ENMeval (version 2.0.6)

evalplot.respCurve.dens: Response curve and density plots for one variable

Description

A wrapper function to plot response curves and density plot.

Usage

evalplot.respCurve.dens(
  mod,
  data,
  envs = NULL,
  var,
  fun = mean,
  type = c(1, 2),
  exp.curve = 0.025,
  nr.curve = 100,
  clamp.tails = TRUE,
  bw.envs = 10
)

Value

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

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 and density are plotted, with no transfer-environment comparison.

var

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

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`).

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) {
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")
# Transfer envs
tr_envs <- envs * 1.5
# Plot
mod <- e@models[[1]]
# Define data as combined training values with coordinates removed
data <- rbind(e@occs, e@bg)[,3:11]
evalplot.respCurve.dens(mod, data, envs = tr_envs, var = "bio1", fun = median)
# Plot training data only, without a transfer environment
evalplot.respCurve.dens(mod, data, var = "bio1", fun = median)
}