACCESS-CM2_ssp585_2.5m\"      #>  [61] \"WorldClim_2.1_ACCESS-CM2_ssp585_5m\"        #>  [62] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_0.5m\"     #>  [63] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_10m\"      #>  [64] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_2.5m\"     #>  [65] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_5m\"       #>  [66] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_0.5m\"     #>  [67] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_10m\"      #>  [68] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_2.5m\"     #>  [69] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_5m\"       #>  [70] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_0.5m\"     #>  [71] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_10m\"      #>  [72] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_2.5m\"     #>  [73] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_5m\"       #>  [74] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_0.5m\"     #>  [75] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_10m\"      #>  [76] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_2.5m\"     #>  [77] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_5m\"       #>  [78] \"WorldClim_2.1_CMCC-ESM2_ssp126_0.5m\"       #>  [79] \"WorldClim_2.1_CMCC-ESM2_ssp126_10m\"        #>  [80] \"WorldClim_2.1_CMCC-ESM2_ssp126_2.5m\"       #>  [81] \"WorldClim_2.1_CMCC-ESM2_ssp126_5m\"         #>  [82] \"WorldClim_2.1_CMCC-ESM2_ssp245_0.5m\"       #>  [83] \"WorldClim_2.1_CMCC-ESM2_ssp245_10m\"        #>  [84] \"WorldClim_2.1_CMCC-ESM2_ssp245_2.5m\"       #>  [85] \"WorldClim_2.1_CMCC-ESM2_ssp245_5m\"         #>  [86] \"WorldClim_2.1_CMCC-ESM2_ssp370_0.5m\"       #>  [87] \"WorldClim_2.1_CMCC-ESM2_ssp370_10m\"        #>  [88] \"WorldClim_2.1_CMCC-ESM2_ssp370_2.5m\"       #>  [89] \"WorldClim_2.1_CMCC-ESM2_ssp370_5m\"         #>  [90] \"WorldClim_2.1_CMCC-ESM2_ssp585_0.5m\"       #>  [91] \"WorldClim_2.1_CMCC-ESM2_ssp585_10m\"        #>  [92] \"WorldClim_2.1_CMCC-ESM2_ssp585_2.5m\"       #>  [93] \"WorldClim_2.1_CMCC-ESM2_ssp585_5m\"         #>  [94] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_0.5m\"   #>  [95] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_10m\"    #>  [96] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_2.5m\"   #>  [97] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_5m\"     #>  [98] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_0.5m\"   #>  [99] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_10m\"    #> [100] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_2.5m\"   #> [101] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_5m\"     #> [102] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_0.5m\"   #> [103] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_10m\"    #> [104] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_2.5m\"   #> [105] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_5m\"     #> [106] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_0.5m\"   #> [107] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_10m\"    #> [108] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_2.5m\"   #> [109] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_5m\"     #> [110] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_0.5m\"     #> [111] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_10m\"      #> [112] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_2.5m\"     #> [113] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_5m\"       #> [114] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_0.5m\"     #> [115] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_10m\"      #> [116] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_2.5m\"     #> [117] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_5m\"       #> [118] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_0.5m\"     #> [119] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_10m\"      #> [120] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_2.5m\"     #> [121] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_5m\"       #> [122] \"WorldClim_2.1_GFDL-ESM4_ssp126_0.5m\"       #> [123] \"WorldClim_2.1_GFDL-ESM4_ssp126_10m\"        #> [124] \"WorldClim_2.1_GFDL-ESM4_ssp126_2.5m\"       #> [125] \"WorldClim_2.1_GFDL-ESM4_ssp126_5m\"         #> [126] \"WorldClim_2.1_GFDL-ESM4_ssp370_0.5m\"       #> [127] \"WorldClim_2.1_GFDL-ESM4_ssp370_10m\"        #> [128] \"WorldClim_2.1_GFDL-ESM4_ssp370_2.5m\"       #> [129] \"WorldClim_2.1_GFDL-ESM4_ssp370_5m\"         #> [130] \"WorldClim_2.1_GISS-E2-1-G_ssp126_0.5m\"     #> [131] \"WorldClim_2.1_GISS-E2-1-G_ssp126_10m\"      #> [132] \"WorldClim_2.1_GISS-E2-1-G_ssp126_2.5m\"     #> [133] \"WorldClim_2.1_GISS-E2-1-G_ssp126_5m\"       #> [134] \"WorldClim_2.1_GISS-E2-1-G_ssp245_0.5m\"     #> [135] \"WorldClim_2.1_GISS-E2-1-G_ssp245_10m\"      #> [136] \"WorldClim_2.1_GISS-E2-1-G_ssp245_2.5m\"     #> [137] \"WorldClim_2.1_GISS-E2-1-G_ssp245_5m\"       #> [138] \"WorldClim_2.1_GISS-E2-1-G_ssp370_0.5m\"     #> [139] \"WorldClim_2.1_GISS-E2-1-G_ssp370_10m\"      #> [140] \"WorldClim_2.1_GISS-E2-1-G_ssp370_2.5m\"     #> [141] \"WorldClim_2.1_GISS-E2-1-G_ssp370_5m\"       #> [142] \"WorldClim_2.1_GISS-E2-1-G_ssp585_0.5m\"     #> [143] \"WorldClim_2.1_GISS-E2-1-G_ssp585_10m\"      #> [144] \"WorldClim_2.1_GISS-E2-1-G_ssp585_2.5m\"     #> [145] \"WorldClim_2.1_GISS-E2-1-G_ssp585_5m\"       #> [146] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_0.5m\" #> [147] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_10m\"  #> [148] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_2.5m\" #> [149] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_5m\"   #> [150] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_0.5m\" #> [151] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\"  #> [152] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_2.5m\" #> [153] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_5m\"   #> [154] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_0.5m\" #> [155] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_10m\"  #> [156] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_2.5m\" #> [157] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_5m\"   #> [158] \"WorldClim_2.1_INM-CM5-0_ssp126_0.5m\"       #> [159] \"WorldClim_2.1_INM-CM5-0_ssp126_10m\"        #> [160] \"WorldClim_2.1_INM-CM5-0_ssp126_2.5m\"       #> [161] \"WorldClim_2.1_INM-CM5-0_ssp126_5m\"         #> [162] \"WorldClim_2.1_INM-CM5-0_ssp245_0.5m\"       #> [163] \"WorldClim_2.1_INM-CM5-0_ssp245_10m\"        #> [164] \"WorldClim_2.1_INM-CM5-0_ssp245_2.5m\"       #> [165] \"WorldClim_2.1_INM-CM5-0_ssp245_5m\"         #> [166] \"WorldClim_2.1_INM-CM5-0_ssp370_0.5m\"       #> [167] \"WorldClim_2.1_INM-CM5-0_ssp370_10m\"        #> [168] \"WorldClim_2.1_INM-CM5-0_ssp370_2.5m\"       #> [169] \"WorldClim_2.1_INM-CM5-0_ssp370_5m\"         #> [170] \"WorldClim_2.1_INM-CM5-0_ssp585_0.5m\"       #> [171] \"WorldClim_2.1_INM-CM5-0_ssp585_10m\"        #> [172] \"WorldClim_2.1_INM-CM5-0_ssp585_2.5m\"       #> [173] \"WorldClim_2.1_INM-CM5-0_ssp585_5m\"         #> [174] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_0.5m\"    #> [175] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_10m\"     #> [176] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_2.5m\"    #> [177] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_5m\"      #> [178] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_0.5m\"    #> [179] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_10m\"     #> [180] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_2.5m\"    #> [181] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_5m\"      #> [182] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_0.5m\"    #> [183] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_10m\"     #> [184] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_2.5m\"    #> [185] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_5m\"      #> [186] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_0.5m\"    #> [187] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_10m\"     #> [188] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_2.5m\"    #> [189] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_5m\"      #> [190] \"WorldClim_2.1_MIROC6_ssp126_0.5m\"          #> [191] \"WorldClim_2.1_MIROC6_ssp126_10m\"           #> [192] \"WorldClim_2.1_MIROC6_ssp126_2.5m\"          #> [193] \"WorldClim_2.1_MIROC6_ssp126_5m\"            #> [194] \"WorldClim_2.1_MIROC6_ssp245_0.5m\"          #> [195] \"WorldClim_2.1_MIROC6_ssp245_10m\"           #> [196] \"WorldClim_2.1_MIROC6_ssp245_2.5m\"          #> [197] \"WorldClim_2.1_MIROC6_ssp245_5m\"            #> [198] \"WorldClim_2.1_MIROC6_ssp370_0.5m\"          #> [199] \"WorldClim_2.1_MIROC6_ssp370_10m\"           #> [200] \"WorldClim_2.1_MIROC6_ssp370_2.5m\"          #> [201] \"WorldClim_2.1_MIROC6_ssp370_5m\"            #> [202] \"WorldClim_2.1_MIROC6_ssp585_0.5m\"          #> [203] \"WorldClim_2.1_MIROC6_ssp585_10m\"           #> [204] \"WorldClim_2.1_MIROC6_ssp585_2.5m\"          #> [205] \"WorldClim_2.1_MIROC6_ssp585_5m\"            #> [206] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"   #> [207] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_10m\"    #> [208] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_2.5m\"   #> [209] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_5m\"     #> [210] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_0.5m\"   #> [211] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_10m\"    #> [212] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_2.5m\"   #> [213] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_5m\"     #> [214] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"   #> [215] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_10m\"    #> [216] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_2.5m\"   #> [217] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_5m\"     #> [218] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"   #> [219] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_10m\"    #> [220] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_2.5m\"   #> [221] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_5m\"     #> [222] \"WorldClim_2.1_MRI-ESM2-0_ssp126_0.5m\"      #> [223] \"WorldClim_2.1_MRI-ESM2-0_ssp126_10m\"       #> [224] \"WorldClim_2.1_MRI-ESM2-0_ssp126_2.5m\"      #> [225] \"WorldClim_2.1_MRI-ESM2-0_ssp126_5m\"        #> [226] \"WorldClim_2.1_MRI-ESM2-0_ssp245_0.5m\"      #> [227] \"WorldClim_2.1_MRI-ESM2-0_ssp245_10m\"       #> [228] \"WorldClim_2.1_MRI-ESM2-0_ssp245_2.5m\"      #> [229] \"WorldClim_2.1_MRI-ESM2-0_ssp245_5m\"        #> [230] \"WorldClim_2.1_MRI-ESM2-0_ssp370_0.5m\"      #> [231] \"WorldClim_2.1_MRI-ESM2-0_ssp370_10m\"       #> [232] \"WorldClim_2.1_MRI-ESM2-0_ssp370_2.5m\"      #> [233] \"WorldClim_2.1_MRI-ESM2-0_ssp370_5m\"        #> [234] \"WorldClim_2.1_MRI-ESM2-0_ssp585_0.5m\"      #> [235] \"WorldClim_2.1_MRI-ESM2-0_ssp585_10m\"       #> [236] \"WorldClim_2.1_MRI-ESM2-0_ssp585_2.5m\"      #> [237] \"WorldClim_2.1_MRI-ESM2-0_ssp585_5m\"        #> [238] \"WorldClim_2.1_UKESM1-0-LL_ssp126_0.5m\"     #> [239] \"WorldClim_2.1_UKESM1-0-LL_ssp126_10m\"      #> [240] \"WorldClim_2.1_UKESM1-0-LL_ssp126_2.5m\"     #> [241] \"WorldClim_2.1_UKESM1-0-LL_ssp126_5m\"       #> [242] \"WorldClim_2.1_UKESM1-0-LL_ssp245_0.5m\"     #> [243] \"WorldClim_2.1_UKESM1-0-LL_ssp245_10m\"      #> [244] \"WorldClim_2.1_UKESM1-0-LL_ssp245_2.5m\"     #> [245] \"WorldClim_2.1_UKESM1-0-LL_ssp245_5m\"       #> [246] \"WorldClim_2.1_UKESM1-0-LL_ssp370_0.5m\"     #> [247] \"WorldClim_2.1_UKESM1-0-LL_ssp370_10m\"      #> [248] \"WorldClim_2.1_UKESM1-0-LL_ssp370_2.5m\"     #> [249] \"WorldClim_2.1_UKESM1-0-LL_ssp370_5m\"       #> [250] \"WorldClim_2.1_UKESM1-0-LL_ssp585_0.5m\"     #> [251] \"WorldClim_2.1_UKESM1-0-LL_ssp585_10m\"      #> [252] \"WorldClim_2.1_UKESM1-0-LL_ssp585_2.5m\"     #> [253] \"WorldClim_2.1_UKESM1-0-LL_ssp585_5m\" help(\"Example\") #> Documentation for the Example dataset #>  #> Description: #>  #>      This dataset is a subset of Beyer2020, used for the vignette of #>      pastclim. Do not use this dataset for any real work, as it might #>      not reflect the most up-to-date version of Beyer2020. #> Documentation for the Barreto et al 2023 dataset #>  #> Description: #>  #>      Spatio-temporal series of monthly temperature and precipitation #>      and 17 derived bioclimatic variables covering the last 5 Ma #>      (Pliocene<e2><80><93>Pleistocene), at intervals of 1,000 years, and a spatial #>      resolution of 1 arc-degrees (see Barreto et al., 2023 for #>      details). #>  #> Details: #>  #>      PALEO-PGEM-Series is downscaled to 1 <c3><97> 1 arc-degrees spatial #>      resolution from the outputs of the PALEO-PGEM emulator (Holden et #>      al., 2019), which emulates reasonable and extensively validated #>      global estimates of monthly temperature and precipitation for the #>      Plio-Pleistocene every 1 kyr at a spatial resolution of ~5 <c3><97> 5 #>      arc-degrees (Holden et al., 2016, 2019). #>  #>      PALEO-PGEM-Series includes the mean and the standard deviation #>      (i.e., standard error) of the emulated climate over 10 stochastic #>      GCM emulations to accommodate aspects of model uncertainty. This #>      allows users to estimate the robustness of their results in the #>      face of the stochastic aspects of the emulations. For more #>      details, see Section 2.4 in Barreto et al. (2023). #>  #>      Note that this is a very large dataset, with 5001 time slices. It #>      takes approximately 1 minute to set up each variable when creating #>      a region_slice or region_series. However, once the object has been #>      created, other operations tend to be much faster (especially if #>      you subset the dataset to a small number of time steps of #>      interest). #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publications: #>  #>      Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. #>      (2023). PALEO-PGEM-Series: A spatial time series of the global #>      climate over the last 5 million years (Plio-Pleistocene). Global #>      Ecology and Biogeography, 32, 1034-1045, doi:10.1111/geb.13683 #>      <https://doi.org/10.1111/geb.13683> #>  #>      Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., #>      Tran, G. T., and Wilkinson, R. D. (2019): PALEO-PGEM v1.0: a #>      statistical emulator of Pliocene<e2><80><93>Pleistocene climate, Geosci. #>      Model Dev., 12, 5137<e2><80><93>5155, doi:10.5194/gmd-12-5137-2019 #>      <https://doi.org/10.5194/gmd-12-5137-2019>. #>  #>  #> ####################################################### #> Documentation for the Beyer2020 dataset #>  #> Description: #>  #>      This dataset covers the last 120k years, at intervals of 1/2 k #>      years, and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial #>      climate, bioclimate and vegetation for the last 120,000 years. Sci #>      Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 #>      <https://doi.org/10.1038/s41597-020-0552-1> #>  #>      The version included in 'pastclim' has the ice sheets masked, as #>      well as internal seas (Black and Caspian Sea) removed. The latter #>      are based on: #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4278> #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4282> #>  #>      As there is no reconstruction of their depth through time, modern #>      outlines were used for all time steps. #>  #>      Also, for bio15, the coefficient of variation was computed after #>      adding one to monthly estimates, and it was multiplied by 100 #>      following <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/deposit/7062281> #>  #>      v1.0.0 Remove ice sheets and internal seas, and use correct #>      formula for bio15. Files can be downloaded from: #>      doi:10.6084/m9.figshare.19723405.v1 #>      <https://doi.org/10.6084/m9.figshare.19723405.v1> #>  #>  #> ####################################################### #> Documentation for _CHELSA 2.1_ #>  #> Description: #>  #>      _CHELSA_ version 2.1 is a database of high spatial resolution #>      global weather and climate data, covering both the present and #>      future projections. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication for the _CHELSA_ dataset: #>  #>      Karger, D.N., Conrad, O., B<c3><b6>hner, J., Kawohl, T., Kreft, H., #>      Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017) #>      Climatologies at high resolution for the Earth land surface areas. #>      Scientific Data. 4 170122. doi:10.1038/sdata.2017.122 #>      <https://doi.org/10.1038/sdata.2017.122> #>  #>      *Present-day reconstructions* are based on the mean for the period #>      1981-2000 and are available at at the high resolution of 0.5 #>      arc-minutes (_CHELSA_2.1_0.5m_). In 'pastclim', the datasets are #>      given a date of 1990 CE (the mid-point of the period of interest). #>      There are 19 <e2><80><9c>bioclimatic<e2><80><9d> variables, as well as monthly estimates #>      for mean temperature, and precipitation. The dataset is very #>      large, as it includes estimates for the oceans as well as the land #>      masses. An alternative to downloading the very large files is to #>      use virtual rasters, which allow the data to remain on the server, #>      with only the pixels required for a given operation being #>      downloaded. Virtual rasters can be used by choosing #>      (_CHELSA_2.1_0.5m_vsi_) #>  #>      *Future projections* are based on the models in CMIP6, downscaled #>      and de-biased using the CHELSA algorithm 2.1. Monthly values of #>      mean temperature, and total precipitation, as well as 19 #>      bioclimatic variables were processed for 5 global climate models #>      (GCMs), and for three Shared Socio-economic Pathways (SSPs): 126, #>      370 and 585. Model and SSP can be chosen by changing the ending of #>      the dataset name _CHELSA_2.1_GCM_SSP_RESm_. #>  #>      Available values for GCM are: \"GFDL-ESM4\", \"IPSL-CM6A-LR\", #>      \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", and \"UKESM1-0-LL\". For SSP, use: #>      \"ssp126\", \"ssp370\", and \"ssp585\". RES is currently limited to #>      \"0.5m\". Example dataset names are #>      _CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_ and #>      _CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_ #>  #>      As for present reconstructions, an alternative to downloading the #>      very large files is to use virtual rasters. Simply append \"_vis\" #>      to the name of the dataset of interest #>      (_CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi_). #>  #>      The dataset are averages over 30 year periods (2011-2040, #>      2041-2070, 2071-2100). In 'pastclim', the midpoints of the periods #>      (2025, 2055, 2075) are used as the time stamps. All 3 periods are #>      automatically downloaded for each combination of GCM model and #>      SSP, and are selected as usual by defining the time in functions #>      such as 'region_slice()'. #>  #>  #> ####################################################### #> Documentation for _CHELSA-TracCE21k_ #>  #> Description: #>  #>      CHELSA-TraCE21k data provides monthly climate data for temperature #>      and precipitation at 30 arc-sec spatial resolution in 100-year #>      time steps for the last 21,000 years. Palaeo-orography at high #>      spatial resolution and at each time step is created by combining #>      high resolution information on glacial cover from current and Last #>      Glacial Maximum (LGM) glacier databases with the interpolation of #>      a dynamic ice sheet model (ICE6G) and a coupling to mean annual #>      temperatures from CCSM3-TraCE21k. Based on the reconstructed #>      palaeo-orography, mean annual temperature and precipitation was #>      downscaled using the CHELSA V1.2 algorithm. #>  #> Details: #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      An alternative to downloading very large files is to use virtual #>      rasters. Simply append \"_vis\" to the name of the dataset of #>      interest (_CHELSA_trace21k_1.0_0.5m_vsi_). This is the recommended #>      approach, and it is currently the only available version of the #>      dataset. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Karger, D.N., Nobis, M.P., Normand, S., Graham, C.H., Zimmermann, #>      N. (2023) CHELSA-TraCE21k <e2><80><93> High resolution (1 km) downscaled #>      transient temperature and precipitation data since the Last #>      Glacial Maximum. Climate of the Past. doi:10.5194/cp-2021-30 #>      <https://doi.org/10.5194/cp-2021-30> #>  #>  #> ####################################################### #> Documentation for the Example dataset #>  #> Description: #>  #>      This dataset is a subset of Beyer2020, used for the vignette of #>      pastclim. Do not use this dataset for any real work, as it might #>      not reflect the most up-to-date version of Beyer2020. #>  #>  #> ####################################################### #> Documentation for _HYDE 3.3_ dataset #>  #> Description: #>  #>      This database presents an update and expansion of the History #>      Database of the Global Environment (HYDE, v 3.3) and replaces #>      former HYDE 3.2 version from 2017. HYDE is and internally #>      consistent combination of updated historical population estimates #>      and land use. Categories include cropland, with a new distinction #>      into irrigated and rain fed crops (other than rice) and irrigated #>      and rain fed rice. Also grazing lands are provided, divided into #>      more intensively used pasture, converted rangeland and #>      non-converted natural (less intensively used) rangeland. #>      Population is represented by maps of total, urban, rural #>      population and population density as well as built-up area. #>  #> Details: #>  #>      The period covered is 10 000 BCE to 2023 CE. Spatial resolution is #>      5 arc minutes (approx. 85 km2 at the equator). The full _HYDE 3.3_ #>      release contains: a Baseline estimate scenario, a Lower estimate #>      scenario and an Upper estimate scenario. Currently only the #>      baseline scenario is available in 'pastclim' #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication for the HYDE 3.2 (there is no current publication for #>      3.3): #>  #>      Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: #>      Anthropogenic land-use estimates for the Holocene; HYDE 3.2, Earth #>      Syst. Sci. Data, 9, 927-953, 2017. doi:10.5194/essd-9-927-2017 #>      <https://doi.org/10.5194/essd-9-927-2017> #>  #>  #> ####################################################### #> Documentation for the Krapp2021 dataset #>  #> Description: #>  #>      This dataset covers the last 800k years, at intervals of 1k years, #>      and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      The units of several variables have been changed to match what is #>      used in WorldClim. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Krapp, M., Beyer, R.M., Edmundson, S.L. et al. A statistics-based #>      reconstruction of high-resolution global terrestrial climate for #>      the last 800,000 years. Sci Data 8, 228 (2021). #>      doi:10.1038/s41597-021-01009-3 #>      <https://doi.org/10.1038/s41597-021-01009-3> #>  #>      The version included in 'pastclim' has the ice sheets masked. #>  #>      Note that, for bio15, we use the corrected version, which follows #>      <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.4.0 Change units to match WorldClim. Fix variable duplication #>      found on earlier versions of the dataset. #>      <https://zenodo.org/records/8415273> #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/record/7065055> #>  #>      v1.0.0 Remove ice sheets and use correct formula for bio15. Files #>      can be downloaded from: doi:10.6084/m9.figshare.19733680.v1 #>      <https://doi.org/10.6084/m9.figshare.19733680.v1> #>  #>  #> ####################################################### #> Documentation for _Paleoclim_ #>  #> Description: #>  #>      _Paleoclim_ is a set of high resolution paleoclimate #>      reconstructions, mostly based on the CESM model, downscaled with #>      the CHELSA dataset to 3 different spatial resolutions: #>      'paleoclim_1.0_2.5m' at 2.5 arc-minutes (~5 km), #>      'paleoclim_1.0_5m' at 5 arc-minutes (~10 km), and #>      'paleoclim_1.0_10m' 10 arc-minutes (~20 km). All 19 biovariables #>      are available. There are only a limited number of time slices #>      available for this dataset; furthermore, currently only time #>      slices from present to 130ka are available in 'pastclim'. #>  #> Details: #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Brown, Hill, Dolan, Carnaval, Haywood (2018) PaleoClim, high #>      spatial resolution paleoclimate surfaces for global land areas. #>      Nature <e2><80><93> Scientific Data. 5:180254 #>  #>  #> ####################################################### #> Documentation for the WorldClim datasets #>  #> Description: #>  #>      WorldClim version 2.1 is a database of high spatial resolution #>      global weather and climate data, covering both the present and #>      future projections. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial #>      resolution climate surfaces for global land areas. International #>      Journal of Climatology 37 (12): 4302-4315. doi:10.1002/joc.5086 #>      <https://doi.org/10.1002/joc.5086> #>  #>      *Present-day reconstructions* are based on the mean for the period #>      1970-2000, and are available at multiple resolutions of 10 #>      arc-minutes, 5 arc-minutes, 2.5 arc-minute and 0.5 arc-minutes. #>      The resolution of interest can be obtained by changing the ending #>      of the dataset name _WorldClim_2.1_RESm_, e.g. _WorldClim_2.1_10m_ #>      or _WorldClim_2.1_5m_ (currently, only 10m and 5m are currently #>      available in 'pastclim'). In 'pastclim', the datasets are given a #>      date of 1985 CE (the mid-point of the period of interest). There #>      are 19 <e2><80><9c>bioclimatic<e2><80><9d> variables, as well as monthly estimates for #>      minimum, mean, and maximum temperature, and precipitation. #>  #>      *Future projections* are based on the models in CMIP6, downscaled #>      and de-biased using WorldClim 2.1 for the present as a baseline. #>      Monthly values of minimum temperature, maximum temperature, and #>      precipitation, as well as 19 bioclimatic variables were processed #>      for 23 global climate models (GCMs), and for four Shared #>      Socio-economic Pathways (SSPs): 126, 245, 370 and 585. Model and #>      SSP can be chosen by changing the ending of the dataset name #>      _WorldClim_2.1_GCM_SSP_RESm_. #>  #>      Available values for GCM are: \"ACCESS-CM2\", \"BCC-CSM2-MR\", #>      \"CMCC-ESM2\", \"EC-Earth3-Veg\", \"FIO-ESM-2-0\", \"GFDL-ESM4\", #>      \"GISS-E2-1-G\", \"HadGEM3-GC31-LL\", \"INM-CM5-0\", \"IPSL-CM6A-LR\", #>      \"MIROC6\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", and \"UKESM1-0-LL\". For #>      SSP, use: \"ssp126\", \"ssp245\", \"ssp370\", and \"ssp585\". RES takes #>      the same values as for present reconstructions (i.e. \"10m\", \"5m\", #>      \"2.5m\", and \"0.5m\"). Example dataset names are #>      _WorldClim_2.1_ACCESS-CM2_ssp245_10m_ and #>      _WorldClim_2.1_MRI-ESM2-0_ssp370_5m_. Four combination (namely #>      _FIO-ESM-2-0_ssp370_, _GFDL-ESM4_ssp245_, _GFDL-ESM4_ssp585_, and #>      _HadGEM3-GC31-LL_ssp370_) are NOT available. #>  #>      The dataset are averages over 20 year periods (2021-2040, #>      2041-2060, 2061-2080, 2081-2100). In 'pastclim', the midpoints of #>      the periods (2030, 2050, 2070, 2090) are used as the time stamps. #>      All 4 periods are automatically downloaded for each combination of #>      GCM model and SSP, and are selected as usual by defining the time #>      in functions such as 'region_slice()'. #>  #>  #> #######################################################"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"formatting-a-custom-dataset-for-pastclim","dir":"Articles","previous_headings":"","what":"Formatting a custom dataset for pastclim","title":"custom dataset","text":"guide aimed formatting data way can used pastclim. pastclim designed extract data netcdf files, format commonly used storing climate reconstructions. netcdf files number advantages, can store compressed information, well allowing access data required (e.g. extracting time steps location interest without reading data memory). expected format pastclim requires time steps given variable stored within single netcdf file. variables combined () flexible: can separate file variable, collate everything within single file, create multiple files including number variables. time variable years since 1950 (.e. negative integers indicating past). number command line tools well R libraries (e.g. cdo, gdal, terra) can help creating editing netcdf files.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"an-example-the-trace21k-chelsea","dir":"Articles","previous_headings":"","what":"An example: the Trace21k-CHELSEA","title":"custom dataset","text":"provide simple example format dataset R. use version Trace21k dataset, downscaled 30 arcsecs using CHELSEA algorithm(available website). data stored geoTIFF files, one file per time step per variable. First, need collate files given variable (use bio01 example) within single netcdf file. original files large, illustrate time steps aggregated 3x3 degrees keep files sizes small. start translating geoTIFF netcdf file. files prefix CHELSA_TraCE21k_bio01_-xxx_V1.0.small.tif, xxx number time step. use 3 time step illustrative purposes. store files single directory, create spatRaster list files directory: NOTE: terra changed way handles time reading netcdf. dev version terra can easily format netcdf files correctly, vignette presents number workarounds needed version CRAN Now need set time axis raster (case, reconstructions every 100 years), generate user friendly names layers raster: Now save data nc file (use temporary directory) can now read custom netcdf file pastclim. expected, one variable (“bio01”) 3 time steps (nlyr). can get times time steps : can slice series plot given time point:  Note reconstructions include ocean ice sheets, much better remove needed ecological/archaeological studies (allows smaller files).","code":"tiffs_path <- system.file(\"extdata/CHELSA_bio01\", package = \"pastclim\") list_of_tiffs <- file.path(tiffs_path, dir(tiffs_path)) bio01 <- terra::rast(list_of_tiffs) library(pastclim) #> Loading required package: terra #> terra 1.7.80 time_bp(bio01) <- c(0, -100, -200) names(bio01) <- paste(\"bio01\", terra::time(bio01), sep = \"_\") nc_name <- file.path(tempdir(), \"CHELSA_TraCE21k_bio01.nc\") terra::writeCDF(bio01,   filename = nc_name, varname = \"bio01\",   compression = 9, overwrite = TRUE ) custom_series <- region_series(   bio_variables = \"bio01\",   dataset = \"custom\",   path_to_nc = nc_name ) custom_series #> class       : SpatRasterDataset  #> subdatasets : 1  #> dimensions  : 174, 360 (nrow, ncol) #> nlyr        : 3  #> resolution  : 1, 1  (x, y) #> extent      : -180.0001, 179.9999, -90.00014, 83.99986  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84 (EPSG:4326)  #> source(s)   : CHELSA_TraCE21k_bio01.nc  #> names       : bio01 get_time_bp_steps(dataset = \"custom\", path_to_nc = nc_name) #> [1]    0 -100 -200 climate_100 <- slice_region_series(custom_series, time_bp = -100) terra::plot(climate_100)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"making-the-data-available-to-others","dir":"Articles","previous_headings":"","what":"Making the data available to others","title":"custom dataset","text":"created suitably formatted netcdf files can used custom datasets pastclim, can add data officially package, thus make available others. necessary steps: Put files freely available repository. Update table used pastclim store information available datasets. table found “./data-raw/data_files/dataset_list_included.csv”. includes following fields: variable: variable name used pastclim ncvar: variable name within nc file (can variable) dataset: name dataset. monthly: boolean whether variable monthly. file_name: name file variable. download_path: URL download file. donwload_function: datasets can easily converted user valid netcdf, possibly leave download_path empty, create internal function downloads converts files. example, see WorldClim datasets. file_name_orig: name original file(s) used create nc dataset. download_path_orig: path original files. version: version dataset created long_name: long name variable abbreviated_name: abbreviated version long_name (used plot labels) time_frame: either year appropriate month units: units variable, displayed plain text table units_exp: units formatted included expression creating plot labels added lines detailing variables dataset, run script “./raw-data/make_data/dataset_list_included.R” store information appropriate dataset pastclim. Provide information new dataset file “./R/dataset_docs”, using roxygen2 syntax. Make sure provide appropriate reference original data, important users can refer back original source. Make Pull Request GitHub.","code":"#>   variable ncvar dataset monthly                 file_name download_path #> 1    bio01  BIO1 Example   FALSE example_climate_v1.3.0.nc               #> 2    bio10 BIO10 Example   FALSE example_climate_v1.3.0.nc               #>   download_function file_name_orig download_path_orig version #> 1                                                       1.3.0 #> 2                                                       1.3.0 #>                             long_name      abbreviated_name time_frame #> 1             annual mean temperature           ann. mean T       year #> 2 mean temperature of warmest quarter mean T of warmest qtr       year #>             units  units_exp dataset_list_v #> 1 degrees Celsius *degree*C*          1.3.9 #> 2 degrees Celsius *degree*C*"},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"worldclim","dir":"Articles","previous_headings":"Present reconstructions","what":"WorldClim","title":"present and future","text":"Present-day reconstructions WorldClim v2.1 based mean period 1970-2000, available multiple resolutions 10 arc-minutes, 5 arc-minutes, 2.5 arc-minute 0.5 arc-minutes. resolution interest can obtained changing ending dataset name WorldClim_2.1_RESm, e.g. WorldClim_2.1_10m WorldClim_2.1_5m. pastclim, datasets given date 1985 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. , annual variables 10m arc-minutes dataset : monthly variables can manipulate data usual way. start downloading dataset: can use region_slice extract data SpatRaster:","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_vars_for_dataset(\"WorldClim_2.1_10m\") #>  [1] \"bio01\"    \"bio02\"    \"bio03\"    \"bio04\"    \"bio05\"    \"bio06\"    #>  [7] \"bio07\"    \"bio08\"    \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    #> [13] \"bio13\"    \"bio14\"    \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    #> [19] \"bio19\"    \"altitude\" get_vars_for_dataset(\"WorldClim_2.1_10m\", monthly = TRUE, annual = FALSE) #>  [1] \"temperature_01\"     \"temperature_02\"     \"temperature_03\"     #>  [4] \"temperature_04\"     \"temperature_05\"     \"temperature_06\"     #>  [7] \"temperature_07\"     \"temperature_08\"     \"temperature_09\"     #> [10] \"temperature_10\"     \"temperature_11\"     \"temperature_12\"     #> [13] \"precipitation_01\"   \"precipitation_02\"   \"precipitation_03\"   #> [16] \"precipitation_04\"   \"precipitation_05\"   \"precipitation_06\"   #> [19] \"precipitation_07\"   \"precipitation_08\"   \"precipitation_09\"   #> [22] \"precipitation_10\"   \"precipitation_11\"   \"precipitation_12\"   #> [25] \"temperature_min_01\" \"temperature_min_02\" \"temperature_min_03\" #> [28] \"temperature_min_04\" \"temperature_min_05\" \"temperature_min_06\" #> [31] \"temperature_min_07\" \"temperature_min_08\" \"temperature_min_09\" #> [34] \"temperature_min_10\" \"temperature_min_11\" \"temperature_min_12\" #> [37] \"temperature_max_01\" \"temperature_max_02\" \"temperature_max_03\" #> [40] \"temperature_max_04\" \"temperature_max_05\" \"temperature_max_06\" #> [43] \"temperature_max_07\" \"temperature_max_08\" \"temperature_max_09\" #> [46] \"temperature_max_10\" \"temperature_max_11\" \"temperature_max_12\" download_dataset(   dataset = \"WorldClim_2.1_10m\",   bio_variables = c(\"bio01\", \"bio02\", \"altitude\") ) climate_present <- region_slice(   time_ce = 1985,   bio_variables = c(\"bio01\", \"bio02\", \"altitude\"),   dataset = \"WorldClim_2.1_10m\" )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"chelsa","dir":"Articles","previous_headings":"Present reconstructions","what":"CHELSA","title":"present and future","text":"Present-day reconstructions CHELSA v2.1 based mean period 1981-2000, available high resolution 0.5 arc-minutes. CHELSA_2.1_0.5m. pastclim, datasets given date 1990 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. , annual variables CHELSA dataset : monthly variables can manipulate data usual way. start downloading dataset: can use region_slice extract data SpatRaster: datasets variable large due high resolution. Besides downloading data, also possible use virtual raster, leaving files server, downloading pixels needed. can achieve using dataset CHELSA_2.1_0.5_vsi. still need download dataset first, rather downloading files, sets virtual raster (fast!): downloaded, can use dataset: ideal just need extract climate number locations, need get full map.","code":"library(pastclim) get_vars_for_dataset(\"CHELSA_2.1_0.5m\") #>  [1] \"bio01\" \"bio02\" \"bio03\" \"bio04\" \"bio05\" \"bio06\" \"bio07\" \"bio08\" \"bio09\" #> [10] \"bio10\" \"bio11\" \"bio12\" \"bio13\" \"bio14\" \"bio15\" \"bio16\" \"bio17\" \"bio18\" #> [19] \"bio19\" get_vars_for_dataset(\"CHELSA_2.1_0.5m\", monthly = TRUE, annual = FALSE) #>  [1] \"temperature_01\"     \"temperature_02\"     \"temperature_03\"     #>  [4] \"temperature_04\"     \"temperature_05\"     \"temperature_06\"     #>  [7] \"temperature_07\"     \"temperature_08\"     \"temperature_09\"     #> [10] \"temperature_10\"     \"temperature_11\"     \"temperature_12\"     #> [13] \"precipitation_01\"   \"precipitation_02\"   \"precipitation_03\"   #> [16] \"precipitation_04\"   \"precipitation_05\"   \"precipitation_06\"   #> [19] \"precipitation_07\"   \"precipitation_08\"   \"precipitation_09\"   #> [22] \"precipitation_10\"   \"precipitation_11\"   \"precipitation_12\"   #> [25] \"temperature_max_01\" \"temperature_max_02\" \"temperature_max_03\" #> [28] \"temperature_max_04\" \"temperature_max_05\" \"temperature_max_06\" #> [31] \"temperature_max_07\" \"temperature_max_08\" \"temperature_max_09\" #> [34] \"temperature_max_10\" \"temperature_max_11\" \"temperature_max_12\" #> [37] \"temperature_min_01\" \"temperature_min_02\" \"temperature_min_03\" #> [40] \"temperature_min_04\" \"temperature_min_05\" \"temperature_min_06\" #> [43] \"temperature_min_07\" \"temperature_min_08\" \"temperature_min_09\" #> [46] \"temperature_min_10\" \"temperature_min_11\" \"temperature_min_12\" download_dataset(   dataset = \"CHELSA_2.1_0.5m\",   bio_variables = c(\"bio01\", \"bio02\") ) climate_present <- region_slice(   time_ce = 1990,   bio_variables = c(\"bio01\", \"bio02\"),   dataset = \"CHELSA_2.1_0.5m\" ) download_dataset(dataset = \"CHELSA_2.1_0.5m_vsi\",                   bio_variables = c(\"bio12\",\"temperature_01\")) climate_present <- region_slice(   time_ce = 1990,   bio_variables = c(\"bio12\",\"temperature_01\"),   dataset = \"CHELSA_2.1_0.5m_vsi\" )"},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"worldclim-1","dir":"Articles","previous_headings":"Future projections","what":"WorldClim","title":"present and future","text":"Future projections based models CMIP6, downscaled de-biased using WorldClim 2.1 present baseline. Monthly values minimum temperature, maximum temperature, precipitation, well 19 bioclimatic variables processed 23 global climate models (GCMs), four Shared Socio-economic Pathways (SSPs): 126, 245, 370 585. Model SSP can chosen changing ending dataset name WorldClim_2.1_GCM_SSP_RESm. complete list available combinations can obtained : , interested HadGEM3-GC31-LL model, ssp set 245 10 arc-minutes, can get available variables: can now download “bio01” “bio02” dataset : datasets averages 20 year periods (2021-2040, 2041-2060, 2061-2080, 2081-2100). pastclim, midpoints periods (2030, 2050, 2070, 2090) used time stamps. 4 periods automatically downloaded combination GCM model SSP, can selected usual defining time region_slice. Alternatively, possible get full time series 4 slices : possible simply use get_time_ce_steps(dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\") get available time points dataset. Help WorldClim datasets (modern future) can accessed help(\"WorldClim_2.1\")","code":"list_available_datasets()[grepl(\"WorldClim_2.1\",list_available_datasets())] #>   [1] \"WorldClim_2.1_0.5m\"                        #>   [2] \"WorldClim_2.1_10m\"                         #>   [3] \"WorldClim_2.1_2.5m\"                        #>   [4] \"WorldClim_2.1_5m\"                          #>   [5] \"WorldClim_2.1_ACCESS-CM2_ssp126_0.5m\"      #>   [6] \"WorldClim_2.1_ACCESS-CM2_ssp126_10m\"       #>   [7] \"WorldClim_2.1_ACCESS-CM2_ssp126_2.5m\"      #>   [8] \"WorldClim_2.1_ACCESS-CM2_ssp126_5m\"        #>   [9] \"WorldClim_2.1_ACCESS-CM2_ssp245_0.5m\"      #>  [10] \"WorldClim_2.1_ACCESS-CM2_ssp245_10m\"       #>  [11] \"WorldClim_2.1_ACCESS-CM2_ssp245_2.5m\"      #>  [12] \"WorldClim_2.1_ACCESS-CM2_ssp245_5m\"        #>  [13] \"WorldClim_2.1_ACCESS-CM2_ssp370_0.5m\"      #>  [14] \"WorldClim_2.1_ACCESS-CM2_ssp370_10m\"       #>  [15] \"WorldClim_2.1_ACCESS-CM2_ssp370_2.5m\"      #>  [16] \"WorldClim_2.1_ACCESS-CM2_ssp370_5m\"        #>  [17] \"WorldClim_2.1_ACCESS-CM2_ssp585_0.5m\"      #>  [18] \"WorldClim_2.1_ACCESS-CM2_ssp585_10m\"       #>  [19] \"WorldClim_2.1_ACCESS-CM2_ssp585_2.5m\"      #>  [20] \"WorldClim_2.1_ACCESS-CM2_ssp585_5m\"        #>  [21] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_0.5m\"     #>  [22] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_10m\"      #>  [23] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_2.5m\"     #>  [24] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_5m\"       #>  [25] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_0.5m\"     #>  [26] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_10m\"      #>  [27] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_2.5m\"     #>  [28] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_5m\"       #>  [29] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_0.5m\"     #>  [30] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_10m\"      #>  [31] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_2.5m\"     #>  [32] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_5m\"       #>  [33] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_0.5m\"     #>  [34] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_10m\"      #>  [35] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_2.5m\"     #>  [36] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_5m\"       #>  [37] \"WorldClim_2.1_CMCC-ESM2_ssp126_0.5m\"       #>  [38] \"WorldClim_2.1_CMCC-ESM2_ssp126_10m\"        #>  [39] \"WorldClim_2.1_CMCC-ESM2_ssp126_2.5m\"       #>  [40] \"WorldClim_2.1_CMCC-ESM2_ssp126_5m\"         #>  [41] \"WorldClim_2.1_CMCC-ESM2_ssp245_0.5m\"       #>  [42] \"WorldClim_2.1_CMCC-ESM2_ssp245_10m\"        #>  [43] \"WorldClim_2.1_CMCC-ESM2_ssp245_2.5m\"       #>  [44] \"WorldClim_2.1_CMCC-ESM2_ssp245_5m\"         #>  [45] \"WorldClim_2.1_CMCC-ESM2_ssp370_0.5m\"       #>  [46] \"WorldClim_2.1_CMCC-ESM2_ssp370_10m\"        #>  [47] \"WorldClim_2.1_CMCC-ESM2_ssp370_2.5m\"       #>  [48] \"WorldClim_2.1_CMCC-ESM2_ssp370_5m\"         #>  [49] \"WorldClim_2.1_CMCC-ESM2_ssp585_0.5m\"       #>  [50] \"WorldClim_2.1_CMCC-ESM2_ssp585_10m\"        #>  [51] \"WorldClim_2.1_CMCC-ESM2_ssp585_2.5m\"       #>  [52] \"WorldClim_2.1_CMCC-ESM2_ssp585_5m\"         #>  [53] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_0.5m\"   #>  [54] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_10m\"    #>  [55] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_2.5m\"   #>  [56] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_5m\"     #>  [57] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_0.5m\"   #>  [58] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_10m\"    #>  [59] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_2.5m\"   #>  [60] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_5m\"     #>  [61] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_0.5m\"   #>  [62] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_10m\"    #>  [63] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_2.5m\"   #>  [64] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_5m\"     #>  [65] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_0.5m\"   #>  [66] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_10m\"    #>  [67] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_2.5m\"   #>  [68] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_5m\"     #>  [69] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_0.5m\"     #>  [70] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_10m\"      #>  [71] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_2.5m\"     #>  [72] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_5m\"       #>  [73] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_0.5m\"     #>  [74] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_10m\"      #>  [75] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_2.5m\"     #>  [76] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_5m\"       #>  [77] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_0.5m\"     #>  [78] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_10m\"      #>  [79] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_2.5m\"     #>  [80] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_5m\"       #>  [81] \"WorldClim_2.1_GFDL-ESM4_ssp126_0.5m\"       #>  [82] \"WorldClim_2.1_GFDL-ESM4_ssp126_10m\"        #>  [83] \"WorldClim_2.1_GFDL-ESM4_ssp126_2.5m\"       #>  [84] \"WorldClim_2.1_GFDL-ESM4_ssp126_5m\"         #>  [85] \"WorldClim_2.1_GFDL-ESM4_ssp370_0.5m\"       #>  [86] \"WorldClim_2.1_GFDL-ESM4_ssp370_10m\"        #>  [87] \"WorldClim_2.1_GFDL-ESM4_ssp370_2.5m\"       #>  [88] \"WorldClim_2.1_GFDL-ESM4_ssp370_5m\"         #>  [89] \"WorldClim_2.1_GISS-E2-1-G_ssp126_0.5m\"     #>  [90] \"WorldClim_2.1_GISS-E2-1-G_ssp126_10m\"      #>  [91] \"WorldClim_2.1_GISS-E2-1-G_ssp126_2.5m\"     #>  [92] \"WorldClim_2.1_GISS-E2-1-G_ssp126_5m\"       #>  [93] \"WorldClim_2.1_GISS-E2-1-G_ssp245_0.5m\"     #>  [94] \"WorldClim_2.1_GISS-E2-1-G_ssp245_10m\"      #>  [95] \"WorldClim_2.1_GISS-E2-1-G_ssp245_2.5m\"     #>  [96] \"WorldClim_2.1_GISS-E2-1-G_ssp245_5m\"       #>  [97] \"WorldClim_2.1_GISS-E2-1-G_ssp370_0.5m\"     #>  [98] \"WorldClim_2.1_GISS-E2-1-G_ssp370_10m\"      #>  [99] \"WorldClim_2.1_GISS-E2-1-G_ssp370_2.5m\"     #> [100] \"WorldClim_2.1_GISS-E2-1-G_ssp370_5m\"       #> [101] \"WorldClim_2.1_GISS-E2-1-G_ssp585_0.5m\"     #> [102] \"WorldClim_2.1_GISS-E2-1-G_ssp585_10m\"      #> [103] \"WorldClim_2.1_GISS-E2-1-G_ssp585_2.5m\"     #> [104] \"WorldClim_2.1_GISS-E2-1-G_ssp585_5m\"       #> [105] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_0.5m\" #> [106] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_10m\"  #> [107] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_2.5m\" #> [108] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_5m\"   #> [109] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_0.5m\" #> [110] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\"  #> [111] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_2.5m\" #> [112] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_5m\"   #> [113] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_0.5m\" #> [114] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_10m\"  #> [115] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_2.5m\" #> [116] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_5m\"   #> [117] \"WorldClim_2.1_INM-CM5-0_ssp126_0.5m\"       #> [118] \"WorldClim_2.1_INM-CM5-0_ssp126_10m\"        #> [119] \"WorldClim_2.1_INM-CM5-0_ssp126_2.5m\"       #> [120] \"WorldClim_2.1_INM-CM5-0_ssp126_5m\"         #> [121] \"WorldClim_2.1_INM-CM5-0_ssp245_0.5m\"       #> [122] \"WorldClim_2.1_INM-CM5-0_ssp245_10m\"        #> [123] \"WorldClim_2.1_INM-CM5-0_ssp245_2.5m\"       #> [124] \"WorldClim_2.1_INM-CM5-0_ssp245_5m\"         #> [125] \"WorldClim_2.1_INM-CM5-0_ssp370_0.5m\"       #> [126] \"WorldClim_2.1_INM-CM5-0_ssp370_10m\"        #> [127] \"WorldClim_2.1_INM-CM5-0_ssp370_2.5m\"       #> [128] \"WorldClim_2.1_INM-CM5-0_ssp370_5m\"         #> [129] \"WorldClim_2.1_INM-CM5-0_ssp585_0.5m\"       #> [130] \"WorldClim_2.1_INM-CM5-0_ssp585_10m\"        #> [131] \"WorldClim_2.1_INM-CM5-0_ssp585_2.5m\"       #> [132] \"WorldClim_2.1_INM-CM5-0_ssp585_5m\"         #> [133] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_0.5m\"    #> [134] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_10m\"     #> [135] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_2.5m\"    #> [136] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_5m\"      #> [137] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_0.5m\"    #> [138] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_10m\"     #> [139] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_2.5m\"    #> [140] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_5m\"      #> [141] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_0.5m\"    #> [142] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_10m\"     #> [143] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_2.5m\"    #> [144] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_5m\"      #> [145] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_0.5m\"    #> [146] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_10m\"     #> [147] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_2.5m\"    #> [148] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_5m\"      #> [149] \"WorldClim_2.1_MIROC6_ssp126_0.5m\"          #> [150] \"WorldClim_2.1_MIROC6_ssp126_10m\"           #> [151] \"WorldClim_2.1_MIROC6_ssp126_2.5m\"          #> [152] \"WorldClim_2.1_MIROC6_ssp126_5m\"            #> [153] \"WorldClim_2.1_MIROC6_ssp245_0.5m\"          #> [154] \"WorldClim_2.1_MIROC6_ssp245_10m\"           #> [155] \"WorldClim_2.1_MIROC6_ssp245_2.5m\"          #> [156] \"WorldClim_2.1_MIROC6_ssp245_5m\"            #> [157] \"WorldClim_2.1_MIROC6_ssp370_0.5m\"          #> [158] \"WorldClim_2.1_MIROC6_ssp370_10m\"           #> [159] \"WorldClim_2.1_MIROC6_ssp370_2.5m\"          #> [160] \"WorldClim_2.1_MIROC6_ssp370_5m\"            #> [161] \"WorldClim_2.1_MIROC6_ssp585_0.5m\"          #> [162] \"WorldClim_2.1_MIROC6_ssp585_10m\"           #> [163] \"WorldClim_2.1_MIROC6_ssp585_2.5m\"          #> [164] \"WorldClim_2.1_MIROC6_ssp585_5m\"            #> [165] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"   #> [166] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_10m\"    #> [167] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_2.5m\"   #> [168] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_5m\"     #> [169] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_0.5m\"   #> [170] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_10m\"    #> [171] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_2.5m\"   #> [172] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_5m\"     #> [173] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"   #> [174] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_10m\"    #> [175] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_2.5m\"   #> [176] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_5m\"     #> [177] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"   #> [178] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_10m\"    #> [179] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_2.5m\"   #> [180] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_5m\"     #> [181] \"WorldClim_2.1_MRI-ESM2-0_ssp126_0.5m\"      #> [182] \"WorldClim_2.1_MRI-ESM2-0_ssp126_10m\"       #> [183] \"WorldClim_2.1_MRI-ESM2-0_ssp126_2.5m\"      #> [184] \"WorldClim_2.1_MRI-ESM2-0_ssp126_5m\"        #> [185] \"WorldClim_2.1_MRI-ESM2-0_ssp245_0.5m\"      #> [186] \"WorldClim_2.1_MRI-ESM2-0_ssp245_10m\"       #> [187] \"WorldClim_2.1_MRI-ESM2-0_ssp245_2.5m\"      #> [188] \"WorldClim_2.1_MRI-ESM2-0_ssp245_5m\"        #> [189] \"WorldClim_2.1_MRI-ESM2-0_ssp370_0.5m\"      #> [190] \"WorldClim_2.1_MRI-ESM2-0_ssp370_10m\"       #> [191] \"WorldClim_2.1_MRI-ESM2-0_ssp370_2.5m\"      #> [192] \"WorldClim_2.1_MRI-ESM2-0_ssp370_5m\"        #> [193] \"WorldClim_2.1_MRI-ESM2-0_ssp585_0.5m\"      #> [194] \"WorldClim_2.1_MRI-ESM2-0_ssp585_10m\"       #> [195] \"WorldClim_2.1_MRI-ESM2-0_ssp585_2.5m\"      #> [196] \"WorldClim_2.1_MRI-ESM2-0_ssp585_5m\"        #> [197] \"WorldClim_2.1_UKESM1-0-LL_ssp126_0.5m\"     #> [198] \"WorldClim_2.1_UKESM1-0-LL_ssp126_10m\"      #> [199] \"WorldClim_2.1_UKESM1-0-LL_ssp126_2.5m\"     #> [200] \"WorldClim_2.1_UKESM1-0-LL_ssp126_5m\"       #> [201] \"WorldClim_2.1_UKESM1-0-LL_ssp245_0.5m\"     #> [202] \"WorldClim_2.1_UKESM1-0-LL_ssp245_10m\"      #> [203] \"WorldClim_2.1_UKESM1-0-LL_ssp245_2.5m\"     #> [204] \"WorldClim_2.1_UKESM1-0-LL_ssp245_5m\"       #> [205] \"WorldClim_2.1_UKESM1-0-LL_ssp370_0.5m\"     #> [206] \"WorldClim_2.1_UKESM1-0-LL_ssp370_10m\"      #> [207] \"WorldClim_2.1_UKESM1-0-LL_ssp370_2.5m\"     #> [208] \"WorldClim_2.1_UKESM1-0-LL_ssp370_5m\"       #> [209] \"WorldClim_2.1_UKESM1-0-LL_ssp585_0.5m\"     #> [210] \"WorldClim_2.1_UKESM1-0-LL_ssp585_10m\"      #> [211] \"WorldClim_2.1_UKESM1-0-LL_ssp585_2.5m\"     #> [212] \"WorldClim_2.1_UKESM1-0-LL_ssp585_5m\" get_vars_for_dataset(dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\") #>  [1] \"bio01\" \"bio02\" \"bio03\" \"bio04\" \"bio05\" \"bio06\" \"bio07\" \"bio08\" \"bio09\" #> [10] \"bio10\" \"bio11\" \"bio12\" \"bio13\" \"bio14\" \"bio15\" \"bio16\" \"bio17\" \"bio18\" #> [19] \"bio19\" download_dataset(   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") ) future_slice <- region_slice(   time_ce = 2030,   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") ) future_series <- region_series(   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"chelsa-1","dir":"Articles","previous_headings":"Future projections","what":"CHELSA","title":"present and future","text":"Future projections based models CMIP6, downscaled de-biased using CHELSA 2.1 present baseline. Monthly values mean temperature, precipitation, well 19 bioclimatic variables processed 5 global climate models (GCMs), three Shared Socio-economic Pathways (SSPs): 126, 370 585. Model SSP can chosen changing ending dataset name CHELSA_2.1_GCM_SSP_0.5m. complete list available combinations can obtained : Note virtual option dataset. , interested GFDL-ESM4 model, ssp set 126 , can get available variables: can now download “bio01” “bio02” dataset, using virtual version, : datasets averages 30 year periods (2011-2040, 2041-2070, 2071-2100). pastclim, midpoints periods (2025, 2055, 2075) used time stamps. 3 periods automatically downloaded given combination GCM model SSP, can selected usual defining time region_slice. Alternatively, possible get full time series 4 slices : possible simply use get_time_ce_steps(dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\") get available time points dataset. Help WorldClim datasets (modern future) can accessed help(\"CHELSA_2.1\")","code":"list_available_datasets()[grepl(\"CHELSA_2.1\",list_available_datasets())] #>  [1] \"CHELSA_2.1_0.5m\"                          #>  [2] \"CHELSA_2.1_0.5m_vsi\"                      #>  [3] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m\"         #>  [4] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi\"     #>  [5] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m\"         #>  [6] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m_vsi\"     #>  [7] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m\"         #>  [8] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m_vsi\"     #>  [9] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m\"      #> [10] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m_vsi\"  #> [11] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m\"      #> [12] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m_vsi\"  #> [13] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m\"      #> [14] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m_vsi\"  #> [15] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"     #> [16] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m_vsi\" #> [17] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"     #> [18] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m_vsi\" #> [19] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"     #> [20] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m_vsi\" #> [21] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m\"        #> [22] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m_vsi\"    #> [23] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m\"        #> [24] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m_vsi\"    #> [25] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m\"        #> [26] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m_vsi\"    #> [27] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m\"       #> [28] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m_vsi\"   #> [29] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m\"       #> [30] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_vsi\"   #> [31] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\"       #> [32] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\" get_vars_for_dataset(dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\", monthly=TRUE) #>  [1] \"bio01\"            \"bio02\"            \"bio03\"            \"bio04\"            #>  [5] \"bio05\"            \"bio06\"            \"bio07\"            \"bio08\"            #>  [9] \"bio09\"            \"bio10\"            \"bio11\"            \"bio12\"            #> [13] \"bio13\"            \"bio14\"            \"bio15\"            \"bio16\"            #> [17] \"bio17\"            \"bio18\"            \"bio19\"            \"temperature_01\"   #> [21] \"temperature_02\"   \"temperature_03\"   \"temperature_04\"   \"temperature_05\"   #> [25] \"temperature_06\"   \"temperature_07\"   \"temperature_08\"   \"temperature_09\"   #> [29] \"temperature_10\"   \"temperature_11\"   \"temperature_12\"   \"precipitation_01\" #> [33] \"precipitation_02\" \"precipitation_03\" \"precipitation_04\" \"precipitation_05\" #> [37] \"precipitation_06\" \"precipitation_07\" \"precipitation_08\" \"precipitation_09\" #> [41] \"precipitation_10\" \"precipitation_11\" \"precipitation_12\" download_dataset(   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") ) future_slice <- region_slice(   time_ce = 2025,   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") ) future_series <- region_series(   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") )"},{"path":"https://evolecolgroup.github.io/pastclim/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michela Leonardi. Author. Emily Y. Hallet. Contributor. Robert Beyer. Contributor. Mario Krapp. Contributor. Andrea Manica. Author, maintainer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Leonardi M, Hallet EY, Beyer R, Krapp M, Manica (2023). “pastclim 1.2: R package easily access use paleoclimatic reconstructions.” Ecography, 2023, e06481. doi:10.1111/ecog.06481.","code":"@Article{pastclim-article,   title = {pastclim 1.2: an R package to easily access and use paleoclimatic reconstructions},   author = {Michela Leonardi and Emily Y. Hallet and Robert Beyer and Mario Krapp and Andrea Manica},   journal = {Ecography},   year = {2023},   volume = {2023},   pages = {e06481},   publisher = {Wiley},   doi = {10.1111/ecog.06481}, }"},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"pastclim-","dir":"","previous_headings":"","what":"Manipulate Time Series of Palaeoclimate Reconstructions","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"R library designed provide easy way extract manipulate palaeoclimate reconstructions ecological anthropological analyses. also able handle time series future reconstructions. functionalities pastclim described Leonardi et al. (2023). Please cite use pastclim research.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"install-the-library","dir":"","previous_headings":"","what":"Install the library","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"pastclim CRAN, easiest way install : version CRAN recommended every day use. New features bug fixes appear first dev branch GitHub, make way CRAN. need early access new features, can install development version pastclim directly GitHub, using devtools, simply get compiled version r-universe. Also, note dev version pastclim tracks changes dev version terra, need upgrade libraries :","code":"install.packages(\"pastclim\") install.packages('terra', repos='https://rspatial.r-universe.dev') install.packages(\"pastclim\", repos = c(\"https://evolecolgroup.r-universe.dev\")"},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"overview-of-functionality","dir":"","previous_headings":"","what":"Overview of functionality","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"dedicated website, can find Articles giving step--step overview package, cheatsheet. also dev version site updated dev branch pastclim (top left dev website, version number red format x.x.x.9xxx, indicating development version). pastclim currently includes data Beyer et al 2020 (reconstruction climate based HadCM3 model last 120k years), Krapp et al 2021 (covers last 800k years statistical emulator HadCM3), Barreto et al 2023 (covering last 5M years using PALEO-PGEM emulator), PaleoClim (providing time steps different palaeoclimate models downscaled higher resolution), CHELSA-Trace21K (transient reconstruction last 21k years, downscaled 1km resolution), HYDE3.3 database land use reconstructions last 10k years, WorldClim CHELSA data (present, future projections number models emission scenarios). details datasets can found . also instructions build use custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"when-something-does-not-work","dir":"","previous_headings":"","what":"When something does not work","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"something work, check issues GitHub see whether problem already reported. , feel free create new issue. Please make sure updated latest version pastclim CRAN, well updating packages system, provide reproducible example developers investigate problem.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Barreto2023.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Barreto et al 2023 dataset — Barreto2023","title":"Documentation for the Barreto et al 2023 dataset — Barreto2023","text":"Spatio-temporal series monthly temperature precipitation 17 derived bioclimatic variables covering last 5 Ma (Pliocene–Pleistocene), intervals 1,000 years, spatial resolution 1 arc-degrees (see Barreto et al., 2023 details).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Barreto2023.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Barreto et al 2023 dataset — Barreto2023","text":"PALEO-PGEM-Series downscaled 1 × 1 arc-degrees spatial resolution outputs PALEO-PGEM emulator (Holden et al., 2019), emulates reasonable extensively validated global estimates monthly temperature precipitation Plio-Pleistocene every 1 kyr spatial resolution ~5 × 5 arc-degrees (Holden et al., 2016, 2019). PALEO-PGEM-Series includes mean standard deviation (.e., standard error) emulated climate 10 stochastic GCM emulations accommodate aspects model uncertainty. allows users estimate robustness results face stochastic aspects emulations. details, see Section 2.4 Barreto et al. (2023). Note large dataset, 5001 time slices. takes approximately 1 minute set variable creating region_slice region_series. However, object created, operations tend much faster (especially subset dataset small number time steps interest). IMPORTANT: use dataset, make sure cite original publications: Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. (2023). PALEO-PGEM-Series: spatial time series global climate last 5 million years (Plio-Pleistocene). Global Ecology Biogeography, 32, 1034-1045, doi:10.1111/geb.13683 Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., Tran, G. T., Wilkinson, R. D. (2019): PALEO-PGEM v1.0: statistical emulator Pliocene–Pleistocene climate, Geosci. Model Dev., 12, 5137–5155, doi:10.5194/gmd-12-5137-2019 .","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Beyer2020.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Beyer2020 dataset — Beyer2020","title":"Documentation for the Beyer2020 dataset — Beyer2020","text":"dataset covers last 120k years, intervals 1/2 k years, resolution 0.5 degrees latitude longitude.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Beyer2020.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Beyer2020 dataset — Beyer2020","text":"IMPORTANT: use dataset, make sure cite original publication: Beyer, R.M., Krapp, M. & Manica, . High-resolution terrestrial climate, bioclimate vegetation last 120,000 years. Sci Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 version included pastclim ice sheets masked, well internal seas (Black Caspian Sea) removed. latter based : https://www.marineregions.org/gazetteer.php?p=details&id=4278 https://www.marineregions.org/gazetteer.php?p=details&id=4282 reconstruction depth time, modern outlines used time steps. Also, bio15, coefficient variation computed adding one monthly estimates, multiplied 100 following https://pubs.usgs.gov/ds/691/ds691.pdf Changelog v1.1.0 Added monthly variables. Files can downloaded : https://zenodo.org/deposit/7062281 v1.0.0 Remove ice sheets internal seas, use correct formula bio15. Files can downloaded : doi:10.6084/m9.figshare.19723405.v1","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_2.1.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for CHELSA 2.1 — CHELSA_2.1","title":"Documentation for CHELSA 2.1 — CHELSA_2.1","text":"CHELSA version 2.1 database high spatial resolution global weather climate data, covering present future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_2.1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for CHELSA 2.1 — CHELSA_2.1","text":"IMPORTANT: use dataset, make sure cite original publication CHELSA dataset: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017) Climatologies high resolution Earth land surface areas. Scientific Data. 4 170122. doi:10.1038/sdata.2017.122 Present-day reconstructions based mean period 1981-2000 available high resolution 0.5 arc-minutes (CHELSA_2.1_0.5m). pastclim, datasets given date 1990 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates mean temperature, precipitation. dataset large, includes estimates oceans well land masses. alternative downloading large files use virtual rasters, allow data remain server, pixels required given operation downloaded. Virtual rasters can used choosing (CHELSA_2.1_0.5m_vsi) Future projections based models CMIP6, downscaled de-biased using CHELSA algorithm 2.1. Monthly values mean temperature, total precipitation, well 19 bioclimatic variables processed 5 global climate models (GCMs), three Shared Socio-economic Pathways (SSPs): 126, 370 585. Model SSP can chosen changing ending dataset name CHELSA_2.1_GCM_SSP_RESm. Available values GCM : \"GFDL-ESM4\", \"IPSL-CM6A-LR\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", \"UKESM1-0-LL\". SSP, use: \"ssp126\", \"ssp370\",\t\"ssp585\". RES currently limited \"0.5m\". Example dataset names CHELSA_2.1_GFDL-ESM4_ssp126_0.5m CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m present reconstructions, alternative downloading large files use virtual rasters. Simply append \"_vis\" name dataset interest (CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi). dataset averages 30 year periods (2011-2040, 2041-2070, 2071-2100). pastclim, midpoints periods (2025, 2055, 2075) used time stamps. 3 periods automatically downloaded combination GCM model SSP, selected usual defining time functions region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_trace21k_1.0.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","title":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","text":"CHELSA-TraCE21k data provides monthly climate data temperature precipitation 30 arc-sec spatial resolution 100-year time steps last 21,000 years. Palaeo-orography high spatial resolution time step created combining high resolution information glacial cover current Last Glacial Maximum (LGM) glacier databases interpolation dynamic ice sheet model (ICE6G) coupling mean annual temperatures CCSM3-TraCE21k. Based reconstructed palaeo-orography, mean annual temperature precipitation downscaled using CHELSA V1.2 algorithm.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_trace21k_1.0.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","text":"details dataset available dedicated website. alternative downloading large files use virtual rasters. Simply append \"_vis\" name dataset interest (CHELSA_trace21k_1.0_0.5m_vsi). recommended approach, currently available version dataset. IMPORTANT: use dataset, make sure cite original publication: Karger, D.N., Nobis, M.P., Normand, S., Graham, C.H., Zimmermann, N. (2023) CHELSA-TraCE21k – High resolution (1 km) downscaled transient temperature precipitation data since Last Glacial Maximum. Climate Past. doi:10.5194/cp-2021-30","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Example.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Example dataset — Example","title":"Documentation for the Example dataset — Example","text":"dataset subset Beyer2020, used vignette pastclim. use dataset real work, might reflect --date version Beyer2020.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/HYDE_3.3_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","title":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","text":"database presents update expansion History Database Global Environment (HYDE, v 3.3) replaces former HYDE 3.2 version 2017. HYDE internally consistent combination updated historical population estimates land use. Categories include cropland, new distinction irrigated rain fed crops (rice) irrigated rain fed rice. Also grazing lands provided, divided intensively used pasture, converted rangeland non-converted natural (less intensively used) rangeland. Population represented maps total, urban, rural population population density well built-area.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/HYDE_3.3_baseline.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","text":"period covered 10 000 BCE 2023 CE. Spatial resolution 5 arc minutes (approx. 85 km2 equator). full HYDE 3.3 release contains: Baseline estimate scenario, Lower estimate scenario Upper estimate scenario. Currently baseline scenario available pastclim details dataset available dedicated website. IMPORTANT: use dataset, make sure cite original publication HYDE 3.2 (current publication 3.3): Klein Goldewijk, K., Beusen, ., Doelman, J., Stehfest, E.: Anthropogenic land-use estimates Holocene; HYDE 3.2, Earth Syst. Sci. Data, 9, 927-953, 2017. doi:10.5194/essd-9-927-2017","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Krapp2021.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Krapp2021 dataset — Krapp2021","title":"Documentation for the Krapp2021 dataset — Krapp2021","text":"dataset covers last 800k years, intervals 1k years, resolution 0.5 degrees latitude longitude.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Krapp2021.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Krapp2021 dataset — Krapp2021","text":"units several variables changed match used WorldClim. IMPORTANT: use dataset, make sure cite original publication: Krapp, M., Beyer, R.M., Edmundson, S.L. et al. statistics-based reconstruction high-resolution global terrestrial climate last 800,000 years. Sci Data 8, 228 (2021). doi:10.1038/s41597-021-01009-3 version included pastclim ice sheets masked. Note , bio15, use corrected version, follows https://pubs.usgs.gov/ds/691/ds691.pdf Changelog v1.4.0 Change units match WorldClim. Fix variable duplication found earlier versions dataset. https://zenodo.org/records/8415273 v1.1.0 Added monthly variables. Files can downloaded : https://zenodo.org/record/7065055 v1.0.0 Remove ice sheets use correct formula bio15. Files can downloaded : doi:10.6084/m9.figshare.19733680.v1","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/WorldClim_2.1.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the WorldClim datasets — WorldClim_2.1","title":"Documentation for the WorldClim datasets — WorldClim_2.1","text":"WorldClim version 2.1 database high spatial resolution global weather climate data, covering present future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/WorldClim_2.1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the WorldClim datasets — WorldClim_2.1","text":"IMPORTANT: use dataset, make sure cite original publication: Fick, S.E. R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces global land areas. International Journal Climatology 37 (12): 4302-4315. doi:10.1002/joc.5086 Present-day reconstructions based mean period 1970-2000, available multiple resolutions 10 arc-minutes, 5 arc-minutes, 2.5 arc-minute 0.5 arc-minutes. resolution interest can obtained changing ending dataset name WorldClim_2.1_RESm, e.g. WorldClim_2.1_10m WorldClim_2.1_5m (currently, 10m 5m currently available pastclim). pastclim, datasets given date 1985 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. Future projections based models CMIP6, downscaled de-biased using WorldClim 2.1 present baseline. Monthly values minimum temperature, maximum temperature, precipitation, well 19 bioclimatic variables processed 23 global climate models (GCMs), four Shared Socio-economic Pathways (SSPs): 126, 245, 370 585. Model SSP can chosen changing ending dataset name WorldClim_2.1_GCM_SSP_RESm. Available values GCM : \"ACCESS-CM2\", \"BCC-CSM2-MR\", \"CMCC-ESM2\", \"EC-Earth3-Veg\", \"FIO-ESM-2-0\", \"GFDL-ESM4\", \"GISS-E2-1-G\", \"HadGEM3-GC31-LL\", \"INM-CM5-0\", \"IPSL-CM6A-LR\", \"MIROC6\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", \"UKESM1-0-LL\". SSP, use: \"ssp126\", \"ssp245\",\t\"ssp370\",\t\"ssp585\". RES takes values present reconstructions (.e. \"10m\", \"5m\", \"2.5m\", \"0.5m\"). Example dataset names WorldClim_2.1_ACCESS-CM2_ssp245_10m WorldClim_2.1_MRI-ESM2-0_ssp370_5m. Four combination (namely FIO-ESM-2-0_ssp370, GFDL-ESM4_ssp245, GFDL-ESM4_ssp585, HadGEM3-GC31-LL_ssp370) available. dataset averages 20 year periods (2021-2040, 2041-2060, 2061-2080, 2081-2100). pastclim, midpoints periods (2030, 2050, 2070, 2090) used time stamps. 4 periods automatically downloaded combination GCM model SSP, selected usual defining time functions region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute bioclimatic variables — bioclim_vars","title":"Compute bioclimatic variables — bioclim_vars","text":"Function compute \"bioclimatic\" variables monthly average temperature precipitation data. modern data, variables generally computed using min maximum temperature, many palaeoclimatic reconstructions average temperature available. variables, exception BIO02 BIO03, can rephrased meaningfully terms mean temperature. function modified version predicts::bcvars.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute bioclimatic variables — bioclim_vars","text":"","code":"bioclim_vars(prec, tavg, ...)  # S4 method for numeric,numeric bioclim_vars(prec, tavg)  # S4 method for SpatRaster,SpatRaster bioclim_vars(prec, tavg, filename = \"\", ...)  # S4 method for SpatRasterDataset,SpatRasterDataset bioclim_vars(prec, tavg, filename = \"\", ...)  # S4 method for matrix,matrix bioclim_vars(prec, tavg)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute bioclimatic variables — bioclim_vars","text":"prec monthly precipitation tavg monthly average temperatures ... additional variables specific methods filename filename save raster (optional).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute bioclimatic variables — bioclim_vars","text":"bioclim variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute bioclimatic variables — bioclim_vars","text":"variables : BIO01 = Annual Mean Temperature BIO04 = Temperature Seasonality (standard deviation x 100) BIO05 = Max Temperature Warmest Month BIO06 = Min Temperature Coldest Month BIO07 = Temperature Annual Range (P5-P6) BIO08 = Mean Temperature Wettest Quarter BIO09 = Mean Temperature Driest Quarter BIO10 = Mean Temperature Warmest Quarter BIO11 = Mean Temperature Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation Wettest Month BIO14 = Precipitation Driest Month BIO15 = Precipitation Seasonality (Coefficient Variation) BIO16 = Precipitation Wettest Quarter BIO17 = Precipitation Driest Quarter BIO18 = Precipitation Warmest Quarter BIO19 = Precipitation Coldest Quarter summary Bioclimatic variables : Nix, 1986. biogeographic analysis Australian elapid snakes. : R. Longmore (ed.). Atlas elapid snakes Australia. Australian Flora Fauna Series 7. Australian Government Publishing Service, Canberra. expanded following ANUCLIM manual","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"BIOME4 classes. — biome4_classes","title":"BIOME4 classes. — biome4_classes","text":"data.frame defining details class","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BIOME4 classes. — biome4_classes","text":"","code":"biome4_classes"},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"BIOME4 classes. — biome4_classes","text":"data.frame multiple columns describe.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if dataset is available. — check_available_dataset","title":"Check if dataset is available. — check_available_dataset","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if dataset is available. — check_available_dataset","text":"","code":"check_available_dataset(dataset, include_custom = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if dataset is available. — check_available_dataset","text":"dataset string defining dataset include_custom boolean whether 'custom' dataset allowed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if dataset is available. — check_available_dataset","text":"TRUE dataset available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if var is available for this dataset. — check_available_variable","title":"Check if var is available for this dataset. — check_available_variable","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if var is available for this dataset. — check_available_variable","text":"","code":"check_available_variable(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if var is available for this dataset. — check_available_variable","text":"variable vector names variables interest dataset dataset interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if var is available for this dataset. — check_available_variable","text":"TRUE var available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that we have a valid pair of coordinate names — check_coords_names","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"internal function checks coords (passed functions) valid set names, , NULL, standard variable names data","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"","code":"check_coords_names(data, coords)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"data data.frame containing locations. coords vector length two giving names \"x\" \"y\" coordinates, points data.frame use standard names.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"vector length 2 valid names, correct order","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Check dataset and path_to_nc params — check_dataset_path","title":"Check dataset and path_to_nc params — check_dataset_path","text":"Check dataset path_to_nc parameters valid. Specifically, path_to_nc set dataset custom (conversely, custom datasets require path_to_nc).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check dataset and path_to_nc params — check_dataset_path","text":"","code":"check_dataset_path(dataset, path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check dataset and path_to_nc params — check_dataset_path","text":"dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\". path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check dataset and path_to_nc params — check_dataset_path","text":"TRUE dataset path valid.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Check multiple time variables — check_time_vars","title":"Check multiple time variables — check_time_vars","text":"Check one set times","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check multiple time variables — check_time_vars","text":"","code":"check_time_vars(time_bp, time_ce, allow_null = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check multiple time variables — check_time_vars","text":"time_bp times bp time_ce times ce allow_null boolean whether can NULL","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check multiple time variables — check_time_vars","text":"times bp","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"Internal function check whether downloaded given variable dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"","code":"check_var_downloaded(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"variable vector names variables interest dataset dataset interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"TRUE variable downloaded.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether variables exist in a netcdf file — check_var_in_nc","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"Internal function test custom nc file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"","code":"check_var_in_nc(bio_variables, path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"bio_variables vector names variables extracted path_to_nc path custom nc file containing palaeoclimate reconstructions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"TRUE variable exists","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean the data path — clean_data_path","title":"Clean the data path — clean_data_path","text":"function deletes old reconstructions superseded data_path. assumes files data_path part pastclim (.e. custom datasets stored directory).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean the data path — clean_data_path","text":"","code":"clean_data_path(ask = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean the data path — clean_data_path","text":"ask boolean whether user asked deleting","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean the data path — clean_data_path","text":"TRUE files deleted successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"Deprecated version location_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"","code":"climate_for_locations(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"... arguments passed location_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a climate slice for a region — climate_for_time_slice","title":"Extract a climate slice for a region — climate_for_time_slice","text":"Deprecated version region_slice()]","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a climate slice for a region — climate_for_time_slice","text":"","code":"climate_for_time_slice(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a climate slice for a region — climate_for_time_slice","text":"... arguments passed region_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a climate slice for a region — climate_for_time_slice","text":"SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to copy the example dataset when a new data path is set — copy_example_data","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"Copy example dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"","code":"copy_example_data()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"TRUE data copied successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract data frame from a region series — df_from_region_series","title":"Extract data frame from a region series — df_from_region_series","text":"Extract climatic information region series organise data frame.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract data frame from a region series — df_from_region_series","text":"","code":"df_from_region_series(x, xy = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract data frame from a region series — df_from_region_series","text":"x climate time series generated region_series() xy boolean whether x y coordinates added dataframe (default TRUE)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract data frame from a region series — df_from_region_series","text":"data.frame cell raster layer (.e. timestep) row, available variables columns.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract data frame from a region series — df_from_region_series","text":"extract data frame region slice, see df_from_region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract data frame from a region slice — df_from_region_slice","title":"Extract data frame from a region slice — df_from_region_slice","text":"Extract climatic information region slice organise data frame. just wrapper around terra::.data.frame().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract data frame from a region slice — df_from_region_slice","text":"","code":"df_from_region_slice(x, xy = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract data frame from a region slice — df_from_region_slice","text":"x climate time slice (.e. terra::SpatRaster) generated region_slice() xy boolean whether x y coordinates added dataframe (default TRUE)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract data frame from a region slice — df_from_region_slice","text":"data.frame cell raster row, available variables columns.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract data frame from a region slice — df_from_region_slice","text":"extract data frame region series, see df_from_region_series().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"Get land mask dataset given time point, compute distance sea land pixel.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"","code":"distance_from_sea(time_bp = NULL, time_ce = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"time_bp time slice years present (negative) time_ce time slice years CE. one time_bp time_ce used. dataset string defining dataset use (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"terra::SpatRaster distances coastline km","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of variation (expressed as percentage) — .cv","title":"Coefficient of variation (expressed as percentage) — .cv","text":"R function compute coefficient variation (expressed percentage). single value, stats::sd = NA. However, one argue cv =0; NA may break code receives . function returns 0 mean close zero.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of variation (expressed as percentage) — .cv","text":"","code":".cv(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of variation (expressed as percentage) — .cv","text":"x vector values","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of variation (expressed as percentage) — .cv","text":"cv","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coefficient of variation (expressed as percentage) — .cv","text":"ODD: abs avoid small (zero) mean e.g. -5:5","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the CHELSA modern and future observations. — download_chelsa","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"function downloads annual monthly variables CHELSA v2.1 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"","code":"download_chelsa(dataset, bio_var, filename)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"dataset name dataset bio_var variable name filename filename stored data_path pastclim (includes full data path)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"TRUE requested CHELSA variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the CHELSA trace21k — download_chelsa_trace21k","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"function downloads annual monthly variables CHELSA trace v1.0 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"","code":"download_chelsa_trace21k(dataset, bio_var, filename = NULL, time_bp = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"dataset name dataset bio_var variable name filename filename stored data_path pastclim (includes full data path) time_bp time steps dataset built (NULL time steps)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"TRUE requested CHELSA variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"dataset huge, download files situations. reason, time_bp set downloading (allowed virtual datasets)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Download palaeoclimate reconstructions. — download_dataset","title":"Download palaeoclimate reconstructions. — download_dataset","text":"function downloads palaeoclimate reconstructions. Files stored data path pastclim, can inspected get_data_path() changed set_data_path()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download palaeoclimate reconstructions. — download_dataset","text":"","code":"download_dataset(dataset, bio_variables = NULL, annual = TRUE, monthly = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download palaeoclimate reconstructions. — download_dataset","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets. bio_variables one variable names downloaded. left NULL, variables available dataset downloaded (parameters annual monthly, see , define types) annual boolean download annual variables monthly boolean download monthly variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download palaeoclimate reconstructions. — download_dataset","text":"TRUE dataset(s) downloaded correctly.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the ETOPO Global relief model — download_etopo","title":"Download the ETOPO Global relief model — download_etopo","text":"function downloads ETOPO2022 global relief model 0.5 1 arc-minute (.e. 30 60 arc-seconds) resolution. large file (>1Gb).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the ETOPO Global relief model — download_etopo","text":"","code":"download_etopo(path = NULL, resolution = 1)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the ETOPO Global relief model — download_etopo","text":"path character. Path download data . left NULL, data downloaded directory returned get_data_path(), automatically named etopo2022_{resolution}s_v1.nc resolution numeric resolution arc-minute (one 0.5, 1). Defaults 1 arc-minute.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the ETOPO Global relief model — download_etopo","text":"dataframe produced curl::multi_download() information download (including error codes)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the paleoclim time series. — download_paleoclim","title":"Download the paleoclim time series. — download_paleoclim","text":"function downloads annual monthly variables Paleoclim V1.0 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the paleoclim time series. — download_paleoclim","text":"","code":"download_paleoclim(dataset, bio_var, filename = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the paleoclim time series. — download_paleoclim","text":"dataset name dataset bio_var variable name filename (USED FUNCTION: data come zip bio variables, generate multiple files, single one)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the paleoclim time series. — download_paleoclim","text":"TRUE requested paleoclim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the worldclim future time series. — download_worldclim_future","title":"Download the worldclim future time series. — download_worldclim_future","text":"function downloads annual monthly variables WorldClim v2.1 dataset future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the worldclim future time series. — download_worldclim_future","text":"","code":"download_worldclim_future(dataset, bio_var, filename = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the worldclim future time series. — download_worldclim_future","text":"dataset name dataset bio_var variable name filename ()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the worldclim future time series. — download_worldclim_future","text":"TRUE requested worldclim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the worldclim future time series. — download_worldclim_future","text":"Note: data come tiffs containing bio (prec/temp) variables given time step. , generate vrt per variable. , since download full set give variable type, create vrts variable type (e.g. bio). use filename get version name, end check generate correctly given programmatic way creating names vrt files.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Download a WorldClim modern observations. — download_worldclim_present","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"function downloads annual monthly variables WorldClim 2.1 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"","code":"download_worldclim_present(dataset, bio_var, filename)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"dataset name dataset bio_var variable name filename file name (full path) file saved","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"TRUE requested WorldClim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"function creates vector paths needed download CHELSA future dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"","code":"filenames_chelsa_future(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"function creates vector paths needed download CHELSA present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"","code":"filenames_chelsa_present(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"function creates vector paths needed download CHELSA trace21k","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"","code":"filenames_chelsa_trace21k(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa present dataset — filenames_paleoclim","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"function creates vector paths needed download CHELSA present dataset. Possible names \"paleoclim_1.0_10m\", \"paleoclim_1.0_5m\", \"paleoclim_1.0_2.5m\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"","code":"filenames_paleoclim(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"function creates vector paths needed download WorldClim present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"","code":"filenames_worldclim_future(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"function creates vector paths needed download WorldClim present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"","code":"filenames_worldclim_present(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the available datasets. — get_available_datasets","title":"Get the available datasets. — get_available_datasets","text":"List datasets available pastclim, can passed functions pastclim values parameter dataset. functions can also used custom datasets setting dataset=\"custom\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the available datasets. — get_available_datasets","text":"","code":"get_available_datasets()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the available datasets. — get_available_datasets","text":"character vector available datasets","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the available datasets. — get_available_datasets","text":"function provides user-friendly list, summarising many datasets available WorldClim. comprehensive list available datasets can obtained list_available_datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the biome classes for a dataset. — get_biome_classes","title":"Get the biome classes for a dataset. — get_biome_classes","text":"Get full list biomes id coded biome variable given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the biome classes for a dataset. — get_biome_classes","text":"","code":"get_biome_classes(dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the biome classes for a dataset. — get_biome_classes","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the biome classes for a dataset. — get_biome_classes","text":"data.frame columns id category.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the data path where climate reconstructions are stored — get_data_path","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"function returns path climate reconstructions stored.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"","code":"get_data_path(silent = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"silent boolean whether message returned data_path set (.e. equal NULL)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"data path","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"path stored option pastclim named data_path. configuration file saved using set_data_path(), path retrieved file named \"pastclim_data.txt\", found directory returned tools::R_user_dir(\"pastclim\",\"config\") (.e. default configuration directory package set R >= 4.0).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the information about a dataset — get_dataset_info","title":"Get the information about a dataset — get_dataset_info","text":"function provides full information given dataset. full list datasets available pastclim can obtained list_available_datasets()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the information about a dataset — get_dataset_info","text":"","code":"get_dataset_info(dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the information about a dataset — get_dataset_info","text":"dataset dataset pastclim","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the information about a dataset — get_dataset_info","text":"text describing dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the variables downloaded for each dataset. — get_downloaded_datasets","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"List downloaded variable dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"","code":"get_downloaded_datasets(data_path = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"data_path leave NULL use default data_path","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"list variable names per dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the ice mask for a dataset. — get_ice_mask","title":"Get the ice mask for a dataset. — get_ice_mask","text":"Get ice mask dataset, either whole series specific time points.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the ice mask for a dataset. — get_ice_mask","text":"","code":"get_ice_mask(time_bp = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the ice mask for a dataset. — get_ice_mask","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the ice mask for a dataset. — get_ice_mask","text":"binary terra::SpatRaster ice mask 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the ice mask for a dataset. — get_ice_mask","text":"Note datasets ice information.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the land mask for a dataset. — get_land_mask","title":"Get the land mask for a dataset. — get_land_mask","text":"Get land mask dataset, either whole series specific time points.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the land mask for a dataset. — get_land_mask","text":"","code":"get_land_mask(time_bp = NULL, time_ce = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the land mask for a dataset. — get_land_mask","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time years CE alternative time_bp.one time_bp time_ce used. available time slices years CE, use get_time_ce_steps(). dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the land mask for a dataset. — get_land_mask","text":"binary terra::SpatRaster land mask 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":null,"dir":"Reference","previous_headings":"","what":"Get time steps for a given MIS — get_mis_time_steps","title":"Get time steps for a given MIS — get_mis_time_steps","text":"Get time steps available given dataset MIS.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get time steps for a given MIS — get_mis_time_steps","text":"","code":"get_mis_time_steps(mis, dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get time steps for a given MIS — get_mis_time_steps","text":"mis string giving mis; must use spelling used mis_boundaries dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get time steps for a given MIS — get_mis_time_steps","text":"vector time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":null,"dir":"Reference","previous_headings":"","what":"Get resolution of a given dataset — get_resolution","title":"Get resolution of a given dataset — get_resolution","text":"Get resolution given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get resolution of a given dataset — get_resolution","text":"","code":"get_resolution(dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get resolution of a given dataset — get_resolution","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get resolution of a given dataset — get_resolution","text":"vector resolution x y axes","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Get sea level estimate — get_sea_level","title":"Get sea level estimate — get_sea_level","text":"function returns estimated sea level Spratt et al. 2016, using long PC1. Sea levels contemporary sea level (note original data reference sea level Holocene ~5k year ago).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get sea level estimate — get_sea_level","text":"","code":"get_sea_level(time_bp)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get sea level estimate — get_sea_level","text":"time_bp time interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get sea level estimate — get_sea_level","text":"vector sea levels meters present level","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":null,"dir":"Reference","previous_headings":"","what":"Get time steps for a given dataset — get_time_bp_steps","title":"Get time steps for a given dataset — get_time_bp_steps","text":"Get time steps (time_bp time_ce) available given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get time steps for a given dataset — get_time_bp_steps","text":"","code":"get_time_bp_steps(dataset, path_to_nc = NULL)  get_time_ce_steps(dataset, path_to_nc = NULL)  get_time_steps(dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get time steps for a given dataset — get_time_bp_steps","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get time steps for a given dataset — get_time_bp_steps","text":"vector time steps (time_bp, time_ce)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the metadata for a variable in a given dataset. — get_var_meta","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"","code":"get_var_meta(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"variable one variable names downloaded dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"metadata (including filename) variable dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a the varname for this variable — get_varname","title":"Get a the varname for this variable — get_varname","text":"Internal function get varname variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a the varname for this variable — get_varname","text":"","code":"get_varname(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a the varname for this variable — get_varname","text":"variable string defining variable name dataset string defining dataset downloaded","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a the varname for this variable — get_varname","text":"name variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a list of variables for a given dataset. — get_vars_for_dataset","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"function lists variables available given dataset. Note spelling use capitals names might differ original publications, pastclim harmonises names variables across different reconstructions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"","code":"get_vars_for_dataset(   dataset,   path_to_nc = NULL,   details = FALSE,   annual = TRUE,   monthly = FALSE )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). path_to_nc path custom nc file containing palaeoclimate reconstructions. custom nc file given, 'details', 'annual' 'monthly' ignored details boolean determining whether output include information including long names variables units. annual boolean show annual variables monthly boolean show monthly variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"vector variable names","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":null,"dir":"Reference","previous_headings":"","what":"Print help to console — help_console","title":"Print help to console — help_console","text":"function prints help file console. based function published R-bloggers: https://www.r-bloggers.com/2013/06/printing-r-help-files---console---knitr-documents/","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print help to console — help_console","text":"","code":"help_console(   topic,   format = c(\"text\", \"html\", \"latex\"),   lines = NULL,   before = NULL,   after = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print help to console — help_console","text":"topic topic help format output formatted lines lines printed string printed output string printed output","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print help to console — help_console","text":"text help file","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the object is a valid region series — is_region_series","title":"Check the object is a valid region series — is_region_series","text":"region series terra::SpatRasterDataset sub-dataset variable, variables number time steps.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the object is a valid region series — is_region_series","text":"","code":"is_region_series(x, strict = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the object is a valid region series — is_region_series","text":"x terra::SpatRasterDataset representing time series regional reconstructions obtained region_series(). strict boolean defining whether preform thorough test (see description details).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the object is a valid region series — is_region_series","text":"TRUE object region series","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check the object is a valid region series — is_region_series","text":"standard test checks sub-datasets (terra::SpatRaster) number layers. thorough test (obtained strict=TRUE) actually checks variables identical time steps comparing result terra::time() applied variable.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Koeppen-Geiger classes. — koeppen_classes","title":"Koeppen-Geiger classes. — koeppen_classes","text":"data.frame defining details class","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Koeppen-Geiger classes. — koeppen_classes","text":"","code":"koeppen_classes"},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Koeppen-Geiger classes. — koeppen_classes","text":"data.frame multiple columns describe.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"Function reconstruct biomes following Köppen Geiger's classification, implemented Beck et al (2018). function translation Matlab function \"KoeppenGeiger\" provided publication. See Table 1 beck et al (2018) rules implemented function.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"","code":"koeppen_geiger(prec, tavg, broad = FALSE, class_names = TRUE, ...)  # S4 method for matrix,matrix koeppen_geiger(prec, tavg, broad = FALSE, class_names = TRUE)  # S4 method for SpatRaster,SpatRaster koeppen_geiger(   prec,   tavg,   broad = FALSE,   class_names = TRUE,   filename = \"\",   ... )  # S4 method for SpatRasterDataset,SpatRasterDataset koeppen_geiger(   prec,   tavg,   broad = FALSE,   class_names = TRUE,   filename = \"\",   ... )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"prec monthly precipitation tavg monthly average temperatures broad boolean whether return broad level classification class_names boolean whether return names classes (addition codes) ... additional variables specific methods filename filename save raster (optional).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"data.frame Köppen Geiger classification","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps 1901–2099 based constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"","code":"prec <- matrix(   c(     66, 51, 53, 53, 33, 34.2, 70.9, 58, 54, 104.3, 81.2, 82.8, 113.3,     97.4, 89, 109.7, 89, 93.4, 99.8, 92.6, 85.3, 102.3, 84, 81.6, 108.6, 88.4,     82.7, 140.1, 120.4, 111.6, 120.4, 113.9, 96.7, 90, 77.4, 79.1   ),   ncol = 12, byrow = TRUE ) tavg <- matrix(   c(     -0.2, 1.7, 2.9, 0.3, 4.2, 5, 4, 9, 9.2, 7.3, 12.6, 12.7, 12.1,     17.2, 17, 15.5, 20.5, 20.3, 17.9, 22.8, 22.9, 17.4, 22.3, 22.4, 13.2, 18.2,     18.6, 8.8, 13, 13.6, 3.5, 6.4, 7.5, 0.3, 2.1, 3.4   ),   ncol = 12, byrow = TRUE ) koeppen_geiger(prec, tavg, broad = TRUE) #>   id broad #> 1 27     4 #> 2 14     3 #> 3 15     3"},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"List all the available datasets. — list_available_datasets","title":"List all the available datasets. — list_available_datasets","text":"List datasets available pastclim. list comprehensive, includes combinations models future scenarios WorldClim. user-friendly list, use get_available_datasets(). functions can also used custom datasets setting dataset=\"custom\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all the available datasets. — list_available_datasets","text":"","code":"list_available_datasets()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all the available datasets. — list_available_datasets","text":"character vector available datasets","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the dataset list — load_dataset_list","title":"Load the dataset list — load_dataset_list","text":"function returns dataframe details variable available every dataset. defaults copy stored within package, checks case updated version stored 'dataset_list_included.csv' tools::R_user_dir(\"pastclim\",\"config\"). latter present, last column, named 'dataset_list_v', provides version table, advanced table used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the dataset list — load_dataset_list","text":"","code":"load_dataset_list(on_cran = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load the dataset list — load_dataset_list","text":"on_cran boolean make function run ci tests using tempdir","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load the dataset list — load_dataset_list","text":"dataset list","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the ETOPO global relief — load_etopo","title":"Load the ETOPO global relief — load_etopo","text":"function loads previously downloaded ETOPO 2022 global relief dataset, 0.5 1 arc-minute (.e. 30 60 arc-seconds) resolution. function assumes file name etopo2022_{resolution}m_v1.nc save file default path appropriate name file format, simply use download_etopo().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the ETOPO global relief — load_etopo","text":"","code":"load_etopo(path = NULL, resolution = 1, version = \"1\")"},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load the ETOPO global relief — load_etopo","text":"path character. Path dataset stored. left NULL, data downloaded directory returned get_data_path() resolution numeric resolution arc-minute (one 0.5, 1). Defaults 1 arc-minute. version character numeric. ETOPO2022 version number. \"1\" supported moment","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load the ETOPO global relief — load_etopo","text":"terra::SpatRaster relief","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of bioclimatic variables for one or more locations. — location_series","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"function extract time series local climate set locations. Note function apply interpolation (opposed location_slice()). coastal location just falls water reconstructions, amend coordinates put firmly land.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"","code":"location_series(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   nn_interpol = FALSE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"x data.frame columns x y coordinates (optional name column), vector cell numbers. See coords standard coordinate names, use custom ones. time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time slice years CE (see time_bp options). available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") bio_variables vector names variables extracted. dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults FALSE (DIFFERENT location_slice(). buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — location_slice","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"function extract local climate set locations appropriate times (selecting closest time slice available specific date associated location).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"","code":"location_slice(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   nn_interpol = TRUE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"x data.frame columns x y coordinates(see coords standard coordinate names, use custom ones), plus optional columns time_bp time_ce (depending units used) name. Alternatively, vector cell numbers. time_bp used time_bp column present x: dates years present (negative values represent time present, .e. 1950, positive values time future) location. time_ce time years CE alternative time_bp.one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") bio_variables vector names variables extracted. dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults TRUE. buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"data.frame climatic variables interest.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"function extract local climate set locations appropriate times (selecting closest time slice available specific date associated location).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"","code":"location_slice_from_region_series(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   region_series,   nn_interpol = TRUE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"x data.frame columns x y coordinates(see coords standard coordinate names, use custom ones), plus optional columns time_bp time_ce (depending units used) name. Alternatively, vector cell numbers. time_bp used time_bp column present x: dates years present (negative values represent time present, .e. 1950, positive values time future) location. time_ce time years CE alternative time_bp. one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") region_series SpatRasterDataset obtained region_series() nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults TRUE. buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"data.frame climatic variables interest.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a binary mask — make_binary_mask","title":"Create a binary mask — make_binary_mask","text":"Create binary mask raster: NAs converted 0s, value 1.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a binary mask — make_binary_mask","text":"","code":"make_binary_mask(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a binary mask — make_binary_mask","text":"x terra::SpatRaster","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a binary mask — make_binary_mask","text":"terra::SpatRaster 0s 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Downscale an ice mask — make_ice_mask","title":"Downscale an ice mask — make_ice_mask","text":"Downscaling ice mask presents issues. mask binary raster, standard downscaling approach still look blocky. can smooth contour applying Gaussian filter. strong filter much matter personal opinion, data compare . function attempts use sensible default value, worth exploring alternative values find good solution.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downscale an ice mask — make_ice_mask","text":"","code":"make_ice_mask(ice_mask_low_res, land_mask_high_res, d = c(0.5, 3))"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downscale an ice mask — make_ice_mask","text":"ice_mask_low_res terra::SpatRaster low resolution ice mask downscale (e.g. obtained get_ice_mask()) land_mask_high_res terra::SpatRaster land masks different times (e.g. obtained make_land_mask()). ice mask cropped matched resolution land mask.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downscale an ice mask — make_ice_mask","text":"terra::SpatRaster ice mask (1's), rest world (sea land) NA's","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a land mask — make_land_mask","title":"Create a land mask — make_land_mask","text":"Create land mask given time step. land mask based simple logic moving ocean given current relief profile ( topography+bathymetry, .e. elevation sea level). Note approach ignores rebound due changing mass distribution ice sheets. LIMITATIONS: land mask show internal lakes/seas land, level unrelated general sea level. specific reconstructions internal lakes (want simply reuse current extents), add onto masks generated function. Also note land mask include ice sheets. means areas permanently covered ice two poles show sea. means , reconstruction including Greenland Antarctica, resulting land mask need modified include appropriate ice sheets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a land mask — make_land_mask","text":"","code":"make_land_mask(relief_rast, time_bp, sea_level = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a land mask — make_land_mask","text":"relief_rast terra::SpatRaster relief time_bp time interest sea_level sea level time interest (left NULL, computed using Spratt 2016)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a land mask — make_land_mask","text":"terra::SpatRaster land masks (land 1's sea NAs), layers different times","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":null,"dir":"Reference","previous_headings":"","what":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"dataset containing beginning end MIS.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"","code":"mis_boundaries"},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"data frame 24 rows 2 variables: mis stage, string start start given MIS, kya end start given MIS, kya","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Mode — mode","title":"Mode — mode","text":"Find mode vector x (note , multiple values frequency, function simply picks first occurring one)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mode — mode","text":"","code":"mode(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mode — mode","text":"x vector","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mode — mode","text":"mode","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/paleoclim_1.0.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for Paleoclim — paleoclim_1.0","title":"Documentation for Paleoclim — paleoclim_1.0","text":"Paleoclim set high resolution paleoclimate reconstructions, mostly based CESM model, downscaled CHELSA dataset 3  different spatial resolutions: paleoclim_1.0_2.5m 2.5 arc-minutes (~5 km), paleoclim_1.0_5m 5 arc-minutes (~10 km), paleoclim_1.0_10m 10 arc-minutes (~20 km). 19 biovariables available. limited number time slices available dataset; furthermore, currently time slices present 130ka available pastclim.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/paleoclim_1.0.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for Paleoclim — paleoclim_1.0","text":"details dataset available dedicated website. IMPORTANT: use dataset, make sure cite original publication: Brown, Hill, Dolan, Carnaval, Haywood (2018) PaleoClim, high spatial resolution paleoclimate surfaces global land areas. Nature – Scientific Data. 5:180254","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim-package.html","id":null,"dir":"Reference","previous_headings":"","what":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","title":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","text":"Methods easily extract manipulate palaeoclimate reconstructions ecological anthropological analyses, described Leonardi et al. (2023) doi:10.1111/ecog.06481 .","code":""},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","text":"Maintainer: Andrea Manica am315@cam.ac.uk Authors: Michela Leonardi contributors: Emily Y. Hallet [contributor] Robert Beyer [contributor] Mario Krapp [contributor]","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":null,"dir":"Reference","previous_headings":"","what":"Read a raster for pastclim — pastclim_rast","title":"Read a raster for pastclim — pastclim_rast","text":"function wrapper around terra::rast(), additional logic correctly import time vrt datasets (time stored custom metadata pastclim-generated vrt files)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read a raster for pastclim — pastclim_rast","text":"","code":"pastclim_rast(   x,   bio_var_orig,   bio_var_pastclim,   var_longname = NULL,   var_units = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read a raster for pastclim — pastclim_rast","text":"x filename raster bio_var_orig variable name present file bio_var_pastclim variable name used pastclim (thus allowing us rename variable)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read a raster for pastclim — pastclim_rast","text":"terra::SpatRaster","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":null,"dir":"Reference","previous_headings":"","what":"Region extents. — region_extent","title":"Region extents. — region_extent","text":"list extents major regions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region extents. — region_extent","text":"","code":"region_extent"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region extents. — region_extent","text":"list vectors giving extents.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":null,"dir":"Reference","previous_headings":"","what":"Region outlines. — region_outline","title":"Region outlines. — region_outline","text":"sf::sf object containing outlines major regions. Outlines span antimeridian split multiple polygons.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region outlines. — region_outline","text":"","code":"region_outline"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region outlines. — region_outline","text":"sf::sf outlines. name names regions","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":null,"dir":"Reference","previous_headings":"","what":"Region outlines unioned. — region_outline_union","title":"Region outlines unioned. — region_outline_union","text":"sf::sf object containing outlines major regions. outline represented single polygon. want multiple polygons, use region_outline.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region outlines unioned. — region_outline_union","text":"","code":"region_outline_union"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region outlines unioned. — region_outline_union","text":"sf::sf outlines. name names regions","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of climate variables for a region — region_series","title":"Extract a time series of climate variables for a region — region_series","text":"function extracts time series one climate variables given dataset covering region (whole world). function returns terra::SpatRasterDataset object, variable sub-dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of climate variables for a region — region_series","text":"","code":"region_series(   time_bp = NULL,   time_ce = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   ext = NULL,   crop = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of climate variables for a region — region_series","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time slices years CE (see time_bp options). available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. bio_variables vector names variables extracted dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. ext extent, coded numeric vector (length=4; order= xmin, xmax, ymin, ymax) terra::SpatExtent object. NULL, full extent reconstruction given. crop polygon used crop reconstructions (e.g. outline continental mass). sf::sfg terra::SpatVector object used define polygon.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of climate variables for a region — region_series","text":"terra::SpatRasterDataset object, variable sub-dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a climate slice for a region — region_slice","title":"Extract a climate slice for a region — region_slice","text":"function extracts slice one climate variables given dataset covering region (whole world). function returns SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a climate slice for a region — region_slice","text":"","code":"region_slice(   time_bp = NULL,   time_ce = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   ext = NULL,   crop = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a climate slice for a region — region_slice","text":"time_bp time slice years present (negative values represent time present, positive values time future). slice needs exist dataset. check slices available, can use get_time_bp_steps(). time_ce time slice years CE. available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. bio_variables vector names variables extracted dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. ext extent, coded numeric vector (length=4; order= xmin, xmax, ymin, ymax) terra::SpatExtent object. NULL, full extent reconstruction given. crop polygon used crop reconstructions (e.g. outline continental mass). sf::sfg terra::SpatVector object used define polygon.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a climate slice for a region — region_slice","text":"SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample points from a region time series — sample_region_series","title":"Sample points from a region time series — sample_region_series","text":"function samples points region time series. Sampling can either performed locations time steps (one value given size), different locations time step (size vector length equal number time steps). sample number points, different locations, time step, provide vector repeating value time step.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample points from a region time series — sample_region_series","text":"","code":"sample_region_series(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample points from a region time series — sample_region_series","text":"x terra::SpatRasterDataset returned region_series() size number points sampled. single value used sample locations across time steps, vector values sample different locations time step. method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample points from a region time series — sample_region_series","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample points from a region time series — sample_region_series","text":"function wraps terra::spatSample() appropriate sample terra::SpatRasters terra::SpatRasterDataset returned region_series().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample points from a region time slice — sample_region_slice","title":"Sample points from a region time slice — sample_region_slice","text":"function samples points region time slice (.e. time point).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample points from a region time slice — sample_region_slice","text":"","code":"sample_region_slice(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample points from a region time slice — sample_region_slice","text":"x terra::SpatRaster returned region_slice() size number points sampled. method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample points from a region time slice — sample_region_slice","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample points from a region time slice — sample_region_slice","text":"function wraps terra::spatSample() appropriate sample terra::SpatRaster returned region_slice(). can also use terra::spatSample() directly slice (standard terra::SpatRaster).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample the same locations from a region time series — sample_rs_fixed","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"Internal function fixed sampling sample_region_series(), used single size given.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"","code":"sample_rs_fixed(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"x terra::SpatRasterDataset returned region_series() size number points sampled; locations across time steps method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample the different number of points from a region time series — sample_rs_variable","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"Internal function sampling different number points timestep region series sample_region_series(), used size vector values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"","code":"sample_rs_variable(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"x terra::SpatRasterDataset returned region_series() size vector number points sampled time step method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the data path where climate reconstructions will be stored — set_data_path","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"function sets path climate reconstructions stored. information stored file names \"pastclim_data.txt\", found directory returned tools::R_user_dir(\"pastclim\",\"config\") (.e. default configuration directory package set R >= 4.0).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"","code":"set_data_path(   path_to_nc = NULL,   ask = TRUE,   write_config = TRUE,   copy_example = TRUE,   on_CRAN = FALSE )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"path_to_nc path file contains downloaded reconstructions. left unset, default location returned tools::R_user_dir(\"pastclim\",\"data\") used ask boolean whether user asked confirm choices write_config boolean whether path saved config file copy_example boolean whether example dataset saved data_path on_CRAN boolean; users need parameters. used set data path temporary directory examples tests run CRAN.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"TRUE path set correctly","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a slice for a time series of climate variables for a region — slice_region_series","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"function extracts time slice time series one climate variables given dataset covering region (whole world).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"","code":"slice_region_series(x, time_bp = NULL, time_ce = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"x climate time series generated region_series() time_bp time slice years present (.e. 1950, negative integers values past). slices need exist dataset. check slices available, can use time_bp(x). time_ce time slice years CE. one time_bp time_ce used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"SpatRaster relevant slice.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"functions extracts sets time years BP (.e. 1950) terra::SpatRaster  terra::SpatRasterDataset. terra::SpatRaster object, time stored unit \"years\", years 0AD. means , summary terra::SpatRaster inspected, times appear time_bp+1950. applies function terra::time() used instead time_bp().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"","code":"time_bp(x)  # S4 method for SpatRaster time_bp(x)  # S4 method for SpatRasterDataset time_bp(x)  time_bp(x) <- value  # S4 method for SpatRaster time_bp(x) <- value  # S4 method for SpatRasterDataset time_bp(x) <- value"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"x terra::SpatRaster value numeric vector times years BP","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"date years BP (negative numbers indicate date past)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a time BP to indexes for a series — time_bp_to_i_series","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"Internal function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"","code":"time_bp_to_i_series(time_bp, time_steps)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"time_bp vector times BP time_steps time steps reconstructions available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"indeces relevant time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the closest index to a given time in years BP — time_bp_to_index","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"Internal function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"","code":"time_bp_to_index(time_bp, time_steps)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"time_bp vector times BP time_steps time steps reconstructions available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"indeces relevant time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"Deprecated version location_series()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"","code":"time_series_for_locations(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"... arguments passed location_series()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Update the dataset list — update_dataset_list","title":"Update the dataset list — update_dataset_list","text":"newer dataset list (includes information files storing data pastclim), download start using 'dataset_list_included.csv' tools::R_user_dir(\"pastclim\",\"config\"). latter present, last column, named 'dataset_list_v', provides version table, advanced table used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update the dataset list — update_dataset_list","text":"","code":"update_dataset_list(on_cran = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update the dataset list — update_dataset_list","text":"on_cran boolean make function run ci tests using tempdir","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update the dataset list — update_dataset_list","text":"TRUE dataset updated","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether a URL is valid — url_is_valid","title":"Test whether a URL is valid — url_is_valid","text":"function check URL points real file","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether a URL is valid — url_is_valid","text":"","code":"url_is_valid(url, verbose = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether a URL is valid — url_is_valid","text":"url url test verbose whether status code outputted message","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether a URL is valid — url_is_valid","text":"boolean whether file exists","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate an netcdf file for pastclim — validate_nc","title":"Validate an netcdf file for pastclim — validate_nc","text":"function validates netcdf file potential dataset pastclim. key checks : ) dimensions (longitude, latitude time) set correctly. b) variables appropriate metadata (longname units)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate an netcdf file for pastclim — validate_nc","text":"","code":"validate_nc(path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate an netcdf file for pastclim — validate_nc","text":"path_to_nc path nc file interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate an netcdf file for pastclim — validate_nc","text":"TRUE file valid.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate pretty variable labels for plotting — var_labels","title":"Generate pretty variable labels for plotting — var_labels","text":"Generate pretty labels (form expression) can used plotting","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate pretty variable labels for plotting — var_labels","text":"","code":"var_labels(x, dataset, with_units = TRUE, abbreviated = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate pretty variable labels for plotting — var_labels","text":"x either character vector names variables, terra::SpatRaster generated [region_slice())] [region_slice())]: R:region_slice()) dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets. with_units boolean defining whether label include units abbreviated boolean defining whether label use abbreviations variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate pretty variable labels for plotting — var_labels","text":"expression can used label plots","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate pretty variable labels for plotting — var_labels","text":"","code":"var_labels(\"bio01\", dataset = \"Example\") #> expression(\"annual mean temperature (\" * degree * C * \")\")  # set the data_path for this example to run on CRAN # users don't need to run this line set_data_path(on_CRAN = TRUE) #> [1] TRUE  # for a SpatRaster climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) terra::plot(climate_20k, main = var_labels(climate_20k, dataset = \"Example\"))  terra::plot(climate_20k, main = var_labels(climate_20k,   dataset = \"Example\",   abbreviated = TRUE ))"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Get metadata from vrt — vrt_get_meta","title":"Get metadata from vrt — vrt_get_meta","text":"function extract metadata information vrt. returns description whole dataset (needed set varname raster) time information band. first checks vrt dataset metadata element key \"pastclim_time_bp\" set TRUE. case, band, extract metadata key \"time\" returns numeric (.e. converting character). Note error returned duplicated time elements bands (whilst duplicated elements valid XML schema VRT, make sense time axis).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get metadata from vrt — vrt_get_meta","text":"","code":"vrt_get_meta(vrt_path)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get metadata from vrt — vrt_get_meta","text":"vrt_path path XML file defining vrt dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get metadata from vrt — vrt_get_meta","text":"list three elements: vector description time_bp defining band, boolean time_bp show determining whether times given time_bp labelling bands terra","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Set vrt metadata — vrt_set_meta","title":"Set vrt metadata — vrt_set_meta","text":"function sets metadata information vrt file. creates dataset wide metadata, well band specific descriptions times.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set vrt metadata — vrt_set_meta","text":"","code":"vrt_set_meta(vrt_path, description, time_vector, time_bp = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set vrt metadata — vrt_set_meta","text":"vrt_path path XML file defining vrt dataset description string description variable dataset time_vector vector descriptions (length number bands) time_bp boolean defining whether time BP (FALSE) CE","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set vrt metadata — vrt_set_meta","text":"TRUE file updated correctly, FALSE update failed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"functions convert years BP used pastclim (negative numbers going past, positive future) standard POSIXct date objects.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"","code":"ybp2date(x)  date2ybp(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"x time years BP using pastclim convention negative numbers indicating years past, POSIXct date object","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"POSIXct date object, vector","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"","code":"ybp2date(-10000) #> [1] \"-8050-01-01 UTC\" ybp2date(0) #> [1] \"1950-01-01 UTC\" # back and forth date2ybp(ybp2date(-10000)) #> [1] -10000"},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-210","dir":"Changelog","previous_headings":"","what":"pastclim 2.1.0","title":"pastclim 2.1.0","text":"Add CHELSA present future datasets (including use virtual rasters avoid downloading data) Add paleoclim multiple resolutions Add CHELSA-TraCE21k (including use virtual rasters avoid downloading data) Re-implement import WorldClim datasets avoid repackaging data (lead faster downloads, force re-download dataset already present). Add functions Koeppen Geiger’s classification monthly means.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-200","dir":"Changelog","previous_headings":"","what":"pastclim 2.0.0","title":"pastclim 2.0.0","text":"CRAN release: 2023-11-02 Allow time defined CE besides BP. NOTE adds parameter number functions. functions used without explicitly naming parameters, old code might give error order parameters now changed). Add Barreto et al 2023 (based PALEO-PGEM, covering last 5 M years) Add WorldClim data (present, future projections multiple models emission scenarios). Add HYDE 3.3 database providing information agriculture population sizes last 10k years. Change units Krapp et al 2021 match datasets. Also, fix data duplication variables now also fixed OSF repository dataset. Improve get_ice_mask(), get_land_mask(), distance_from_sea() work series rather just slices. Speed region_*() functions subsetting extent/cropping.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-124","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.4","title":"pastclim 1.2.4","text":"CRAN release: 2023-04-25 Updates time handled stay sync changes terra.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-123","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.3","title":"pastclim 1.2.3","text":"CRAN release: 2023-01-06 Added lai Krapp2021 (variable now also present original OSF repository dataset). Change column names data.frame returned location_series() match location_slice() Allow interpolation nearest neighbours location_series(), allow buffer estimates returned location_*() functions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-122","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.2","title":"pastclim 1.2.2","text":"Update Krapp2021 files make compatible terra now handles time. Users re-download datasets. Old files can removed clean_data_path()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-121","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.1","title":"pastclim 1.2.1","text":"Small updates CRAN submission.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-120","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.0","title":"pastclim 1.2.0","text":"Provide additional information variables units, create pretty labels plots. Names locations now stored automatically outputs. Update time handled work terra 1.6-41 (now imports units netcdf files).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-110","dir":"Changelog","previous_headings":"","what":"pastclim 1.1.0","title":"pastclim 1.1.0","text":"Expand functionality handle time series regions; rename functions extract data regions locations make consistent. Old code still work, raise warning functions deprecated. Remove need pastclimData, now put data user dir returned R>=4.0.0. removes need re-downloading data upgrading R. Add monthly variables Beyer2020 Krapp2021.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-101","dir":"Changelog","previous_headings":"","what":"pastclim 1.0.1","title":"pastclim 1.0.1","text":"Fix bug information extracted just one location.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-100","dir":"Changelog","previous_headings":"","what":"pastclim 1.0.0","title":"pastclim 1.0.0","text":"Initial public release","code":""}]
+[{"path":"https://evolecolgroup.github.io/pastclim/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://www.contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to pastclim","title":"Contributing to pastclim","text":"document outlines contribute development pastclim. package maintained voluntary basis, help always appreciated.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"the-basic-process-of-contributing","dir":"","previous_headings":"","what":"The basic process of contributing","title":"Contributing to pastclim","text":"Development work pastclim occurs dev branch. , want propose changes, work dev. Start forking project onto github repository, make changes directly fork (either dev branch, make custom branch). updating documentation checking tests pass (see ), start Pull Request. proposed changes reviewed, might asked fix/improve code. can iterative process, requiring rounds revision depending complexity code. Functions documented using roxygen. changes affects documentation , rebuild . root directory package, simply run: implemented new functionality, patched bug, consider whether add appropriate unit test. pastclim uses testthat framework unit tests. make sure tests work : Finally, submit push request, check changes don’t break build. can check, also builds vignette runs tests.: Make sure resolved warnings notes raised devtools::check()! followed 3 steps, ready make Pull Request. changes go automatic continuous integration, check impact changes multiple platforms. everything goes well, see green tick submission.","code":"devtools::document() devtools::test() devtools::check()"},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to pastclim","text":"spot typos, spelling mistakes, grammatical errors documentation, fix directly file describes function. .R file R directory, .Rd file man directory. .Rd files automatically generated roxygen2 edited hand. recommend study first roxygen2 comments work.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"functional-changes","dir":"","previous_headings":"","what":"Functional changes","title":"Contributing to pastclim","text":"want make change impacts functioning pastclim, ’s good idea first file issue explaining mind. change meant fix bug, add minimal reprex. good reprex also perfect starting point writing unit test, accompany functional change code. Unit tests also essential fixing bugs, can demonstrate fix work, prevent future changes undoing work. unit testing, use testthat; find tests tests, file dedicated function, following convention test_my_function.R naming files. creating tests, try make use built-datasets, rather adding data files package. Ideally, body Pull Request include phrase Fixes #issue-number, issue_number number Github. way, Pull Request automatically linked issue, issue closed Pull Request merged . user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html. continuous integration checks Pull Request reduce test coverage.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Functional changes","what":"Code style","title":"Contributing to pastclim","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. Lots commenting code helps mantainability; , doubt, always add explanation new code.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to pastclim","text":"Please note tidyverse project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC BY 4.0","title":"CC BY 4.0","text":"Attribution 4.0 International ======================================================================= Creative Commons Corporation (“Creative Commons”) law firm provide legal services legal advice. Distribution Creative Commons public licenses create lawyer-client relationship. Creative Commons makes licenses related information available “-” basis. 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Creative Commons may contacted creativecommons.org.","code":"Considerations for licensors: Our public licenses are  intended for use by those authorized to give the public  permission to use material in ways otherwise restricted by  copyright and certain other rights. Our licenses are  irrevocable. Licensors should read and understand the terms  and conditions of the license they choose before applying it.  Licensors should also secure all rights necessary before  applying our licenses so that the public can reuse the  material as expected. Licensors should clearly mark any  material not subject to the license. This includes other CC-  licensed material, or material used under an exception or  limitation to copyright. More considerations for licensors: wiki.creativecommons.org/Considerations_for_licensors   Considerations for the public: By using one of our public  licenses, a licensor grants the public permission to use the  licensed material under specified terms and conditions. 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More considerations  for the public:  wiki.creativecommons.org/Considerations_for_licensees"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"install-the-library","dir":"Articles","previous_headings":"","what":"Install the library","title":"pastclim overview","text":"pastclim CRAN, easiest way install : want latest development version, can get GitHub. install GitHub, need use devtools; haven’t done already, install CRAN install.packages(\"devtools\"). Also, note dev version pastclim tracks changes dev version terra, need upgrade : dedicated website, can find Articles giving step--step overview package, cheatsheet. also version site updated dev version (top left, version number red, format x.x.x.9xxx, indicating development version). want build vignette directly R installing pastclim GitHub, can : read directly R : Depending operating system use, might need additional packages build vignette.","code":"install.packages(\"pastclim\") install.packages(\"terra\", repos = \"https://rspatial.r-universe.dev\") devtools::install_github(\"EvolEcolGroup/pastclim\", ref = \"dev\") devtools::install_github(\"EvolEcolGroup/pastclim\", ref = \"dev\", build_vignettes = TRUE) vignette(\"pastclim_overview\", package = \"pastclim\")"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"download-the-data","dir":"Articles","previous_headings":"","what":"Download the data","title":"pastclim overview","text":"need download climatic reconstructions able real work pastclim. Pastclim currently includes data Beyer et al 2020 (Beyer2020, reconstruction climate based HadCM3 model last 120k years), Krapp et al 2021 (Krapp2021, covers last 800k years statistical emulator HadCM3), Barreto et al 2023 (Barreto2023), covering last 5M years using PALEO-PGEM emulator), CHELSA-TraCE21k, covering last 21k years high spatial temporal resolution (CHELSA_trace21k_0.5m_vsi), HYDE3.3 database land use reconstructions last 10k years (HYDE_3.3_baseline) paleoclim dataset, selected time steps last 120k years various resolutions (paleoclim_RESm), WorldClim CHELSA data (WorldClim_2.1_ CHELSA_2.1_, present, future projections number models emission scenarios). information datasets can found , using help page given dataset. detailed instructions use WorldClim CHELSA datasets present future reconstructions can found article also instructions build use custom datasets, need familiarity handling netcdf files. list datasets available can obtained typing: Please aware using dataset made available pastclim require cite pastclim well original publication presenting dataset. reference cite pastclim can obtained typing reference associated dataset choice (case “Beyer2020”) displayed together general information dataset command: datasets available pastclim, functions help download data choose variables. start pastclim first time, need set path reconstructions stored using set_data_path. default, package data path used: Press 1 happy offered choices, pastclim remember data path future sessions. Note data path look different example, depends user name operating system. prefer using custom path (e.g. “~/my_reconstructions”), can set : package includes small dataset, Example, use vignette suitable running real analyses; real datasets large (100s Mb Gb), need specify want download (see ). Let us start inspecting Example dataset. can get list variables available dataset : available time steps can obtained : can also query resolution dataset: , *Example” dataset resolution 1x1 degree. Beyer2020 Krapp2021, can get list available variables dataset : Note , default, annual variables shown. see available monthly variables, simply use: monthly variables, months coded “_xx” end variable names; e.g. “temperature_02” mean monthly temperature February. thorough description variable (including units) can obtained : able get available time steps download dataset. pastclim offers interface download necessary files data path. inspect datasets variables already downloaded data path, can use: Let’s now download bio01 bio05 Beyer2020 dataset (operation might take several minutes, datasets large; R pause download complete): Note multiple variables can packed together single file, get_downloaded_datasets() might list variables ones chose download (depends dataset). upgrading pastclim, new version various datasets might become available. make previously downloaded datasets obsolete, might suddenly told pastclim variables re-downloaded. can lead accumulation old datasets data path. function clean_data_path() can used delete old files longer needed.","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_available_datasets() #> Barreto2023, Beyer2020, CHELSA_trace21k_1.0_0.5m_vsi, Example, HYDE_3.3_baseline, Krapp2021, paleoclim_1.0_10m, paleoclim_1.0_2.5m, paleoclim_1.0_5m #> for present day reconstructions, use \"WorldClim_2.1_RESm\" or \"CHELSA_2.4_RESm\" where RES is an available resolution. #> for future predictions, use \"WorldClim_2.1_GCM_SSP_RESm\" or \"CHELSA_2.1_GCM_SSP_RESm\", where GCM is the GCM model, SSP is the Shared Socio-economic Pathways scenario. #> use help(\"WorldClim_2.1\") or help(\"CHELSA_2.1\") for a list of available options citation(\"pastclim\") #> To cite pastclim in publications use: #>  #>   Leonardi M, Hallet EY, Beyer R, Krapp M, Manica A (2023). \"pastclim #>   1.2: an R package to easily access and use paleoclimatic #>   reconstructions.\" _Ecography_, *2023*, e06481. doi:10.1111/ecog.06481 #>   <https://doi.org/10.1111/ecog.06481>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Article{pastclim-article, #>     title = {pastclim 1.2: an R package to easily access and use paleoclimatic reconstructions}, #>     author = {Michela Leonardi and Emily Y. Hallet and Robert Beyer and Mario Krapp and Andrea Manica}, #>     journal = {Ecography}, #>     year = {2023}, #>     volume = {2023}, #>     pages = {e06481}, #>     publisher = {Wiley}, #>     doi = {10.1111/ecog.06481}, #>   } help(\"Beyer2020\") #> Documentation for the Beyer2020 dataset #>  #> Description: #>  #>      This dataset covers the last 120k years, at intervals of 1/2 k #>      years, and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial #>      climate, bioclimate and vegetation for the last 120,000 years. Sci #>      Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 #>      <https://doi.org/10.1038/s41597-020-0552-1> #>  #>      The version included in 'pastclim' has the ice sheets masked, as #>      well as internal seas (Black and Caspian Sea) removed. The latter #>      are based on: #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4278> #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4282> #>  #>      As there is no reconstruction of their depth through time, modern #>      outlines were used for all time steps. #>  #>      Also, for bio15, the coefficient of variation was computed after #>      adding one to monthly estimates, and it was multiplied by 100 #>      following <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/deposit/7062281> #>  #>      v1.0.0 Remove ice sheets and internal seas, and use correct #>      formula for bio15. Files can be downloaded from: #>      doi:10.6084/m9.figshare.19723405.v1 #>      <https://doi.org/10.6084/m9.figshare.19723405.v1> library(pastclim) set_data_path() #> The data_path will be set to /home/andrea/.local/share/R/pastclim. #> A copy of the Example dataset will be copied there. #> This path will be saved by pastclim for future use. #> Proceed?  #>  #> 1: Yes #> 2: No set_data_path(path_to_nc = \"~/my_reconstructions\") get_vars_for_dataset(dataset = \"Example\") #> [1] \"bio01\" \"bio10\" \"bio12\" \"biome\" get_time_bp_steps(dataset = \"Example\") #> [1] -20000 -15000 -10000  -5000      0 get_resolution(dataset = \"Example\") #> [1] 1 1 get_vars_for_dataset(dataset = \"Beyer2020\") #>  [1] \"bio01\"    \"bio04\"    \"bio05\"    \"bio06\"    \"bio07\"    \"bio08\"    #>  [7] \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    \"bio13\"    \"bio14\"    #> [13] \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    \"bio19\"    \"npp\"      #> [19] \"lai\"      \"biome\"    \"altitude\" \"rugosity\" get_vars_for_dataset(dataset = \"Krapp2021\") #>  [1] \"bio01\"    \"bio04\"    \"bio05\"    \"bio06\"    \"bio07\"    \"bio08\"    #>  [7] \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    \"bio13\"    \"bio14\"    #> [13] \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    \"bio19\"    \"npp\"      #> [19] \"biome\"    \"lai\"      \"altitude\" \"rugosity\" get_vars_for_dataset(dataset = \"Beyer2020\", annual = FALSE, monthly = TRUE) #>  [1] \"temperature_01\"       \"temperature_02\"       \"temperature_03\"       #>  [4] \"temperature_04\"       \"temperature_05\"       \"temperature_06\"       #>  [7] \"temperature_07\"       \"temperature_08\"       \"temperature_09\"       #> [10] \"temperature_10\"       \"temperature_11\"       \"temperature_12\"       #> [13] \"precipitation_01\"     \"precipitation_02\"     \"precipitation_03\"     #> [16] \"precipitation_04\"     \"precipitation_05\"     \"precipitation_06\"     #> [19] \"precipitation_07\"     \"precipitation_08\"     \"precipitation_09\"     #> [22] \"precipitation_10\"     \"precipitation_11\"     \"precipitation_12\"     #> [25] \"cloudiness_01\"        \"cloudiness_02\"        \"cloudiness_03\"        #> [28] \"cloudiness_04\"        \"cloudiness_05\"        \"cloudiness_06\"        #> [31] \"cloudiness_07\"        \"cloudiness_08\"        \"cloudiness_09\"        #> [34] \"cloudiness_10\"        \"cloudiness_11\"        \"cloudiness_12\"        #> [37] \"relative_humidity_01\" \"relative_humidity_02\" \"relative_humidity_03\" #> [40] \"relative_humidity_04\" \"relative_humidity_05\" \"relative_humidity_06\" #> [43] \"relative_humidity_07\" \"relative_humidity_08\" \"relative_humidity_09\" #> [46] \"relative_humidity_10\" \"relative_humidity_11\" \"relative_humidity_12\" #> [49] \"wind_speed_01\"        \"wind_speed_02\"        \"wind_speed_03\"        #> [52] \"wind_speed_04\"        \"wind_speed_05\"        \"wind_speed_06\"        #> [55] \"wind_speed_07\"        \"wind_speed_08\"        \"wind_speed_09\"        #> [58] \"wind_speed_10\"        \"wind_speed_11\"        \"wind_speed_12\"        #> [61] \"mo_npp_01\"            \"mo_npp_02\"            \"mo_npp_03\"            #> [64] \"mo_npp_04\"            \"mo_npp_05\"            \"mo_npp_06\"            #> [67] \"mo_npp_07\"            \"mo_npp_08\"            \"mo_npp_09\"            #> [70] \"mo_npp_10\"            \"mo_npp_11\"            \"mo_npp_12\" get_vars_for_dataset(dataset = \"Example\", details = TRUE) #>   variable                           long_name           units #> 1    bio01             annual mean temperature degrees Celsius #> 2    bio10 mean temperature of warmest quarter degrees Celsius #> 3    bio12                annual precipitation     mm per year #> 4    biome                 biome (from BIOME4) get_downloaded_datasets() #> $Example #> [1] \"bio01\" \"bio10\" \"bio12\" \"biome\" download_dataset(dataset = \"Beyer2020\", bio_variables = c(\"bio01\", \"bio05\"))"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"get-climate-for-locations","dir":"Articles","previous_headings":"","what":"Get climate for locations","title":"pastclim overview","text":"Often want get climate specific locations. can using function location_slice. function, get slices climate times relevant locations interest. Let us consider five possible locations interest: Iho Eleru (Late Stone Age inland site Nigeria), La Riera (Late Palaeolithic coastal site Spain), Chalki (Mesolithic site Greek island), Oronsay (Mesolithic site Scottish Hebrides), Atlantis (fabled submersed city mentioned Plato). site date (realistic, made ) interested associating climatic reconstructions. extract climatic conditions bio01 bio12: pastclim finds closest time step (slice) available given date, outputs slice used column time_bp_slice (Example dataset use vignette temporal resolution 5k years). Note Chalki Atlantis available (get NA) appropriate time steps. occurs location, reconstructions, either water ice, pastclim can return estimate. instances, due discretisation space raster. can interpolate climate among nearest neighbours, thus using climate reconstructions neighbouring pixels location just one land pixels: Chalki, can see problem indeed , since small island, well represented reconstructions (bear mind Example dataset coarse spatial resolution), can reconstruct appropriate climate interpolating. Atlantis, hand, middle ocean, information climate might became submerged (assuming ever existed…). Note nn_interpol = TRUE default function. Sometimes, want get time series climatic reconstructions, thus allowing us see climate changed time: resulting dataframe can subsetted get time series location (small Example dataset contains 5 time slices): Also note locations, climate can available certain time steps, depending sea level ice sheet extent. case Oronsay: can quickly plot bio01 time locations:  expected, don’t data Atlantis (always underwater), also fail retrieve data Chalki. location_series interpolate nearest neighbours default (, differs location_slice behaviour). rationale behaviour interested whether locations might end underwater, want grab climate estimates submerged. However, cases (Chalki) might necessary allow interpolation. Pretty labels environmental variables can generated var_labels:  Note climatic reconstructions extracted Example dataset, coarse, used base real inference environmental conditions. note also higher resolution always better. important consider appropriate spatial scale relevant question hand. Sometimes, might necessary downscale simulations (see section end vignette), cases might want get estimates cover area around specific location (e.g. comparing proxies capture climatology broad area, certain sediment cores capture pollen broader region). location_slice location_series can provide mean estimates areas around location coordinates setting buffer parameter (see help pages functions details).","code":"locations <- data.frame(   name = c(\"Iho Eleru\", \"La Riera\", \"Chalki\", \"Oronsay\", \"Atlantis\"),   longitude = c(5, -4, 27, -6, -24), latitude = c(7, 44, 36, 56, 31),   time_bp = c(-11200, -18738, -10227, -10200, -11600) ) locations #>        name longitude latitude time_bp #> 1 Iho Eleru         5        7  -11200 #> 2  La Riera        -4       44  -18738 #> 3    Chalki        27       36  -10227 #> 4   Oronsay        -6       56  -10200 #> 5  Atlantis       -24       31  -11600 location_slice(   x = locations, bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\", nn_interpol = FALSE ) #>        name longitude latitude time_bp time_bp_slice     bio01    bio12 #> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.595 #> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.570 #> 3    Chalki        27       36  -10227        -10000        NA       NA #> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.824 #> 5  Atlantis       -24       31  -11600        -10000        NA       NA location_slice(   x = locations, bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\", nn_interpol = TRUE ) #>        name longitude latitude time_bp time_bp_slice     bio01     bio12 #> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.5950 #> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.5703 #> 3    Chalki        27       36  -10227        -10000 17.432425  723.1012 #> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.8245 #> 5  Atlantis       -24       31  -11600        -10000        NA        NA locations_ts <- location_series(   x = locations,   bio_variables = c(\"bio01\", \"bio12\"),   dataset = \"Example\" ) subset(locations_ts, name == \"Iho Eleru\") #>          name longitude latitude time_bp    bio01    bio12 #> 1   Iho Eleru         5        7  -20000 22.55133 1577.238 #> 1.1 Iho Eleru         5        7  -15000 23.27008 1850.715 #> 1.2 Iho Eleru         5        7  -10000 25.34670 2204.595 #> 1.3 Iho Eleru         5        7   -5000 25.65009 2109.735 #> 1.4 Iho Eleru         5        7       0 26.77033 1840.845 subset(locations_ts, name == \"Oronsay\") #>        name longitude latitude time_bp    bio01    bio12 #> 4   Oronsay        -6       56  -20000       NA       NA #> 4.1 Oronsay        -6       56  -15000       NA       NA #> 4.2 Oronsay        -6       56  -10000 6.937467 1362.824 #> 4.3 Oronsay        -6       56   -5000 8.167976 1462.253 #> 4.4 Oronsay        -6       56       0 8.185000 1434.490 library(ggplot2) ggplot(data = locations_ts, aes(x = time_bp, y = bio01, group = name)) +   geom_line(aes(col = name)) +   geom_point(aes(col = name)) #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_line()`). #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_point()`). library(ggplot2) ggplot(data = locations_ts, aes(x = time_bp, y = bio01, group = name)) +   geom_line(aes(col = name)) +   geom_point(aes(col = name)) +   labs(     y = var_labels(\"bio01\", dataset = \"Example\", abbreviated = TRUE),     x = \"time BP (yr)\"   ) #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_line()`). #> Warning: Removed 12 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"get-climate-for-a-region","dir":"Articles","previous_headings":"","what":"Get climate for a region","title":"pastclim overview","text":"Instead focussing specific locations, might want look whole region. given time step, can extract slice climate returns raster (technically SpatRaster object defined terra library, meaning can perform standard terra raster operations object). interact SpatRaster objects, need library terra loaded (otherwise might get errors correct method found, e.g. plotting). pastclim automatically loads terra, able work terra objects without problem: plot three variables (layers raster):  can add informative labels var_labels:  possible also load time series rasters function region_series. case, function returns SpatRasterDataset, variable sub-dataset: sub-dataset SpatRaster, time steps layers: Note terra stores dates years AD, BP. can inspect times years BP : can plot time series given variable (relabel plots use years bp):  plot climate variables given time step, can slice time series:  Instead giving minimum maximum time step, can also provide specific time steps region_series. Note pastclim function get vector time steps given MIS dataset. example, MIS 1, get: can use:","code":"climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_20k #> class       : SpatRaster  #> dimensions  : 150, 360, 3  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> sources     : example_climate_v1.3.0.nc:BIO1   #>               example_climate_v1.3.0.nc:BIO10   #>               example_climate_v1.3.0.nc:BIO12   #> varnames    : bio01 (annual mean temperature)  #>               bio10 (mean temperature of warmest quarter)  #>               bio12 (annual precipitation)  #> names       :           bio01,           bio10,       bio12  #> unit        : degrees Celsius, degrees Celsius, mm per year  #> time (years): -18050 terra::plot(climate_20k) terra::plot(climate_20k,   main = var_labels(climate_20k, dataset = \"Example\", abbreviated = TRUE) ) climate_region <- region_series(   time_bp = list(min = -15000, max = 0),   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_region #> class       : SpatRasterDataset  #> subdatasets : 3  #> dimensions  : 150, 360 (nrow, ncol) #> nlyr        : 4, 4, 4  #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source(s)   : example_climate_v1.3.0.nc  #> names       : bio01, bio10, bio12 climate_region$bio01 #> class       : SpatRaster  #> dimensions  : 150, 360, 4  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source      : example_climate_v1.3.0.nc:BIO1  #> varname     : bio01 (annual mean temperature)  #> names       :    bio01_-15000,    bio01_-10000,     bio01_-5000,         bio01_0  #> unit        : degrees Celsius, degrees Celsius, degrees Celsius, degrees Celsius  #> time (years): -13050 to 1950 time_bp(climate_region) #> [1] -15000 -10000  -5000      0 terra::plot(climate_region$bio01, main = time_bp(climate_region)) slice_10k <- slice_region_series(climate_region, time_bp = -10000) terra::plot(slice_10k) mis1_steps <- get_mis_time_steps(mis = 1, dataset = \"Example\") mis1_steps #> [1] -10000  -5000      0 climate_mis1 <- region_series(   time_bp = mis1_steps,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) climate_mis1 #> class       : SpatRasterDataset  #> subdatasets : 3  #> dimensions  : 150, 360 (nrow, ncol) #> nlyr        : 3, 3, 3  #> resolution  : 1, 1  (x, y) #> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84  #> source(s)   : example_climate_v1.3.0.nc  #> names       : bio01, bio10, bio12"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"cropping","dir":"Articles","previous_headings":"","what":"Cropping","title":"pastclim overview","text":"Often want focus given region. number preset rectangular extents pastclim: can get corners European extent: can extract climate Europe setting ext region_slice:  can see plot, cutting Europe using rectangular shape keeps portion Northern Africa map. pastclim includes number pre-generated masks main continental masses, stored dataset region_outline sf::sfc object. can get list : can use function crop within region_slice keep area within desired outline.  can combine multiple regions together. example, can crop Africa Eurasia unioning two individual outlines:  Note outlines cross antimeridian split multiple polygons (can used without projecting rasters). Eurasia, eastern end Siberia left hand side plot. continent_outlines_union provides outlines single polygons (case want use projection). can also use custom outline (.e. polygon, coded terra::vect object) mask limit area covered raster. Note need reuse first vertex last vertex, close polygon:  region_series takes ext crop options region_slice limit extent climatic reconstructions.","code":"names(region_extent) #> [1] \"Africa\"    \"America\"   \"Asia\"      \"Europe\"    \"Eurasia\"   \"N_America\" #> [7] \"Oceania\"   \"S_America\" region_extent$Europe #> [1] -15  70  33  75 europe_climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   ext = region_extent$Europe ) terra::plot(europe_climate_20k) names(region_outline) #> [1] \"Africa\"    \"Eurasia\"   \"N_America\" \"Oceania\"   \"S_America\" \"Europe\" europe_climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = region_outline$Europe ) terra::plot(europe_climate_20k) library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE afr_eurasia <- sf::st_union(region_outline$Africa, region_outline$Eurasia) climate_20k_afr_eurasia <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = afr_eurasia ) terra::plot(climate_20k_afr_eurasia) custom_vec <- terra::vect(\"POLYGON ((0 70, 25 70, 50 80, 170 80, 170 10,                               119 2.4, 119 0.8, 116 -7.6, 114 -12, 100 -40,                               -25 -40, -25 64, 0 70))\") climate_20k_custom <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   crop = custom_vec ) terra::plot(climate_20k_custom)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"working-with-biomes-and-ice-sheets","dir":"Articles","previous_headings":"","what":"Working with biomes and ice sheets","title":"pastclim overview","text":"Beyer2020 Krapp2021 datasets include categorical variable detailing extension biomes. can get biome 20k years ago plot :  Note legend massive. plotting multiple time slices, best use legned=FALSE plotting statement avoid legend. need plot extent specific biome, example desert, can simply set levels NA:  climate reconstructions show areas permanent ice. Ice sheets stored class 28 “biome” variable. can retrieve directly ice land (biome categories) masks :  can also add ice sheets plots climatic variables. First, need turn ice mask polygons: can add polygons layer (.e. variable) climate slice following code (note , add polygons every panel figure, need create function used argument fun within plot):  cases, multiple time points variable want see ice sheets change:  Note add ice sheets instance, build function takes single parameter index image (.e. 1 4 plot ) use subset list ice outlines. Sometimes interesting measure distance coastline (e.g. modelling species rely brackish water, determine distance marine resources archaeology). pastclim, can use use distance_from_sea, accounts sea level change based landmask:","code":"get_biome_classes(\"Example\") #>    id                           category #> 1   0                       Water bodies #> 2   1          Tropical evergreen forest #> 3   2     Tropical semi-deciduous forest #> 4   3 Tropical deciduous forest/woodland #> 5   4         Temperate deciduous forest #> 6   5           Temperate conifer forest #> 7   6                  Warm mixed forest #> 8   7                  Cool mixed forest #> 9   8                Cool conifer forest #> 10  9                  Cold mixed forest #> 11 10      Evegreen taiga/montane forest #> 12 11     Deciduous taiga/montane forest #> 13 12                   Tropical savanna #> 14 13      Tropical xerophytic shrubland #> 15 14     Temperate xerophytic shrubland #> 16 15     Temperate sclerophyll woodland #> 17 16      Temperate broadleaved savanna #> 18 17              Open conifer woodland #> 19 18                    Boreal parkland #> 20 19                 Tropical grassland #> 21 20                Temperate grassland #> 22 21                             Desert #> 23 22                      Steppe tundra #> 24 23                       Shrub tundra #> 25 24                 Dwarf shrub tundra #> 26 25             Prostrate shrub tundra #> 27 26    Cushion forb lichen moss tundra #> 28 27                             Barren #> 29 28                           Land ice biome_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"biome\"),   dataset = \"Example\" ) plot(biome_20k) biome_20k$desert <- biome_20k$biome biome_20k$desert[biome_20k$desert != 21] <- NA terra::plot(biome_20k$desert) ice_mask <- get_ice_mask(-20000, dataset = \"Example\") land_mask <- get_land_mask(-20000, dataset = \"Example\") terra::plot(c(ice_mask, land_mask)) ice_mask_vect <- as.polygons(ice_mask) plot(climate_20k,   fun = function() polys(ice_mask_vect, col = \"gray\", lwd = 0.5) ) europe_climate <- region_series(   time_bp = c(-20000, -15000, -10000, 0),   bio_variables = c(\"bio01\"),   dataset = \"Example\",   ext = region_extent$Europe ) ice_masks <- get_ice_mask(c(-20000, -15000, -10000, 0),   dataset = \"Example\" ) ice_poly_list <- lapply(ice_masks, as.polygons) plot(europe_climate$bio01,   main = time_bp(europe_climate),   fun = function(i) {     polys(ice_poly_list[[i]],       col = \"gray\",       lwd = 0.5     )   } ) distances_sea <- distance_from_sea(time_bp = c(-20000, 0), dataset = \"Example\") #> |---------|---------|---------|---------|=========================================                                          |---------|---------|---------|---------|========================================= distances_sea_australia <- crop(distances_sea, terra::ext(100, 170, -60, 20)) plot(distances_sea_australia, main = time_bp(distances_sea_australia))"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"adding-locations-to-region-plots","dir":"Articles","previous_headings":"","what":"Adding locations to region plots","title":"pastclim overview","text":"plot locations region plots, first need create SpatVector object dataframe metadata, specifying names columns x y coordinates: can add climate slice following code (note , add points every panel figure, need create function used argument fun within plot):  points within extent region plotted (, case, European locations). can combine ice sheets locations single plot:","code":"locations_vect <- vect(locations, geom = c(\"longitude\", \"latitude\")) locations_vect #>  class       : SpatVector  #>  geometry    : points  #>  dimensions  : 5, 2  (geometries, attributes) #>  extent      : -24, 27, 7, 56  (xmin, xmax, ymin, ymax) #>  coord. ref. :   #>  names       :      name    time_bp #>  type        :     <chr>      <num> #>  values      : Iho Eleru  -1.12e+04 #>                 La Riera -1.874e+04 #>                   Chalki -1.023e+04 plot(europe_climate_20k,   fun = function() points(locations_vect, col = \"red\", cex = 2) ) plot(europe_climate_20k,   fun = function() {     polys(ice_mask_vect, col = \"gray\", lwd = 0.5)     points(locations_vect, col = \"red\", cex = 2)   } )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"set-the-samples-within-the-background","dir":"Articles","previous_headings":"","what":"Set the samples within the background","title":"pastclim overview","text":"many studies, want set environmental conditions given set location within background time period. Let us start visualising background time step interest PCA:  can now get climatic conditions locations time step compute PCA scores based axes defined background: now can plot points top background  want pool background multiple time steps, can simple use region_series get series, transform data frame df_from_region_series.","code":"bio_vars <- c(\"bio01\", \"bio10\", \"bio12\") climate_10k <- region_slice(-10000,   bio_variables = bio_vars,   dataset = \"Example\" ) climate_values_10k <- df_from_region_slice(climate_10k) climate_10k_pca <- prcomp(climate_values_10k[, bio_vars],   scale = TRUE, center = TRUE ) plot(climate_10k_pca$x[, 2] ~ climate_10k_pca$x[, 1],   pch = 20, col = \"lightgray\",   xlab = \"PC1\", ylab = \"PC2\" ) locations_10k <- data.frame(   longitude = c(0, 90, 20, 5), latitude = c(20, 45, 50, 47),   time_bp = c(-9932, -9753, -10084, -10249) ) climate_loc_10k <- location_slice(   x = locations_10k[, c(\"longitude\", \"latitude\")],   time_bp = locations_10k$time_bp, bio_variables = bio_vars,   dataset = \"Example\" ) locations_10k_pca_scores <- predict(climate_10k_pca,   newdata = climate_loc_10k[, bio_vars] ) plot(climate_10k_pca$x[, 2] ~ climate_10k_pca$x[, 1],   pch = 20, col = \"lightgray\",   xlab = \"PC1\", ylab = \"PC2\" ) points(locations_10k_pca_scores, pch = 20, col = \"red\")"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"random-sampling-of-background","dir":"Articles","previous_headings":"","what":"Random sampling of background","title":"pastclim overview","text":"instances (e.g. underlying raster large handle), might desirable sample background instead using values. interested single time step, can simply generate raster time slice interest, use sample_region_slice: samples multiple time steps, need sample background proportionally number points time step. , example, wanted 30 samples 20k years ago 50 samples 10k years ago: use data build PCA.","code":"climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\"),   dataset = \"Example\" ) this_sample <- sample_region_slice(climate_20k, size = 100) head(this_sample) #>    cell     x     y     bio01    bio10 #> 1 30435  14.5   5.5  19.20609 21.04144 #> 2 11098 117.5  59.5 -17.02355 10.63760 #> 3 46402 141.5 -38.5  11.20755 14.61647 #> 4 28719  98.5  10.5  23.08009 25.09301 #> 5 32526 -54.5  -0.5  21.08426 22.37266 #> 6 21694 -86.5  29.5  11.34134 22.99179 climate_ts <- region_series(   time_bp = c(-20000, -10000),   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\",   ext = terra::ext(region_extent$Europe) ) sampled_climate <- sample_region_series(climate_ts, size = c(3, 5)) sampled_climate #>          cell    x    y     bio01     bio10     bio12 time_bp #> -20000.1 3010 19.5 39.5 11.405086 19.345711 1000.7158  -20000 #> -20000.2 3367 36.5 35.5 13.070941 21.583271  658.2184  -20000 #> -20000.3 2729 -6.5 42.5  1.555535  8.271003 1393.2020  -20000 #> -10000.1 3274 28.5 36.5 17.501736 28.305464  973.9351  -10000 #> -10000.2 2697 46.5 43.5  9.789731 25.253517  433.5954  -10000 #> -10000.3 1652 21.5 55.5  5.449332 16.197599  750.3496  -10000 #> -10000.4  723 27.5 66.5 -7.382193  2.715217  322.7987  -10000 #> -10000.5 3461 45.5 34.5 20.095482 35.738621  223.0041  -10000"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html","id":"downscaling","dir":"Articles","previous_headings":"","what":"Downscaling","title":"pastclim overview","text":"pastclim contain built-code change spatial resolution climatic reconstructions, possible downscale data using relevant function terra package. first need extract region time choice, case Europe 10,000 years ago  can downscale using disagg() function terra package, requiring aggregation factor expressed number cells direction (horizontally, vertically, , needed, layers). example used 25 horizontally vertically, using bilinear interpolation.  Note , whilst smoothed climate, land mask changed, thus still blocky edges.","code":"europe_10k <- region_slice(   dataset = \"Example\",   bio_variables = c(\"bio01\"),   time_bp = -10000, ext = region_extent$Europe ) terra::plot(europe_10k) europe_ds <- terra::disagg(europe_10k, fact = 25, method = \"bilinear\") terra::plot(europe_ds)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a1_available_datasets.html","id":"overview-of-datasets-available-in-pastclim","dir":"Articles","previous_headings":"","what":"Overview of datasets available in pastclim","title":"available datasets","text":"number datasets available pastclim. possible use custom datasets long properly formatted (look article format custom datasets interested). possible get list available datasets : comprehensive list can obtained : dataset, can get detailed information using help function: provide full documentation dataset (sorted alphabetical order):","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_available_datasets() #> Barreto2023, Beyer2020, CHELSA_trace21k_1.0_0.5m_vsi, Example, HYDE_3.3_baseline, Krapp2021, paleoclim_1.0_10m, paleoclim_1.0_2.5m, paleoclim_1.0_5m #> for present day reconstructions, use \"WorldClim_2.1_RESm\" or \"CHELSA_2.4_RESm\" where RES is an available resolution. #> for future predictions, use \"WorldClim_2.1_GCM_SSP_RESm\" or \"CHELSA_2.1_GCM_SSP_RESm\", where GCM is the GCM model, SSP is the Shared Socio-economic Pathways scenario. #> use help(\"WorldClim_2.1\") or help(\"CHELSA_2.1\") for a list of available options list_available_datasets() #>   [1] \"Barreto2023\"                               #>   [2] \"Beyer2020\"                                 #>   [3] \"CHELSA_2.1_0.5m\"                           #>   [4] \"CHELSA_2.1_0.5m_vsi\"                       #>   [5] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m\"          #>   [6] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi\"      #>   [7] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m\"          #>   [8] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m_vsi\"      #>   [9] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m\"          #>  [10] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m_vsi\"      #>  [11] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m\"       #>  [12] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m_vsi\"   #>  [13] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m\"       #>  [14] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m_vsi\"   #>  [15] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m\"       #>  [16] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m_vsi\"   #>  [17] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"      #>  [18] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m_vsi\"  #>  [19] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"      #>  [20] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m_vsi\"  #>  [21] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"      #>  [22] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m_vsi\"  #>  [23] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m\"         #>  [24] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m_vsi\"     #>  [25] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m\"         #>  [26] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m_vsi\"     #>  [27] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m\"         #>  [28] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m_vsi\"     #>  [29] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m\"        #>  [30] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m_vsi\"    #>  [31] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m\"        #>  [32] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_vsi\"    #>  [33] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\"        #>  [34] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\"    #>  [35] \"CHELSA_trace21k_1.0_0.5m_vsi\"              #>  [36] \"Example\"                                   #>  [37] \"HYDE_3.3_baseline\"                         #>  [38] \"Krapp2021\"                                 #>  [39] \"paleoclim_1.0_10m\"                         #>  [40] \"paleoclim_1.0_2.5m\"                        #>  [41] \"paleoclim_1.0_5m\"                          #>  [42] \"WorldClim_2.1_0.5m\"                        #>  [43] \"WorldClim_2.1_10m\"                         #>  [44] \"WorldClim_2.1_2.5m\"                        #>  [45] \"WorldClim_2.1_5m\"                          #>  [46] \"WorldClim_2.1_ACCESS-CM2_ssp126_0.5m\"      #>  [47] \"WorldClim_2.1_ACCESS-CM2_ssp126_10m\"       #>  [48] \"WorldClim_2.1_ACCESS-CM2_ssp126_2.5m\"      #>  [49] \"WorldClim_2.1_ACCESS-CM2_ssp126_5m\"        #>  [50] \"WorldClim_2.1_ACCESS-CM2_ssp245_0.5m\"      #>  [51] \"WorldClim_2.1_ACCESS-CM2_ssp245_10m\"       #>  [52] \"WorldClim_2.1_ACCESS-CM2_ssp245_2.5m\"      #>  [53] \"WorldClim_2.1_ACCESS-CM2_ssp245_5m\"        #>  [54] \"WorldClim_2.1_ACCESS-CM2_ssp370_0.5m\"      #>  [55] \"WorldClim_2.1_ACCESS-CM2_ssp370_10m\"       #>  [56] \"WorldClim_2.1_ACCESS-CM2_ssp370_2.5m\"      #>  [57] \"WorldClim_2.1_ACCESS-CM2_ssp370_5m\"        #>  [58] \"WorldClim_2.1_ACCESS-CM2_ssp585_0.5m\"      #>  [59] \"WorldClim_2.1_ACCESS-CM2_ssp585_10m\"       #>  [60] \"WorldClim_2.1_ACCESS-CM2_ssp585_2.5m\"      #>  [61] \"WorldClim_2.1_ACCESS-CM2_ssp585_5m\"        #>  [62] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_0.5m\"     #>  [63] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_10m\"      #>  [64] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_2.5m\"     #>  [65] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_5m\"       #>  [66] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_0.5m\"     #>  [67] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_10m\"      #>  [68] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_2.5m\"     #>  [69] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_5m\"       #>  [70] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_0.5m\"     #>  [71] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_10m\"      #>  [72] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_2.5m\"     #>  [73] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_5m\"       #>  [74] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_0.5m\"     #>  [75] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_10m\"      #>  [76] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_2.5m\"     #>  [77] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_5m\"       #>  [78] \"WorldClim_2.1_CMCC-ESM2_ssp126_0.5m\"       #>  [79] \"WorldClim_2.1_CMCC-ESM2_ssp126_10m\"        #>  [80] \"WorldClim_2.1_CMCC-ESM2_ssp126_2.5m\"       #>  [81] \"WorldClim_2.1_CMCC-ESM2_ssp126_5m\"         #>  [82] \"WorldClim_2.1_CMCC-ESM2_ssp245_0.5m\"       #>  [83] \"WorldClim_2.1_CMCC-ESM2_ssp245_10m\"        #>  [84] \"WorldClim_2.1_CMCC-ESM2_ssp245_2.5m\"       #>  [85] \"WorldClim_2.1_CMCC-ESM2_ssp245_5m\"         #>  [86] \"WorldClim_2.1_CMCC-ESM2_ssp370_0.5m\"       #>  [87] \"WorldClim_2.1_CMCC-ESM2_ssp370_10m\"        #>  [88] \"WorldClim_2.1_CMCC-ESM2_ssp370_2.5m\"       #>  [89] \"WorldClim_2.1_CMCC-ESM2_ssp370_5m\"         #>  [90] \"WorldClim_2.1_CMCC-ESM2_ssp585_0.5m\"       #>  [91] \"WorldClim_2.1_CMCC-ESM2_ssp585_10m\"        #>  [92] \"WorldClim_2.1_CMCC-ESM2_ssp585_2.5m\"       #>  [93] \"WorldClim_2.1_CMCC-ESM2_ssp585_5m\"         #>  [94] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_0.5m\"   #>  [95] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_10m\"    #>  [96] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_2.5m\"   #>  [97] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_5m\"     #>  [98] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_0.5m\"   #>  [99] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_10m\"    #> [100] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_2.5m\"   #> [101] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_5m\"     #> [102] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_0.5m\"   #> [103] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_10m\"    #> [104] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_2.5m\"   #> [105] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_5m\"     #> [106] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_0.5m\"   #> [107] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_10m\"    #> [108] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_2.5m\"   #> [109] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_5m\"     #> [110] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_0.5m\"     #> [111] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_10m\"      #> [112] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_2.5m\"     #> [113] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_5m\"       #> [114] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_0.5m\"     #> [115] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_10m\"      #> [116] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_2.5m\"     #> [117] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_5m\"       #> [118] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_0.5m\"     #> [119] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_10m\"      #> [120] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_2.5m\"     #> [121] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_5m\"       #> [122] \"WorldClim_2.1_GFDL-ESM4_ssp126_0.5m\"       #> [123] \"WorldClim_2.1_GFDL-ESM4_ssp126_10m\"        #> [124] \"WorldClim_2.1_GFDL-ESM4_ssp126_2.5m\"       #> [125] \"WorldClim_2.1_GFDL-ESM4_ssp126_5m\"         #> [126] \"WorldClim_2.1_GFDL-ESM4_ssp370_0.5m\"       #> [127] \"WorldClim_2.1_GFDL-ESM4_ssp370_10m\"        #> [128] \"WorldClim_2.1_GFDL-ESM4_ssp370_2.5m\"       #> [129] \"WorldClim_2.1_GFDL-ESM4_ssp370_5m\"         #> [130] \"WorldClim_2.1_GISS-E2-1-G_ssp126_0.5m\"     #> [131] \"WorldClim_2.1_GISS-E2-1-G_ssp126_10m\"      #> [132] \"WorldClim_2.1_GISS-E2-1-G_ssp126_2.5m\"     #> [133] \"WorldClim_2.1_GISS-E2-1-G_ssp126_5m\"       #> [134] \"WorldClim_2.1_GISS-E2-1-G_ssp245_0.5m\"     #> [135] \"WorldClim_2.1_GISS-E2-1-G_ssp245_10m\"      #> [136] \"WorldClim_2.1_GISS-E2-1-G_ssp245_2.5m\"     #> [137] \"WorldClim_2.1_GISS-E2-1-G_ssp245_5m\"       #> [138] \"WorldClim_2.1_GISS-E2-1-G_ssp370_0.5m\"     #> [139] \"WorldClim_2.1_GISS-E2-1-G_ssp370_10m\"      #> [140] \"WorldClim_2.1_GISS-E2-1-G_ssp370_2.5m\"     #> [141] \"WorldClim_2.1_GISS-E2-1-G_ssp370_5m\"       #> [142] \"WorldClim_2.1_GISS-E2-1-G_ssp585_0.5m\"     #> [143] \"WorldClim_2.1_GISS-E2-1-G_ssp585_10m\"      #> [144] \"WorldClim_2.1_GISS-E2-1-G_ssp585_2.5m\"     #> [145] \"WorldClim_2.1_GISS-E2-1-G_ssp585_5m\"       #> [146] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_0.5m\" #> [147] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_10m\"  #> [148] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_2.5m\" #> [149] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_5m\"   #> [150] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_0.5m\" #> [151] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\"  #> [152] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_2.5m\" #> [153] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_5m\"   #> [154] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_0.5m\" #> [155] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_10m\"  #> [156] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_2.5m\" #> [157] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_5m\"   #> [158] \"WorldClim_2.1_INM-CM5-0_ssp126_0.5m\"       #> [159] \"WorldClim_2.1_INM-CM5-0_ssp126_10m\"        #> [160] \"WorldClim_2.1_INM-CM5-0_ssp126_2.5m\"       #> [161] \"WorldClim_2.1_INM-CM5-0_ssp126_5m\"         #> [162] \"WorldClim_2.1_INM-CM5-0_ssp245_0.5m\"       #> [163] \"WorldClim_2.1_INM-CM5-0_ssp245_10m\"        #> [164] \"WorldClim_2.1_INM-CM5-0_ssp245_2.5m\"       #> [165] \"WorldClim_2.1_INM-CM5-0_ssp245_5m\"         #> [166] \"WorldClim_2.1_INM-CM5-0_ssp370_0.5m\"       #> [167] \"WorldClim_2.1_INM-CM5-0_ssp370_10m\"        #> [168] \"WorldClim_2.1_INM-CM5-0_ssp370_2.5m\"       #> [169] \"WorldClim_2.1_INM-CM5-0_ssp370_5m\"         #> [170] \"WorldClim_2.1_INM-CM5-0_ssp585_0.5m\"       #> [171] \"WorldClim_2.1_INM-CM5-0_ssp585_10m\"        #> [172] \"WorldClim_2.1_INM-CM5-0_ssp585_2.5m\"       #> [173] \"WorldClim_2.1_INM-CM5-0_ssp585_5m\"         #> [174] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_0.5m\"    #> [175] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_10m\"     #> [176] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_2.5m\"    #> [177] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_5m\"      #> [178] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_0.5m\"    #> [179] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_10m\"     #> [180] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_2.5m\"    #> [181] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_5m\"      #> [182] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_0.5m\"    #> [183] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_10m\"     #> [184] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_2.5m\"    #> [185] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_5m\"      #> [186] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_0.5m\"    #> [187] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_10m\"     #> [188] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_2.5m\"    #> [189] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_5m\"      #> [190] \"WorldClim_2.1_MIROC6_ssp126_0.5m\"          #> [191] \"WorldClim_2.1_MIROC6_ssp126_10m\"           #> [192] \"WorldClim_2.1_MIROC6_ssp126_2.5m\"          #> [193] \"WorldClim_2.1_MIROC6_ssp126_5m\"            #> [194] \"WorldClim_2.1_MIROC6_ssp245_0.5m\"          #> [195] \"WorldClim_2.1_MIROC6_ssp245_10m\"           #> [196] \"WorldClim_2.1_MIROC6_ssp245_2.5m\"          #> [197] \"WorldClim_2.1_MIROC6_ssp245_5m\"            #> [198] \"WorldClim_2.1_MIROC6_ssp370_0.5m\"          #> [199] \"WorldClim_2.1_MIROC6_ssp370_10m\"           #> [200] \"WorldClim_2.1_MIROC6_ssp370_2.5m\"          #> [201] \"WorldClim_2.1_MIROC6_ssp370_5m\"            #> [202] \"WorldClim_2.1_MIROC6_ssp585_0.5m\"          #> [203] \"WorldClim_2.1_MIROC6_ssp585_10m\"           #> [204] \"WorldClim_2.1_MIROC6_ssp585_2.5m\"          #> [205] \"WorldClim_2.1_MIROC6_ssp585_5m\"            #> [206] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"   #> [207] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_10m\"    #> [208] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_2.5m\"   #> [209] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_5m\"     #> [210] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_0.5m\"   #> [211] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_10m\"    #> [212] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_2.5m\"   #> [213] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_5m\"     #> [214] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"   #> [215] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_10m\"    #> [216] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_2.5m\"   #> [217] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_5m\"     #> [218] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"   #> [219] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_10m\"    #> [220] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_2.5m\"   #> [221] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_5m\"     #> [222] \"WorldClim_2.1_MRI-ESM2-0_ssp126_0.5m\"      #> [223] \"WorldClim_2.1_MRI-ESM2-0_ssp126_10m\"       #> [224] \"WorldClim_2.1_MRI-ESM2-0_ssp126_2.5m\"      #> [225] \"WorldClim_2.1_MRI-ESM2-0_ssp126_5m\"        #> [226] \"WorldClim_2.1_MRI-ESM2-0_ssp245_0.5m\"      #> [227] \"WorldClim_2.1_MRI-ESM2-0_ssp245_10m\"       #> [228] \"WorldClim_2.1_MRI-ESM2-0_ssp245_2.5m\"      #> [229] \"WorldClim_2.1_MRI-ESM2-0_ssp245_5m\"        #> [230] \"WorldClim_2.1_MRI-ESM2-0_ssp370_0.5m\"      #> [231] \"WorldClim_2.1_MRI-ESM2-0_ssp370_10m\"       #> [232] \"WorldClim_2.1_MRI-ESM2-0_ssp370_2.5m\"      #> [233] \"WorldClim_2.1_MRI-ESM2-0_ssp370_5m\"        #> [234] \"WorldClim_2.1_MRI-ESM2-0_ssp585_0.5m\"      #> [235] \"WorldClim_2.1_MRI-ESM2-0_ssp585_10m\"       #> [236] \"WorldClim_2.1_MRI-ESM2-0_ssp585_2.5m\"      #> [237] \"WorldClim_2.1_MRI-ESM2-0_ssp585_5m\"        #> [238] \"WorldClim_2.1_UKESM1-0-LL_ssp126_0.5m\"     #> [239] \"WorldClim_2.1_UKESM1-0-LL_ssp126_10m\"      #> [240] \"WorldClim_2.1_UKESM1-0-LL_ssp126_2.5m\"     #> [241] \"WorldClim_2.1_UKESM1-0-LL_ssp126_5m\"       #> [242] \"WorldClim_2.1_UKESM1-0-LL_ssp245_0.5m\"     #> [243] \"WorldClim_2.1_UKESM1-0-LL_ssp245_10m\"      #> [244] \"WorldClim_2.1_UKESM1-0-LL_ssp245_2.5m\"     #> [245] \"WorldClim_2.1_UKESM1-0-LL_ssp245_5m\"       #> [246] \"WorldClim_2.1_UKESM1-0-LL_ssp370_0.5m\"     #> [247] \"WorldClim_2.1_UKESM1-0-LL_ssp370_10m\"      #> [248] \"WorldClim_2.1_UKESM1-0-LL_ssp370_2.5m\"     #> [249] \"WorldClim_2.1_UKESM1-0-LL_ssp370_5m\"       #> [250] \"WorldClim_2.1_UKESM1-0-LL_ssp585_0.5m\"     #> [251] \"WorldClim_2.1_UKESM1-0-LL_ssp585_10m\"      #> [252] \"WorldClim_2.1_UKESM1-0-LL_ssp585_2.5m\"     #> [253] \"WorldClim_2.1_UKESM1-0-LL_ssp585_5m\" help(\"Example\") #> Documentation for the Example dataset #>  #> Description: #>  #>      This dataset is a subset of Beyer2020, used for the vignette of #>      pastclim. Do not use this dataset for any real work, as it might #>      not reflect the most up-to-date version of Beyer2020. #> Documentation for the Barreto et al 2023 dataset #>  #> Description: #>  #>      Spatio-temporal series of monthly temperature and precipitation #>      and 17 derived bioclimatic variables covering the last 5 Ma #>      (Pliocene<e2><80><93>Pleistocene), at intervals of 1,000 years, and a spatial #>      resolution of 1 arc-degrees (see Barreto et al., 2023 for #>      details). #>  #> Details: #>  #>      PALEO-PGEM-Series is downscaled to 1 <c3><97> 1 arc-degrees spatial #>      resolution from the outputs of the PALEO-PGEM emulator (Holden et #>      al., 2019), which emulates reasonable and extensively validated #>      global estimates of monthly temperature and precipitation for the #>      Plio-Pleistocene every 1 kyr at a spatial resolution of ~5 <c3><97> 5 #>      arc-degrees (Holden et al., 2016, 2019). #>  #>      PALEO-PGEM-Series includes the mean and the standard deviation #>      (i.e., standard error) of the emulated climate over 10 stochastic #>      GCM emulations to accommodate aspects of model uncertainty. This #>      allows users to estimate the robustness of their results in the #>      face of the stochastic aspects of the emulations. For more #>      details, see Section 2.4 in Barreto et al. (2023). #>  #>      Note that this is a very large dataset, with 5001 time slices. It #>      takes approximately 1 minute to set up each variable when creating #>      a region_slice or region_series. However, once the object has been #>      created, other operations tend to be much faster (especially if #>      you subset the dataset to a small number of time steps of #>      interest). #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publications: #>  #>      Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. #>      (2023). PALEO-PGEM-Series: A spatial time series of the global #>      climate over the last 5 million years (Plio-Pleistocene). Global #>      Ecology and Biogeography, 32, 1034-1045, doi:10.1111/geb.13683 #>      <https://doi.org/10.1111/geb.13683> #>  #>      Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., #>      Tran, G. T., and Wilkinson, R. D. (2019): PALEO-PGEM v1.0: a #>      statistical emulator of Pliocene<e2><80><93>Pleistocene climate, Geosci. #>      Model Dev., 12, 5137<e2><80><93>5155, doi:10.5194/gmd-12-5137-2019 #>      <https://doi.org/10.5194/gmd-12-5137-2019>. #>  #>  #> ####################################################### #> Documentation for the Beyer2020 dataset #>  #> Description: #>  #>      This dataset covers the last 120k years, at intervals of 1/2 k #>      years, and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial #>      climate, bioclimate and vegetation for the last 120,000 years. Sci #>      Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 #>      <https://doi.org/10.1038/s41597-020-0552-1> #>  #>      The version included in 'pastclim' has the ice sheets masked, as #>      well as internal seas (Black and Caspian Sea) removed. The latter #>      are based on: #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4278> #>  #>      <https://www.marineregions.org/gazetteer.php?p=details&id=4282> #>  #>      As there is no reconstruction of their depth through time, modern #>      outlines were used for all time steps. #>  #>      Also, for bio15, the coefficient of variation was computed after #>      adding one to monthly estimates, and it was multiplied by 100 #>      following <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/deposit/7062281> #>  #>      v1.0.0 Remove ice sheets and internal seas, and use correct #>      formula for bio15. Files can be downloaded from: #>      doi:10.6084/m9.figshare.19723405.v1 #>      <https://doi.org/10.6084/m9.figshare.19723405.v1> #>  #>  #> ####################################################### #> Documentation for _CHELSA 2.1_ #>  #> Description: #>  #>      _CHELSA_ version 2.1 is a database of high spatial resolution #>      global weather and climate data, covering both the present and #>      future projections. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication for the _CHELSA_ dataset: #>  #>      Karger, D.N., Conrad, O., B<c3><b6>hner, J., Kawohl, T., Kreft, H., #>      Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017) #>      Climatologies at high resolution for the Earth land surface areas. #>      Scientific Data. 4 170122. doi:10.1038/sdata.2017.122 #>      <https://doi.org/10.1038/sdata.2017.122> #>  #>      *Present-day reconstructions* are based on the mean for the period #>      1981-2000 and are available at at the high resolution of 0.5 #>      arc-minutes (_CHELSA_2.1_0.5m_). In 'pastclim', the datasets are #>      given a date of 1990 CE (the mid-point of the period of interest). #>      There are 19 <e2><80><9c>bioclimatic<e2><80><9d> variables, as well as monthly estimates #>      for mean temperature, and precipitation. The dataset is very #>      large, as it includes estimates for the oceans as well as the land #>      masses. An alternative to downloading the very large files is to #>      use virtual rasters, which allow the data to remain on the server, #>      with only the pixels required for a given operation being #>      downloaded. Virtual rasters can be used by choosing #>      (_CHELSA_2.1_0.5m_vsi_) #>  #>      *Future projections* are based on the models in CMIP6, downscaled #>      and de-biased using the CHELSA algorithm 2.1. Monthly values of #>      mean temperature, and total precipitation, as well as 19 #>      bioclimatic variables were processed for 5 global climate models #>      (GCMs), and for three Shared Socio-economic Pathways (SSPs): 126, #>      370 and 585. Model and SSP can be chosen by changing the ending of #>      the dataset name _CHELSA_2.1_GCM_SSP_RESm_. #>  #>      Available values for GCM are: \"GFDL-ESM4\", \"IPSL-CM6A-LR\", #>      \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", and \"UKESM1-0-LL\". For SSP, use: #>      \"ssp126\", \"ssp370\", and \"ssp585\". RES is currently limited to #>      \"0.5m\". Example dataset names are #>      _CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_ and #>      _CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_ #>  #>      As for present reconstructions, an alternative to downloading the #>      very large files is to use virtual rasters. Simply append \"_vis\" #>      to the name of the dataset of interest #>      (_CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi_). #>  #>      The dataset are averages over 30 year periods (2011-2040, #>      2041-2070, 2071-2100). In 'pastclim', the midpoints of the periods #>      (2025, 2055, 2075) are used as the time stamps. All 3 periods are #>      automatically downloaded for each combination of GCM model and #>      SSP, and are selected as usual by defining the time in functions #>      such as 'region_slice()'. #>  #>  #> ####################################################### #> Documentation for _CHELSA-TracCE21k_ #>  #> Description: #>  #>      CHELSA-TraCE21k data provides monthly climate data for temperature #>      and precipitation at 30 arc-sec spatial resolution in 100-year #>      time steps for the last 21,000 years. Palaeo-orography at high #>      spatial resolution and at each time step is created by combining #>      high resolution information on glacial cover from current and Last #>      Glacial Maximum (LGM) glacier databases with the interpolation of #>      a dynamic ice sheet model (ICE6G) and a coupling to mean annual #>      temperatures from CCSM3-TraCE21k. Based on the reconstructed #>      palaeo-orography, mean annual temperature and precipitation was #>      downscaled using the CHELSA V1.2 algorithm. #>  #> Details: #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      An alternative to downloading very large files is to use virtual #>      rasters. Simply append \"_vis\" to the name of the dataset of #>      interest (_CHELSA_trace21k_1.0_0.5m_vsi_). This is the recommended #>      approach, and it is currently the only available version of the #>      dataset. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Karger, D.N., Nobis, M.P., Normand, S., Graham, C.H., Zimmermann, #>      N. (2023) CHELSA-TraCE21k <e2><80><93> High resolution (1 km) downscaled #>      transient temperature and precipitation data since the Last #>      Glacial Maximum. Climate of the Past. doi:10.5194/cp-2021-30 #>      <https://doi.org/10.5194/cp-2021-30> #>  #>  #> ####################################################### #> Documentation for the Example dataset #>  #> Description: #>  #>      This dataset is a subset of Beyer2020, used for the vignette of #>      pastclim. Do not use this dataset for any real work, as it might #>      not reflect the most up-to-date version of Beyer2020. #>  #>  #> ####################################################### #> Documentation for _HYDE 3.3_ dataset #>  #> Description: #>  #>      This database presents an update and expansion of the History #>      Database of the Global Environment (HYDE, v 3.3) and replaces #>      former HYDE 3.2 version from 2017. HYDE is and internally #>      consistent combination of updated historical population estimates #>      and land use. Categories include cropland, with a new distinction #>      into irrigated and rain fed crops (other than rice) and irrigated #>      and rain fed rice. Also grazing lands are provided, divided into #>      more intensively used pasture, converted rangeland and #>      non-converted natural (less intensively used) rangeland. #>      Population is represented by maps of total, urban, rural #>      population and population density as well as built-up area. #>  #> Details: #>  #>      The period covered is 10 000 BCE to 2023 CE. Spatial resolution is #>      5 arc minutes (approx. 85 km2 at the equator). The full _HYDE 3.3_ #>      release contains: a Baseline estimate scenario, a Lower estimate #>      scenario and an Upper estimate scenario. Currently only the #>      baseline scenario is available in 'pastclim' #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication for the HYDE 3.2 (there is no current publication for #>      3.3): #>  #>      Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: #>      Anthropogenic land-use estimates for the Holocene; HYDE 3.2, Earth #>      Syst. Sci. Data, 9, 927-953, 2017. doi:10.5194/essd-9-927-2017 #>      <https://doi.org/10.5194/essd-9-927-2017> #>  #>  #> ####################################################### #> Documentation for the Krapp2021 dataset #>  #> Description: #>  #>      This dataset covers the last 800k years, at intervals of 1k years, #>      and a resolution of 0.5 degrees in latitude and longitude. #>  #> Details: #>  #>      The units of several variables have been changed to match what is #>      used in WorldClim. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Krapp, M., Beyer, R.M., Edmundson, S.L. et al. A statistics-based #>      reconstruction of high-resolution global terrestrial climate for #>      the last 800,000 years. Sci Data 8, 228 (2021). #>      doi:10.1038/s41597-021-01009-3 #>      <https://doi.org/10.1038/s41597-021-01009-3> #>  #>      The version included in 'pastclim' has the ice sheets masked. #>  #>      Note that, for bio15, we use the corrected version, which follows #>      <https://pubs.usgs.gov/ds/691/ds691.pdf> #>  #>      Changelog #>  #>      v1.4.0 Change units to match WorldClim. Fix variable duplication #>      found on earlier versions of the dataset. #>      <https://zenodo.org/records/8415273> #>  #>      v1.1.0 Added monthly variables. Files can be downloaded from: #>      <https://zenodo.org/record/7065055> #>  #>      v1.0.0 Remove ice sheets and use correct formula for bio15. Files #>      can be downloaded from: doi:10.6084/m9.figshare.19733680.v1 #>      <https://doi.org/10.6084/m9.figshare.19733680.v1> #>  #>  #> ####################################################### #> Documentation for _Paleoclim_ #>  #> Description: #>  #>      _Paleoclim_ is a set of high resolution paleoclimate #>      reconstructions, mostly based on the CESM model, downscaled with #>      the CHELSA dataset to 3 different spatial resolutions: #>      'paleoclim_1.0_2.5m' at 2.5 arc-minutes (~5 km), #>      'paleoclim_1.0_5m' at 5 arc-minutes (~10 km), and #>      'paleoclim_1.0_10m' 10 arc-minutes (~20 km). All 19 biovariables #>      are available. There are only a limited number of time slices #>      available for this dataset; furthermore, currently only time #>      slices from present to 130ka are available in 'pastclim'. #>  #> Details: #>  #>      More details on the dataset are available on its dedicated #>      website. #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Brown, Hill, Dolan, Carnaval, Haywood (2018) PaleoClim, high #>      spatial resolution paleoclimate surfaces for global land areas. #>      Nature <e2><80><93> Scientific Data. 5:180254 #>  #>  #> ####################################################### #> Documentation for the WorldClim datasets #>  #> Description: #>  #>      WorldClim version 2.1 is a database of high spatial resolution #>      global weather and climate data, covering both the present and #>      future projections. #>  #> Details: #>  #>      IMPORTANT: If you use this dataset, make sure to cite the original #>      publication: #>  #>      Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial #>      resolution climate surfaces for global land areas. International #>      Journal of Climatology 37 (12): 4302-4315. doi:10.1002/joc.5086 #>      <https://doi.org/10.1002/joc.5086> #>  #>      *Present-day reconstructions* are based on the mean for the period #>      1970-2000, and are available at multiple resolutions of 10 #>      arc-minutes, 5 arc-minutes, 2.5 arc-minute and 0.5 arc-minutes. #>      The resolution of interest can be obtained by changing the ending #>      of the dataset name _WorldClim_2.1_RESm_, e.g. _WorldClim_2.1_10m_ #>      or _WorldClim_2.1_5m_ (currently, only 10m and 5m are currently #>      available in 'pastclim'). In 'pastclim', the datasets are given a #>      date of 1985 CE (the mid-point of the period of interest). There #>      are 19 <e2><80><9c>bioclimatic<e2><80><9d> variables, as well as monthly estimates for #>      minimum, mean, and maximum temperature, and precipitation. #>  #>      *Future projections* are based on the models in CMIP6, downscaled #>      and de-biased using WorldClim 2.1 for the present as a baseline. #>      Monthly values of minimum temperature, maximum temperature, and #>      precipitation, as well as 19 bioclimatic variables were processed #>      for 23 global climate models (GCMs), and for four Shared #>      Socio-economic Pathways (SSPs): 126, 245, 370 and 585. Model and #>      SSP can be chosen by changing the ending of the dataset name #>      _WorldClim_2.1_GCM_SSP_RESm_. #>  #>      Available values for GCM are: \"ACCESS-CM2\", \"BCC-CSM2-MR\", #>      \"CMCC-ESM2\", \"EC-Earth3-Veg\", \"FIO-ESM-2-0\", \"GFDL-ESM4\", #>      \"GISS-E2-1-G\", \"HadGEM3-GC31-LL\", \"INM-CM5-0\", \"IPSL-CM6A-LR\", #>      \"MIROC6\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", and \"UKESM1-0-LL\". For #>      SSP, use: \"ssp126\", \"ssp245\", \"ssp370\", and \"ssp585\". RES takes #>      the same values as for present reconstructions (i.e. \"10m\", \"5m\", #>      \"2.5m\", and \"0.5m\"). Example dataset names are #>      _WorldClim_2.1_ACCESS-CM2_ssp245_10m_ and #>      _WorldClim_2.1_MRI-ESM2-0_ssp370_5m_. Four combination (namely #>      _FIO-ESM-2-0_ssp370_, _GFDL-ESM4_ssp245_, _GFDL-ESM4_ssp585_, and #>      _HadGEM3-GC31-LL_ssp370_) are NOT available. #>  #>      The dataset are averages over 20 year periods (2021-2040, #>      2041-2060, 2061-2080, 2081-2100). In 'pastclim', the midpoints of #>      the periods (2030, 2050, 2070, 2090) are used as the time stamps. #>      All 4 periods are automatically downloaded for each combination of #>      GCM model and SSP, and are selected as usual by defining the time #>      in functions such as 'region_slice()'. #>  #>  #> #######################################################"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"formatting-a-custom-dataset-for-pastclim","dir":"Articles","previous_headings":"","what":"Formatting a custom dataset for pastclim","title":"custom dataset","text":"guide aimed formatting data way can used pastclim. pastclim designed extract data netcdf files, format commonly used storing climate reconstructions. netcdf files number advantages, can store compressed information, well allowing access data required (e.g. extracting time steps location interest without reading data memory). expected format pastclim requires time steps given variable stored within single netcdf file. variables combined () flexible: can separate file variable, collate everything within single file, create multiple files including number variables. time variable years since 1950 (.e. negative integers indicating past). number command line tools well R libraries (e.g. cdo, gdal, terra) can help creating editing netcdf files.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"an-example-the-trace21k-chelsea","dir":"Articles","previous_headings":"","what":"An example: the Trace21k-CHELSEA","title":"custom dataset","text":"provide simple example format dataset R. use version Trace21k dataset, downscaled 30 arcsecs using CHELSEA algorithm(available website). data stored geoTIFF files, one file per time step per variable. First, need collate files given variable (use bio01 example) within single netcdf file. original files large, illustrate time steps aggregated 3x3 degrees keep files sizes small. start translating geoTIFF netcdf file. files prefix CHELSA_TraCE21k_bio01_-xxx_V1.0.small.tif, xxx number time step. use 3 time step illustrative purposes. store files single directory, create spatRaster list files directory: NOTE: terra changed way handles time reading netcdf. dev version terra can easily format netcdf files correctly, vignette presents number workarounds needed version CRAN Now need set time axis raster (case, reconstructions every 100 years), generate user friendly names layers raster: Now save data nc file (use temporary directory) can now read custom netcdf file pastclim. expected, one variable (“bio01”) 3 time steps (nlyr). can get times time steps : can slice series plot given time point:  Note reconstructions include ocean ice sheets, much better remove needed ecological/archaeological studies (allows smaller files).","code":"tiffs_path <- system.file(\"extdata/CHELSA_bio01\", package = \"pastclim\") list_of_tiffs <- file.path(tiffs_path, dir(tiffs_path)) bio01 <- terra::rast(list_of_tiffs) library(pastclim) #> Loading required package: terra #> terra 1.7.80 time_bp(bio01) <- c(0, -100, -200) names(bio01) <- paste(\"bio01\", terra::time(bio01), sep = \"_\") nc_name <- file.path(tempdir(), \"CHELSA_TraCE21k_bio01.nc\") terra::writeCDF(bio01,   filename = nc_name, varname = \"bio01\",   compression = 9, overwrite = TRUE ) custom_series <- region_series(   bio_variables = \"bio01\",   dataset = \"custom\",   path_to_nc = nc_name ) custom_series #> class       : SpatRasterDataset  #> subdatasets : 1  #> dimensions  : 174, 360 (nrow, ncol) #> nlyr        : 3  #> resolution  : 1, 1  (x, y) #> extent      : -180.0001, 179.9999, -90.00014, 83.99986  (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84 (EPSG:4326)  #> source(s)   : CHELSA_TraCE21k_bio01.nc  #> names       : bio01 get_time_bp_steps(dataset = \"custom\", path_to_nc = nc_name) #> [1]    0 -100 -200 climate_100 <- slice_region_series(custom_series, time_bp = -100) terra::plot(climate_100)"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html","id":"making-the-data-available-to-others","dir":"Articles","previous_headings":"","what":"Making the data available to others","title":"custom dataset","text":"created suitably formatted netcdf files can used custom datasets pastclim, can add data officially package, thus make available others. necessary steps: Put files freely available repository. Update table used pastclim store information available datasets. table found “./data-raw/data_files/dataset_list_included.csv”. includes following fields: variable: variable name used pastclim ncvar: variable name within nc file (can variable) dataset: name dataset. monthly: boolean whether variable monthly. file_name: name file variable. download_path: URL download file. donwload_function: datasets can easily converted user valid netcdf, possibly leave download_path empty, create internal function downloads converts files. example, see WorldClim datasets. file_name_orig: name original file(s) used create nc dataset. download_path_orig: path original files. version: version dataset created long_name: long name variable abbreviated_name: abbreviated version long_name (used plot labels) time_frame: either year appropriate month units: units variable, displayed plain text table units_exp: units formatted included expression creating plot labels added lines detailing variables dataset, run script “./raw-data/make_data/dataset_list_included.R” store information appropriate dataset pastclim. Provide information new dataset file “./R/dataset_docs”, using roxygen2 syntax. Make sure provide appropriate reference original data, important users can refer back original source. Make Pull Request GitHub.","code":"#>   variable ncvar dataset monthly                 file_name download_path #> 1    bio01  BIO1 Example   FALSE example_climate_v1.3.0.nc               #> 2    bio10 BIO10 Example   FALSE example_climate_v1.3.0.nc               #>   download_function file_name_orig download_path_orig version #> 1                                                       1.3.0 #> 2                                                       1.3.0 #>                             long_name      abbreviated_name time_frame #> 1             annual mean temperature           ann. mean T       year #> 2 mean temperature of warmest quarter mean T of warmest qtr       year #>             units  units_exp dataset_list_v #> 1 degrees Celsius *degree*C*          1.3.9 #> 2 degrees Celsius *degree*C*"},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"worldclim","dir":"Articles","previous_headings":"Present reconstructions","what":"WorldClim","title":"present and future","text":"Present-day reconstructions WorldClim v2.1 based mean period 1970-2000, available multiple resolutions 10 arc-minutes, 5 arc-minutes, 2.5 arc-minute 0.5 arc-minutes. resolution interest can obtained changing ending dataset name WorldClim_2.1_RESm, e.g. WorldClim_2.1_10m WorldClim_2.1_5m. pastclim, datasets given date 1985 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. , annual variables 10m arc-minutes dataset : monthly variables can manipulate data usual way. start downloading dataset: can use region_slice extract data SpatRaster:","code":"library(pastclim) #> Loading required package: terra #> terra 1.7.80 get_vars_for_dataset(\"WorldClim_2.1_10m\") #>  [1] \"bio01\"    \"bio02\"    \"bio03\"    \"bio04\"    \"bio05\"    \"bio06\"    #>  [7] \"bio07\"    \"bio08\"    \"bio09\"    \"bio10\"    \"bio11\"    \"bio12\"    #> [13] \"bio13\"    \"bio14\"    \"bio15\"    \"bio16\"    \"bio17\"    \"bio18\"    #> [19] \"bio19\"    \"altitude\" get_vars_for_dataset(\"WorldClim_2.1_10m\", monthly = TRUE, annual = FALSE) #>  [1] \"temperature_01\"     \"temperature_02\"     \"temperature_03\"     #>  [4] \"temperature_04\"     \"temperature_05\"     \"temperature_06\"     #>  [7] \"temperature_07\"     \"temperature_08\"     \"temperature_09\"     #> [10] \"temperature_10\"     \"temperature_11\"     \"temperature_12\"     #> [13] \"precipitation_01\"   \"precipitation_02\"   \"precipitation_03\"   #> [16] \"precipitation_04\"   \"precipitation_05\"   \"precipitation_06\"   #> [19] \"precipitation_07\"   \"precipitation_08\"   \"precipitation_09\"   #> [22] \"precipitation_10\"   \"precipitation_11\"   \"precipitation_12\"   #> [25] \"temperature_min_01\" \"temperature_min_02\" \"temperature_min_03\" #> [28] \"temperature_min_04\" \"temperature_min_05\" \"temperature_min_06\" #> [31] \"temperature_min_07\" \"temperature_min_08\" \"temperature_min_09\" #> [34] \"temperature_min_10\" \"temperature_min_11\" \"temperature_min_12\" #> [37] \"temperature_max_01\" \"temperature_max_02\" \"temperature_max_03\" #> [40] \"temperature_max_04\" \"temperature_max_05\" \"temperature_max_06\" #> [43] \"temperature_max_07\" \"temperature_max_08\" \"temperature_max_09\" #> [46] \"temperature_max_10\" \"temperature_max_11\" \"temperature_max_12\" download_dataset(   dataset = \"WorldClim_2.1_10m\",   bio_variables = c(\"bio01\", \"bio02\", \"altitude\") ) climate_present <- region_slice(   time_ce = 1985,   bio_variables = c(\"bio01\", \"bio02\", \"altitude\"),   dataset = \"WorldClim_2.1_10m\" )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"chelsa","dir":"Articles","previous_headings":"Present reconstructions","what":"CHELSA","title":"present and future","text":"Present-day reconstructions CHELSA v2.1 based mean period 1981-2000, available high resolution 0.5 arc-minutes. CHELSA_2.1_0.5m. pastclim, datasets given date 1990 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. , annual variables CHELSA dataset : monthly variables can manipulate data usual way. start downloading dataset: can use region_slice extract data SpatRaster: datasets variable large due high resolution. Besides downloading data, also possible use virtual raster, leaving files server, downloading pixels needed. can achieve using dataset CHELSA_2.1_0.5_vsi. still need download dataset first, rather downloading files, sets virtual raster (fast!): downloaded, can use dataset: ideal just need extract climate number locations, need get full map.","code":"library(pastclim) get_vars_for_dataset(\"CHELSA_2.1_0.5m\") #>  [1] \"bio01\" \"bio02\" \"bio03\" \"bio04\" \"bio05\" \"bio06\" \"bio07\" \"bio08\" \"bio09\" #> [10] \"bio10\" \"bio11\" \"bio12\" \"bio13\" \"bio14\" \"bio15\" \"bio16\" \"bio17\" \"bio18\" #> [19] \"bio19\" get_vars_for_dataset(\"CHELSA_2.1_0.5m\", monthly = TRUE, annual = FALSE) #>  [1] \"temperature_01\"     \"temperature_02\"     \"temperature_03\"     #>  [4] \"temperature_04\"     \"temperature_05\"     \"temperature_06\"     #>  [7] \"temperature_07\"     \"temperature_08\"     \"temperature_09\"     #> [10] \"temperature_10\"     \"temperature_11\"     \"temperature_12\"     #> [13] \"precipitation_01\"   \"precipitation_02\"   \"precipitation_03\"   #> [16] \"precipitation_04\"   \"precipitation_05\"   \"precipitation_06\"   #> [19] \"precipitation_07\"   \"precipitation_08\"   \"precipitation_09\"   #> [22] \"precipitation_10\"   \"precipitation_11\"   \"precipitation_12\"   #> [25] \"temperature_max_01\" \"temperature_max_02\" \"temperature_max_03\" #> [28] \"temperature_max_04\" \"temperature_max_05\" \"temperature_max_06\" #> [31] \"temperature_max_07\" \"temperature_max_08\" \"temperature_max_09\" #> [34] \"temperature_max_10\" \"temperature_max_11\" \"temperature_max_12\" #> [37] \"temperature_min_01\" \"temperature_min_02\" \"temperature_min_03\" #> [40] \"temperature_min_04\" \"temperature_min_05\" \"temperature_min_06\" #> [43] \"temperature_min_07\" \"temperature_min_08\" \"temperature_min_09\" #> [46] \"temperature_min_10\" \"temperature_min_11\" \"temperature_min_12\" download_dataset(   dataset = \"CHELSA_2.1_0.5m\",   bio_variables = c(\"bio01\", \"bio02\") ) climate_present <- region_slice(   time_ce = 1990,   bio_variables = c(\"bio01\", \"bio02\"),   dataset = \"CHELSA_2.1_0.5m\" ) download_dataset(dataset = \"CHELSA_2.1_0.5m_vsi\",                   bio_variables = c(\"bio12\",\"temperature_01\")) climate_present <- region_slice(   time_ce = 1990,   bio_variables = c(\"bio12\",\"temperature_01\"),   dataset = \"CHELSA_2.1_0.5m_vsi\" )"},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"worldclim-1","dir":"Articles","previous_headings":"Future projections","what":"WorldClim","title":"present and future","text":"Future projections based models CMIP6, downscaled de-biased using WorldClim 2.1 present baseline. Monthly values minimum temperature, maximum temperature, precipitation, well 19 bioclimatic variables processed 23 global climate models (GCMs), four Shared Socio-economic Pathways (SSPs): 126, 245, 370 585. Model SSP can chosen changing ending dataset name WorldClim_2.1_GCM_SSP_RESm. complete list available combinations can obtained : , interested HadGEM3-GC31-LL model, ssp set 245 10 arc-minutes, can get available variables: can now download “bio01” “bio02” dataset : datasets averages 20 year periods (2021-2040, 2041-2060, 2061-2080, 2081-2100). pastclim, midpoints periods (2030, 2050, 2070, 2090) used time stamps. 4 periods automatically downloaded combination GCM model SSP, can selected usual defining time region_slice. Alternatively, possible get full time series 4 slices : possible simply use get_time_ce_steps(dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\") get available time points dataset. Help WorldClim datasets (modern future) can accessed help(\"WorldClim_2.1\")","code":"list_available_datasets()[grepl(\"WorldClim_2.1\",list_available_datasets())] #>   [1] \"WorldClim_2.1_0.5m\"                        #>   [2] \"WorldClim_2.1_10m\"                         #>   [3] \"WorldClim_2.1_2.5m\"                        #>   [4] \"WorldClim_2.1_5m\"                          #>   [5] \"WorldClim_2.1_ACCESS-CM2_ssp126_0.5m\"      #>   [6] \"WorldClim_2.1_ACCESS-CM2_ssp126_10m\"       #>   [7] \"WorldClim_2.1_ACCESS-CM2_ssp126_2.5m\"      #>   [8] \"WorldClim_2.1_ACCESS-CM2_ssp126_5m\"        #>   [9] \"WorldClim_2.1_ACCESS-CM2_ssp245_0.5m\"      #>  [10] \"WorldClim_2.1_ACCESS-CM2_ssp245_10m\"       #>  [11] \"WorldClim_2.1_ACCESS-CM2_ssp245_2.5m\"      #>  [12] \"WorldClim_2.1_ACCESS-CM2_ssp245_5m\"        #>  [13] \"WorldClim_2.1_ACCESS-CM2_ssp370_0.5m\"      #>  [14] \"WorldClim_2.1_ACCESS-CM2_ssp370_10m\"       #>  [15] \"WorldClim_2.1_ACCESS-CM2_ssp370_2.5m\"      #>  [16] \"WorldClim_2.1_ACCESS-CM2_ssp370_5m\"        #>  [17] \"WorldClim_2.1_ACCESS-CM2_ssp585_0.5m\"      #>  [18] \"WorldClim_2.1_ACCESS-CM2_ssp585_10m\"       #>  [19] \"WorldClim_2.1_ACCESS-CM2_ssp585_2.5m\"      #>  [20] \"WorldClim_2.1_ACCESS-CM2_ssp585_5m\"        #>  [21] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_0.5m\"     #>  [22] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_10m\"      #>  [23] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_2.5m\"     #>  [24] \"WorldClim_2.1_BCC-CSM2-MR_ssp126_5m\"       #>  [25] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_0.5m\"     #>  [26] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_10m\"      #>  [27] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_2.5m\"     #>  [28] \"WorldClim_2.1_BCC-CSM2-MR_ssp245_5m\"       #>  [29] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_0.5m\"     #>  [30] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_10m\"      #>  [31] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_2.5m\"     #>  [32] \"WorldClim_2.1_BCC-CSM2-MR_ssp370_5m\"       #>  [33] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_0.5m\"     #>  [34] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_10m\"      #>  [35] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_2.5m\"     #>  [36] \"WorldClim_2.1_BCC-CSM2-MR_ssp585_5m\"       #>  [37] \"WorldClim_2.1_CMCC-ESM2_ssp126_0.5m\"       #>  [38] \"WorldClim_2.1_CMCC-ESM2_ssp126_10m\"        #>  [39] \"WorldClim_2.1_CMCC-ESM2_ssp126_2.5m\"       #>  [40] \"WorldClim_2.1_CMCC-ESM2_ssp126_5m\"         #>  [41] \"WorldClim_2.1_CMCC-ESM2_ssp245_0.5m\"       #>  [42] \"WorldClim_2.1_CMCC-ESM2_ssp245_10m\"        #>  [43] \"WorldClim_2.1_CMCC-ESM2_ssp245_2.5m\"       #>  [44] \"WorldClim_2.1_CMCC-ESM2_ssp245_5m\"         #>  [45] \"WorldClim_2.1_CMCC-ESM2_ssp370_0.5m\"       #>  [46] \"WorldClim_2.1_CMCC-ESM2_ssp370_10m\"        #>  [47] \"WorldClim_2.1_CMCC-ESM2_ssp370_2.5m\"       #>  [48] \"WorldClim_2.1_CMCC-ESM2_ssp370_5m\"         #>  [49] \"WorldClim_2.1_CMCC-ESM2_ssp585_0.5m\"       #>  [50] \"WorldClim_2.1_CMCC-ESM2_ssp585_10m\"        #>  [51] \"WorldClim_2.1_CMCC-ESM2_ssp585_2.5m\"       #>  [52] \"WorldClim_2.1_CMCC-ESM2_ssp585_5m\"         #>  [53] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_0.5m\"   #>  [54] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_10m\"    #>  [55] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_2.5m\"   #>  [56] \"WorldClim_2.1_EC-Earth3-Veg_ssp126_5m\"     #>  [57] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_0.5m\"   #>  [58] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_10m\"    #>  [59] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_2.5m\"   #>  [60] \"WorldClim_2.1_EC-Earth3-Veg_ssp245_5m\"     #>  [61] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_0.5m\"   #>  [62] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_10m\"    #>  [63] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_2.5m\"   #>  [64] \"WorldClim_2.1_EC-Earth3-Veg_ssp370_5m\"     #>  [65] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_0.5m\"   #>  [66] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_10m\"    #>  [67] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_2.5m\"   #>  [68] \"WorldClim_2.1_EC-Earth3-Veg_ssp585_5m\"     #>  [69] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_0.5m\"     #>  [70] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_10m\"      #>  [71] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_2.5m\"     #>  [72] \"WorldClim_2.1_FIO-ESM-2-0_ssp126_5m\"       #>  [73] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_0.5m\"     #>  [74] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_10m\"      #>  [75] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_2.5m\"     #>  [76] \"WorldClim_2.1_FIO-ESM-2-0_ssp245_5m\"       #>  [77] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_0.5m\"     #>  [78] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_10m\"      #>  [79] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_2.5m\"     #>  [80] \"WorldClim_2.1_FIO-ESM-2-0_ssp585_5m\"       #>  [81] \"WorldClim_2.1_GFDL-ESM4_ssp126_0.5m\"       #>  [82] \"WorldClim_2.1_GFDL-ESM4_ssp126_10m\"        #>  [83] \"WorldClim_2.1_GFDL-ESM4_ssp126_2.5m\"       #>  [84] \"WorldClim_2.1_GFDL-ESM4_ssp126_5m\"         #>  [85] \"WorldClim_2.1_GFDL-ESM4_ssp370_0.5m\"       #>  [86] \"WorldClim_2.1_GFDL-ESM4_ssp370_10m\"        #>  [87] \"WorldClim_2.1_GFDL-ESM4_ssp370_2.5m\"       #>  [88] \"WorldClim_2.1_GFDL-ESM4_ssp370_5m\"         #>  [89] \"WorldClim_2.1_GISS-E2-1-G_ssp126_0.5m\"     #>  [90] \"WorldClim_2.1_GISS-E2-1-G_ssp126_10m\"      #>  [91] \"WorldClim_2.1_GISS-E2-1-G_ssp126_2.5m\"     #>  [92] \"WorldClim_2.1_GISS-E2-1-G_ssp126_5m\"       #>  [93] \"WorldClim_2.1_GISS-E2-1-G_ssp245_0.5m\"     #>  [94] \"WorldClim_2.1_GISS-E2-1-G_ssp245_10m\"      #>  [95] \"WorldClim_2.1_GISS-E2-1-G_ssp245_2.5m\"     #>  [96] \"WorldClim_2.1_GISS-E2-1-G_ssp245_5m\"       #>  [97] \"WorldClim_2.1_GISS-E2-1-G_ssp370_0.5m\"     #>  [98] \"WorldClim_2.1_GISS-E2-1-G_ssp370_10m\"      #>  [99] \"WorldClim_2.1_GISS-E2-1-G_ssp370_2.5m\"     #> [100] \"WorldClim_2.1_GISS-E2-1-G_ssp370_5m\"       #> [101] \"WorldClim_2.1_GISS-E2-1-G_ssp585_0.5m\"     #> [102] \"WorldClim_2.1_GISS-E2-1-G_ssp585_10m\"      #> [103] \"WorldClim_2.1_GISS-E2-1-G_ssp585_2.5m\"     #> [104] \"WorldClim_2.1_GISS-E2-1-G_ssp585_5m\"       #> [105] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_0.5m\" #> [106] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_10m\"  #> [107] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_2.5m\" #> [108] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp126_5m\"   #> [109] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_0.5m\" #> [110] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\"  #> [111] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_2.5m\" #> [112] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_5m\"   #> [113] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_0.5m\" #> [114] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_10m\"  #> [115] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_2.5m\" #> [116] \"WorldClim_2.1_HadGEM3-GC31-LL_ssp585_5m\"   #> [117] \"WorldClim_2.1_INM-CM5-0_ssp126_0.5m\"       #> [118] \"WorldClim_2.1_INM-CM5-0_ssp126_10m\"        #> [119] \"WorldClim_2.1_INM-CM5-0_ssp126_2.5m\"       #> [120] \"WorldClim_2.1_INM-CM5-0_ssp126_5m\"         #> [121] \"WorldClim_2.1_INM-CM5-0_ssp245_0.5m\"       #> [122] \"WorldClim_2.1_INM-CM5-0_ssp245_10m\"        #> [123] \"WorldClim_2.1_INM-CM5-0_ssp245_2.5m\"       #> [124] \"WorldClim_2.1_INM-CM5-0_ssp245_5m\"         #> [125] \"WorldClim_2.1_INM-CM5-0_ssp370_0.5m\"       #> [126] \"WorldClim_2.1_INM-CM5-0_ssp370_10m\"        #> [127] \"WorldClim_2.1_INM-CM5-0_ssp370_2.5m\"       #> [128] \"WorldClim_2.1_INM-CM5-0_ssp370_5m\"         #> [129] \"WorldClim_2.1_INM-CM5-0_ssp585_0.5m\"       #> [130] \"WorldClim_2.1_INM-CM5-0_ssp585_10m\"        #> [131] \"WorldClim_2.1_INM-CM5-0_ssp585_2.5m\"       #> [132] \"WorldClim_2.1_INM-CM5-0_ssp585_5m\"         #> [133] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_0.5m\"    #> [134] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_10m\"     #> [135] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_2.5m\"    #> [136] \"WorldClim_2.1_IPSL-CM6A-LR_ssp126_5m\"      #> [137] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_0.5m\"    #> [138] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_10m\"     #> [139] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_2.5m\"    #> [140] \"WorldClim_2.1_IPSL-CM6A-LR_ssp245_5m\"      #> [141] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_0.5m\"    #> [142] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_10m\"     #> [143] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_2.5m\"    #> [144] \"WorldClim_2.1_IPSL-CM6A-LR_ssp370_5m\"      #> [145] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_0.5m\"    #> [146] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_10m\"     #> [147] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_2.5m\"    #> [148] \"WorldClim_2.1_IPSL-CM6A-LR_ssp585_5m\"      #> [149] \"WorldClim_2.1_MIROC6_ssp126_0.5m\"          #> [150] \"WorldClim_2.1_MIROC6_ssp126_10m\"           #> [151] \"WorldClim_2.1_MIROC6_ssp126_2.5m\"          #> [152] \"WorldClim_2.1_MIROC6_ssp126_5m\"            #> [153] \"WorldClim_2.1_MIROC6_ssp245_0.5m\"          #> [154] \"WorldClim_2.1_MIROC6_ssp245_10m\"           #> [155] \"WorldClim_2.1_MIROC6_ssp245_2.5m\"          #> [156] \"WorldClim_2.1_MIROC6_ssp245_5m\"            #> [157] \"WorldClim_2.1_MIROC6_ssp370_0.5m\"          #> [158] \"WorldClim_2.1_MIROC6_ssp370_10m\"           #> [159] \"WorldClim_2.1_MIROC6_ssp370_2.5m\"          #> [160] \"WorldClim_2.1_MIROC6_ssp370_5m\"            #> [161] \"WorldClim_2.1_MIROC6_ssp585_0.5m\"          #> [162] \"WorldClim_2.1_MIROC6_ssp585_10m\"           #> [163] \"WorldClim_2.1_MIROC6_ssp585_2.5m\"          #> [164] \"WorldClim_2.1_MIROC6_ssp585_5m\"            #> [165] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"   #> [166] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_10m\"    #> [167] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_2.5m\"   #> [168] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp126_5m\"     #> [169] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_0.5m\"   #> [170] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_10m\"    #> [171] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_2.5m\"   #> [172] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp245_5m\"     #> [173] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"   #> [174] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_10m\"    #> [175] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_2.5m\"   #> [176] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp370_5m\"     #> [177] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"   #> [178] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_10m\"    #> [179] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_2.5m\"   #> [180] \"WorldClim_2.1_MPI-ESM1-2-HR_ssp585_5m\"     #> [181] \"WorldClim_2.1_MRI-ESM2-0_ssp126_0.5m\"      #> [182] \"WorldClim_2.1_MRI-ESM2-0_ssp126_10m\"       #> [183] \"WorldClim_2.1_MRI-ESM2-0_ssp126_2.5m\"      #> [184] \"WorldClim_2.1_MRI-ESM2-0_ssp126_5m\"        #> [185] \"WorldClim_2.1_MRI-ESM2-0_ssp245_0.5m\"      #> [186] \"WorldClim_2.1_MRI-ESM2-0_ssp245_10m\"       #> [187] \"WorldClim_2.1_MRI-ESM2-0_ssp245_2.5m\"      #> [188] \"WorldClim_2.1_MRI-ESM2-0_ssp245_5m\"        #> [189] \"WorldClim_2.1_MRI-ESM2-0_ssp370_0.5m\"      #> [190] \"WorldClim_2.1_MRI-ESM2-0_ssp370_10m\"       #> [191] \"WorldClim_2.1_MRI-ESM2-0_ssp370_2.5m\"      #> [192] \"WorldClim_2.1_MRI-ESM2-0_ssp370_5m\"        #> [193] \"WorldClim_2.1_MRI-ESM2-0_ssp585_0.5m\"      #> [194] \"WorldClim_2.1_MRI-ESM2-0_ssp585_10m\"       #> [195] \"WorldClim_2.1_MRI-ESM2-0_ssp585_2.5m\"      #> [196] \"WorldClim_2.1_MRI-ESM2-0_ssp585_5m\"        #> [197] \"WorldClim_2.1_UKESM1-0-LL_ssp126_0.5m\"     #> [198] \"WorldClim_2.1_UKESM1-0-LL_ssp126_10m\"      #> [199] \"WorldClim_2.1_UKESM1-0-LL_ssp126_2.5m\"     #> [200] \"WorldClim_2.1_UKESM1-0-LL_ssp126_5m\"       #> [201] \"WorldClim_2.1_UKESM1-0-LL_ssp245_0.5m\"     #> [202] \"WorldClim_2.1_UKESM1-0-LL_ssp245_10m\"      #> [203] \"WorldClim_2.1_UKESM1-0-LL_ssp245_2.5m\"     #> [204] \"WorldClim_2.1_UKESM1-0-LL_ssp245_5m\"       #> [205] \"WorldClim_2.1_UKESM1-0-LL_ssp370_0.5m\"     #> [206] \"WorldClim_2.1_UKESM1-0-LL_ssp370_10m\"      #> [207] \"WorldClim_2.1_UKESM1-0-LL_ssp370_2.5m\"     #> [208] \"WorldClim_2.1_UKESM1-0-LL_ssp370_5m\"       #> [209] \"WorldClim_2.1_UKESM1-0-LL_ssp585_0.5m\"     #> [210] \"WorldClim_2.1_UKESM1-0-LL_ssp585_10m\"      #> [211] \"WorldClim_2.1_UKESM1-0-LL_ssp585_2.5m\"     #> [212] \"WorldClim_2.1_UKESM1-0-LL_ssp585_5m\" get_vars_for_dataset(dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\") #>  [1] \"bio01\" \"bio02\" \"bio03\" \"bio04\" \"bio05\" \"bio06\" \"bio07\" \"bio08\" \"bio09\" #> [10] \"bio10\" \"bio11\" \"bio12\" \"bio13\" \"bio14\" \"bio15\" \"bio16\" \"bio17\" \"bio18\" #> [19] \"bio19\" download_dataset(   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") ) future_slice <- region_slice(   time_ce = 2030,   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") ) future_series <- region_series(   dataset = \"WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m\",   bio_variables = c(\"bio01\", \"bio02\") )"},{"path":"https://evolecolgroup.github.io/pastclim/articles/a3_pastclim_present_and_future.html","id":"chelsa-1","dir":"Articles","previous_headings":"Future projections","what":"CHELSA","title":"present and future","text":"Future projections based models CMIP6, downscaled de-biased using CHELSA 2.1 present baseline. Monthly values mean temperature, precipitation, well 19 bioclimatic variables processed 5 global climate models (GCMs), three Shared Socio-economic Pathways (SSPs): 126, 370 585. Model SSP can chosen changing ending dataset name CHELSA_2.1_GCM_SSP_0.5m. complete list available combinations can obtained : Note virtual option dataset. , interested GFDL-ESM4 model, ssp set 126 , can get available variables: can now download “bio01” “bio02” dataset, using virtual version, : datasets averages 30 year periods (2011-2040, 2041-2070, 2071-2100). pastclim, midpoints periods (2025, 2055, 2075) used time stamps. 3 periods automatically downloaded given combination GCM model SSP, can selected usual defining time region_slice. Alternatively, possible get full time series 4 slices : possible simply use get_time_ce_steps(dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\") get available time points dataset. Help WorldClim datasets (modern future) can accessed help(\"CHELSA_2.1\")","code":"list_available_datasets()[grepl(\"CHELSA_2.1\",list_available_datasets())] #>  [1] \"CHELSA_2.1_0.5m\"                          #>  [2] \"CHELSA_2.1_0.5m_vsi\"                      #>  [3] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m\"         #>  [4] \"CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi\"     #>  [5] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m\"         #>  [6] \"CHELSA_2.1_GFDL-ESM4_ssp370_0.5m_vsi\"     #>  [7] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m\"         #>  [8] \"CHELSA_2.1_GFDL-ESM4_ssp585_0.5m_vsi\"     #>  [9] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m\"      #> [10] \"CHELSA_2.1_IPSL-CM6A-LR_ssp126_0.5m_vsi\"  #> [11] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m\"      #> [12] \"CHELSA_2.1_IPSL-CM6A-LR_ssp370_0.5m_vsi\"  #> [13] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m\"      #> [14] \"CHELSA_2.1_IPSL-CM6A-LR_ssp585_0.5m_vsi\"  #> [15] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m\"     #> [16] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp126_0.5m_vsi\" #> [17] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m\"     #> [18] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m_vsi\" #> [19] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m\"     #> [20] \"CHELSA_2.1_MPI-ESM1-2-HR_ssp585_0.5m_vsi\" #> [21] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m\"        #> [22] \"CHELSA_2.1_MRI-ESM2-0_ssp126_0.5m_vsi\"    #> [23] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m\"        #> [24] \"CHELSA_2.1_MRI-ESM2-0_ssp370_0.5m_vsi\"    #> [25] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m\"        #> [26] \"CHELSA_2.1_MRI-ESM2-0_ssp585_0.5m_vsi\"    #> [27] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m\"       #> [28] \"CHELSA_2.1_UKESM1-0-LL_ssp126_0.5m_vsi\"   #> [29] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m\"       #> [30] \"CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m_vsi\"   #> [31] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\"       #> [32] \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\" get_vars_for_dataset(dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m\", monthly=TRUE) #>  [1] \"bio01\"            \"bio02\"            \"bio03\"            \"bio04\"            #>  [5] \"bio05\"            \"bio06\"            \"bio07\"            \"bio08\"            #>  [9] \"bio09\"            \"bio10\"            \"bio11\"            \"bio12\"            #> [13] \"bio13\"            \"bio14\"            \"bio15\"            \"bio16\"            #> [17] \"bio17\"            \"bio18\"            \"bio19\"            \"temperature_01\"   #> [21] \"temperature_02\"   \"temperature_03\"   \"temperature_04\"   \"temperature_05\"   #> [25] \"temperature_06\"   \"temperature_07\"   \"temperature_08\"   \"temperature_09\"   #> [29] \"temperature_10\"   \"temperature_11\"   \"temperature_12\"   \"precipitation_01\" #> [33] \"precipitation_02\" \"precipitation_03\" \"precipitation_04\" \"precipitation_05\" #> [37] \"precipitation_06\" \"precipitation_07\" \"precipitation_08\" \"precipitation_09\" #> [41] \"precipitation_10\" \"precipitation_11\" \"precipitation_12\" download_dataset(   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") ) future_slice <- region_slice(   time_ce = 2025,   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") ) future_series <- region_series(   dataset = \"CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi\",   bio_variables = c(\"bio01\", \"bio02\") )"},{"path":"https://evolecolgroup.github.io/pastclim/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michela Leonardi. Author. Emily Y. Hallet. Contributor. Robert Beyer. Contributor. Mario Krapp. Contributor. Andrea V. Pozzi. Contributor. Andrea Manica. Author, maintainer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Leonardi M, Hallet EY, Beyer R, Krapp M, Manica (2023). “pastclim 1.2: R package easily access use paleoclimatic reconstructions.” Ecography, 2023, e06481. doi:10.1111/ecog.06481.","code":"@Article{pastclim-article,   title = {pastclim 1.2: an R package to easily access and use paleoclimatic reconstructions},   author = {Michela Leonardi and Emily Y. Hallet and Robert Beyer and Mario Krapp and Andrea Manica},   journal = {Ecography},   year = {2023},   volume = {2023},   pages = {e06481},   publisher = {Wiley},   doi = {10.1111/ecog.06481}, }"},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"pastclim-","dir":"","previous_headings":"","what":"Manipulate Time Series of Palaeoclimate Reconstructions","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"R library designed provide easy way extract manipulate palaeoclimate reconstructions ecological anthropological analyses. also able handle time series future reconstructions. functionalities pastclim described Leonardi et al. (2023). Please cite use pastclim research.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"install-the-library","dir":"","previous_headings":"","what":"Install the library","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"pastclim CRAN, easiest way install : version CRAN recommended every day use. New features bug fixes appear first dev branch GitHub, make way CRAN. need early access new features, can install development version pastclim directly GitHub, using devtools, simply get compiled version r-universe. Also, note dev version pastclim tracks changes dev version terra, need upgrade libraries :","code":"install.packages(\"pastclim\") install.packages('terra', repos='https://rspatial.r-universe.dev') install.packages(\"pastclim\", repos = c(\"https://evolecolgroup.r-universe.dev\")"},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"overview-of-functionality","dir":"","previous_headings":"","what":"Overview of functionality","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"dedicated website, can find Articles giving step--step overview package, cheatsheet. also dev version site updated dev branch pastclim (top left dev website, version number red format x.x.x.9xxx, indicating development version). pastclim currently includes data Beyer et al 2020 (reconstruction climate based HadCM3 model last 120k years), Krapp et al 2021 (covers last 800k years statistical emulator HadCM3), Barreto et al 2023 (covering last 5M years using PALEO-PGEM emulator), PaleoClim (providing time steps different palaeoclimate models downscaled higher resolution), CHELSA-Trace21K (transient reconstruction last 21k years, downscaled 1km resolution), HYDE3.3 database land use reconstructions last 10k years, WorldClim CHELSA data (present, future projections number models emission scenarios). details datasets can found . also instructions build use custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/index.html","id":"when-something-does-not-work","dir":"","previous_headings":"","what":"When something does not work","title":"Manipulate Time Series of Palaeoclimate Reconstructions","text":"something work, check issues GitHub see whether problem already reported. , feel free create new issue. Please make sure updated latest version pastclim CRAN, well updating packages system, provide reproducible example developers investigate problem.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Barreto2023.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Barreto et al 2023 dataset — Barreto2023","title":"Documentation for the Barreto et al 2023 dataset — Barreto2023","text":"Spatio-temporal series monthly temperature precipitation 17 derived bioclimatic variables covering last 5 Ma (Pliocene–Pleistocene), intervals 1,000 years, spatial resolution 1 arc-degrees (see Barreto et al., 2023 details).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Barreto2023.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Barreto et al 2023 dataset — Barreto2023","text":"PALEO-PGEM-Series downscaled 1 × 1 arc-degrees spatial resolution outputs PALEO-PGEM emulator (Holden et al., 2019), emulates reasonable extensively validated global estimates monthly temperature precipitation Plio-Pleistocene every 1 kyr spatial resolution ~5 × 5 arc-degrees (Holden et al., 2016, 2019). PALEO-PGEM-Series includes mean standard deviation (.e., standard error) emulated climate 10 stochastic GCM emulations accommodate aspects model uncertainty. allows users estimate robustness results face stochastic aspects emulations. details, see Section 2.4 Barreto et al. (2023). Note large dataset, 5001 time slices. takes approximately 1 minute set variable creating region_slice region_series. However, object created, operations tend much faster (especially subset dataset small number time steps interest). IMPORTANT: use dataset, make sure cite original publications: Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. (2023). PALEO-PGEM-Series: spatial time series global climate last 5 million years (Plio-Pleistocene). Global Ecology Biogeography, 32, 1034-1045, doi:10.1111/geb.13683 Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., Tran, G. T., Wilkinson, R. D. (2019): PALEO-PGEM v1.0: statistical emulator Pliocene–Pleistocene climate, Geosci. Model Dev., 12, 5137–5155, doi:10.5194/gmd-12-5137-2019 .","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Beyer2020.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Beyer2020 dataset — Beyer2020","title":"Documentation for the Beyer2020 dataset — Beyer2020","text":"dataset covers last 120k years, intervals 1/2 k years, resolution 0.5 degrees latitude longitude.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Beyer2020.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Beyer2020 dataset — Beyer2020","text":"IMPORTANT: use dataset, make sure cite original publication: Beyer, R.M., Krapp, M. & Manica, . High-resolution terrestrial climate, bioclimate vegetation last 120,000 years. Sci Data 7, 236 (2020). doi:10.1038/s41597-020-0552-1 version included pastclim ice sheets masked, well internal seas (Black Caspian Sea) removed. latter based : https://www.marineregions.org/gazetteer.php?p=details&id=4278 https://www.marineregions.org/gazetteer.php?p=details&id=4282 reconstruction depth time, modern outlines used time steps. Also, bio15, coefficient variation computed adding one monthly estimates, multiplied 100 following https://pubs.usgs.gov/ds/691/ds691.pdf Changelog v1.1.0 Added monthly variables. Files can downloaded : https://zenodo.org/deposit/7062281 v1.0.0 Remove ice sheets internal seas, use correct formula bio15. Files can downloaded : doi:10.6084/m9.figshare.19723405.v1","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_2.1.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for CHELSA 2.1 — CHELSA_2.1","title":"Documentation for CHELSA 2.1 — CHELSA_2.1","text":"CHELSA version 2.1 database high spatial resolution global weather climate data, covering present future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_2.1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for CHELSA 2.1 — CHELSA_2.1","text":"IMPORTANT: use dataset, make sure cite original publication CHELSA dataset: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017) Climatologies high resolution Earth land surface areas. Scientific Data. 4 170122. doi:10.1038/sdata.2017.122 Present-day reconstructions based mean period 1981-2000 available high resolution 0.5 arc-minutes (CHELSA_2.1_0.5m). pastclim, datasets given date 1990 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates mean temperature, precipitation. dataset large, includes estimates oceans well land masses. alternative downloading large files use virtual rasters, allow data remain server, pixels required given operation downloaded. Virtual rasters can used choosing (CHELSA_2.1_0.5m_vsi) Future projections based models CMIP6, downscaled de-biased using CHELSA algorithm 2.1. Monthly values mean temperature, total precipitation, well 19 bioclimatic variables processed 5 global climate models (GCMs), three Shared Socio-economic Pathways (SSPs): 126, 370 585. Model SSP can chosen changing ending dataset name CHELSA_2.1_GCM_SSP_RESm. Available values GCM : \"GFDL-ESM4\", \"IPSL-CM6A-LR\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", \"UKESM1-0-LL\". SSP, use: \"ssp126\", \"ssp370\",\t\"ssp585\". RES currently limited \"0.5m\". Example dataset names CHELSA_2.1_GFDL-ESM4_ssp126_0.5m CHELSA_2.1_UKESM1-0-LL_ssp370_0.5m present reconstructions, alternative downloading large files use virtual rasters. Simply append \"_vis\" name dataset interest (CHELSA_2.1_GFDL-ESM4_ssp126_0.5m_vsi). dataset averages 30 year periods (2011-2040, 2041-2070, 2071-2100). pastclim, midpoints periods (2025, 2055, 2075) used time stamps. 3 periods automatically downloaded combination GCM model SSP, selected usual defining time functions region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_trace21k_1.0.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","title":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","text":"CHELSA-TraCE21k data provides monthly climate data temperature precipitation 30 arc-sec spatial resolution 100-year time steps last 21,000 years. Palaeo-orography high spatial resolution time step created combining high resolution information glacial cover current Last Glacial Maximum (LGM) glacier databases interpolation dynamic ice sheet model (ICE6G) coupling mean annual temperatures CCSM3-TraCE21k. Based reconstructed palaeo-orography, mean annual temperature precipitation downscaled using CHELSA V1.2 algorithm.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/CHELSA_trace21k_1.0.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for CHELSA-TracCE21k — CHELSA_trace21k_1.0","text":"details dataset available dedicated website. alternative downloading large files use virtual rasters. Simply append \"_vis\" name dataset interest (CHELSA_trace21k_1.0_0.5m_vsi). recommended approach, currently available version dataset. IMPORTANT: use dataset, make sure cite original publication: Karger, D.N., Nobis, M.P., Normand, S., Graham, C.H., Zimmermann, N. (2023) CHELSA-TraCE21k – High resolution (1 km) downscaled transient temperature precipitation data since Last Glacial Maximum. Climate Past. doi:10.5194/cp-2021-30","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Example.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Example dataset — Example","title":"Documentation for the Example dataset — Example","text":"dataset subset Beyer2020, used vignette pastclim. use dataset real work, might reflect --date version Beyer2020.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/HYDE_3.3_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","title":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","text":"database presents update expansion History Database Global Environment (HYDE, v 3.3) replaces former HYDE 3.2 version 2017. HYDE internally consistent combination updated historical population estimates land use. Categories include cropland, new distinction irrigated rain fed crops (rice) irrigated rain fed rice. Also grazing lands provided, divided intensively used pasture, converted rangeland non-converted natural (less intensively used) rangeland. Population represented maps total, urban, rural population population density well built-area.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/HYDE_3.3_baseline.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for HYDE 3.3 dataset — HYDE_3.3_baseline","text":"period covered 10 000 BCE 2023 CE. Spatial resolution 5 arc minutes (approx. 85 km2 equator). full HYDE 3.3 release contains: Baseline estimate scenario, Lower estimate scenario Upper estimate scenario. Currently baseline scenario available pastclim details dataset available dedicated website. IMPORTANT: use dataset, make sure cite original publication HYDE 3.2 (current publication 3.3): Klein Goldewijk, K., Beusen, ., Doelman, J., Stehfest, E.: Anthropogenic land-use estimates Holocene; HYDE 3.2, Earth Syst. Sci. Data, 9, 927-953, 2017. doi:10.5194/essd-9-927-2017","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Krapp2021.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the Krapp2021 dataset — Krapp2021","title":"Documentation for the Krapp2021 dataset — Krapp2021","text":"dataset covers last 800k years, intervals 1k years, resolution 0.5 degrees latitude longitude.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/Krapp2021.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the Krapp2021 dataset — Krapp2021","text":"units several variables changed match used WorldClim. IMPORTANT: use dataset, make sure cite original publication: Krapp, M., Beyer, R.M., Edmundson, S.L. et al. statistics-based reconstruction high-resolution global terrestrial climate last 800,000 years. Sci Data 8, 228 (2021). doi:10.1038/s41597-021-01009-3 version included pastclim ice sheets masked. Note , bio15, use corrected version, follows https://pubs.usgs.gov/ds/691/ds691.pdf Changelog v1.4.0 Change units match WorldClim. Fix variable duplication found earlier versions dataset. https://zenodo.org/records/8415273 v1.1.0 Added monthly variables. Files can downloaded : https://zenodo.org/record/7065055 v1.0.0 Remove ice sheets use correct formula bio15. Files can downloaded : doi:10.6084/m9.figshare.19733680.v1","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/WorldClim_2.1.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for the WorldClim datasets — WorldClim_2.1","title":"Documentation for the WorldClim datasets — WorldClim_2.1","text":"WorldClim version 2.1 database high spatial resolution global weather climate data, covering present future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/WorldClim_2.1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for the WorldClim datasets — WorldClim_2.1","text":"IMPORTANT: use dataset, make sure cite original publication: Fick, S.E. R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces global land areas. International Journal Climatology 37 (12): 4302-4315. doi:10.1002/joc.5086 Present-day reconstructions based mean period 1970-2000, available multiple resolutions 10 arc-minutes, 5 arc-minutes, 2.5 arc-minute 0.5 arc-minutes. resolution interest can obtained changing ending dataset name WorldClim_2.1_RESm, e.g. WorldClim_2.1_10m WorldClim_2.1_5m (currently, 10m 5m currently available pastclim). pastclim, datasets given date 1985 CE (mid-point period interest). 19 “bioclimatic” variables, well monthly estimates minimum, mean, maximum temperature, precipitation. Future projections based models CMIP6, downscaled de-biased using WorldClim 2.1 present baseline. Monthly values minimum temperature, maximum temperature, precipitation, well 19 bioclimatic variables processed 23 global climate models (GCMs), four Shared Socio-economic Pathways (SSPs): 126, 245, 370 585. Model SSP can chosen changing ending dataset name WorldClim_2.1_GCM_SSP_RESm. Available values GCM : \"ACCESS-CM2\", \"BCC-CSM2-MR\", \"CMCC-ESM2\", \"EC-Earth3-Veg\", \"FIO-ESM-2-0\", \"GFDL-ESM4\", \"GISS-E2-1-G\", \"HadGEM3-GC31-LL\", \"INM-CM5-0\", \"IPSL-CM6A-LR\", \"MIROC6\", \"MPI-ESM1-2-HR\", \"MRI-ESM2-0\", \"UKESM1-0-LL\". SSP, use: \"ssp126\", \"ssp245\",\t\"ssp370\",\t\"ssp585\". RES takes values present reconstructions (.e. \"10m\", \"5m\", \"2.5m\", \"0.5m\"). Example dataset names WorldClim_2.1_ACCESS-CM2_ssp245_10m WorldClim_2.1_MRI-ESM2-0_ssp370_5m. Four combination (namely FIO-ESM-2-0_ssp370, GFDL-ESM4_ssp245, GFDL-ESM4_ssp585, HadGEM3-GC31-LL_ssp370) available. dataset averages 20 year periods (2021-2040, 2041-2060, 2061-2080, 2081-2100). pastclim, midpoints periods (2030, 2050, 2070, 2090) used time stamps. 4 periods automatically downloaded combination GCM model SSP, selected usual defining time functions region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute bioclimatic variables — bioclim_vars","title":"Compute bioclimatic variables — bioclim_vars","text":"Function compute \"bioclimatic\" variables monthly average temperature precipitation data. modern data, variables generally computed using min maximum temperature, many palaeoclimatic reconstructions average temperature available. variables, exception BIO02 BIO03, can rephrased meaningfully terms mean temperature. function modified version predicts::bcvars.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute bioclimatic variables — bioclim_vars","text":"","code":"bioclim_vars(prec, tavg, ...)  # S4 method for numeric,numeric bioclim_vars(prec, tavg)  # S4 method for SpatRaster,SpatRaster bioclim_vars(prec, tavg, filename = \"\", ...)  # S4 method for SpatRasterDataset,SpatRasterDataset bioclim_vars(prec, tavg, filename = \"\", ...)  # S4 method for matrix,matrix bioclim_vars(prec, tavg)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute bioclimatic variables — bioclim_vars","text":"prec monthly precipitation tavg monthly average temperatures ... additional variables specific methods filename filename save raster (optional).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute bioclimatic variables — bioclim_vars","text":"bioclim variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/bioclim_vars-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute bioclimatic variables — bioclim_vars","text":"variables : BIO01 = Annual Mean Temperature BIO04 = Temperature Seasonality (standard deviation x 100) BIO05 = Max Temperature Warmest Month BIO06 = Min Temperature Coldest Month BIO07 = Temperature Annual Range (P5-P6) BIO08 = Mean Temperature Wettest Quarter BIO09 = Mean Temperature Driest Quarter BIO10 = Mean Temperature Warmest Quarter BIO11 = Mean Temperature Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation Wettest Month BIO14 = Precipitation Driest Month BIO15 = Precipitation Seasonality (Coefficient Variation) BIO16 = Precipitation Wettest Quarter BIO17 = Precipitation Driest Quarter BIO18 = Precipitation Warmest Quarter BIO19 = Precipitation Coldest Quarter summary Bioclimatic variables : Nix, 1986. biogeographic analysis Australian elapid snakes. : R. Longmore (ed.). Atlas elapid snakes Australia. Australian Flora Fauna Series 7. Australian Government Publishing Service, Canberra. expanded following ANUCLIM manual","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"BIOME4 classes. — biome4_classes","title":"BIOME4 classes. — biome4_classes","text":"data.frame defining details class","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BIOME4 classes. — biome4_classes","text":"","code":"biome4_classes"},{"path":"https://evolecolgroup.github.io/pastclim/reference/biome4_classes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"BIOME4 classes. — biome4_classes","text":"data.frame multiple columns describe.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if dataset is available. — check_available_dataset","title":"Check if dataset is available. — check_available_dataset","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if dataset is available. — check_available_dataset","text":"","code":"check_available_dataset(dataset, include_custom = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if dataset is available. — check_available_dataset","text":"dataset string defining dataset include_custom boolean whether 'custom' dataset allowed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if dataset is available. — check_available_dataset","text":"TRUE dataset available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if var is available for this dataset. — check_available_variable","title":"Check if var is available for this dataset. — check_available_variable","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if var is available for this dataset. — check_available_variable","text":"","code":"check_available_variable(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if var is available for this dataset. — check_available_variable","text":"variable vector names variables interest dataset dataset interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_available_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if var is available for this dataset. — check_available_variable","text":"TRUE var available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that we have a valid pair of coordinate names — check_coords_names","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"internal function checks coords (passed functions) valid set names, , NULL, standard variable names data","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"","code":"check_coords_names(data, coords)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"data data.frame containing locations. coords vector length two giving names \"x\" \"y\" coordinates, points data.frame use standard names.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_coords_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that we have a valid pair of coordinate names — check_coords_names","text":"vector length 2 valid names, correct order","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Check dataset and path_to_nc params — check_dataset_path","title":"Check dataset and path_to_nc params — check_dataset_path","text":"Check dataset path_to_nc parameters valid. Specifically, path_to_nc set dataset custom (conversely, custom datasets require path_to_nc).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check dataset and path_to_nc params — check_dataset_path","text":"","code":"check_dataset_path(dataset, path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check dataset and path_to_nc params — check_dataset_path","text":"dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\". path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_dataset_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check dataset and path_to_nc params — check_dataset_path","text":"TRUE dataset path valid.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Check multiple time variables — check_time_vars","title":"Check multiple time variables — check_time_vars","text":"Check one set times","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check multiple time variables — check_time_vars","text":"","code":"check_time_vars(time_bp, time_ce, allow_null = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check multiple time variables — check_time_vars","text":"time_bp times bp time_ce times ce allow_null boolean whether can NULL","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_time_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check multiple time variables — check_time_vars","text":"times bp","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"Internal function check whether downloaded given variable dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"","code":"check_var_downloaded(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"variable vector names variables interest dataset dataset interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_downloaded.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to check whether we have downloaded a given variable for a dataset — check_var_downloaded","text":"TRUE variable downloaded.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether variables exist in a netcdf file — check_var_in_nc","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"Internal function test custom nc file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"","code":"check_var_in_nc(bio_variables, path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"bio_variables vector names variables extracted path_to_nc path custom nc file containing palaeoclimate reconstructions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/check_var_in_nc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether variables exist in a netcdf file — check_var_in_nc","text":"TRUE variable exists","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean the data path — clean_data_path","title":"Clean the data path — clean_data_path","text":"function deletes old reconstructions superseded data_path. assumes files data_path part pastclim (.e. custom datasets stored directory).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean the data path — clean_data_path","text":"","code":"clean_data_path(ask = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean the data path — clean_data_path","text":"ask boolean whether user asked deleting","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/clean_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean the data path — clean_data_path","text":"TRUE files deleted successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"Deprecated version location_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"","code":"climate_for_locations(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"... arguments passed location_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_locations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — climate_for_locations","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a climate slice for a region — climate_for_time_slice","title":"Extract a climate slice for a region — climate_for_time_slice","text":"Deprecated version region_slice()]","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a climate slice for a region — climate_for_time_slice","text":"","code":"climate_for_time_slice(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a climate slice for a region — climate_for_time_slice","text":"... arguments passed region_slice()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/climate_for_time_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a climate slice for a region — climate_for_time_slice","text":"SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to copy the example dataset when a new data path is set — copy_example_data","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"Copy example dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"","code":"copy_example_data()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/copy_example_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to copy the example dataset when a new data path is set — copy_example_data","text":"TRUE data copied successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract data frame from a region series — df_from_region_series","title":"Extract data frame from a region series — df_from_region_series","text":"Extract climatic information region series organise data frame.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract data frame from a region series — df_from_region_series","text":"","code":"df_from_region_series(x, xy = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract data frame from a region series — df_from_region_series","text":"x climate time series generated region_series() xy boolean whether x y coordinates added dataframe (default TRUE)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract data frame from a region series — df_from_region_series","text":"data.frame cell raster layer (.e. timestep) row, available variables columns.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract data frame from a region series — df_from_region_series","text":"extract data frame region slice, see df_from_region_slice().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract data frame from a region slice — df_from_region_slice","title":"Extract data frame from a region slice — df_from_region_slice","text":"Extract climatic information region slice organise data frame. just wrapper around terra::.data.frame().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract data frame from a region slice — df_from_region_slice","text":"","code":"df_from_region_slice(x, xy = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract data frame from a region slice — df_from_region_slice","text":"x climate time slice (.e. terra::SpatRaster) generated region_slice() xy boolean whether x y coordinates added dataframe (default TRUE)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract data frame from a region slice — df_from_region_slice","text":"data.frame cell raster row, available variables columns.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/df_from_region_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract data frame from a region slice — df_from_region_slice","text":"extract data frame region series, see df_from_region_series().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"Get land mask dataset given time point, compute distance sea land pixel.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"","code":"distance_from_sea(time_bp = NULL, time_ce = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"time_bp time slice years present (negative) time_ce time slice years CE. one time_bp time_ce used. dataset string defining dataset use (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/distance_from_sea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a raster of distances from the sea for each land pixel. — distance_from_sea","text":"terra::SpatRaster distances coastline km","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of variation (expressed as percentage) — .cv","title":"Coefficient of variation (expressed as percentage) — .cv","text":"R function compute coefficient variation (expressed percentage). single value, stats::sd = NA. However, one argue cv =0; NA may break code receives . function returns 0 mean close zero.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of variation (expressed as percentage) — .cv","text":"","code":".cv(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of variation (expressed as percentage) — .cv","text":"x vector values","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of variation (expressed as percentage) — .cv","text":"cv","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/dot-cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coefficient of variation (expressed as percentage) — .cv","text":"ODD: abs avoid small (zero) mean e.g. -5:5","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the CHELSA modern and future observations. — download_chelsa","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"function downloads annual monthly variables CHELSA v2.1 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"","code":"download_chelsa(dataset, bio_var, filename)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"dataset name dataset bio_var variable name filename filename stored data_path pastclim (includes full data path)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the CHELSA modern and future observations. — download_chelsa","text":"TRUE requested CHELSA variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the CHELSA trace21k — download_chelsa_trace21k","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"function downloads annual monthly variables CHELSA trace v1.0 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"","code":"download_chelsa_trace21k(dataset, bio_var, filename = NULL, time_bp = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"dataset name dataset bio_var variable name filename filename stored data_path pastclim (includes full data path) time_bp time steps dataset built (NULL time steps)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"TRUE requested CHELSA variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_chelsa_trace21k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the CHELSA trace21k — download_chelsa_trace21k","text":"dataset huge, download files situations. reason, time_bp set downloading (allowed virtual datasets)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Download palaeoclimate reconstructions. — download_dataset","title":"Download palaeoclimate reconstructions. — download_dataset","text":"function downloads palaeoclimate reconstructions. Files stored data path pastclim, can inspected get_data_path() changed set_data_path()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download palaeoclimate reconstructions. — download_dataset","text":"","code":"download_dataset(dataset, bio_variables = NULL, annual = TRUE, monthly = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download palaeoclimate reconstructions. — download_dataset","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets. bio_variables one variable names downloaded. left NULL, variables available dataset downloaded (parameters annual monthly, see , define types) annual boolean download annual variables monthly boolean download monthly variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download palaeoclimate reconstructions. — download_dataset","text":"TRUE dataset(s) downloaded correctly.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the ETOPO Global relief model — download_etopo","title":"Download the ETOPO Global relief model — download_etopo","text":"function downloads ETOPO2022 global relief model 0.5 1 arc-minute (.e. 30 60 arc-seconds) resolution. large file (>1Gb).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the ETOPO Global relief model — download_etopo","text":"","code":"download_etopo(path = NULL, resolution = 1)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the ETOPO Global relief model — download_etopo","text":"path character. Path download data . left NULL, data downloaded directory returned get_data_path(), automatically named etopo2022_{resolution}s_v1.nc resolution numeric resolution arc-minute (one 0.5, 1). Defaults 1 arc-minute.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_etopo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the ETOPO Global relief model — download_etopo","text":"dataframe produced curl::multi_download() information download (including error codes)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the paleoclim time series. — download_paleoclim","title":"Download the paleoclim time series. — download_paleoclim","text":"function downloads annual monthly variables Paleoclim V1.0 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the paleoclim time series. — download_paleoclim","text":"","code":"download_paleoclim(dataset, bio_var, filename = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the paleoclim time series. — download_paleoclim","text":"dataset name dataset bio_var variable name filename (USED FUNCTION: data come zip bio variables, generate multiple files, single one)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_paleoclim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the paleoclim time series. — download_paleoclim","text":"TRUE requested paleoclim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the worldclim future time series. — download_worldclim_future","title":"Download the worldclim future time series. — download_worldclim_future","text":"function downloads annual monthly variables WorldClim v2.1 dataset future projections.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the worldclim future time series. — download_worldclim_future","text":"","code":"download_worldclim_future(dataset, bio_var, filename = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the worldclim future time series. — download_worldclim_future","text":"dataset name dataset bio_var variable name filename ()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the worldclim future time series. — download_worldclim_future","text":"TRUE requested worldclim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_future.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the worldclim future time series. — download_worldclim_future","text":"Note: data come tiffs containing bio (prec/temp) variables given time step. , generate vrt per variable. , since download full set give variable type, create vrts variable type (e.g. bio). use filename get version name, end check generate correctly given programmatic way creating names vrt files.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Download a WorldClim modern observations. — download_worldclim_present","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"function downloads annual monthly variables WorldClim 2.1 dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"","code":"download_worldclim_present(dataset, bio_var, filename)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"dataset name dataset bio_var variable name filename file name (full path) file saved","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/download_worldclim_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download a WorldClim modern observations. — download_worldclim_present","text":"TRUE requested WorldClim variable downloaded successfully","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"function creates vector paths needed download CHELSA future dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"","code":"filenames_chelsa_future(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa future dataset — filenames_chelsa_future","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"function creates vector paths needed download CHELSA present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"","code":"filenames_chelsa_present(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa present dataset — filenames_chelsa_present","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"function creates vector paths needed download CHELSA trace21k","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"","code":"filenames_chelsa_trace21k(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_chelsa_trace21k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa trace 21k — filenames_chelsa_trace21k","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the chelsa present dataset — filenames_paleoclim","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"function creates vector paths needed download CHELSA present dataset. Possible names \"paleoclim_1.0_10m\", \"paleoclim_1.0_5m\", \"paleoclim_1.0_2.5m\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"","code":"filenames_paleoclim(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"dataset name dataset interest (currently unused) bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_paleoclim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the chelsa present dataset — filenames_paleoclim","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"function creates vector paths needed download WorldClim present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"","code":"filenames_worldclim_future(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_future.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_future","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"function creates vector paths needed download WorldClim present dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"","code":"filenames_worldclim_present(dataset, bio_var)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"dataset name dataset interest bio_var variable interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/filenames_worldclim_present.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate file names to download the WorldClim present dataset — filenames_worldclim_present","text":"vector times, one per band","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the available datasets. — get_available_datasets","title":"Get the available datasets. — get_available_datasets","text":"List datasets available pastclim, can passed functions pastclim values parameter dataset. functions can also used custom datasets setting dataset=\"custom\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the available datasets. — get_available_datasets","text":"","code":"get_available_datasets()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the available datasets. — get_available_datasets","text":"character vector available datasets","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_available_datasets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the available datasets. — get_available_datasets","text":"function provides user-friendly list, summarising many datasets available WorldClim. comprehensive list available datasets can obtained list_available_datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the biome classes for a dataset. — get_biome_classes","title":"Get the biome classes for a dataset. — get_biome_classes","text":"Get full list biomes id coded biome variable given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the biome classes for a dataset. — get_biome_classes","text":"","code":"get_biome_classes(dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the biome classes for a dataset. — get_biome_classes","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_biome_classes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the biome classes for a dataset. — get_biome_classes","text":"data.frame columns id category.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the data path where climate reconstructions are stored — get_data_path","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"function returns path climate reconstructions stored.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"","code":"get_data_path(silent = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"silent boolean whether message returned data_path set (.e. equal NULL)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"data path","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_data_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the data path where climate reconstructions are stored — get_data_path","text":"path stored option pastclim named data_path. configuration file saved using set_data_path(), path retrieved file named \"pastclim_data.txt\", found directory returned tools::R_user_dir(\"pastclim\",\"config\") (.e. default configuration directory package set R >= 4.0).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the information about a dataset — get_dataset_info","title":"Get the information about a dataset — get_dataset_info","text":"function provides full information given dataset. full list datasets available pastclim can obtained list_available_datasets()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the information about a dataset — get_dataset_info","text":"","code":"get_dataset_info(dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the information about a dataset — get_dataset_info","text":"dataset dataset pastclim","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_dataset_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the information about a dataset — get_dataset_info","text":"text describing dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the variables downloaded for each dataset. — get_downloaded_datasets","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"List downloaded variable dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"","code":"get_downloaded_datasets(data_path = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"data_path leave NULL use default data_path","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_downloaded_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the variables downloaded for each dataset. — get_downloaded_datasets","text":"list variable names per dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the ice mask for a dataset. — get_ice_mask","title":"Get the ice mask for a dataset. — get_ice_mask","text":"Get ice mask dataset, either whole series specific time points.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the ice mask for a dataset. — get_ice_mask","text":"","code":"get_ice_mask(time_bp = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the ice mask for a dataset. — get_ice_mask","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the ice mask for a dataset. — get_ice_mask","text":"binary terra::SpatRaster ice mask 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_ice_mask.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the ice mask for a dataset. — get_ice_mask","text":"Note datasets ice information.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the land mask for a dataset. — get_land_mask","title":"Get the land mask for a dataset. — get_land_mask","text":"Get land mask dataset, either whole series specific time points.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the land mask for a dataset. — get_land_mask","text":"","code":"get_land_mask(time_bp = NULL, time_ce = NULL, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the land mask for a dataset. — get_land_mask","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time years CE alternative time_bp.one time_bp time_ce used. available time slices years CE, use get_time_ce_steps(). dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_land_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the land mask for a dataset. — get_land_mask","text":"binary terra::SpatRaster land mask 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":null,"dir":"Reference","previous_headings":"","what":"Get time steps for a given MIS — get_mis_time_steps","title":"Get time steps for a given MIS — get_mis_time_steps","text":"Get time steps available given dataset MIS.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get time steps for a given MIS — get_mis_time_steps","text":"","code":"get_mis_time_steps(mis, dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get time steps for a given MIS — get_mis_time_steps","text":"mis string giving mis; must use spelling used mis_boundaries dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_mis_time_steps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get time steps for a given MIS — get_mis_time_steps","text":"vector time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":null,"dir":"Reference","previous_headings":"","what":"Get resolution of a given dataset — get_resolution","title":"Get resolution of a given dataset — get_resolution","text":"Get resolution given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get resolution of a given dataset — get_resolution","text":"","code":"get_resolution(dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get resolution of a given dataset — get_resolution","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_resolution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get resolution of a given dataset — get_resolution","text":"vector resolution x y axes","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Get sea level estimate — get_sea_level","title":"Get sea level estimate — get_sea_level","text":"function returns estimated sea level Spratt et al. 2016, using long PC1. Sea levels contemporary sea level (note original data reference sea level Holocene ~5k year ago).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get sea level estimate — get_sea_level","text":"","code":"get_sea_level(time_bp)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get sea level estimate — get_sea_level","text":"time_bp time interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_sea_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get sea level estimate — get_sea_level","text":"vector sea levels meters present level","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":null,"dir":"Reference","previous_headings":"","what":"Get time steps for a given dataset — get_time_bp_steps","title":"Get time steps for a given dataset — get_time_bp_steps","text":"Get time steps (time_bp time_ce) available given dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get time steps for a given dataset — get_time_bp_steps","text":"","code":"get_time_bp_steps(dataset, path_to_nc = NULL)  get_time_ce_steps(dataset, path_to_nc = NULL)  get_time_steps(dataset, path_to_nc = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get time steps for a given dataset — get_time_bp_steps","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_time_bp_steps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get time steps for a given dataset — get_time_bp_steps","text":"vector time steps (time_bp, time_ce)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the metadata for a variable in a given dataset. — get_var_meta","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"Internal getter function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"","code":"get_var_meta(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"variable one variable names downloaded dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_var_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the metadata for a variable in a given dataset. — get_var_meta","text":"metadata (including filename) variable dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a the varname for this variable — get_varname","title":"Get a the varname for this variable — get_varname","text":"Internal function get varname variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a the varname for this variable — get_varname","text":"","code":"get_varname(variable, dataset)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a the varname for this variable — get_varname","text":"variable string defining variable name dataset string defining dataset downloaded","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_varname.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a the varname for this variable — get_varname","text":"name variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a list of variables for a given dataset. — get_vars_for_dataset","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"function lists variables available given dataset. Note spelling use capitals names might differ original publications, pastclim harmonises names variables across different reconstructions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"","code":"get_vars_for_dataset(   dataset,   path_to_nc = NULL,   details = FALSE,   annual = TRUE,   monthly = FALSE )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). path_to_nc path custom nc file containing palaeoclimate reconstructions. custom nc file given, 'details', 'annual' 'monthly' ignored details boolean determining whether output include information including long names variables units. annual boolean show annual variables monthly boolean show monthly variables","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/get_vars_for_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a list of variables for a given dataset. — get_vars_for_dataset","text":"vector variable names","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":null,"dir":"Reference","previous_headings":"","what":"Print help to console — help_console","title":"Print help to console — help_console","text":"function prints help file console. based function published R-bloggers: https://www.r-bloggers.com/2013/06/printing-r-help-files---console---knitr-documents/","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print help to console — help_console","text":"","code":"help_console(   topic,   format = c(\"text\", \"html\", \"latex\"),   lines = NULL,   before = NULL,   after = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print help to console — help_console","text":"topic topic help format output formatted lines lines printed string printed output string printed output","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/help_console.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print help to console — help_console","text":"text help file","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the object is a valid region series — is_region_series","title":"Check the object is a valid region series — is_region_series","text":"region series terra::SpatRasterDataset sub-dataset variable, variables number time steps.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the object is a valid region series — is_region_series","text":"","code":"is_region_series(x, strict = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the object is a valid region series — is_region_series","text":"x terra::SpatRasterDataset representing time series regional reconstructions obtained region_series(). strict boolean defining whether preform thorough test (see description details).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the object is a valid region series — is_region_series","text":"TRUE object region series","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/is_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check the object is a valid region series — is_region_series","text":"standard test checks sub-datasets (terra::SpatRaster) number layers. thorough test (obtained strict=TRUE) actually checks variables identical time steps comparing result terra::time() applied variable.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Koeppen-Geiger classes. — koeppen_classes","title":"Koeppen-Geiger classes. — koeppen_classes","text":"data.frame defining details class","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Koeppen-Geiger classes. — koeppen_classes","text":"","code":"koeppen_classes"},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_classes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Koeppen-Geiger classes. — koeppen_classes","text":"data.frame multiple columns describe.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"Function reconstruct biomes following Köppen Geiger's classification, implemented Beck et al (2018). function translation Matlab function \"KoeppenGeiger\" provided publication. See Table 1 beck et al (2018) rules implemented function.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"","code":"koeppen_geiger(prec, tavg, broad = FALSE, class_names = TRUE, ...)  # S4 method for matrix,matrix koeppen_geiger(prec, tavg, broad = FALSE, class_names = TRUE)  # S4 method for SpatRaster,SpatRaster koeppen_geiger(   prec,   tavg,   broad = FALSE,   class_names = TRUE,   filename = \"\",   ... )  # S4 method for SpatRasterDataset,SpatRasterDataset koeppen_geiger(   prec,   tavg,   broad = FALSE,   class_names = TRUE,   filename = \"\",   ... )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"prec monthly precipitation tavg monthly average temperatures broad boolean whether return broad level classification class_names boolean whether return names classes (addition codes) ... additional variables specific methods filename filename save raster (optional).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"data.frame Köppen Geiger classification","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps 1901–2099 based constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/koeppen_geiger-methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reconstruct biomes based on the Köppen Geiger's classification — koeppen_geiger","text":"","code":"prec <- matrix(   c(     66, 51, 53, 53, 33, 34.2, 70.9, 58, 54, 104.3, 81.2, 82.8, 113.3,     97.4, 89, 109.7, 89, 93.4, 99.8, 92.6, 85.3, 102.3, 84, 81.6, 108.6, 88.4,     82.7, 140.1, 120.4, 111.6, 120.4, 113.9, 96.7, 90, 77.4, 79.1   ),   ncol = 12, byrow = TRUE ) tavg <- matrix(   c(     -0.2, 1.7, 2.9, 0.3, 4.2, 5, 4, 9, 9.2, 7.3, 12.6, 12.7, 12.1,     17.2, 17, 15.5, 20.5, 20.3, 17.9, 22.8, 22.9, 17.4, 22.3, 22.4, 13.2, 18.2,     18.6, 8.8, 13, 13.6, 3.5, 6.4, 7.5, 0.3, 2.1, 3.4   ),   ncol = 12, byrow = TRUE ) koeppen_geiger(prec, tavg, broad = TRUE) #>   id broad #> 1 27     4 #> 2 14     3 #> 3 15     3"},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"List all the available datasets. — list_available_datasets","title":"List all the available datasets. — list_available_datasets","text":"List datasets available pastclim. list comprehensive, includes combinations models future scenarios WorldClim. user-friendly list, use get_available_datasets(). functions can also used custom datasets setting dataset=\"custom\"","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all the available datasets. — list_available_datasets","text":"","code":"list_available_datasets()"},{"path":"https://evolecolgroup.github.io/pastclim/reference/list_available_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all the available datasets. — list_available_datasets","text":"character vector available datasets","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the dataset list — load_dataset_list","title":"Load the dataset list — load_dataset_list","text":"function returns dataframe details variable available every dataset. defaults copy stored within package, checks case updated version stored 'dataset_list_included.csv' tools::R_user_dir(\"pastclim\",\"config\"). latter present, last column, named 'dataset_list_v', provides version table, advanced table used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the dataset list — load_dataset_list","text":"","code":"load_dataset_list(on_cran = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load the dataset list — load_dataset_list","text":"on_cran boolean make function run ci tests using tempdir","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_dataset_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load the dataset list — load_dataset_list","text":"dataset list","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the ETOPO global relief — load_etopo","title":"Load the ETOPO global relief — load_etopo","text":"function loads previously downloaded ETOPO 2022 global relief dataset, 0.5 1 arc-minute (.e. 30 60 arc-seconds) resolution. function assumes file name etopo2022_{resolution}m_v1.nc save file default path appropriate name file format, simply use download_etopo().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the ETOPO global relief — load_etopo","text":"","code":"load_etopo(path = NULL, resolution = 1, version = \"1\")"},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load the ETOPO global relief — load_etopo","text":"path character. Path dataset stored. left NULL, data downloaded directory returned get_data_path() resolution numeric resolution arc-minute (one 0.5, 1). Defaults 1 arc-minute. version character numeric. ETOPO2022 version number. \"1\" supported moment","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/load_etopo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load the ETOPO global relief — load_etopo","text":"terra::SpatRaster relief","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of bioclimatic variables for one or more locations. — location_series","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"function extract time series local climate set locations. Note function apply interpolation (opposed location_slice()). coastal location just falls water reconstructions, amend coordinates put firmly land.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"","code":"location_series(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   nn_interpol = FALSE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"x data.frame columns x y coordinates (optional name column), vector cell numbers. See coords standard coordinate names, use custom ones. time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time slice years CE (see time_bp options). available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") bio_variables vector names variables extracted. dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults FALSE (DIFFERENT location_slice(). buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of bioclimatic variables for one or more locations. — location_series","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — location_slice","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"function extract local climate set locations appropriate times (selecting closest time slice available specific date associated location).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"","code":"location_slice(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   nn_interpol = TRUE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"x data.frame columns x y coordinates(see coords standard coordinate names, use custom ones), plus optional columns time_bp time_ce (depending units used) name. Alternatively, vector cell numbers. time_bp used time_bp column present x: dates years present (negative values represent time present, .e. 1950, positive values time future) location. time_ce time years CE alternative time_bp.one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") bio_variables vector names variables extracted. dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults TRUE. buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — location_slice","text":"data.frame climatic variables interest.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"function extract local climate set locations appropriate times (selecting closest time slice available specific date associated location).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"","code":"location_slice_from_region_series(   x,   time_bp = NULL,   time_ce = NULL,   coords = NULL,   region_series,   nn_interpol = TRUE,   buffer = FALSE,   directions = 8 )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"x data.frame columns x y coordinates(see coords standard coordinate names, use custom ones), plus optional columns time_bp time_ce (depending units used) name. Alternatively, vector cell numbers. time_bp used time_bp column present x: dates years present (negative values represent time present, .e. 1950, positive values time future) location. time_ce time years CE alternative time_bp. one time_bp time_ce used. coords vector length two giving names \"x\" \"y\" coordinates, found data. left NULL, function try guess columns based standard names c(\"x\", \"y\"), c(\"X\",\"Y\"), c(\"longitude\", \"latitude\"), c(\"lon\", \"lat\") region_series SpatRasterDataset obtained region_series() nn_interpol boolean determining whether nearest neighbour interpolation used estimate climate cells lack information (.e. water ice). default, interpolation performed first ring nearest neighbours; climate available, NA returned location. number neighbours can changed argument directions. nn_interpol defaults TRUE. buffer boolean determining whether variable returned mean buffer around focal cell. set TRUE, overrides nn_interpol (provides estimates buffer locations cells NA). buffer size determined argument directions. buffer defaults FALSE. directions character matrix indicate directions cells considered connected using nn_interpol buffer. following character values allowed: \"rook\" \"4\" horizontal vertical neighbours; \"bishop\" get diagonal neighbours; \"queen\" \"8\" get vertical, horizontal diagonal neighbours; \"16\" knight one-cell queen move neighbours. directions matrix odd dimensions logical (0, 1) values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/location_slice_from_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract local climate for one or more locations for a given time slice. — location_slice_from_region_series","text":"data.frame climatic variables interest.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a binary mask — make_binary_mask","title":"Create a binary mask — make_binary_mask","text":"Create binary mask raster: NAs converted 0s, value 1.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a binary mask — make_binary_mask","text":"","code":"make_binary_mask(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a binary mask — make_binary_mask","text":"x terra::SpatRaster","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_binary_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a binary mask — make_binary_mask","text":"terra::SpatRaster 0s 1s","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Downscale an ice mask — make_ice_mask","title":"Downscale an ice mask — make_ice_mask","text":"Downscaling ice mask presents issues. mask binary raster, standard downscaling approach still look blocky. can smooth contour applying Gaussian filter. strong filter much matter personal opinion, data compare . function attempts use sensible default value, worth exploring alternative values find good solution.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downscale an ice mask — make_ice_mask","text":"","code":"make_ice_mask(ice_mask_low_res, land_mask_high_res, d = c(0.5, 3))"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downscale an ice mask — make_ice_mask","text":"ice_mask_low_res terra::SpatRaster low resolution ice mask downscale (e.g. obtained get_ice_mask()) land_mask_high_res terra::SpatRaster land masks different times (e.g. obtained make_land_mask()). ice mask cropped matched resolution land mask.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_ice_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downscale an ice mask — make_ice_mask","text":"terra::SpatRaster ice mask (1's), rest world (sea land) NA's","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a land mask — make_land_mask","title":"Create a land mask — make_land_mask","text":"Create land mask given time step. land mask based simple logic moving ocean given current relief profile ( topography+bathymetry, .e. elevation sea level). Note approach ignores rebound due changing mass distribution ice sheets. LIMITATIONS: land mask show internal lakes/seas land, level unrelated general sea level. specific reconstructions internal lakes (want simply reuse current extents), add onto masks generated function. Also note land mask include ice sheets. means areas permanently covered ice two poles show sea. means , reconstruction including Greenland Antarctica, resulting land mask need modified include appropriate ice sheets.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a land mask — make_land_mask","text":"","code":"make_land_mask(relief_rast, time_bp, sea_level = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a land mask — make_land_mask","text":"relief_rast terra::SpatRaster relief time_bp time interest sea_level sea level time interest (left NULL, computed using Spratt 2016)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/make_land_mask.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a land mask — make_land_mask","text":"terra::SpatRaster land masks (land 1's sea NAs), layers different times","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":null,"dir":"Reference","previous_headings":"","what":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"dataset containing beginning end MIS.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"","code":"mis_boundaries"},{"path":"https://evolecolgroup.github.io/pastclim/reference/mis_boundaries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Time boundaries of marine isotope stages (MIS). — mis_boundaries","text":"data frame 24 rows 2 variables: mis stage, string start start given MIS, kya end start given MIS, kya","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Mode — mode","title":"Mode — mode","text":"Find mode vector x (note , multiple values frequency, function simply picks first occurring one)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mode — mode","text":"","code":"mode(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mode — mode","text":"x vector","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/mode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mode — mode","text":"mode","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/paleoclim_1.0.html","id":null,"dir":"Reference","previous_headings":"","what":"Documentation for Paleoclim — paleoclim_1.0","title":"Documentation for Paleoclim — paleoclim_1.0","text":"Paleoclim set high resolution paleoclimate reconstructions, mostly based CESM model, downscaled CHELSA dataset 3  different spatial resolutions: paleoclim_1.0_2.5m 2.5 arc-minutes (~5 km), paleoclim_1.0_5m 5 arc-minutes (~10 km), paleoclim_1.0_10m 10 arc-minutes (~20 km). 19 biovariables available. limited number time slices available dataset; furthermore, currently time slices present 130ka available pastclim.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/paleoclim_1.0.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Documentation for Paleoclim — paleoclim_1.0","text":"details dataset available dedicated website. IMPORTANT: use dataset, make sure cite original publication: Brown, Hill, Dolan, Carnaval, Haywood (2018) PaleoClim, high spatial resolution paleoclimate surfaces global land areas. Nature – Scientific Data. 5:180254","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim-package.html","id":null,"dir":"Reference","previous_headings":"","what":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","title":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","text":"Methods easily extract manipulate palaeoclimate reconstructions ecological anthropological analyses, described Leonardi et al. (2023) doi:10.1111/ecog.06481 .","code":""},{"path":[]},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"pastclim: Manipulate Time Series of Palaeoclimate Reconstructions — pastclim-package","text":"Maintainer: Andrea Manica am315@cam.ac.uk Authors: Michela Leonardi contributors: Emily Y. Hallet [contributor] Robert Beyer [contributor] Mario Krapp [contributor] Andrea V. Pozzi [contributor]","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":null,"dir":"Reference","previous_headings":"","what":"Read a raster for pastclim — pastclim_rast","title":"Read a raster for pastclim — pastclim_rast","text":"function wrapper around terra::rast(), additional logic correctly import time vrt datasets (time stored custom metadata pastclim-generated vrt files)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read a raster for pastclim — pastclim_rast","text":"","code":"pastclim_rast(   x,   bio_var_orig,   bio_var_pastclim,   var_longname = NULL,   var_units = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read a raster for pastclim — pastclim_rast","text":"x filename raster bio_var_orig variable name present file bio_var_pastclim variable name used pastclim (thus allowing us rename variable)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/pastclim_rast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read a raster for pastclim — pastclim_rast","text":"terra::SpatRaster","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":null,"dir":"Reference","previous_headings":"","what":"Region extents. — region_extent","title":"Region extents. — region_extent","text":"list extents major regions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region extents. — region_extent","text":"","code":"region_extent"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_extent.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region extents. — region_extent","text":"list vectors giving extents.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":null,"dir":"Reference","previous_headings":"","what":"Region outlines. — region_outline","title":"Region outlines. — region_outline","text":"sf::sf object containing outlines major regions. Outlines span antimeridian split multiple polygons.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region outlines. — region_outline","text":"","code":"region_outline"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region outlines. — region_outline","text":"sf::sf outlines. name names regions","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":null,"dir":"Reference","previous_headings":"","what":"Region outlines unioned. — region_outline_union","title":"Region outlines unioned. — region_outline_union","text":"sf::sf object containing outlines major regions. outline represented single polygon. want multiple polygons, use region_outline.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Region outlines unioned. — region_outline_union","text":"","code":"region_outline_union"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_outline_union.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Region outlines unioned. — region_outline_union","text":"sf::sf outlines. name names regions","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of climate variables for a region — region_series","title":"Extract a time series of climate variables for a region — region_series","text":"function extracts time series one climate variables given dataset covering region (whole world). function returns terra::SpatRasterDataset object, variable sub-dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of climate variables for a region — region_series","text":"","code":"region_series(   time_bp = NULL,   time_ce = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   ext = NULL,   crop = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of climate variables for a region — region_series","text":"time_bp time slices years present (negative values represent time present, positive values time future). parameter can vector times (slices need exist dataset), list min max element setting range values, left NULL retrieve time steps. check slices available, can use get_time_bp_steps(). time_ce time slices years CE (see time_bp options). available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. bio_variables vector names variables extracted dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. ext extent, coded numeric vector (length=4; order= xmin, xmax, ymin, ymax) terra::SpatExtent object. NULL, full extent reconstruction given. crop polygon used crop reconstructions (e.g. outline continental mass). sf::sfg terra::SpatVector object used define polygon.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of climate variables for a region — region_series","text":"terra::SpatRasterDataset object, variable sub-dataset.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a climate slice for a region — region_slice","title":"Extract a climate slice for a region — region_slice","text":"function extracts slice one climate variables given dataset covering region (whole world). function returns SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a climate slice for a region — region_slice","text":"","code":"region_slice(   time_bp = NULL,   time_ce = NULL,   bio_variables,   dataset,   path_to_nc = NULL,   ext = NULL,   crop = NULL )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a climate slice for a region — region_slice","text":"time_bp time slice years present (negative values represent time present, positive values time future). slice needs exist dataset. check slices available, can use get_time_bp_steps(). time_ce time slice years CE. available time slices years CE, use get_time_ce_steps(). one time_bp time_ce used. bio_variables vector names variables extracted dataset string defining dataset use. set \"custom\", single nc file used \"path_to_nc\" path_to_nc path custom nc file containing palaeoclimate reconstructions. variables interest need included file. ext extent, coded numeric vector (length=4; order= xmin, xmax, ymin, ymax) terra::SpatExtent object. NULL, full extent reconstruction given. crop polygon used crop reconstructions (e.g. outline continental mass). sf::sfg terra::SpatVector object used define polygon.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a climate slice for a region — region_slice","text":"SpatRaster terra::SpatRaster object, variable layer.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample points from a region time series — sample_region_series","title":"Sample points from a region time series — sample_region_series","text":"function samples points region time series. Sampling can either performed locations time steps (one value given size), different locations time step (size vector length equal number time steps). sample number points, different locations, time step, provide vector repeating value time step.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample points from a region time series — sample_region_series","text":"","code":"sample_region_series(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample points from a region time series — sample_region_series","text":"x terra::SpatRasterDataset returned region_series() size number points sampled. single value used sample locations across time steps, vector values sample different locations time step. method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample points from a region time series — sample_region_series","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample points from a region time series — sample_region_series","text":"function wraps terra::spatSample() appropriate sample terra::SpatRasters terra::SpatRasterDataset returned region_series().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample points from a region time slice — sample_region_slice","title":"Sample points from a region time slice — sample_region_slice","text":"function samples points region time slice (.e. time point).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample points from a region time slice — sample_region_slice","text":"","code":"sample_region_slice(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample points from a region time slice — sample_region_slice","text":"x terra::SpatRaster returned region_slice() size number points sampled. method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample points from a region time slice — sample_region_slice","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_region_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample points from a region time slice — sample_region_slice","text":"function wraps terra::spatSample() appropriate sample terra::SpatRaster returned region_slice(). can also use terra::spatSample() directly slice (standard terra::SpatRaster).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample the same locations from a region time series — sample_rs_fixed","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"Internal function fixed sampling sample_region_series(), used single size given.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"","code":"sample_rs_fixed(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"x terra::SpatRasterDataset returned region_series() size number points sampled; locations across time steps method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample the same locations from a region time series — sample_rs_fixed","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample the different number of points from a region time series — sample_rs_variable","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"Internal function sampling different number points timestep region series sample_region_series(), used size vector values.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"","code":"sample_rs_variable(x, size, method = \"random\", replace = FALSE, na.rm = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"x terra::SpatRasterDataset returned region_series() size vector number points sampled time step method one sampling methods terra::spatSample(). defaults \"random\" replace boolean determining whether sample replacement na.rm boolean determining whether NAs removed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/sample_rs_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample the different number of points from a region time series — sample_rs_variable","text":"data.frame sampled cells respective values climate variables.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the data path where climate reconstructions will be stored — set_data_path","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"function sets path climate reconstructions stored. information stored file names \"pastclim_data.txt\", found directory returned tools::R_user_dir(\"pastclim\",\"config\") (.e. default configuration directory package set R >= 4.0).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"","code":"set_data_path(   path_to_nc = NULL,   ask = TRUE,   write_config = TRUE,   copy_example = TRUE,   on_CRAN = FALSE )"},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"path_to_nc path file contains downloaded reconstructions. left unset, default location returned tools::R_user_dir(\"pastclim\",\"data\") used ask boolean whether user asked confirm choices write_config boolean whether path saved config file copy_example boolean whether example dataset saved data_path on_CRAN boolean; users need parameters. used set data path temporary directory examples tests run CRAN.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/set_data_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the data path where climate reconstructions will be stored — set_data_path","text":"TRUE path set correctly","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a slice for a time series of climate variables for a region — slice_region_series","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"function extracts time slice time series one climate variables given dataset covering region (whole world).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"","code":"slice_region_series(x, time_bp = NULL, time_ce = NULL)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"x climate time series generated region_series() time_bp time slice years present (.e. 1950, negative integers values past). slices need exist dataset. check slices available, can use time_bp(x). time_ce time slice years CE. one time_bp time_ce used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/slice_region_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a slice for a time series of climate variables for a region — slice_region_series","text":"SpatRaster relevant slice.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"functions extracts sets time years BP (.e. 1950) terra::SpatRaster  terra::SpatRasterDataset. terra::SpatRaster object, time stored unit \"years\", years 0AD. means , summary terra::SpatRaster inspected, times appear time_bp+1950. applies function terra::time() used instead time_bp().","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"","code":"time_bp(x)  # S4 method for SpatRaster time_bp(x)  # S4 method for SpatRasterDataset time_bp(x)  time_bp(x) <- value  # S4 method for SpatRaster time_bp(x) <- value  # S4 method for SpatRasterDataset time_bp(x) <- value"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"x terra::SpatRaster value numeric vector times years BP","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set time in years before present for SpatRaster and SpatRasterDataset — time_bp","text":"date years BP (negative numbers indicate date past)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a time BP to indexes for a series — time_bp_to_i_series","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"Internal function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"","code":"time_bp_to_i_series(time_bp, time_steps)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"time_bp vector times BP time_steps time steps reconstructions available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_i_series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a time BP to indexes for a series — time_bp_to_i_series","text":"indeces relevant time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the closest index to a given time in years BP — time_bp_to_index","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"Internal function","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"","code":"time_bp_to_index(time_bp, time_steps)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"time_bp vector times BP time_steps time steps reconstructions available","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_bp_to_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the closest index to a given time in years BP — time_bp_to_index","text":"indeces relevant time steps","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"Deprecated version location_series()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"","code":"time_series_for_locations(...)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"... arguments passed location_series()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/time_series_for_locations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a time series of bioclimatic variables for one or more locations. — time_series_for_locations","text":"data.frame climatic variables interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Update the dataset list — update_dataset_list","title":"Update the dataset list — update_dataset_list","text":"newer dataset list (includes information files storing data pastclim), download start using 'dataset_list_included.csv' tools::R_user_dir(\"pastclim\",\"config\"). latter present, last column, named 'dataset_list_v', provides version table, advanced table used.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update the dataset list — update_dataset_list","text":"","code":"update_dataset_list(on_cran = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update the dataset list — update_dataset_list","text":"on_cran boolean make function run ci tests using tempdir","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/update_dataset_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update the dataset list — update_dataset_list","text":"TRUE dataset updated","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether a URL is valid — url_is_valid","title":"Test whether a URL is valid — url_is_valid","text":"function check URL points real file","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether a URL is valid — url_is_valid","text":"","code":"url_is_valid(url, verbose = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether a URL is valid — url_is_valid","text":"url url test verbose whether status code outputted message","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/url_is_valid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether a URL is valid — url_is_valid","text":"boolean whether file exists","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate an netcdf file for pastclim — validate_nc","title":"Validate an netcdf file for pastclim — validate_nc","text":"function validates netcdf file potential dataset pastclim. key checks : ) dimensions (longitude, latitude time) set correctly. b) variables appropriate metadata (longname units)","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate an netcdf file for pastclim — validate_nc","text":"","code":"validate_nc(path_to_nc)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate an netcdf file for pastclim — validate_nc","text":"path_to_nc path nc file interest","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/validate_nc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate an netcdf file for pastclim — validate_nc","text":"TRUE file valid.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate pretty variable labels for plotting — var_labels","title":"Generate pretty variable labels for plotting — var_labels","text":"Generate pretty labels (form expression) can used plotting","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate pretty variable labels for plotting — var_labels","text":"","code":"var_labels(x, dataset, with_units = TRUE, abbreviated = FALSE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate pretty variable labels for plotting — var_labels","text":"x either character vector names variables, terra::SpatRaster generated [region_slice())] [region_slice())]: R:region_slice()) dataset string defining dataset downloaded (list possible values can obtained list_available_datasets()). function work custom datasets. with_units boolean defining whether label include units abbreviated boolean defining whether label use abbreviations variable","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate pretty variable labels for plotting — var_labels","text":"expression can used label plots","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/var_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate pretty variable labels for plotting — var_labels","text":"","code":"var_labels(\"bio01\", dataset = \"Example\") #> expression(\"annual mean temperature (\" * degree * C * \")\")  # set the data_path for this example to run on CRAN # users don't need to run this line set_data_path(on_CRAN = TRUE) #> [1] TRUE  # for a SpatRaster climate_20k <- region_slice(   time_bp = -20000,   bio_variables = c(\"bio01\", \"bio10\", \"bio12\"),   dataset = \"Example\" ) terra::plot(climate_20k, main = var_labels(climate_20k, dataset = \"Example\"))  terra::plot(climate_20k, main = var_labels(climate_20k,   dataset = \"Example\",   abbreviated = TRUE ))"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Get metadata from vrt — vrt_get_meta","title":"Get metadata from vrt — vrt_get_meta","text":"function extract metadata information vrt. returns description whole dataset (needed set varname raster) time information band. first checks vrt dataset metadata element key \"pastclim_time_bp\" set TRUE. case, band, extract metadata key \"time\" returns numeric (.e. converting character). Note error returned duplicated time elements bands (whilst duplicated elements valid XML schema VRT, make sense time axis).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get metadata from vrt — vrt_get_meta","text":"","code":"vrt_get_meta(vrt_path)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get metadata from vrt — vrt_get_meta","text":"vrt_path path XML file defining vrt dataset","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_get_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get metadata from vrt — vrt_get_meta","text":"list three elements: vector description time_bp defining band, boolean time_bp show determining whether times given time_bp labelling bands terra","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Set vrt metadata — vrt_set_meta","title":"Set vrt metadata — vrt_set_meta","text":"function sets metadata information vrt file. creates dataset wide metadata, well band specific descriptions times.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set vrt metadata — vrt_set_meta","text":"","code":"vrt_set_meta(vrt_path, description, time_vector, time_bp = TRUE)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set vrt metadata — vrt_set_meta","text":"vrt_path path XML file defining vrt dataset description string description variable dataset time_vector vector descriptions (length number bands) time_bp boolean defining whether time BP (FALSE) CE","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/vrt_set_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set vrt metadata — vrt_set_meta","text":"TRUE file updated correctly, FALSE update failed","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"functions convert years BP used pastclim (negative numbers going past, positive future) standard POSIXct date objects.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"","code":"ybp2date(x)  date2ybp(x)"},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"x time years BP using pastclim convention negative numbers indicating years past, POSIXct date object","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"POSIXct date object, vector","code":""},{"path":"https://evolecolgroup.github.io/pastclim/reference/ybp2date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert years BP from pastclim to lubridate date, or vice versa — ybp2date","text":"","code":"ybp2date(-10000) #> [1] \"-8050-01-01 UTC\" ybp2date(0) #> [1] \"1950-01-01 UTC\" # back and forth date2ybp(ybp2date(-10000)) #> [1] -10000"},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-210","dir":"Changelog","previous_headings":"","what":"pastclim 2.1.0","title":"pastclim 2.1.0","text":"Add CHELSA present future datasets (including use virtual rasters avoid downloading data) Add paleoclim multiple resolutions Add CHELSA-TraCE21k (including use virtual rasters avoid downloading data) Re-implement import WorldClim datasets avoid repackaging data (lead faster downloads, force re-download dataset already present). Add functions Koeppen Geiger’s classification monthly means.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-200","dir":"Changelog","previous_headings":"","what":"pastclim 2.0.0","title":"pastclim 2.0.0","text":"CRAN release: 2023-11-02 Allow time defined CE besides BP. NOTE adds parameter number functions. functions used without explicitly naming parameters, old code might give error order parameters now changed). Add Barreto et al 2023 (based PALEO-PGEM, covering last 5 M years) Add WorldClim data (present, future projections multiple models emission scenarios). Add HYDE 3.3 database providing information agriculture population sizes last 10k years. Change units Krapp et al 2021 match datasets. Also, fix data duplication variables now also fixed OSF repository dataset. Improve get_ice_mask(), get_land_mask(), distance_from_sea() work series rather just slices. Speed region_*() functions subsetting extent/cropping.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-124","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.4","title":"pastclim 1.2.4","text":"CRAN release: 2023-04-25 Updates time handled stay sync changes terra.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-123","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.3","title":"pastclim 1.2.3","text":"CRAN release: 2023-01-06 Added lai Krapp2021 (variable now also present original OSF repository dataset). Change column names data.frame returned location_series() match location_slice() Allow interpolation nearest neighbours location_series(), allow buffer estimates returned location_*() functions.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-122","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.2","title":"pastclim 1.2.2","text":"Update Krapp2021 files make compatible terra now handles time. Users re-download datasets. Old files can removed clean_data_path()","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-121","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.1","title":"pastclim 1.2.1","text":"Small updates CRAN submission.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-120","dir":"Changelog","previous_headings":"","what":"pastclim 1.2.0","title":"pastclim 1.2.0","text":"Provide additional information variables units, create pretty labels plots. Names locations now stored automatically outputs. Update time handled work terra 1.6-41 (now imports units netcdf files).","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-110","dir":"Changelog","previous_headings":"","what":"pastclim 1.1.0","title":"pastclim 1.1.0","text":"Expand functionality handle time series regions; rename functions extract data regions locations make consistent. Old code still work, raise warning functions deprecated. Remove need pastclimData, now put data user dir returned R>=4.0.0. removes need re-downloading data upgrading R. Add monthly variables Beyer2020 Krapp2021.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-101","dir":"Changelog","previous_headings":"","what":"pastclim 1.0.1","title":"pastclim 1.0.1","text":"Fix bug information extracted just one location.","code":""},{"path":"https://evolecolgroup.github.io/pastclim/news/index.html","id":"pastclim-100","dir":"Changelog","previous_headings":"","what":"pastclim 1.0.0","title":"pastclim 1.0.0","text":"Initial public release","code":""}]