View source: R/generateSpFromPCA.R
generateSpFromPCA  R Documentation 
This functions generates a virtual species distribution by computing a PCA among environmental variables, and simulating the response of the species along the two first axes of the PCA. The response to axes of the PCA is determined with gaussian functions.
generateSpFromPCA(
raster.stack,
rescale = TRUE,
niche.breadth = "any",
axes = c(1, 2),
means = NULL,
sds = NULL,
pca = NULL,
sample.points = FALSE,
nb.points = 10000,
plot = TRUE
)
raster.stack 
a SpatRaster object, in which each layer represent an environmental variable. 
rescale 

niche.breadth 

axes 
a vector of values. Which axes would you like to keep in your PCA? At least 2 axes should be included (Only 1 axis currently not supported) 
means 
a vector containing as many numeric values as axes. Will be used to define the means of the gaussian response functions to the axes of the PCA. 
sds 
a vector containing as many numeric values as axes. Will be used to define the standard deviations of the gaussian response functions to the axes of the PCA. 
pca 
a 
sample.points 

nb.points 
a numeric value. Only useful if 
plot 

Online tutorial for this function
This function proceeds in 3 steps:
A PCA of environmental conditions is generated
Gaussian responses to the first two axes are computed
These responses are multiplied to obtain the final environmental suitability
If rescale = TRUE
, the final environmental suitability is rescaled
between 0 and 1,
with the formula (val  min) / (max  min).
The shape of gaussian responses can be randomly generated by the function or
defined manually by choosing
means
and sds
. The random generation is constrained
by the argument niche.breadth
, which controls the range of possible
standard deviation values. This range of values is based on
a fraction of the axis:
"any"
: the standard deviations can have values from 1% to
50% of axes' ranges. For example if the first axis of the PCA ranges from
5 to +5,
then sd values along this axis can range from 0.1 to 5.
"narrow"
: the standard deviations are limited between 1% and
10% of axes' ranges. For example if the first axis of the PCA ranges from
5 to +5,
then sd values along this axis can range from 0.1 to 1.
"wide"
: the standard deviations are limited between 10% and
50% of axes' ranges. For example if the first axis of the PCA ranges from
5 to +5,
then sd values along this axis can range from 1 to 5.
a list
with 3 elements:
approach
: the approach used to generate the species,
i.e., "pca"
details
: the details and parameters used to generate
the species
suitab.raster
: the virtual species distribution, as a
SpatRaster object containing the
environmental suitability
The structure of the virtualspecies object can be seen using str()
To perform the PCA, the function has to transform the raster into a matrix.
This may not be feasible if the raster is too large for the
computer's memory.
In this case, you should perform the PCA on a sample of your raster with
set sample.points = TRUE
and choose the number of points to sample
with
nb.points
.
Boris Leroy leroy.boris@gmail.com
with help from C. N. Meynard, C. Bellard & F. Courchamp
generateSpFromFun
to generate a virtual species with
the responses to each environmental variables.
# Create an example stack with four 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)))
names(env) < c("var1", "var2", "var3", "var4")
plot(env) # Illustration of the variables
# Generating a species with the PCA
generateSpFromPCA(raster.stack = env)
# The top part of the plot shows the PCA and the response functions along
# the two axes.
# The bottom part shows the probabilities of occurrence of the virtual
# species.
# Defining manually the response to axes
generateSpFromPCA(raster.stack = env,
means = c(2, 0),
sds = c(0.6, 1.5))
# This species can be seen as occupying intermediate altitude ranges of a
# conic mountain.
# Beyond the first two axes
generateSpFromPCA(raster.stack = env,
axes = c(1, 3))
sp < generateSpFromPCA(raster.stack = env,
axes = 1:3)
plotResponse(sp, axes = c(1, 2))
plotResponse(sp, axes = c(1, 3))
plotResponse(sp, axes = c(2, 3))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.