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Add a control to a BiodiversityModel object to limit extrapolation
Source:R/add_limits_extrapolation.R
add_limits_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 limit 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.
* "mess"
- Constraints the predictions using the
Multivariate Environmental Similarity Surfaces (MESS) 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. Not yet implemented!
See also details for further explanations.
Usage
add_limits_extrapolation(
x,
layer,
method = "mcp",
mcp_buffer = 0,
novel = "within",
limits_clip = FALSE
)
# S4 method for class 'BiodiversityDistribution'
add_limits_extrapolation(
x,
layer,
method = "mcp",
mcp_buffer = 0,
novel = "within",
limits_clip = FALSE
)
Arguments
- x
distribution()
(i.e.BiodiversityDistribution
) object.- layer
A
SpatRaster
orsf
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"
,"mess"
or"shape"
.- mcp_buffer
A
numeric
distance to buffer the mcp (Default0
). 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 formethod = "nt2"
, formethod = "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 limit 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) { # \dontrun{
# To add a zone layer for extrapolation constraints.
x <- distribution(background) |>
add_predictors(covariates) |>
add_limits_extrapolation(method = "zones", layer = zones)
} # }