drmr: A Bayesian approach to Dynamic Range Models in R

Predicting how species distributions will respond to environmental change is a critical challenge. Dynamic Range Models (DRMs) offer a powerful mechanistic approach by explicitly modeling the influence of environmental drivers on demographic processes. However, the widespread adoption of DRMs has been hindered by their inherent complexity and a critical gap in the available software. While many tools can simulate range dynamics, none provide a user-friendly framework to statistically estimate the functional relationships between environmental conditions and demographic rates from spatio-temporal data. To make this approach more accessible, we introduce drmr, an open-source R package for building, fitting, evaluating, and forecasting age-structured DRMs within a user-friendly Bayesian framework. From spatio-temporal species observations, the drmr package allows users to explicitly estimate how environmental drivers affect demographic processes such as recruitment and survival. We demonstrate the package's utility through case studies on summer flounder (Paralichthys dentatus) and red-bellied woodpecker (Melanerpes carolinus). In both cases, we fit DRMs with environment-dependent recruitment and survival. Additionally, we show how the package visualizes estimated relationships between environmental conditions and demographic rates. The drmr package bridges a critical gap between complex, process-explicit models that are parameterized a priori, and statistically accessible correlative models that are easily fit to data. By lowering the barrier to entry, it provides a powerful and accessible tool for ecologists to test mechanistic hypotheses and generate more robust ecological forecasts in the face of global change.