Macroecology
Julius-Maximilians-Universität Würzburg
NOTE: Load the workspace (RData file) from the previous exercise. It has the objects for this exercise
Cargar los siguientes paquetes:
library(terra)
library(sp)
Cargar las variables ambientales
aet <- rast("exercises_data/AET.bil")
#Check if they are "projected"
crs(aet)
#If they were not, without a geographic reference, we have to define it (the default, geographic coordinates)
crs(aet) <- "epsg:4326"
Crop the raster of AET to the exten of our study domain (the Americas, created before at 1º resolution)
aet_amer <- crop(aet,ext(amer_ras))
Aggregate the values to a larger resolution
aet_amer1 <- aggregate(aet_amer,2)
AET values in the ocean are 255, we need to transform them to NA
aet_amer1_vals <- values(aet_amer1)
aet_amer1_vals <- ifelse(aet_amer1_vals==255,NA,aet_amer1_vals)
aet_amer1_nas <- aet_amer1
values(aet_amer1_nas) <- aet_amer1_vals
Get the coordinates for the bat species richness raster
bats_ras_coords <- xyFromCell(bats_rast, 1:length(values(bats_rast)))
Get the values of AET for the sites (gridcells) where we have bats. Change NAs for 0s
bats_ras_aet <- extract(aet_amer1_nas,bats_ras_coords)
bats_ras_rich <- values(bats_rast)
bats_ras_rich[is.na(bats_ras_rich)] <- 0
bats_ras_aet[is.na(bats_ras_aet)] <- 0
#Correlative approach: spp richness ~ environment Correlation between bat species richness and AET
cor(bats_ras_aet[,1], bats_ras_rich)
cor.test(bats_ras_aet[,1], bats_ras_rich)
#Consider spatial autocorrelation
library(SpatialPack)
modified.ttest(bats_ras_aet[,1], bats_ras_rich, bats_ras_coords)
Repeat the correlations for the different resolutions of our previous exercise
How do they look? Are there any differences among scales? Which ones?