create_env_nsets: Generate multiple sets of related environmental variables.

In charliem2003/sdmProfiling: A Spatially-explicit Species Distribution Model Evaluation Tool

Generate multiple sets of related environmental variables.

Description

Essentially a wrapper around create_env_set. Each set of environmental variables (can be just 1 variable) that are related but have some random variation between them. The first variable is generated using unconditional Gaussian simulation using a variogram model with the specified model and psill parameter, using vgm from the gstat package, plus a random range value extracted using the specified distribution. New conditional Gaussian simulations are then made for subsequent variables using a random subsample of cells from the first variable plus a new random range value. Each set starts from a newly generated base variables.

Usage

create_env_nsets(
  cellDims = c(100, 100),
  sets = c(5, 4, 3, 1),
  model = "Sph",
  psill = 1.5,
  dep1 = 1,
  rangeFun = "vrangeFun",
  propSamp = 0.25
)

Arguments

cellDims	vector containing variable dimensions (n cell width, n cell height)
sets	a vector with the number of variables for each set (generated by create_env_set). Each set is generated from a new randomly generated base environmental variable.
model	model for variogram; default = "Sph" (see vgm for options)
psill	(partial) sill of the variogram model; default = 1.5 (see vgm for options)
dep1	multiplied by the psill in submodels (default = 1)
rangeFun	name of function, or specified function, to generate random number for the range parameter of the variogram model. E.g. function() exp(runif(1, 1, 6))
propSamp	proportion of cells to sample for each submodel (default = 0.5)

Details

The model and psill are kept constant across all variables, but a new range value is drawn from the specified distribution each time. The distribution is specified using a user-generated function. The user can either input the name of the function or define the function as the argument.

Subsequent variables are generated based on a random sample of the 1st variable. The number of cells selected is defined by propsamp; the larger the value the more similar the new variable will be to the original. The psill and model type of the variogram is kept constant across all new variables, although the psill can be altered using the parameter dep1, which is multiplied by the psill of the first variable (must be >0 and <2; default = 1).

Value

A raster stack of environmental variables. For each variable all values are standardised between 0 and 1. The name of each raster relates to the number of the variable set, and then the number of the variable within that set.

Author(s)

Charlie Marsh ([e-mail]) & Yoni Gavish, based on the original code from http://santiago.begueria.es/2010/10/generating-spatially-correlated-random-fields-with-r/

References

Variations on this method have been used to generate virtual species in:

Gavish, Y., Marsh, C.J., Kuemmerlen, M., Stoll, S., Haase, P., Kunin, W.E., 2017. Accounting for biotic interactions through alpha-diversity constraints in stacked species distribution models. Methods in Ecology and Evolution 8, 1092–1102. https://doi.org/10.1111/2041-210X.12731

Marsh, C.J., Gavish, Y., Kunin, W.E., Brummitt, N.A., 2019. Mind the gap: Can downscaling Area of Occupancy overcome sampling gaps when assessing IUCN Red List status? Diversity and Distributions 025, 1832–1845. https://doi.org/10.1111/ddi.12983

Examples

### generate 3 sets of related variables.
### set1 = 4 variables; set 2 = 3 variables; set 3 = 1 variable

# the sampling distribution for range can either be a saved function
vrangeFun <- function() exp(runif(1, 1, 6))

envSet <- create_env_nsets(cellDims = c(100, 100),
                           sets     = c(4, 3, 1),
                           model    = "Sph",
                           psill    = 1.5,
                           dep1     = 1,
                           rangeFun = vrangeFun,
                           propSamp = 0.25)

# or we can define the function within the argument itself
envSet <- create_env_nsets(cellDims = c(100, 100),
                           sets     = c(4, 3, 1),
                           model    = "Sph",
                           psill    = 1.5,
                           dep1     = 1,
                           rangeFun = function() 100 + runif(1, -100, 100),
                           propSamp = 0.05)

# the output is a raster stack which can be plotted
plot(envSet)

charliem2003/sdmProfiling documentation built on June 13, 2022, 4:43 a.m.