Fitting individual-based models of spatial population dynamics to long-term monitoring data

Abstract

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration, integrating heterogeneous data sources such as parameter estimates from published literature and observational data from monitoring schemes. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades; results suggest that reproductive success was a key factor driving the observed range expansion. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR, an approach that can improve predictive performance compared to cSDMs and can be adopted for any population with some prior knowledge of demographic and dispersal parameters plus spatiotemporal observations of abundance or presence/absence.