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

Abstract

Abstract 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 the 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 to 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 outperformed the mechanistic models that were not tailored for the specific situations. Mechanistic species distribution models may provide a significant advantage in prediction compared to more commonly used correlative models when predicting 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.

Keywords

• agent‐based models
• approximate Bayesian computation
• HMSC
• mechanistic models
• species distribution models