A high-throughput, species-explicit approach to quantifying biodiversity at scale for assessment and monitoring

Accurately quantifying biodiversity at scale is critical for conservation, particularly as new conservation initiatives require robust metrics to assess and monitor ecological outcomes. Advances in remote sensing, analytical methods, and expanding in situ observation networks now provide unprecedented capacity to model biodiversity at fine spatial resolution across large regions. Historically, large-scale biodiversity assessments relied on coarse proxies such as climate, land cover extent, or expert knowledge, which can obscure species-level responses. There is an urgent need for approaches that link biodiversity outcomes to species explicit habitat estimates and reveal environmental drivers of biodiversity patterns. We present a high-throughput species distribution modeling workflow that provides a species explicit approach to quantifying biodiversity as a function of fine-scale habitat suitability. Using Google Earth Engine and a machine learning framework, we evaluated how high resolution, multi-scale environmental covariates, primarily derived from remote sensing and representing climate, land cover, disturbance, and landscape configuration, influence species distributions. We then stacked species models to generate regional biodiversity layers that reflect species-level habitat suitability rather than coarse habitat surrogates. We applied this approach to 191 bird and mammal species across the North American Great Plains using occurrence data from eBird and GBIF. For each species, we implemented a covariate selection process to identify the optimal spatial scale for each predictor and built final models using only the best-scale covariates. Model performance was consistently high, producing detailed spatial predictions within expected species’ ranges. The models reveal how species respond to environmental drivers across scales, providing nuanced insights into biodiversity patterns in heterogeneous landscapes. While we are using this workflow to assess and monitor biodiversity outcomes from rangeland restoration on private lands, it is readily extendable. It can support hindcasting to determine drivers of biodiversity change, multispecies connectivity modeling, or integration with trait-based indices for functional diversity. The derived species richness layers provide a scientifically rigorous, spatially explicit measure of biodiversity that can guide conservation prioritization and management, scenario planning, and biodiversity finance strategies. By moving beyond coarse proxies, this approach offers a scalable, species-centered method for assessing and managing biodiversity across regional and global scales.