Merge pull request #397 from bjohnso005/biomodelos

added guidance text for indic, diver, & mask

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: wallace
-Version: 2023.02.22
-Date: 2023-02-22
+Version: 2023.03.16
+Date: 2023-03-16
 Title: A Modular Platform for Reproducible Modeling of Species Niches 
     and Distributions
 Description: The 'shiny' application Wallace is a modular platform for 

inst/shiny/Rmd/gtext_diver.Rmd

@@ -7,6 +7,29 @@ output: html_document
 
 **ORIENTATION**
 
-Diversity can refer to a local measure of any dimension of biodiversity (taxonomic, functional or phylogenetic). There are countless indexes to measure local diversity for each dimension that take (or not) different aspects into account going from the simplest counts to indexes including abundances, relatedness etc. Generally, the simplest indexes (counts) are more widely used. This is the case for taxonomic diversity with species richness (count of species), for phylogenetic diversity with Faith’s PD (1992 count of phylogeny branches or nodes) and for functional diversity with functional richness (niche space occupied by the community). 
+Alpha diversity can refer to a local measure of any dimension of biodiversity (taxonomic, functional or phylogenetic). There are countless indices to measure local diversity for each dimension ranging from the simplest counts to indexes that include abundance and relatedness. Generally, the simplest indexes (counts) are the most widely used partly because species abundance data are less commonly available. This is the case for taxonomic diversity, with species richness (count of species) as the most widely used. For phylogenetic diversity, Faith's (1992) count of phylogenetic branches or nodes is common, and for functional diversity, functional richness (niche space occupied by the community) is commonly used.
+
+Range estimates from multiple species can be combined to calculate assemblage-level estimates of diversity. For example, intersecting species' ranges can be used to estimate potential species richness (Calabrese et al., 2014). The benefits and disadvantages of using binary maps for macroecological applications have been well documented (Graham and Hijmans 2006). Nonetheless, estimates of species diversity can be calculated from distribution maps such as binary SDMs.
+
+In the **Estimate Diversity** component, Wallace users can perform analyses and visualizations of community diversity metrics by stacking individual species distribution maps. Community metrics are calculated at the pixel level (One pixel is treated as one community). Wallace currently allows users to: 1) Compute species richness (Module: *Calculate Richness*) as the number of species in each pixel, and 2) Calculate species endemism (Module: *Calculate Endemism*) as the number of species found in a pixel divided by the total number of pixels in which they are found.
+
+---
+
+**ORIENTACIÓN**
+
+La diversidad alfa puede referirse a una medida local de cualquier dimensión de la biodiversidad (taxonómica, funcional o filogenética). Existen innumerables índices para medir la diversidad local para cada dimensión que van desde los conteos más simples hasta índices que incluyen abundancia y parentesco. En general, los índices más simples (conteos) son los más utilizados, en parte porque los datos de abundancia de especies están disponibles con menos frecuencia. Este es el caso de la diversidad taxonómica, siendo la riqueza de especies (recuento de especies) la más utilizada. Para la diversidad filogenética, es común el conteo de ramas o nodos filogenéticos de Faith (1992), y para la diversidad funcional, se usa comúnmente la riqueza funcional (espacio de nicho ocupado por la comunidad).
+
+Las estimaciones de rango de múltiples especies se pueden combinar para calcular estimaciones de diversidad a nivel de ensamblaje. Por ejemplo, los rangos de especies que se cruzan se pueden usar para estimar la riqueza potencial de especies (Calabrese et al., 2014). Los beneficios y desventajas de usar mapas binarios para aplicaciones macroecológicas han sido bien documentados (Graham y Hijmans 2006). No obstante, las estimaciones de la diversidad de especies se pueden calcular a partir de mapas de distribución como los SDM binarios.
+
+En el componente **Estimate Diversity**, los usuarios de Wallace pueden realizar análisis y visualizaciones de métricas de diversidad de la comunidad apilando mapas de distribución de especies individuales. Las métricas de la comunidad se calculan a nivel de píxel (un píxel se trata como una comunidad). Wallace actualmente permite a los usuarios: 1) Calcular la riqueza de especies (Módulo: *Calculate Richness*) como el número de especies en cada píxel, y 2) Calcular el endemismo de especies (Módulo: *Calculate Endemism*) como el número de especies encontradas en un píxel dividido por el número total de píxeles en los que se encuentran.
+
+---
+
+**REFERENCES**
+
+Calabrese, J.M., Certain, G., Kraan, C., & Dormann, C.F. (2014). Stacking species distribution models and adjusting bias by linking them to macroecological models: Stacking species distribution models. *Global Ecology and Biogeography*, 23(1), 99–112. <a href="https://doi.org/10.1111/geb.12102" target="_blank">DOI:10.1111/geb.12102</a>
+
+Faith, D.P. (1992). Conservation evaluation and phylogenetic diversity. *Biological Conservation*, 61, 1–10. <a href="https://doi.org/10.1016/0006-3207(92)91201-3" target="_blank">DOI:10.1016/0006-3207(92)91201-3</a>
+
+Graham, C.H., & Hijmans, R.J. (2006). A comparison of methods for mapping species ranges and species richness. *Global Ecology and Biogeography*, 15(6), 578-587. <a href=" https://doi.org/10.1111/j.1466-8238.2006.00257.x" target="_blank">DOI:10.1111/j.1466-8238.2006.00257.x</a>
 
-Component Diversity performs analyzes and visualizations of community diversity metrics based on individual species distribution maps. Community metrics are calculated at the pixel level (One pixel is treated as one community). Wallace currently allows to: 1) Compute species richness (Module Species richness) as the number of species in each pixel, 2) calculate the species endemism (Module Species Endemism) as the number of species found in a pixel divided by the total number of pixels in which they are found

inst/shiny/Rmd/gtext_indic.Rmd

@@ -1,23 +1,33 @@
 ---
-  title: "indic"
+title: "indic"
 output: html_document
 ---
+
 ### **Component: Calculate Indicators**
 
-**Background**
+**ORIENTATION**
+
+Translating a species’ current distribution into meaningful conservation metrics in a repeatable and transparent way to inform conservation planning and decision-making remains an outstanding issue in conservation biology (IUCN 2022). By using a species distribution model (SDM), as well as landscape requirements (e.g., forest cover), we can mask the output of an SDM to only those areas likely to be suitable to estimate the species’ current range (as we implement in the component **Mask Prediction**, Merow et al. 2022). From these reduced model outputs, the upper bounds of IUCN metrics regarding extent of occurrence (EOO) and area of occupancy (AOO) can be calculated to inform the assessment of a species’ conservation status, in combination with other information (Kass et al. 2021). In addition, we can calculate the proportion of a species’ range size that is protected, that is threatened, or that is associated with different land cover types. If past or future model projections or geospatial data on habitat for masking are available, we can also calculate and visualize change in these metrics over time. These change metrics can then inform IUCN red-listing assessments and conservation planning (Galante et al. 2023). 
+In *Wallace*, users can calculate these metrics using functions from the ‘changeRangeR’ package, as implemented in three modules:  1) Calculate Area Metrics to calculate range size, EOO based on SDM or occurrence data and AOO based on SDM or occurrence data; 2) Calculate Ratio Overlap to calculate proportions of overlap between a species’ or multiple species’ ranges with a shapefile or raster; and 3) Calculate Change Over Time to visualize change in metrics over time if data from multiple years are available. 
+
+Users can calculate metrics based on SDMs made in `wallace` (*Wallace SDM*), models transferred in space and time within `wallace` (*Transferred SDM*), user-provided SDMs uploaded in the *Upload User Prediction* module of the **Mask Prediction** component (*User uploaded SDM)*, or masked models that were masked within `wallace` in the **Mask Prediction** component (*Masked SDM*). 
+
+---
 
-  Translating a species’ current distribution into meaningful conservation metrics in a repeatable and transparent way to inform conservation planning and decision-making remains an outstanding issue in conservation biology. By using a species distribution model (SDM), as well as landscape requirements (e.g., forest cover), we can mask the output of an SDM to only those areas likely to be suitable to estimate the species’ current
-range (as we implement in the Wallace component Mask). From these reduced model outputs, upper bounds of IUCN metrics regarding area of occupancy (AOO) and extent of occurrence (EOO) can be calculated to inform the assessment of a species’ conservation status, in combination with other information. In
-addition, we can calculate the proportion of a species’ range size that is protected, that is threatened, or that is associated with different land cover types. If past or future model transfers or geospatial data
-on habitat for masking are available, we can also calculate and visualize indic in these metrics over time. These indic metrics can then inform IUCN red-listing assessments and forward-thinking conservation planning.
+**ORIENTACIÓN**
 
-**Implementation**
+Traducir la distribución actual de una especie en métricas de conservación significativas de una manera repetible y transparente para informar la planificación de la conservación y la toma de decisiones sigue siendo un tema pendiente en la biología de la conservación (UICN 2022). Mediante el uso de un modelo de distribución de especies (SDM), así como los requisitos del paisaje (por ejemplo, la cobertura forestal), podemos enmascarar el resultado de un SDM solo en aquellas áreas que probablemente sean adecuadas para estimar el rango actual de la especie (como lo implementamos en el componente **Mask Prediction**, Merow et al. 2022). A partir de estos resultados reducidos del modelo, se pueden calcular los límites superiores de las métricas de la UICN con respecto a la extensión de ocurrencia (EOO) y el área de ocupación (AOO) para informar la evaluación del estado de conservación de una especie, en combinación con otra información (Kass et al. 2021). Además, podemos calcular la proporción del área de distribución de una especie que está protegida, amenazada o asociada con diferentes tipos de cobertura terrestre. Si se encuentran disponibles las proyecciones de modelos pasadas o futuras o los datos geoespaciales sobre el hábitat para el enmascaramiento, también podemos calcular y visualizar el cambio en estas métricas a lo largo del tiempo. Estas métricas de cambio pueden luego informar las evaluaciones de la lista roja de la UICN y la planificación de la conservación (Galante et al. 2023).
+
+En Wallace, los usuarios pueden calcular estas métricas utilizando funciones del paquete 'changeRangeR', tal como se implementan en tres módulos: 1) Calculate Area Metrics para calcular el tamaño del rango, EOO basado en SDM o datos de ocurrencia y AOO basado en SDM o datos de ocurrencia; 2) Calculate Ratio Overlap para calcular las proporciones de superposición entre los rangos de una especie o varias especies con un archivo en formato shapefile o ráster; y 3) Calculate Change Over Time para visualizar el cambio en las métricas a lo largo del tiempo si hay datos disponibles de varios años.
+
+Los usuarios pueden calcular métricas basadas en SDM realizados en `wallace` (*Wallace SDM*), modelos transferidos en el espacio y el tiempo dentro de `wallace` (*Transferred SDM*), SDM proporcionados por el usuario cargados en el módulo Cargar predicción de usuario del componente **Mask Prediction** (*User uploaded SDM*), o modelos enmascarados que se enmascararon dentro de `wallace` en el componente **Mask Prediction** (*Masked SDM*).
+
+---
 
-  In Wallace we can calculate these metrics for the using the changeRangeR package, as implemented in three modules:  Areas to calculate range size, EOO based on sdm or occurrence data and AOO based on sdm, masked sdm, or occurrence data; Overlap with a shapefile; and indic over Time.
-Beyond single species, we can combine models from multiple species to calculate community-level metrics of conservation interest  - for that, see the Diversity component.
+**REFERENCES**
 
-**References**
+Galante, P.J., Chang, S., Paz, A., Aiello-Lammens, M., Gerstner, B.E., Johnson, B.A., Kass, J.M., Merow, C., Noguera-Urbano, E.A., Pinilla-Buitrago, G.E., and Blair, M.E. (2023). changeRangeR: an R package for reproducible biodiversity change metrics from species distribution estimates. *Conservation Science & Practice*, 5(1), e12863. <a href="https://doi.org/10.1111/csp2.12863" target="_blank">DOI:10.1111/csp2.12863</a>  
 
-IUCN Standards and Petitions Committee. 2019. Guidelines for Using the IUCN Red List Categories and Criteria. Version 14. Prepared by the Standards and Petitions Committee.
+IUCN. (2022). Guidelines for using the IUCN red list categories and criteria. Version 15.1. IUCN Retrieved from <a href="https://www.iucnredlist.org/resources/redlistguidelines" target="_blank">www.iucnredlist.org/resources/redlistguidelines</a>
 
-Merow, C., Galante, P., Gerstner, B., Johnson, B., Kass, J.M., Paz, A., Rosauer, D., Serra, P., Anderson, R.P., Blair, M. “changeRangeR: Translating species’ distributions into conservation metrics”. In prep.
+Kass, J.M., Meenan, S.I., Tinoco, N., Burneo, S.F., & Anderson, R.P. (2021). Improving area of occupancy estimates for parapatric species using distribution models and support vector machines. *Ecological Applications*, 31(1). <a href="https://doi.org/10.1002/eap.2228" target="_blank">DOI:10.1002/eap.2228</a>