Data from: Agent-based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data

Species distribution models (SDMs) have been widely used in ecology to
understand how species relate to environmental variation. Most SDMs are
correlative and they lack explicit reference to the underlying processes
and therefore the reliability of their predictions might be questionable.
Mechanistic models that incorporate components that relate to underlying
processes such as trophic interactions or dispersal have been less
utilized due to their case-specificity and difficulties related to their
parametrization, which typically requires significantly more data than
parametrization of correlative models.We compare correlative and
mechanistic species distribution models in prediction tasks under
different scenarios. We define a mechanistic agent-based models of
resource-consumer dynamics to generate data with known processes and
parameter values. We fit correlative and mechanistic models to these data
to study under which conditions mechanistic models might give more
accurate predictions and how robust they are for possible model
misspecification. The mechanistic models provided better extrapolation
predictions than the correlative model in a simulated setting, when the
model used for fitting the data matched the data-generating model. The
mechanistic model predictions were sensitive to the correctness of the
model, and the quality of them dropped significantly even under slight
model misspecification. In real data analyses, the correlative models
consistently out-performed the mechanistic models that were not tailored
for the specific situations. Mechanistic species distribution models may
provide significant advantage in prediction compared to more commonly used
correlative models, when predicting to new environmental conditions.
However, this requires that the model is carefully tailored for the
specific system, because the predictions from the mechanistic models are
sensitive to model misspecification.