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Add a range of a species as predictor to a distribution object

Source: R/add_predictors.R
add_predictor_range.Rd

This function allows to add a species range which is usually drawn by experts in a separate process as spatial explicit prior. Both sf and SpatRaster-objects are supported as input.

Users are advised to look at the "bossMaps" R-package presented as part of Merow et al. (2017), which allows flexible calculation of non-linear distance transforms from the boundary of the range. Outputs of this package could be added directly to this function. Note that this function adds the range as predictor and not as offset. For this purpose a separate function add_offset_range() exists.

Additional options allow to include the range either as "binary" or as "distance" transformed predictor. The difference being that the range is either directly included as presence-only predictor or alternatively with a linear distance transform from the range boundary. The parameter "distance_max" can be specified to constrain this distance transform.

Usage

add_predictor_range(
  x,
  layer,
  method = "distance",
  distance_max = NULL,
  fraction = NULL,
  priors = NULL
)

# S4 method for BiodiversityDistribution,SpatRaster
add_predictor_range(
  x,
  layer,
  method = "precomputed_range",
  fraction = NULL,
  priors = NULL
)

# S4 method for BiodiversityDistribution,sf
add_predictor_range(
  x,
  layer,
  method = "distance",
  distance_max = Inf,
  fraction = NULL,
  priors = NULL
)

Arguments

x

distribution() (i.e. BiodiversityDistribution) object.

layer

A sf or SpatRaster object with the range for the target feature.

method

character describing how the range should be included ("binary" | "distance").

distance_max

Numeric threshold on the maximum distance (Default: NULL).

fraction

An optional SpatRaster object that is multiplied with digitized raster layer. Can be used to for example to remove or reduce the expected value (Default: NULL).

priors

A PriorList object. Default is set to NULL which uses default prior assumptions.

References

  • Merow, C., Wilson, A. M., & Jetz, W. (2017). Integrating occurrence data and expert maps for improved species range predictions. Global Ecology and Biogeography, 26(2), 243–258. https://doi.org/10.1111/geb.12539

Examples

if (FALSE) {
distribution(background) |>
  add_predictor_range(range, method = "distance", distance_max = 2)
}