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Add a control to a BiodiversityModel object to control extrapolation

Source: R/add_control_extrapolation.R
add_control_extrapolation.Rd

One of the main aims of species distribution models (SDMs) is to project in space and time. For projections a common issue is extrapolation as - unconstrained - SDMs can indicate areas as suitable which are unlikely to be occupied by species or habitats (often due to historic or biotic factors). To some extent this can be related to an insufficient quantification of the niche (e.g. niche truncation by considering only a subset of observations within the actual distribution), in other cases there can also be general barriers or constraints that limit any projections (e.g. islands). This control method adds some of those options to a model distribution object. Currently supported methods are:

* "zones" - This is a wrapper to allow the addition of zones to a distribution model object, similar to what is also possible via distribution(). Required is a spatial layer that describes a environmental zoning.

* "mcp" - Rather than using an external or additional layer, this option constraints predictions by a certain distance of points in its vicinity. Buffer distances have to be in the unit of the projection used and can be configured via "mcp_buffer".

* "nt2" - Constraints the predictions using the multivariate combination novelty index (NT2) following Mesgaran et al. (2014). This method is also available in the similarity() function.

* "shape" - This is an implementation of the 'shape' method introduced by Velazco et al. (2023). Through a user defined threshold it effectively limits model extrapolation so that no projections are made beyond the extent judged as defensible and informed by the training observations.

See also details for further explanations.

Usage

add_control_extrapolation(
  x,
  layer,
  method = "mcp",
  mcp_buffer = 0,
  novel = "within",
  limits_clip = FALSE
)

# S4 method for BiodiversityDistribution
add_control_extrapolation(
  x,
  layer,
  method = "mcp",
  mcp_buffer = 0,
  novel = "within",
  limits_clip = FALSE
)

Arguments

x

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

layer

A SpatRaster or sf object that limits the prediction surface when intersected with input data (Default: NULL).

method

A character vector describing the method used for controlling extrapolation. Available options are "zones", "mcp" (Default), or "nt2" or "shape".

mcp_buffer

A numeric distance to buffer the mcp (Default 0). Only used if "mcp" is used.

novel

Which conditions are to be masked out respectively, either the novel conditions within only "within" (Default) or also including outside reference conditions "outside". Only use for method = "nt2", for method = "mess" this variable is always "within".

limits_clip

logical Should the limits clip all predictors before fitting a model (TRUE) or just the prediction (FALSE, default).

Value

Adds extrapolation control option to a distribution object.

Details

For method "zones" a zoning layer can be supplied which is then used to intersect the provided training points with. Any projections made with the model can then be constrained so as to not project into areas that do not consider any training points and are unlikely to have any. Examples for zones are for the separation of islands and mainlands, biomes, or lithological soil conditions.

If no layer is available, it is also possible to constraint predictions by the distance to a minimum convex polygon surrounding the training points with method "mcp" (optionally buffered). This can make sense particular for rare species or those fully sampled across their niche.

For the "NT2" and "MESS" index it is possible to constrain the prediction to conditions within (novel = "within") or also include outside (novel = "outside") conditions.

Note

The method "zones" is also possible directly within distribution().

References

  • Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M., & Guisan, A. (2006). Are niche‐based species distribution models transferable in space?. Journal of biogeography, 33(10), 1689-1703. https://doi.org/10.1111/j.1365-2699.2006.01466.x

  • Chevalier, M., Broennimann, O., Cornuault, J., & Guisan, A. (2021). Data integration methods to account for spatial niche truncation effects in regional projections of species distribution. Ecological Applications, 31(7), e02427. https://doi.org/10.1002/eap.2427

  • Velazco, S. J. E., Brooke, M. R., De Marco Jr., P., Regan, H. M., & Franklin, J. (2023). How far can I extrapolate my species distribution model? Exploring Shape, a novel method. Ecography, 11, e06992. https://doi.org/10.1111/ecog.06992

  • Mesgaran, M. B., R. D. Cousens, B. L. Webber, and J. Franklin. (2014) Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions 20:1147-1159.

Examples

if (FALSE) {
 # To add a zone layer for extrapolation constraints.
 x <- distribution(background) |>
   add_predictors(covariates) |>
   add_control_extrapolation(method = "zones", layer = zones)
}