# Create an example stack with six environmental variables
a <- matrix(rep(dnorm(1:100, 50, sd = 25)),
nrow = 100, ncol = 100, byrow = TRUE)
env <- c(rast(a * dnorm(1:100, 50, sd = 25)),
rast(a * 1:100),
rast(a * logisticFun(1:100, alpha = 10, beta = 70)),
rast(t(a)),
rast(exp(a)),
rast(log(a)))
names(env) <- paste("Var", 1:6, sep = "")
# More than 6 variables: by default a PCA approach will be used
sp <- generateRandomSp(env)
# limiting the distribution to a specific extent
limit <- ext(1, 50, 1, 50)
limitDistribution(sp, area = limit)
# Example of a raster of habitat patches
habitat.raster <- setValues(sp$pa.raster,
sample(c(0, 1), size = ncell(sp$pa.raster),
replace = TRUE))
plot(habitat.raster) # 1 = suitable habitat; 0 = unsuitable habitat
sp <- limitDistribution(sp, geographical.limit = "raster", area = habitat.raster)
par(mfrow = c(2, 1))
plot(sp$pa.raster)
plot(sp$occupied.area) # Species could not occur in many cells because
# habitat patches were unsuitable
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