sabinaNSDM: An R package for spatially nested hierarchical species distribution modelling

Abstract

Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge. To address this gap, we introduce the sabinaNSDM R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions. sabinaNSDM streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs. This paper outlines the workflow and functions integrated into the sabinaNSDM package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.