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

library(knitr)
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
root <- rprojroot::find_rstudio_root_file()

library(raster)

natearth <- brick(unzip("C:/Users/FRS/Dropbox/GIS.layers/NaturalEarth/CrossBlended_Hypso_ShadedRelief_50m.zip", exdir = tempdir())[4])
ne <- crop(natearth, extent(c(-175, -15, -60, 90)))
ne.redux <- aggregate(ne, fact = 8)

data(regions)
#plot(regions, add = TRUE, border = "black")

locs <- as.data.frame(readr::read_csv(paste0(root, "/data/locs_30m.csv")))
coordinates(locs) <- c("longitude", "latitude")
projection(locs) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
#plot(locs, add = TRUE)
# rSDM::occmap(locs[locs$species == "macloviana", ])
# rSDM::occmap(locs[locs$species == "maritima", ])

library(RColorBrewer)
mypal <- brewer.pal(5, "Set1")

pdf("figures/Fig1.pdf", paper = "a4")
#svglite::svglite("manuscript/figures/Fig1.svg", width = 9, height = 11)
plotRGB(ne.redux)
plot(regions, add = TRUE, border = "dim gray", lwd = 2)
#spp <- unique(locs$species)
spp <- c("maritima", "microglochin", "canescens", "magellanica", "macloviana")
for (i in 1:5) {
  points(locs[locs$species == spp[i], ], pch = 21, bg = mypal[i], col = "gray20", cex = 1)
}
legend(x = -160, y = 10, legend = spp, pt.bg = mypal,
       pch = 21, box.col = "white", title = "Carex species", bg = "white")
dev.off()

library(ggplot2)
library(mapr)
occs <- locs[, c("species")]
names(occs) <- "name"
basemap <- map_ggmap(occs, zoom = 2, size = 2)
basemap + xlim(-165, -22) + ylim(-50, 95)