virtualspecies (version 1.6)

generateSpFromBCA: Generate a virtual species distribution from a Between Component Analysis of environmental variables

Description

A Between Component Analysis is similar to a PCA, except that two sets of environmental conditions (e.g. current and future) will be used. This function is useful to generate species designed to test the extrapolation capacity of models, e.g. for climate change extrapolations

Usage

generateSpFromBCA(
  raster.stack.current,
  raster.stack.future,
  rescale = TRUE,
  niche.breadth = "any",
  means = NULL,
  sds = NULL,
  bca = NULL,
  sample.points = FALSE,
  nb.points = 10000,
  plot = TRUE
)

Value

a list with 4 elements:

• approach: the approach used to generate the species, i.e., "bca"

• details: the details and parameters used to generate the species

• suitab.raster.current: the virtual species distribution, as a SpatRaster object containing the current environmental suitability

• suitab.raster.future: the virtual species distribution, as a SpatRaster object containing the future environmental suitability

The structure of the virtualspecies object can be seen using str()

Arguments

raster.stack.current

a SpatRaster object, in which each layer represent an environmental variable from the "current" time horizon.

raster.stack.future

a SpatRaster object, in which each layer represent an environmental variable from a "future" time horizon.

rescale

TRUE of FALSE. Should the output suitability raster be rescaled between 0 and 1?

niche.breadth

"any", "narrow" or "wide". This parameter defines how tolerant is the species regarding environmental conditions by adjusting the standard deviations of the gaussian functions. See details.

means

a vector containing two numeric values. Will be used to define the means of the gaussian response functions to the axes of the BCA.

sds

a vector containing two numeric values. Will be used to define the standard deviations of the gaussian response functions to the axes of the BCA.

bca

a bca object. You can provide a bca object that you already computed yourself with generateSpFromBCA

sample.points

TRUE of FALSE. If you have large raster files then use this parameter to sample a number of points equal to nb.points. However the representation of your environmental variables will not be complete.

nb.points

a numeric value. Only useful if sample.points = TRUE. The number of sampled points from the raster, to perform the PCA. A too small value may not be representative of the environmental conditions in your rasters.

plot

TRUE or FALSE. If TRUE, the generated virtual species will be plotted.

Author

Robin Delsol, Boris Leroy

Maintainer: Boris Leroy

Details

This function generates a virtual species distribution by computing a Between Component Analysis based on two different stacks of environmental variables. The response of the species is then simulated along the two first axes of the BCA with gaussian functions in the same way as in generateSpFromPCA.

A Between Component Analysis is used to separate two sets of environmental conditions. This function proceeds in 4 steps:

1. A Principal Component Analysis is generated based on both set of environmental conditions

2. A BCA of this PCA is generated using the function bca from package ade4. Note that at this step we choose one random point from raster.stack.future, and we use this single point as if it was a third set of environmental conditions for the BCA. This trick allows us to subtly change the shape of the bca in order to generate different types of conditions.

3. Gaussian responses to the first two axes are computed

4. 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.

If a bca object is provided, the output bca object will contain the new environments coordinates along the provided bca axes.

See Also

generateSpFromFun to generate a virtual species with the responses to each environmental variables.generateSpFromPCA to generate a virtual species with the PCA of environmental variables.

Examples

# Create two example stacks with four environmental variables each
a <- matrix(rep(dnorm(1:100, 50, sd = 25)), 
            nrow = 100, ncol = 100, byrow = TRUE)

env1 <- c(rast(a * dnorm(1:100, 50, sd = 25)),
          rast(a * 1:100),
          rast(a),
          rast(t(a)))
names(env1) <- c("var1", "var2", "var3", "var4")
plot(env1) # Illustration of the variables

b <- matrix(rep(dnorm(1:100, 25, sd = 50)), 
            nrow = 100, ncol = 100, byrow = TRUE)

env2 <- c(rast(b * dnorm(1:100, 50, sd = 25)),
          rast(b * 1:100),
          rast(b),
          rast(t(b)))

names(env2) <- c("var1", "var2", "var3", "var4")
plot(env2) # Illustration of the variables 

# Generating a species with the BCA

generateSpFromBCA(raster.stack.current = env1, raster.stack.future = env2)

# The left part of the plot shows the BCA and the response functions along
# the two axes.
# The top-right part shows environmental suitability of the virtual
# species in the current environment.
# The bottom-right part shows environmental suitability of the virtual
# species in the future environment. 


# Defining manually the response to axes

generateSpFromBCA(raster.stack.current = env1, raster.stack.future = env2,
           means = c(-2, 0),
           sds = c(0.6, 1.5))