Data from: Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications

Aim: Species distribution models are used across evolution, ecology,
conservation, and epidemiology to make critical decisions and study
biological phenomena, often in cases where experimental approaches are
intractable. Choices regarding optimal models, methods, and data are
typically made based on discrimination accuracy: a model’s ability to
predict subsets of species occurrence data that were withheld during model
construction. However, empirical applications of these models often
involve making biological inferences based on continuous estimates of
relative habitat suitability as a function of environmental predictor
variables. We term the reliability of these biological inferences
“functional accuracy.” We explore the link between discrimination accuracy
and functional accuracy. Methods: Using a simulation approach we
investigate whether models that make good predictions of species
distributions correctly infer the underlying relationship between
environmental predictors and the suitability of habitat. Results: We
demonstrate that discrimination accuracy is only informative when models
are simple and similar in structure to the true niche, or when data
partitioning is geographically structured. However, the utility of
discrimination accuracy for selecting models with high functional accuracy
was low in all cases. Main conclusions: These results suggest that many
empirical studies and decisions are based on criteria that are unrelated
to models’ usefulness for their intended purpose. We argue that empirical
modeling studies need to place significantly more emphasis on biological
insight into the plausibility of models, and that the current approach of
maximizing discrimination accuracy at the expense of other considerations
is detrimental to both the empirical and methodological literature in this
active field. Finally, we argue that future development of the field must
include an increased emphasis on simulation; methodological studies based
on ability to predict withheld occurrence data may be largely
uninformative about best practices for applications where interpretation
of models relies on estimating ecological processes, and will unduly
penalize more biologically informative modeling approaches.