ENMeval (version 2.0.6)

evalplot.density: Plot Density Plots of variables

Description

This function plots densities of a given environmental variable based on a maxent.jar or maxnet model.

Usage

evalplot.density(data, envs = NULL, var, bw.envs = 10)

Value

A ggplot object of the response curve.

Arguments

data

Data frame of training data (occurrences + background).

envs

Raster data (SpatRaster) of environmental variables for model projection. If `NULL` (default), only the training-data density is plotted, with no transfer-environment comparison.

var

A character string specifying the variable name for the response curve.

bw.envs

The smoothing bandwidth to be used in the environmental variables

Author

Gonzalo E. Pinilla-Buitrago

References

Pinilla-Buitrago, G.E., Kass, J.M., & Anderson, R.P. (2026). Extrapolation strategy matters when transferring ecological niche models: new visualization tools for informed decisions. Ecography, e08590. https://doi.org/10.1002/ecog.08590

Examples

if (FALSE) {
library(ENMeval)
occs <- read.csv(file.path(system.file(package="predicts"), "/ex/bradypus.csv"))[,2:3]
envs <- terra::rast(list.files(path=paste(system.file(package="predicts"), "/ex", sep=""),
                        pattern="tif$", full.names=TRUE))
# No biome
envs <- envs[[!(names(envs) %in% "biome")]]
occs.z <- cbind(occs, terra::extract(envs, occs, ID = FALSE))
bg <- as.data.frame(predicts::backgroundSample(envs, n = 10000))
names(bg) <- names(occs)
bg.z <- cbind(bg, terra::extract(envs, bg, ID = FALSE))
os <- list(abs.auc.diff = FALSE, pred.type = "cloglog", validation.bg = "partition")
ps <- list(orientation = "lat_lat")
e.maxnet <- ENMevaluate(occs, envs, bg,
                       tune.args = list(fc = "LQ", rm = 1),
                        partitions = "block", other.settings = os, partition.settings = ps,
                        algorithm = "maxnet", overlap = TRUE)
# Transfer envs
tr_envs <- envs * 1.5
# Define data as combined training values with coordinates removed
data <- rbind(e.maxnet@occs, e.maxnet@bg)[,3:11]
# Plot
evalplot.density(data, envs = tr_envs, var = "bio5")
# Plot training data only, without a transfer environment
evalplot.density(data, var = "bio5")
}