Plot Response Curve for Maxent Models

This function plots a response curve for a given environmental variable based on a maxent.jar or maxnet model. It allows plotting clamping on or off and supports multiple variables via a wrapper that combines plots using the patchwork package.

Usage

evalplot.respCurve(
  mod,
  data,
  envs = NULL,
  var,
  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 curve is plotted, with no transfer-environment comparison.

var

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

fun

If maxent.jar a function to compute constant values for other variables (default is `mean`). Maxnet models always use mean.

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 ggplot object of the response curve.

Details

The type 1 option (default) sets the focal variable to values along a range "r" from its minimum to maximum (buffered by exp.curve) while setting all other variables to static values defined by fun (which defaults to their means), then makes a model prediction for this table. The type 2 option sets the focal variable to one static value along r while keeping all other variables at their original values, makes a model predicton for this table, then repeats this process for all values along the range, resulting in 100 model predictions for an r of length 100. The final curve for type 2 plots the means of these prediction tables.

The original maxent.jar software and the dismo package implemented type 1 response curves, but the pdp package and the predicts package implement type 2, so the user can choose which to visualize in order to directly compare to one of these outputs.

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
# Plot
# Plot with clamp tails
mod <- e@models[[1]]
# Define data as combined training values with coordinates removed
data <- rbind(e@occs, e@bg)[,3:11]
# Plot
evalplot.respCurve(mod, data, envs = tr_envs, var = "bio1")
# Without tails
evalplot.respCurve(mod, data, envs = tr_envs, var = "bio1", clamp.tails = FALSE)
} # }