Plot Response Curves for All Variables with Shared Y-Axis

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.respCurves(
  mod,
  data,
  envs = NULL,
  fun = mean,
  type = c(1, 2),
  exp.curve = 0.025,
  nr.curve = 100,
  clamp.tails = TRUE
)

Arguments

mod

A maxent.jar or maxnet model object.

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 response curves are plotted, with no transfer-environment comparison.

fun

A function to compute constant values for other variables (default is `median`).

type

Number (1 or 2) to specify type of response curve to plot. See details for explanation.

exp.curve

Numeric value indicating the range expansion for plotting (default is 0.025).

nr.curve

Integer specifying the number of points for the response curve (default is 100).

clamp.tails

Logical; if `TRUE`, clamping tails in plot (default is `TRUE`).

Value

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

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

Author

Gonzalo E. Pinilla- Buitrago

Examples

if (FALSE) { # \dontrun{
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 <- 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
mod <- e@models[[1]]
# Define data as combined training values with coordinates removed
data <- rbind(e@occs, e@bg)[,3:11]
# Plot
evalplot.respCurves(mod, data, envs = tr_envs)
# Plot training data only, without a transfer environment
evalplot.respCurves(mod, data)
} # }