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WEGE is an R package that allows the user to calculate the WEGE index for a particular area. Additionally it also calculates rasters of KBA criteria (A1a, A1b, A1e, and B1) Weighted endemism, the EDGE score, Evolutionary Distinctiveness and Extinction risk.
The package can currently only be installed through GitHub:
# install.packages("remotes")
remotes::install_github("harithmorgadinho/wege_ind")A get_edge example:
library(WEGE)
library(sp)
#> Warning: package 'sp' was built under R version 3.5.2
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.1.3, PROJ 4.9.3
species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)) {
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
temp <- Polygon(rbind(temp0,temp0[1,]))
range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
input$ED <- runif(nrow(input),1,30)
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
target_area <- Polygon(rbind(temp0,temp0[1,]))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
get_edge(target_area = target_area,input = input,species = 'binomial',category = 'category')
#> [[1]]
#> [1] 23.60822A get_wege example:
library(sp)
library(sf)
library(WEGE)
species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)){
temp <- Polygon(cbind(runif(4,1,50),runif(4,1,50)))
range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
target_area <- Polygon(cbind(runif(4,1,50),runif(4,1,50)))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
get_wege(target_area,input,species = 'binomial',category = 'category')
#> [[1]]
#> [1] 312.7381A get_kba-criteria example:
library(WEGE)
library(sp)
library(sf)
species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)){
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
temp <- Polygon(rbind(temp0,temp0[1,]))
range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
target_area <- Polygon(rbind(temp0,temp0[1,]))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
get_kba_criteria(target_area,input)
#> species area category area_kba perc_kba A1a A1b A1e B1
#> 1 a 2.947391e-05 CR 1.786149e-06 6.060102 yes no no no
#> 2 b 2.768724e-04 EN 7.429253e-05 26.832764 yes no no yes
#> 3 e 2.434958e-04 CR 1.244859e-04 51.124459 yes no no yes
#> 4 f 3.128119e-04 CR 3.224454e-05 10.307965 yes no no yes
#> 5 g 4.479853e-04 NT 1.918042e-04 42.814836 no no no yes
#> 6 h 2.102851e-04 NT 1.337322e-04 63.595688 no no no yes
#> 7 j 1.013406e-04 EN 3.655669e-05 36.073100 yes no no yes
#> 8 k 4.449901e-04 VU 1.025432e-04 23.043938 no yes no yes
#> 9 m 3.871863e-04 CR 1.013974e-04 26.188272 yes no no yes
#> 10 n 4.809546e-04 NT 2.858035e-04 59.424224 no no no yes
#> 11 o 9.440874e-05 NT 2.502407e-05 26.506091 no no no yes
#> 12 q 5.152714e-04 CR 2.376042e-04 46.112449 yes no no yes
#> 13 r 5.082501e-05 EN 3.078056e-05 60.561836 yes no no yes
#> 14 s 2.523799e-04 VU 1.587406e-04 62.897513 no yes no yes
#> 15 t 2.591122e-04 LC 8.592068e-05 33.159645 no no no yes
#> 16 u 5.776036e-05 LC 5.589551e-05 96.771400 no no no yes
#> 17 v 6.707679e-05 NT 1.720175e-05 25.644862 no no no yes
#> 18 w 2.207896e-04 NT 9.193474e-05 41.639073 no no no yes
#> 19 x 6.142535e-04 VU 2.235084e-04 36.386998 no yes no yes
#> 20 y 9.276385e-05 CR 4.771320e-05 51.435119 yes no no yes
#> 21 z 3.607598e-04 EN 9.905803e-05 27.458169 yes no no yesA raster example example:
library(WEGE)
library(sp)
library(sf)
library(raster)
#> Warning: package 'raster' was built under R version 3.5.2
species <- sample(letters, 10)
range_list <- list()
for (i in seq_along(species)) {
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
temp <- Polygon(rbind(temp0,temp0[1,]))
range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
input$ed <- runif(runif(10,1,50))
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
target_area <- Polygon(rbind(temp0,temp0[1,]))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
spat_ras(target_area,input,species = 'binomial',ed='ed', res = 1)
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#> class : RasterStack
#> dimensions : 24, 31, 744, 10 (nrow, ncol, ncell, nlayers)
#> resolution : 1, 1 (x, y)
#> extent : 13.19312, 44.19312, 8.554813, 32.55481 (xmin, xmax, ymin, ymax)
#> crs : NA
#> names : A1a, A1b, A1e, B1, GE, ED, EDGE, WEGE, WE, KBAs
#> min values : 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, -4.475515, 0.000000, 0.000000, 0.000000
#> max values : 1.000000, 1.000000, 1.000000, 1.000000, 0.501100, 3.891238, 0.000000, 0.005011, 0.000600, 1.000000Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C., Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020). Wege: A New Metric for Ranking Locations for Biodiversity Conservation. bioRxiv. https://doi.org/10.1101/2020.01.17.910299
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.