Data from: How far can I extrapolate my species distribution model? Exploring Shape, a novel method

Species distribution and ecological niche models (hereafter SDMs) are
popular tools with broad applications in ecology, biodiversity
conservation, and environmental science. Many SDM applications require
projecting models in environmental conditions non-analog to those used for
model training (extrapolation), giving predictions that may be
statistically unsupported and biologically meaningless. We introduce a
novel method, Shape, a model-agnostic approach that calculates the
extrapolation degree for a given projection data point by its multivariate
distance to the nearest training data point. Such distances are
relativized by a factor that reflects the dispersion of the training data
in environmental space. Distinct from other approaches, Shape incorporates
an adjustable threshold to control the binary discrimination between
acceptable and unacceptable extrapolation degrees. We compared Shape’s
performance to five extrapolation metrics based on their ability to detect
analog environmental conditions in environmental space and improve SDMs
suitability predictions. To do so, we used 760 virtual species to define
different modeling conditions determined by species niche tolerance,
distribution equilibrium condition, sample size, and algorithm. All
algorithms had trouble predicting species niches. However, we found a
substantial improvement in model predictions when model projections were
truncated independently of extrapolation metrics. Shape’s performance was
dependent on extrapolation threshold used to truncate models. Because of
this versatility, our approach showed similar or better performance than
the previous approaches and could better deal with all modeling conditions
and algorithms. Our extrapolation metric is simple to interpret, captures
the complex shapes of the data in environmental space, and can use any
extrapolation threshold to define whether model predictions are retained
based on the extrapolation degrees. These properties make this approach
more broadly applicable than existing methods for creating and applying
SDMs. We hope this method and accompanying tools support modelers to
explore, detect, and reduce extrapolation errors to achieve more reliable
models.