randPointsBatchExtract: Updates a list of random points with environmental data from...

In adamlilith/enmSdm: Tools for Modeling Niches and Distributions of Species

Updates a list of random points with environmental data from rasters

Description

This function is called using a list object typically generated using the randPointsBatch function. To each set of random points represented in that list it adds environmental data extracted from a raster stack or brick.

Usage

randPointsBatchExtract(rands, rast, verbose = FALSE)

Arguments

rands	A list object typically generated using the randPointsBatch function.
rast	A raster, raster stack, or raster brick from which to extract data.
verbose	Logical, if TRUE display progress.

Value

A list.

See Also

randPointsRespectingSelf, randPointsRespectingSelfOther1, randPointsRespectingSelfOther2, randPointsBatch, randPointsBatchSampled, randPointsBatchNicheOverlap

Examples

library(dismo)
library(raster)

data(lemurs, package='enmSdm')
longLat <- c('decimalLongitude', 'decimalLatitude')

mad <- raster::getData('GADM', country='MDG', level=0)
elev <- raster::getData('alt', country='MDG', mask=TRUE, res=2.5)

# plot data as-is
plot(mad)
species <- sort(unique(lemurs$species))

for (i in seq_along(species)) {
	
	thisLemur <- lemurs[lemurs$species == species[i], longLat]
	points(thisLemur, pch=i, col=i)
	
}

legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species))

# geographically thin presences of each species
thinLemurs <- data.frame()

for (i in seq_along(species)) {
	
	thisLemur <- lemurs[lemurs$species == species[i], ]
	thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat)
	thinLemurs <- rbind(thinLemurs, thinned)
	
}

# plot geographically thinned data
plot(mad)

for (i in seq_along(species)) {
	
	thisLemur <- thinLemurs[thinLemurs$species == species[i], longLat]
	points(thisLemur, pch=i, col=i)
	
}

legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species))

# randomize one species with respect to itself 
x <- thinLemurs[thinLemurs$species == 'Eulemur fulvus', longLat]

set.seed(123)
x1rand <- randPointsRespectingSelf(x=x, rast=elev, tol=24000, verbose=TRUE)

# plot observed and randomized occurrences
plot(mad)
points(x, pch=16)
points(x1rand, col='red')

# randomize two species with respect to selves and others
species1 <- species[1]
species2 <- species[3]

x1 <- thinLemurs[thinLemurs$species == species1, longLat]
x2 <- thinLemurs[thinLemurs$species == species2, longLat]

set.seed(123)
tol1 <- tol2 <- tol12 <- 16000
x12rand <- randPointsRespectingSelfOther2(x1=x1, x2=x2, rast=elev,
	tol1=tol1, tol2=tol2, tol12=tol12, verbose=TRUE)

# plot geographically thinned data
plot(mad)
points(x1, pch=21, bg='cornflowerblue')
points(x2, pch=24, bg='cornflowerblue')
points(x12rand$x1rand, pch=1, col='red')
points(x12rand$x2rand, pch=2, col='red')

legend('bottomleft', legend=c(species1, species2,
	legend=paste('rand', species1), paste('rand', species2)),
	pch=c(21, 24, 1, 2), col=c('black', 'black', 'red', 'red'),
	pt.bg=c('cornflowerblue', 'cornflowerblue', NA, NA))

### batch mode
## Not run: 

# download climate data
clim <- raster::getData('worldclim', var='bio', res=2.5)

# lemur data
data(lemurs, package='enmSdm')
longLat <- c('decimalLongitude', 'decimalLatitude')

# geographically thin presences of each species
thinLemurs <- data.frame()

for (i in seq_along(species)) {
	
	thisLemur <- lemurs[lemurs$species == species[i], ]
	thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat)
	thinLemurs <- rbind(thinLemurs, thinned)
	
}

# randomize two species with respect to selves and others
species1 <- species[1]
species2 <- species[3]

x1 <- thinLemurs[thinLemurs$species == species1, longLat]
x2 <- thinLemurs[thinLemurs$species == species2, longLat]

# create null distributions
set.seed(123)
tol1 <- tol2 <- tol12 <- 24000
iterations <- 100 # for analysis set this to 100 or more
# for testing use a small number!

x12rand <- randPointsBatch('randPointsRespectingSelfOther2', x1=x1, x2=x2,
	rast=clim[[1]], tol1=tol1, tol2=tol2, tol12=tol12, iterations=iterations,
	verbose=TRUE)

# get environment that was sampled to use as background
bg <- randPointsBatchSampled(x12rand)
bgEnv <- raster::extract(clim, bg)

# create PCA of environmental space
vars <- paste0('bio', 1:19)
bgPca <- princomp(bgEnv[ , vars], cor=TRUE)

x1env <- raster::extract(clim, x1)
x2env <- raster::extract(clim, x2)

nas1 <- omnibus::naRows(x1env)
nas2 <- omnibus::naRows(x2env)

if (length(nas1) > 0) x1env <- x1env[-nas1, ]
if (length(nas2) > 0) x2env <- x2env[-nas2, ]

# observed niche overlap
obsOverlap <- enmSdm::nicheOverlap(
	x1=x1env,
	x2=x2env,
	env=bgPca,
	vars=vars,
	bins=100,
	cor=TRUE
)

# extract climate at randomized sites
x12rand <- randPointsBatchExtract(x12rand, clim, verbose=TRUE)

# null niche overlap
nullOverlap <- randPointsBatchNicheOverlap(
	rands=x12rand,
	env=bgPca,
	vars=vars,
	bins=100,
	cor=TRUE
)

hist(nullOverlap$d, 20, main='Niche Overlap',
	xlab='Schoener\'s D', xlim=c(0, 1))
abline(v=obsOverlap[['d']], col='blue', lwd=3)
legend('topright', legend='Observed', lwd=3, col='blue')


## End(Not run)

adamlilith/enmSdm documentation built on Jan. 6, 2023, 11 a.m.