Downloading bioclimatic variables from Worldclim.org

install.packages('dismo')
install.packages('rgdal')
library(dismo)
library(rgdal)

Set working directory to save bioclim data

setwd('Set/your/working/directory')

When downloading Worldclim data through R, at the 0.5 resolution (30 Arc-seconds- the highest available)

you can only download data by tile. For our study region, we need to download four tiles, then

merge them into one large rasterstack, then crop by the extent needed.

Download each tile for Eastern North America. Be sure to plot one raster from each stack to take a look
at where each one is. Rasterstacks can be subset using square brackets . i.e., plot(env[[1]])
env1<-getData(name="worldclim", var="bio", res=0.5, download=T, lat=36, lon=-78)
env2<-getData(name="worldclim", var="bio", res=0.5, download=T, lat=36, lon=-100)
env3<-getData(name="worldclim", var="bio", res=0.5, download=T, lat=20, lon=-78)
env4<-getData(name="worldclim", var="bio", res=0.5, download=T, lat=20, lon=-100)

Merge the four tiles into one rasterstack called envall (raster functions use the Raster package, which is included in dismo)

env12<-merge(env1, env2)
enva123<-merge(env12, env3)
envall<-merge(env123, env4)

Cropping
# Define the extent of the study region we want
e<-extent(c(-103.057, -51.625, 15.139, 52.582))
# crop bioclim data to this extent
Bio<-crop(envall, e)

Load occurrences and check on an environmental layer

#### Load the occurrence localities from GBIF, and plot them on this map then save these rasters as GeoTiffs for future use.
Locs<-readOGR("Set/your/working/directory", "Locs")
##### Add these points on top of a bio layer (using plot(Bio[[1]], or similar command).
points(Locs)

Save environmental data as Geotiffs

#### Save these rasters as GeoTiffs. For this we need to use a for loop to iterate through the layers
## of the rasterstack, saving each one with the correct name and extension.
#First, set the working directory to where you want to save the rasters
setwd('Set/your/working/directory'')
for (i in 1:nlayers(Bio)){
  # writeRaster is a function that will write each layer with the correct name. As the 
  # loop iterates through, each layer in turn becomes "i". For the names, we will
  # use the names from any of the rasterstacks we downloaded from worldclim
  writeRaster(Bio[[i]], filename = names(env1[[i]]), format='GTiff')
}

## Load rasters back into R. For pattern, the \\ tells R that anything can come before the pattern,
# and the $ tells R that this pattern should only be at the end of the file's name.
bio<-stack(list.files(path = 'Set/your/working/directory', pattern = '\\.tif$', full.names = T))

In R, manipulating rasters is very easy.

# Take a look at at the first few values of Bio1:
names(bio[[1]])
head(values(bio[[1]]), 100)
# Remember, temperature values are multiplied by 10 to efficiently store the data (floats are larger). Divide this raster by 10 to see
# the real values for Mean Annual Temperature.
real.temps<-bio[[1]]/10
head(values(real.temps), 100)