randPointsBatchNicheOverlap: Niche overlap for a set of iterated "randPointsRespecting~"...

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

Niche overlap for a set of iterated "randPointsRespecting~" functions

Description

This function is called using a list object typically generated using the randPointsBatchExtract function (plus sometimes followed by the randPointsBatchExtract and randPointsBatchSampled functions). It calculates niche overlap for each set of points. Essentially it is a wrapper for nicheOverlap.

Usage

randPointsBatchNicheOverlap(rands, env, vars, x = NULL, bins = 100, cor = TRUE)

Arguments

rands	A list object typically generated using the randPointsBatchExtract function.
env	Either a data frame, matrix, or any object that can be coerced to a data frame containing environmental data at available background sites, or an object of class princomp representing a principal components analysis generated using the princomp function with argument scores = TRUE.
vars	Either a character list naming columns in x1, x2, and x3 to be used as environmental data, or positive integers indexing the columns to be used as environmental data.
x	Either NULL (default) or a data frame, matrix, SpatialPointsDataFrame, or other object that can be coerced to a data frame. If supplied then the objects (usually species) represented in rands are compared to this set of environmental values. This argument must be supplied if rands was generated using randPointsRespectingSelf or randPointsRespectingSelfOther1.
bins	Number of bins into which to divide the environmental space (default is 100 on each side).
cor	Logical, if TRUE (default), then the PCA used to construct the environmental space will use the correlation matrix (this is highly recommended if the variables are on different scales). This is ignored if env is an object of class princomp.

Value

Data frame.

See Also

randPointsRespectingSelf, randPointsRespectingSelfOther1, randPointsRespectingSelfOther2, randPointsBatch, randPointsBatchSampled, randPointsBatchExtract

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.