The rENM Framework: A Modular System for Reconstructing and Analyzing Long-Term Ecological Niche Dynamics

Abstract

Retrospective ecological niche modeling (rENM) combines historical species occurrence records with historical environmental data to reconstruct the spatio-temporal dynamics of species-environment relationships under changing conditions. Despite growing recognition that those relationships can be nonstationary, time-series approaches to ecological niche modeling remain uncommon, and the tools to support them at scale are limited. Here, we describe the rENM Framework, an experimental, open-source suite of R packages that automates a complete rENM workflow spanning data preparation, ensemble time-series construction, trend analysis, AI interpretation, and report generation. The framework integrates eBird occurrence records with environmental variables derived from NASA’s MERRA-2 reanalysis across a 45-year study period (1980–2024) and executes a complete analysis for any species with eBird data through a single function call. By treating climatic suitability as a dynamic ecological response surface rather than a static baseline, the framework produces the following analytical products that complement conventional ecological niche modeling approaches: suitability time series, long-term trend and acceleration maps, centroid displacement estimates, bioclimatic velocity metrics, variable contribution trajectories, and hotspot analyses identifying areas of accelerating suitability decline. We illustrate the framework’s outputs with a representative run for Cassin’s Sparrow (Peucaea cassinii), a grassland species of conservation concern in the arid southwestern United States and the focal species throughout our development work. The framework’s automated, unsupervised pipeline makes systematic application across large numbers of species tractable, with direct implications for conservation assessments, such as State Wildlife Action Plans, where species-specific analytical capacity is often limited by available resources. The rENM Framework is openly available on GitHub and archived on Zenodo.