evalplot.envSim.map {ENMeval}R Documentation

Similarity maps for partition groups

Description

Maps environmental similarity of reference partitions (occurrences or background) to all cells with values in the raster. This function uses raster data, and thus cannot map similarity values using only tables of environmental values for occurrences or background. Further, this function does not calculate similarity for categorical variables.

Usage

evalplot.envSim.map(
  e = NULL,
  envs,
  occs.z = NULL,
  bg.z = NULL,
  occs.grp = NULL,
  bg.grp = NULL,
  ref.data = "occs",
  sim.type = "mess",
  categoricals = NULL,
  envs.vars = NULL,
  bb.buf = 0,
  occs.testing.z = NULL,
  plot.bg.pts = FALSE,
  sim.palette = NULL,
  pts.size = 1.5,
  gradient.colors = c("red", "white", "blue"),
  na.color = "gray",
  return.tbl = FALSE,
  return.ras = FALSE,
  quiet = FALSE
)

Arguments

e

ENMevaluation object (optional)

envs

RasterStack: environmental predictor variables used to build the models in "e"; categorical variables should be removed before input, as they cannot be used to calculate MESS

occs.z

data frame: longitude, latitude, and environmental predictor variable values for occurrence records, in that order (optional); the first two columns must be named "longitude" and "latitude"

bg.z

data frame: longitude, latitude, and environmental predictor variable values for background records, in that order (optional); the first two columns must be named "longitude" and "latitude"

occs.grp

numeric vector: partition groups for occurrence records (optional)

bg.grp

numeric vector: partition groups for background records (optional)

ref.data

character: the reference to calculate MESS based on occurrences ("occs") or background ("bg"), with default "occs"

sim.type

character: either "mess" for Multivariate Environmental Similarity Surface, "most_diff" for most different variable, or "most_sim" for most similar variable; uses similarity function from package rmaxent

categoricals

character vector: names of categorical variables in input RasterStack or data frames to be removed from the analysis; these must be specified as this function was intended for use with continuous data only

envs.vars

character vector: names of a predictor variable to plot similarities for; if left NULL, calculations are done with respect to all variables (optional)

bb.buf

numeric: distance used to buffer (extend) the mapping extent in map units; for latitude/longitude, this is in degrees (optional)

occs.testing.z

data frame: longitude, latitude, and environmental predictor variable values for fully withheld testing records, in that order; this is for use only with the "testing" partition option when an ENMevaluation object is not input (optional)

plot.bg.pts

boolean: if TRUE, plot background points when using ref.data = "bg"

sim.palette

character: RColorBrewer palette name to use for plotting discrete variables; if NULL, default is "Set1"

pts.size

numeric: custom point size for ggplot

gradient.colors

character vector: colors used for ggplot2::scale_fill_gradient2

na.color

character: color used for NA values

return.tbl

boolean: if TRUE, return the data frames of similarity values used to make the ggplot instead of the plot itself

return.ras

boolean: if TRUE, return the RasterStack of similarity values used to make the ggplot instead of the plot itself

quiet

boolean: if TRUE, silence all function messages (but not errors)

Details

When fully withheld testing groups are used, make sure to input either an ENMevaluation object or the argument occs.testing.z. In the resulting plot, partition 1 refers to the training data, while partition 2 refers to the fully withheld testing group.

There are two variations for this plot. If "histogram", histograms are plotted showing the MESS estimates for each partition group. If "raster", rasters are plotted showing the geographical MESS estimates for each partition group. With sim.type option "mess", the similarity between environmental values associated with the validation occurrences (per partition group) and those associated with the entire study extent (specified by the extent of the input RasterStack "envs") are calculated, and the minimum similarity per grid is returned. Higher negative values indicate greater environmental difference between the validation occurrences and the study extent, and higher positive values indicate greater similarity. This function uses the 'similarity()' function to calculate the similarities. See the below reference for details on MESS.

Value

A ggplot of MESS calculations for data partitions.

References

Elith, J., Kearney, M., and Phillips, S. (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1: 330-342. doi: 10.1111/j.2041-210X.2010.00036.x


[Package ENMeval version 2.0.1 Index]