WEGE index

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.

Install

The package can currently only be installed through GitHub:

# install.packages("remotes")
remotes::install_github("harithmorgadinho/wege_ind")

Usage

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] 23.60822

A 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] 312.7381

A 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 yes

A 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)
#> 1 7442 7443 7444 7445 7446 7447 7448 7449 74410 74411 74412 74413 74414 74415 74416 74417 74418 74419 74420 74421 74422 74423 74424 74425 74426 74427 74428 74429 74430 74431 74432 74433 74434 74435 74436 74437 74438 74439 74440 74441 74442 74443 74444 74445 74446 74447 74448 74449 74450 74451 74452 74453 74454 74455 74456 74457 74458 74459 74460 74461 74462 74463 74464 74465 74466 74467 74468 74469 74470 74471 74472 74473 74474 74475 74476 74477 74478 74479 74480 74481 74482 74483 74484 74485 74486 74487 74488 74489 74490 74491 74492 74493 74494 74495 74496 74497 74498 74499 744100 744101 744102 744103 744104 744105 744106 744107 744108 744109 744110 744111 744112 744113 744114 744115 744116 744117 744118 744119 744120 744121 744122 744123 744124 744125 744126 744127 744128 744129 744130 744131 744132 744133 744134 744135 744136 744137 744138 744139 744140 744141 744142 744143 744144 744145 744146 744147 744148 744149 744150 744151 744152 744153 744154 744155 744156 744157 744158 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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,  1.000000

Citation:

Farooq, 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