evalplot.densities: Density plots for All Variables with Shared Y-Axis

In ENMeval: Automated Tuning and Evaluations of Ecological Niche Models

Density plots for All Variables with Shared Y-Axis

Description

A wrapper function to plot response curves for all contributing variables and combine them using patchwork. The plots share a common y-axis label.

Usage

evalplot.densities(data, envs = NULL, vars = NULL, bw.envs = 10)

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 densities are plotted, with no transfer-environment comparison.
vars	Vector specifying the variable names for the response curve. Default is all variables (from 'envs' if supplied, otherwise from 'data').
bw.envs	The smoothing bandwidth to be used in the environmental variables

Value

A combined patchwork plot of all response curves with a shared y-axis label.

Author(s)

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

## Not run: 
occs <- read.csv(file.path(system.file(package="predicts"), "/ex/bradypus.csv"))[,2:3]
envs <- 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 <- 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@occs, e@bg)[,3:11]
# Plot
evalplot.densities(data, envs = tr_envs)
# Plot training data only, without a transfer environment
evalplot.densities(data)

## End(Not run)

ENMeval documentation built on Sept. 11, 2026, 5:08 p.m.