In dcarver1/gapAnalysisR: An R package to calculate conservation indicators using species distribution models

library(tmap)
tmap_mode("view")

# function for flitering list based on character values
include <- function (theList, toMatch){
  matches <- unique (grep(paste(toMatch,collapse="|"),
                          theList, value=TRUE))
  return(matches)
}

# function for replacing na values with 0 
removeNA = function(rast){
  rast[is.na(rast)] <- 0
  return(rast)
}

# function for extending all rasters to an equal extent
extend_all =function(rasters){
  extent(Reduce(extend,rasters))
}

# function for adding all rasters together
sum_all = function(rasters, extent){
    re = lapply(rasters, function(r){extend(r, extent, value=0)})
    Reduce("+",re)
  }

Species Richness Map

Combined all species threshold maps to display species richness.

#replaces all NA values with zeros some when added to othe raster elements are not canceled out
rasterList <- lapply(X = raster_List, FUN = removeNA)

# Matches the extent of all rasters and adds them together
r_sum = sum_all(rasterList, extend_all(rasterList))

#Replace all zeros with NA
r_sum[r_sum == 0]<-NA

try(qtm(r_sum), FALSE)
# check write Clause and output the file 
if(writeRasters==TRUE){
  try(raster::writeRaster(x = r_sum, filename = paste0(outputFolder, "/richnessMap_",Sys.Date() ,".tif"), overwrite=TRUE))
}

Summary of Conservation Statistics for all Species

The conservation statistic is based on all known occurrance records for the species. The other conservation metrics are based on records that contain latitude and longitude values.

Ex situ Conservation

The table below shows the ex situ conservation summary. SRSex is a gross comparison of germplasm (G) and reference (H) records. GRSex analyzes how comprehensively the G records cover the maxent model spatially. ERSex analyzes how well the G records cover the maxent model with regard to ecosystems covered. All of the conservation metrics are on a scale from 0-100, with 0 = poor conservation and 100 = perfectly sufficient conservation. The final ex situ conservation score is called FCSex and is a mean of the three ex situ conservation scores.

DT::datatable(exsituSummary)

GRSex

Map of the potential distribution, with previous germplasm collection points surrounded by a 50 km buffer overlaid. Only germplasm points are displayed on the map.

Areas of Native Range = 0

Potential Distribution = 1

Area Where samples have been collected = 2

suppressMessages(require(rgdal))
suppressMessages(require(raster))

#importFrom("methods", "as")
#importFrom("stats", "complete.cases", "filter", "median")
#importFrom("utils", "data", "memory.limit", "read.csv", "write.csv")
if(missing(bufferDistance)){
  bufferDistance <- 50000
}
  # create a empty vector to hold the rastes 
  vector = c()

  for(i in 1:length(sort(species_list))){
  # select species G occurrences
    occData <- occurrenceData %>%
      dplyr::filter(taxon == species_list[i]) %>%
      dplyr::filter(type == "G")%>%
      dplyr::select(longitude,latitude)

    sp::coordinates(occData) <- ~longitude+latitude
    sp::proj4string(occData) <- CRS("+proj=longlat +datum=WGS84")
  # select raster with species name
    for(j in 1:length(raster_List)){
      if(grepl(j, i, ignore.case = TRUE)){
        sdm <- raster_List[[j]]
      }
    }
  # convert SDM from binary to 1-NA for mask and area
  sdmMask <- sdm
  sdmMask[sdmMask == 0] <- NA
  # buffer G points
  buffer <- geobuffer::geobuffer_pts(xy = occData,
                                       dist_m = bufferDistance,
                                       output = 'sf')

  # rasterizing and making it into a mask
  buffer_rs <- fasterize::fasterize(buffer, sdm)
  buffer_rs[!is.na(buffer_rs[])] <- 1
  buffer_rs <- buffer_rs * sdmMask
  buffer_rs[is.na(buffer_rs)] <- 0
  gapMap <- sdmMask - buffer_rs
  vector <- append(vector,gapMap)
}

rasterListG <- lapply(X = vector, FUN = removeNA)

# Matches the extent of all rasters and adds them together
g_sum = sum_all(rasterListG, extend_all(rasterList))

#Replace all zeros with NA
g_sum[g_sum == 0]<-NA

try(qtm(g_sum), FALSE)
# check write Clause and output the file 
if(writeRasters==TRUE){
  try(raster::writeRaster(x = g_sum, filename = paste0(outputFolder, "/exsituGapMap_",Sys.Date() ,".tif"), overwrite=TRUE))
}

In Situ Conservation

The table below shows the in situ conservation summary. SRSin reports the proportion of occurrences which fall within protected areas (WDPA database 2019). GRSin analyzes how comprehensively protected areas cover the maxent model spatially. ERSin analyzes how well protected areas cover the maxent model with regard to ecosystems covered. All of the conservation metrics are on a scale from 0-100, with 0 = poor conservation and 100 = perfectly sufficient conservation. The final in situ conservation score is called FCSin and is a mean of the three in situ conservation scores.

DT::datatable(insituSummary)

GRSin

Map of the potential distribution, with distribution occurring within existing protected areas (WDPA 2019) highlighted.

Areas of Native Range = 0

Potential Distribution = 1

Protected Lands within the Potential Distribution = 2

proArea <- raster(system.file("data/protectedArea/wdpa_reclass.tif",
                                package = "gapAnalysisR"))
proArea <- raster::crop(x = proArea, y= r_sum)
#flip the values for 1 and NA 
proArea[proArea== 1] <- 2
proArea[is.na(proArea)] <- 1
proArea[proArea == 2] <- NA

inGapMap <- proArea * r_sum
try(qtm(inGapMap), FALSE)
# check write Clause and output the file 
if(writeRasters==TRUE){
  try(raster::writeRaster(x = inGapMap, filename = paste0(outputFolder, "/insituGapMap_",Sys.Date() ,".tif"), overwrite=TRUE))
}

Combined Summary

This table shows the combined ex situ and in situ conservation metrics. FCSc-mean is the final combined conservation score. We categorize taxa based on the final combined scores (0-25 = high priority (HP) for further conservation work; 25-50 medium (MP); 50-75 low (LP); and 75-100 sufficiently conserved (SC)