Nothing
#' @title spat_ras
#' @description A function to get the WEGE index value for a provided polygon.
#' @param target_area Either a sp or sf object to which to calculate the WEGE index.
#' @param input Species ranges, either from a shapefile or from a georeferenced species list with a column for species, two columns for coordinates and one for
#' the IUCN category.
#' @param x name of the longitude column.
#' @param y name of the latitude column.
#' @param species name of the species column.
#' @param ed name of the evolutionary distinctiveness column.
#' @param category name of IUCN the category column. Terminology must be as
#' follows: DD for Data Deficient, LC for Least Concern, NT for Near Threatened,
#' EN, for Endangered, CR for Critically Endangered, EW for Extinct in the wild
#' and EX for Extinct.
#' @param show_progress Progress of the analysis by showing the number of the grid where the function is calculating the different indices.
#' @param res grid-cell size to use to calculate the range of the species in
#' case a georeferenced species list was provided.
#' @example examples/spat_ras_function.R
#' @return A RasterStack with rasters for each KBA criteria (A1a,A1b,A1e,B1) and indices calculated (GE,ED,EDGE,WEGE)
#' @export
spat_ras <- function(target_area, input, x, y, species='binomial',
category = 'category', show_progress = FALSE, ed, res = 1) {
if (is.null(input[[species]])) {
stop(paste0("No column found with the name - ",paste(species)))
}
if (is.null(input[[category]])) {
stop(paste0("No column found with the name - ",paste(category)))
}
if (!missing(ed)) {
if (is.null(input[[ed]])) {
stop(paste0("No column found with the name - ",paste(ed)))
}}
if (any(class(input) %in% "sf")) {
input_cl <- 'sf_ob'}else {input_cl <- 'df_ob'}
if (any(class(target_area) %in% 'SpatialPolygonsDataFrame')) {
target_area <- sf::st_as_sf(target_area)
}
if (any(class(input) %in% 'SpatialPolygonsDataFrame')) {
input <- sf::st_as_sf(input)
if (!sf::st_crs(target_area) == sf::st_crs(input)) {
stop("Inputs have a different projection")
}
}
if (any(class(input) %in% 'SpatialPolygonsDataFrame')) {
input <- sf::st_as_sf(input)
if (!sf::st_crs(target_area) == sf::st_crs(input)) {
stop("Inputs have a different projection")
}
}
if (any(class(input) %in% "data.frame")) {
crs_ta <- sf::st_crs(target_area)
input <- sf::st_as_sf(x = input,coords = c(x,y),crs = crs_ta)
}
sps_grid <- sf::st_intersects(input,target_area)
intersected_object_t <- t(sps_grid)
sp_numbers <- unlist(intersected_object_t[1:nrow(target_area)])
sp <- unique(input[[species]][sp_numbers])
if (identical(sp, character(0))) {
stop("No species found in selected area")
}
if (input_cl == 'df_ob') {
rgrid <- raster(raster::extent(input), resolution = res,crs = sp::CRS(crs_ta$proj4string))
rgrid[] <- 1:raster::ncell(rgrid)
rgrid <- sf::st_as_sf(raster::rasterToPolygons(rgrid))
iucn_to_grid_range <- function(iucn_shp,grid_to_use) {
r_grid_sf <- grid_to_use
sf_to_intersect <- iucn_shp
sf::st_crs(r_grid_sf) <- sf::st_crs(sf_to_intersect)
sps_grid <- sf::st_intersects(sf_to_intersect,r_grid_sf)
intersected_object <- sps_grid
area <- unlist(lapply(intersected_object,length))
sp_range_df <- cbind.data.frame(species = iucn_shp[[species]],area=area/(res*res*10000))
return(sp_range_df)
}
input <- input[input[[species]] %in% sp,]
input_combined <- stats::aggregate(input,
by = list(input$BINOMIAL),
FUN = mean)
input_combined <- input_combined[,c('Group.1','geometry')]
colnames(input_combined)[1] <- species
tmp <- iucn_to_grid_range(iucn_shp = input_combined,grid_to_use = rgrid)
}else {
area_input <- function(input,sp) {
temp <- sf::st_area(input[input[[species]] %in% sp,])
attributes(temp) <- NULL
temp <- sum(temp)
return(temp <- temp/1000000)
}
all_area <- lapply(sp,area_input,input = input)
tmp <- cbind.data.frame(species = sp,area = unlist(all_area))
}
tmp <- merge(tmp,input[,c(species,'category')],by.x = 'species',by.y =
species)
tmp <- tmp[,-4]
er_df <- cbind.data.frame(status = c('DD','LC','NT','VU','EN','CR','EW','EX'),
ER = c(0.0513,0.0009,0.0071,0.0513,0.4276,0.9688,1,1
))
tmp <- unique(tmp)
tmp <- merge(tmp, er_df, by.x = category, by.y = 'status', sort = TRUE)
if (!missing(ed)) {
tmp <- merge(tmp, input[,c(species,ed)], by.x = 'species', by.y = species,
sort = TRUE)
tmp <- tmp[,-6]
}
rgrid <- raster(raster::extent(target_area), resolution = res,crs = sp::CRS(crs_ta$proj4string))
rgrid[] <- 1:raster::ncell(rgrid)
rgrid <- sf::st_as_sf(raster::rasterToPolygons(rgrid))
iucn_to_grid <- function(iucn_shp,grid_to_use) {
sf::st_crs(grid_to_use) <- sf::st_crs(iucn_shp)
sps_grid <- sf::st_intersects(iucn_shp,grid_to_use)
intersected_object <- sps_grid
intersected_object_t <- t(intersected_object)
list_final <- list()
for (i in seq_along(intersected_object_t)) {
if(show_progress == TRUE) {
cat(i,length(intersected_object_t))
}
list_final[i] <- intersected_object_t[i]
}
names(list_final) <- 1:nrow(grid_to_use)
li_2 <- lapply(seq_along(list_final), function(i) {
list_final[[i]] <- as.character(iucn_shp[[species]][list_final[[i]]])
})
li_3 <- li_2
names(li_3) <- 1:nrow(grid_to_use)
return(li_3)
}
sp_grid <- iucn_to_grid(iucn_shp = input,grid_to_use = rgrid)
kba_size <- res*res*10000
tmp[tmp$area < kba_size,]$area = kba_size
tmp$perc_kba <- 100*(kba_size/tmp$area)
min_rangeA1a <- 0.5
min_rangeA1b <- 1
min_rangeB1 <- 10
df_final = data.frame()
for (i in seq_along(sp_grid)) {
temp_df <- tmp[tmp$species %in% sp_grid[[i]],]
if (nrow(temp_df) == 0) {
we_temp <- 0
wege_temp <- 0
ed_temp <- 0
edge_temp <- 0
ge_temp <- 0
KBA_A1a_temp <- 0
KBA_A1b_temp <- 0
KBA_A1e_temp <- 0
KBA_B1_temp <- 0
}else{
we_temp <- lapply(1, function(x) sum(1/temp_df$area))
wege_temp <- lapply(1, function(x) sum(sqrt(1/temp_df$area)*temp_df$ER))
if (!missing(ed)) {
edge_temp <- lapply(1, function(x) sum(log((temp_df$ed)*(temp_df$ER + 1
))))
ed_temp <- lapply(1, function(x) sum(temp_df$ed))}else{
ed_temp <- 0
edge_temp <- 0
}
ge_temp <- lapply(1, function(x) sum(temp_df$ER))
KBA_A1a_temp <- lapply(1, function(x) if (any(temp_df[temp_df$category == "CR" | temp_df$category == "EN",]$perc_kba > min_rangeA1a)) {1} else {0})
KBA_A1b_temp <- lapply(1, function(x) if (any(temp_df[temp_df$category == "VU",]$perc_kba > min_rangeA1b)) {1} else {0})
KBA_A1e_temp <- lapply(1, function(x) if (any(temp_df[temp_df$category == "CR" | temp_df$category == "EN",]$perc_kba == 100)) {1
} else {0})
KBA_B1_temp <- lapply(1, function(x) if (any(temp_df$perc_kba >
min_rangeB1)) {1} else {0})
}
df_temp <- cbind.data.frame(i,we=unlist(we_temp),wege=unlist(wege_temp),GE = unlist(ge_temp),ED = unlist(ed_temp),EDGE = unlist(edge_temp),kba_A1a = unlist(KBA_A1a_temp),kba_A1b = unlist(KBA_A1b_temp),kba_A1e = unlist(KBA_A1e_temp),kba_B1 = unlist(KBA_B1_temp))
df_final <- rbind(df_final,df_temp)
}
r <- raster(raster::extent(target_area), resolution = res,crs =
CRS(crs_ta$proj4string))
r_GE <- r
r_GE[] <- df_final$GE
r_ED <- r
r_ED[] <- df_final$ED
r_EDGE <- r
r_EDGE[] <- df_final$EDGE
r_wege <- r
r_wege[] <- df_final$wege
r_we <- r
r_we[] <- df_final$we
r_A1a <- r
r_A1a[] <- df_final$kba_A1a
r_A1b <- r
r_A1b[] <- df_final$kba_A1b
r_A1e <- r
r_A1e[] <- df_final$kba_A1e
r_B1 <- r
r_B1[] <- df_final$kba_B1
r_Kbas <- sum(r_A1a,r_A1b,r_A1e,r_B1)
r_Kbas[r_Kbas > 0] <- 1
raster_stack <- raster::stack(r_A1a,r_A1b,r_A1e,r_B1,r_GE,r_ED,r_EDGE,r_wege,r_we,r_Kbas)
names(raster_stack) <- c('A1a','A1b','A1e','B1','ER','ED','EDGE','WEGE','WE','KBAs')
raster::plot(raster_stack)
return(raster_stack)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.