Balancing structural complexity with ecological insight in Spatio‐temporal species distribution models

1. The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insightgained from SDMs.

2. Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane Grus grus in a spatio temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs.

3. Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.

4. Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability but can be a useful approach to improving insight gained from model outputs.