Fast species distribution model

This function computes species distribution models using two modelling algorithms: generalized linear models, and maximum entropy (only if rJava is available). Note: this is an experimental function, and may change in the future.

sdm(
  x,
  layers = NULL,
  pol = NULL,
  thin = TRUE,
  thin.size = 500,
  algorithm = "all",
  size = 50,
  width = 50000,
  mask = FALSE,
  predictors,
  background = NULL
)

Arguments

x

A dataframe containing the species occurrences and geographic coordinates. Column 1 labeled as "species", column 2 "lon", column 3 "lat".

layers

A SpatRaster of predictor variables for fitting species distribution models from species occurrences.

pol

A vector polygon specifying the calibration area or boundary to account for a more realistic dispersal capacity and ecological limitation of a species. If NULL, the extent of input points is used.

thin

Whether to spatially thin occurrences

thin.size

The size of the thin occurrences.

algorithm

Character. The choice of algorithm to run the species distribution model. For now, the available algorithms include:

• “all”: Calls all available algorithms: both GLM and MAXENT.

• “GLM”: Calls only Generalized linear model.

• “MAXENT”: Calls only Maximum entropy.

size

Minimum number of points required to successfully run a species distribution model especially for species with few occurrences.

width

Width of buffer in meter if x is in longitude/latitude CRS.

mask

logical. Should layers be used to mask? Only used if pol is a SpatVector.

predictors

If predicting to new time points, the climate layers for the time points.

background

A dataframe of background points, specifying 2 columns with long lat or x and y as nulls for species distribution modeling, often using a vector of probability weights.

Value

A list with the following objects:

• ensemble_raster The ensembled raster that predicts the potential species distribution based on the algorithms selected.

• data The dataframe of occurrences used to implement the model.

• polygon Map polygons of the predicted distributions analogous to extent-of-occurrence range polygon.

• indiv_models Raster layers for the separate models that predict the potential species distribution.

References

Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., Fandos, G., Feng, X., Guillera‐Arroita, G., Guisan, A., Lahoz‐Monfort, J.J., Leitão, P.J., Park, D.S., Peterson, A.T., Rapacciuolo, G., Schmatz, D.R., Schröder, B., Serra‐Diaz, J.M., Thuiller, W., Yates, K.L., Zimmermann, N.E. and Merow, C. (2020), A standard protocol for reporting species distribution models. Ecography, 43: 1261-1277.

Examples

# \donttest{
# get predictor variables
library(predicts)
f <- system.file("ex/bio.tif", package="predicts")
preds <- rast(f)
#plot(preds)

# get species occurrences
b <- file.path(system.file(package="predicts"), "ex/bradypus.csv")
d <- read.csv(b)

# fit ensemble model for four algorithms
# m <- sdm(d, layers = preds, predictors = preds, algorithm = "all")
# plot(m$ensemble_raster)
# plot(m$polygon, add=TRUE)
# }