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Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-07-09

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Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-07-11

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diff --git a/search.json b/search.json index ff7fc5db2f..cea9a126ba 100644 --- a/search.json +++ b/search.json @@ -1,4 +1,60 @@ [ + { + "objectID": "index.html", + "href": "index.html", + "title": "Forecasting Challenge", + "section": "", + "text": "We invite you to submit forecasts!\nThe NEON Ecological Forecasting Challenge is an open platform for the ecological and data science communities to forecast data from the National Ecological Observatory Network before they are collected.\nThe Challenge is hosted by the Ecological Forecasting Initiative Research Coordination Network and sponsored by the U.S. National Science Foundation." + }, + { + "objectID": "index.html#why-a-forecasting-challenge", + "href": "index.html#why-a-forecasting-challenge", + "title": "Forecasting Challenge", + "section": "Why a forecasting challenge?", + "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of predictability in ecology. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe Challenge is an excellent focal project in university courses.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the NEON Challenge\n9366\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-07-09\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n11.03\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n10" + }, + { + "objectID": "index.html#the-challenge-is-a-platform", + "href": "index.html#the-challenge-is-a-platform", + "title": "Forecasting Challenge", + "section": "The Challenge is a platform", + "text": "The Challenge is a platform\nOur platform is designed to empower you to contribute by providing target data, numerical weather forecasts, and tutorials. We automatically score your forecasts using the latest NEON data. All forecasts and scores are publicly available through cloud storage and discoverable through our catalog.\n \nFigure from Thomas et al. 2023" + }, + { + "objectID": "index.html#contact", + "href": "index.html#contact", + "title": "Forecasting Challenge", + "section": "Contact", + "text": "Contact\neco4cast.initiative@gmail.com" + }, + { + "objectID": "index.html#acknowledgements", + "href": "index.html#acknowledgements", + "title": "Forecasting Challenge", + "section": "Acknowledgements", + "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-07-11" + }, + { + "objectID": "learn-more.html", + "href": "learn-more.html", + "title": "Learn more", + "section": "", + "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting\n\n\n\n\n\nLewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628" + }, + { + "objectID": "learn-more.html#tutorials", + "href": "learn-more.html#tutorials", + "title": "Learn more", + "section": "", + "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting" + }, + { + "objectID": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", + "href": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", + "title": "Learn more", + "section": "", + "text": "Lewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628" + }, { "objectID": "targets.html", "href": "targets.html", @@ -25,7 +81,7 @@ "href": "targets.html#sec-targets", "title": "What to forecast", "section": "Explore the targets and themes", - "text": "Explore the targets and themes\nInformation on the targets files for the five “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where PT30M is a 30-minute mean, P1D is a daily mean, and P1W is a weekly total.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nTerrestrial fluxesAquaticsPhenologyBeetle communitiesTick populations\n\n\n\nThe exchange of water and carbon dioxide between the atmosphere and the land is akin to earth’s terrestrial ecosystems breathing rate and lung capacity. \nThe terrestrial flux theme challenges you to forecast the gas exchange at up to 47 sites across the U.S.\nThere are two variables and two time-steps (or duration) that you can forecast.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nforecast horizon\nLatency\n\n\n\n\nle\nP1D\ndaily mean latent heat flux (W/m2)\n30 days\n~ 5 days\n\n\nnee\nP1D\ndaily mean Net ecosystem exchange (gC/m2/day)\n30 days\n~ 5 days\n\n\nle\nPT30M\n30 minute mean latent heat flux (W/m2)\n10 days\n~ 5 days\n\n\nnee\nPT30M\n30 minute mean net ecosystem exchange (umol/m2/s)\n10 days\n~ 5 days\n\n\n\n\n\n\nDaily mean\nThe daily mean target file is located at the following URL.\n\nurl_P1D <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nterrestrial_targets <- read_csv(url_P1D, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBART\n2017-02-06\nP1D\nle\n11.5794551\n\n\nneon4cast\nBART\n2017-02-07\nP1D\nle\n4.8951620\n\n\nneon4cast\nBART\n2017-02-09\nP1D\nle\n7.5281656\n\n\nneon4cast\nBART\n2017-02-11\nP1D\nle\n1.1577581\n\n\nneon4cast\nBART\n2017-02-12\nP1D\nle\n0.1999174\n\n\nneon4cast\nBART\n2017-02-13\nP1D\nle\n10.9325370\n\n\n\n\n\nand the time series for the focal sites\n\nterrestrial_targets |> \n filter(site_id %in% terrestrial_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\n\n\n30 minute\nThe 30 minute duration targets are designed for forecasting sub-daily carbon and water dynamics. The URL is found at:\n\nurl_PT30M <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html\n\n\n\n\nFreshwater surface water temperature, dissolved oxygen, and chlorophyll-a all influence drinking water quality, are critical for life in aquatic environments, and can represent the health of the ecosystem.\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 7 lakes and 27 river/stream NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n30 days\n~ 3 days\n\n\noxygen\nP1D\nSurface Mean Daily Dissolved Oxygen Concentration (mgL)\n30 days\n~ 3 days\n\n\ntemperature\nP1D\nSurface Mean Daily Water Temperature (Celsius)\n30 days\n~ 3 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nARIK\n2016-08-12\nP1D\noxygen\n3.402153\n\n\nneon4cast\nARIK\n2016-08-13\nP1D\noxygen\n4.156236\n\n\nneon4cast\nARIK\n2016-08-14\nP1D\noxygen\n4.071263\n\n\nneon4cast\nARIK\n2016-08-15\nP1D\noxygen\n3.909114\n\n\nneon4cast\nARIK\n2016-08-16\nP1D\noxygen\n3.862653\n\n\nneon4cast\nARIK\n2016-08-17\nP1D\noxygen\n4.354618\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 859 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nWater temperature at multiple depths measured at the UTC 00 hour are available for the 7 NEON lake sites. These data can be used for model development but will not be used for forecast evaluation.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/supporting_data/project_id=neon4cast/aquatics-expanded-observations.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Aquatics.html\n\n\n\nPhenology (the changes in plant canopies over the year) has been identified as one of the primary ecological fingerprints of global climate change.\nThe greenness and redness, as measured by a camera looking down at the top of vegetation are a quantitative measure of phenology. The phenology theme challenges you to forecast daily mean greeness and/or redness at up-to 47 terrestrial NEON sites.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\nread_csv(url, show_col_types = FALSE) |> \n distinct(variable, duration) |> \n left_join(target_metadata, by = c(\"variable\",\"duration\")) |> \n filter(variable %in% c(\"gcc_90\",\"rcc_90\")) |> \n select(-class) |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\ngcc_90\nP1D\nGreen chromatic coordinate is the ratio of the green digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\nrcc_90\nP1D\nRed chromatic coordinate is the ratio of the Red digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nphenology_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2017-05-30\nP1D\ngcc_90\n0.41659\n\n\nneon4cast\nABBY\n2017-05-31\nP1D\ngcc_90\n0.41570\n\n\nneon4cast\nABBY\n2017-06-01\nP1D\ngcc_90\n0.41780\n\n\nneon4cast\nABBY\n2017-06-02\nP1D\ngcc_90\n0.41539\n\n\nneon4cast\nABBY\n2017-06-03\nP1D\ngcc_90\n0.42216\n\n\nneon4cast\nABBY\n2017-06-04\nP1D\ngcc_90\n0.41659\n\n\n\n\n\nand the time series for the focal sites\n\nphenology_targets |> \n filter(site_id %in% phenology_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 8202 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Phenology.html\n\n\n\nSentinel species (such as beetles) can give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity.\nThe beetles theme challenges you to forecast weekly ground beetles (Family: Carabidae) abundance and richness (two measures of biodiversity) at up-to 47 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nabundance\nP1W\nTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site\n1 year\n~ 6 months\n\n\nrichness\nP1W\nTotal number of unique ‘species’ in a sampling bout for each NEON site each week.\n1 year\n~ 6 months\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nbeetles_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nabundance\n1.0489796\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nrichness\n14.0000000\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nabundance\n4.4535714\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nrichness\n13.0000000\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nabundance\n0.0553571\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nrichness\n10.0000000\n\n\n\n\n\nand the time series for the focal sites\n\nbeetles_targets |> \n filter(site_id %in% beetles_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Beetles.html\n\n\n\nTarget species for the tick population forecasts are Amblyomma americanum nymphal ticks. A. americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness. The species is present in the eastern United States, and their populations are expanding. There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.\nThe beetles theme challenges you to forecast weekly Amblyomma americanum nymphal tick abundance at up-to 9 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\namblyomma_americanum\nP1W\nThe density of Amblyomma americanum nymphs per week (ticks per 1600m^2)\n1 year\n~ 6 months\n\n\n\n\n\nThe weekly target file is located at the following URL.\n\n\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n[1] \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n\nYou can directly load it into R using the following\n\nticks_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBLAN\n2015-04-20\nP1W\namblyomma_americanum\n0.000000\n\n\nneon4cast\nBLAN\n2015-05-11\nP1W\namblyomma_americanum\n9.815951\n\n\nneon4cast\nBLAN\n2015-06-01\nP1W\namblyomma_americanum\n10.000000\n\n\nneon4cast\nBLAN\n2015-06-08\nP1W\namblyomma_americanum\n19.393939\n\n\nneon4cast\nBLAN\n2015-06-22\nP1W\namblyomma_americanum\n3.137255\n\n\nneon4cast\nBLAN\n2015-07-13\nP1W\namblyomma_americanum\n3.661327\n\n\n\n\n\nand the time series for the focal sites\n\nticks_targets |> \n filter(site_id %in% ticks_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Ticks.html" + "text": "Explore the targets and themes\nInformation on the targets files for the five “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where PT30M is a 30-minute mean, P1D is a daily mean, and P1W is a weekly total.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nTerrestrial fluxesAquaticsPhenologyBeetle communitiesTick populations\n\n\n\nThe exchange of water and carbon dioxide between the atmosphere and the land is akin to earth’s terrestrial ecosystems breathing rate and lung capacity. \nThe terrestrial flux theme challenges you to forecast the gas exchange at up to 47 sites across the U.S.\nThere are two variables and two time-steps (or duration) that you can forecast.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nforecast horizon\nLatency\n\n\n\n\nle\nP1D\ndaily mean latent heat flux (W/m2)\n30 days\n~ 5 days\n\n\nnee\nP1D\ndaily mean Net ecosystem exchange (gC/m2/day)\n30 days\n~ 5 days\n\n\nle\nPT30M\n30 minute mean latent heat flux (W/m2)\n10 days\n~ 5 days\n\n\nnee\nPT30M\n30 minute mean net ecosystem exchange (umol/m2/s)\n10 days\n~ 5 days\n\n\n\n\n\n\nDaily mean\nThe daily mean target file is located at the following URL.\n\nurl_P1D <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nterrestrial_targets <- read_csv(url_P1D, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBART\n2017-02-06\nP1D\nle\n11.5794551\n\n\nneon4cast\nBART\n2017-02-07\nP1D\nle\n4.8951620\n\n\nneon4cast\nBART\n2017-02-09\nP1D\nle\n7.5281656\n\n\nneon4cast\nBART\n2017-02-11\nP1D\nle\n1.1577581\n\n\nneon4cast\nBART\n2017-02-12\nP1D\nle\n0.1999174\n\n\nneon4cast\nBART\n2017-02-13\nP1D\nle\n10.9325370\n\n\n\n\n\nand the time series for the focal sites\n\nterrestrial_targets |> \n filter(site_id %in% terrestrial_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\n\n\n30 minute\nThe 30 minute duration targets are designed for forecasting sub-daily carbon and water dynamics. The URL is found at:\n\nurl_PT30M <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html\n\n\n\n\nFreshwater surface water temperature, dissolved oxygen, and chlorophyll-a all influence drinking water quality, are critical for life in aquatic environments, and can represent the health of the ecosystem.\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 7 lakes and 27 river/stream NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n30 days\n~ 3 days\n\n\noxygen\nP1D\nSurface Mean Daily Dissolved Oxygen Concentration (mgL)\n30 days\n~ 3 days\n\n\ntemperature\nP1D\nSurface Mean Daily Water Temperature (Celsius)\n30 days\n~ 3 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nARIK\n2016-08-12\nP1D\noxygen\n3.402153\n\n\nneon4cast\nARIK\n2016-08-13\nP1D\noxygen\n4.156236\n\n\nneon4cast\nARIK\n2016-08-14\nP1D\noxygen\n4.071263\n\n\nneon4cast\nARIK\n2016-08-15\nP1D\noxygen\n3.909114\n\n\nneon4cast\nARIK\n2016-08-16\nP1D\noxygen\n3.862653\n\n\nneon4cast\nARIK\n2016-08-17\nP1D\noxygen\n4.354618\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 859 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nWater temperature at multiple depths measured at the UTC 00 hour are available for the 7 NEON lake sites. These data can be used for model development but will not be used for forecast evaluation.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/supporting_data/project_id=neon4cast/aquatics-expanded-observations.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Aquatics.html\n\n\n\nPhenology (the changes in plant canopies over the year) has been identified as one of the primary ecological fingerprints of global climate change.\nThe greenness and redness, as measured by a camera looking down at the top of vegetation are a quantitative measure of phenology. The phenology theme challenges you to forecast daily mean greeness and/or redness at up-to 47 terrestrial NEON sites.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\nread_csv(url, show_col_types = FALSE) |> \n distinct(variable, duration) |> \n left_join(target_metadata, by = c(\"variable\",\"duration\")) |> \n filter(variable %in% c(\"gcc_90\",\"rcc_90\")) |> \n select(-class) |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\ngcc_90\nP1D\nGreen chromatic coordinate is the ratio of the green digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\nrcc_90\nP1D\nRed chromatic coordinate is the ratio of the Red digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nphenology_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2017-05-30\nP1D\ngcc_90\n0.41659\n\n\nneon4cast\nABBY\n2017-05-31\nP1D\ngcc_90\n0.41570\n\n\nneon4cast\nABBY\n2017-06-01\nP1D\ngcc_90\n0.41780\n\n\nneon4cast\nABBY\n2017-06-02\nP1D\ngcc_90\n0.41539\n\n\nneon4cast\nABBY\n2017-06-03\nP1D\ngcc_90\n0.42216\n\n\nneon4cast\nABBY\n2017-06-04\nP1D\ngcc_90\n0.41659\n\n\n\n\n\nand the time series for the focal sites\n\nphenology_targets |> \n filter(site_id %in% phenology_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 8206 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Phenology.html\n\n\n\nSentinel species (such as beetles) can give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity.\nThe beetles theme challenges you to forecast weekly ground beetles (Family: Carabidae) abundance and richness (two measures of biodiversity) at up-to 47 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nabundance\nP1W\nTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site\n1 year\n~ 6 months\n\n\nrichness\nP1W\nTotal number of unique ‘species’ in a sampling bout for each NEON site each week.\n1 year\n~ 6 months\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nbeetles_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nabundance\n1.0489796\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nrichness\n14.0000000\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nabundance\n4.4535714\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nrichness\n13.0000000\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nabundance\n0.0553571\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nrichness\n10.0000000\n\n\n\n\n\nand the time series for the focal sites\n\nbeetles_targets |> \n filter(site_id %in% beetles_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Beetles.html\n\n\n\nTarget species for the tick population forecasts are Amblyomma americanum nymphal ticks. A. americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness. The species is present in the eastern United States, and their populations are expanding. There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.\nThe beetles theme challenges you to forecast weekly Amblyomma americanum nymphal tick abundance at up-to 9 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\namblyomma_americanum\nP1W\nThe density of Amblyomma americanum nymphs per week (ticks per 1600m^2)\n1 year\n~ 6 months\n\n\n\n\n\nThe weekly target file is located at the following URL.\n\n\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n[1] \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n\nYou can directly load it into R using the following\n\nticks_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBLAN\n2015-04-20\nP1W\namblyomma_americanum\n0.000000\n\n\nneon4cast\nBLAN\n2015-05-11\nP1W\namblyomma_americanum\n9.815951\n\n\nneon4cast\nBLAN\n2015-06-01\nP1W\namblyomma_americanum\n10.000000\n\n\nneon4cast\nBLAN\n2015-06-08\nP1W\namblyomma_americanum\n19.393939\n\n\nneon4cast\nBLAN\n2015-06-22\nP1W\namblyomma_americanum\n3.137255\n\n\nneon4cast\nBLAN\n2015-07-13\nP1W\namblyomma_americanum\n3.661327\n\n\n\n\n\nand the time series for the focal sites\n\nticks_targets |> \n filter(site_id %in% ticks_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Ticks.html" }, { "objectID": "targets.html#explore-the-sites", @@ -145,61 +201,5 @@ "title": "Forecast performance", "section": "Aggregated scores", "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since in the last 30 days for the daily or sub-daily variables and the past year for the weekly variables\n\nLearn about the continuous ranked probability score here\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chrophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum" - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "Forecasting Challenge", - "section": "", - "text": "We invite you to submit forecasts!\nThe NEON Ecological Forecasting Challenge is an open platform for the ecological and data science communities to forecast data from the National Ecological Observatory Network before they are collected.\nThe Challenge is hosted by the Ecological Forecasting Initiative Research Coordination Network and sponsored by the U.S. National Science Foundation." - }, - { - "objectID": "index.html#why-a-forecasting-challenge", - "href": "index.html#why-a-forecasting-challenge", - "title": "Forecasting Challenge", - "section": "Why a forecasting challenge?", - "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of predictability in ecology. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe Challenge is an excellent focal project in university courses.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the NEON Challenge\n9366\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-07-07\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n11.02\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n10" - }, - { - "objectID": "index.html#the-challenge-is-a-platform", - "href": "index.html#the-challenge-is-a-platform", - "title": "Forecasting Challenge", - "section": "The Challenge is a platform", - "text": "The Challenge is a platform\nOur platform is designed to empower you to contribute by providing target data, numerical weather forecasts, and tutorials. We automatically score your forecasts using the latest NEON data. All forecasts and scores are publicly available through cloud storage and discoverable through our catalog.\n \nFigure from Thomas et al. 2023" - }, - { - "objectID": "index.html#contact", - "href": "index.html#contact", - "title": "Forecasting Challenge", - "section": "Contact", - "text": "Contact\neco4cast.initiative@gmail.com" - }, - { - "objectID": "index.html#acknowledgements", - "href": "index.html#acknowledgements", - "title": "Forecasting Challenge", - "section": "Acknowledgements", - "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-07-09" - }, - { - "objectID": "learn-more.html", - "href": "learn-more.html", - "title": "Learn more", - "section": "", - "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting\n\n\n\n\n\nLewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628" - }, - { - "objectID": "learn-more.html#tutorials", - "href": "learn-more.html#tutorials", - "title": "Learn more", - "section": "", - "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting" - }, - { - "objectID": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", - "href": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", - "title": "Learn more", - "section": "", - "text": "Lewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. 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2024-07-08 + 2024-07-09 https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/targets/collection.json - 2024-07-08 + 2024-07-09 diff --git a/targets.html b/targets.html index 1d57c043d7..ab3c154ee0 100644 --- a/targets.html +++ b/targets.html @@ -690,7 +690,7 @@

30 minute

facet_grid(variable~site_id, scales = "free_y") + theme_bw()
-
Warning: Removed 8202 rows containing missing values or values outside the scale range
+
Warning: Removed 8206 rows containing missing values or values outside the scale range
 (`geom_point()`).
@@ -972,8 +972,8 @@

30 minute

Explore the sites

-
- +
+



The following table lists all the sites in the NEON Ecological Forecasting Challenge. The columns with “theme” names incidate whether that site is included in that theme’s target file.

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