In Pakillo/Carex.bipolar: SDM analyses of bipolar Carex species

library(knitr)
opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, out.width = '100%')
root <- rprojroot::find_rstudio_root_file()

library(Carex.bipolar)
library(readr)
library(dplyr)
library(ENMeval)
library(rSDM)

Load data

locs <- as.data.frame(read_csv(file.path(root, "data/locs_30m.csv")))
bioclim <- as.data.frame(read_csv(file.path(root, "data/bioclim_pres_30m.csv")))

Select only occurrences of this species (defined in makefile):

species <- "canescens"
if (species != "allspp") locs <- locs[locs$species == species, ]

Load present bioclim rasters

bioclim.pres <- read_presclim()

Map

rSDM::occmap(locs, ras = bioclim.pres[[1]], main = "Occurrences")  # Map

ENMeval

modeval <- ENMevaluate(occ = locs[, c("longitude", "latitude")], 
                 env = bioclim.pres, 
                 RMvalues = c(0.5, 1, 1.5, 2), 
                 fc = c("LQ", "LQH"), 
                 method = "randomkfold", kfolds = 10, 
                 clamp = TRUE, 
                 rasterPreds = TRUE,
                 parallel = TRUE, numCores = 4)

results <- modeval@results
kable(results)
eval.plot(results)
maps <- modeval@predictions
plot(maps)
plot(maps[[which(results$delta.AICc == 0)]], main = "Models with lowest AICc")

for (i in which(results$delta.AICc == 0)) {
  response(modeval@models[[i]])
}

save(modeval, file = paste0(root, "/analyses/output/fullspp_ENMeval/", species, "_modeval.rda"))

rm(list = ls())

devtools::session_info()

Pakillo/Carex.bipolar documentation built on July 27, 2020, 2:09 p.m.