Spatially nested species distribution models (N‐ SDM ): An effective tool to overcome niche truncation for more robust inference and projections

作者：Antoine Guisan, Mathieu Chevalier, Antoine Adde, Alejandra Zarzo‐Arias, Teresa Goicolea, Olivier Broennimann, Blaise Petitpierre, Daniel Scherrer, Pierre‐Louis Rey, Flavien Collart, Federico Riva, Bart Steen, Rubén G. Mateo · 发表于：Journal of Ecology · 年份：2025 · DOI：10.1111/1365-2745.70063 · 被引用次数：38 · 研究领域：Species Distribution and Climate Change、Genetic diversity and population structure、Wildlife Ecology and Conservation

Abstract Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N‐SDMs), where a whole range, coarse‐grain SDM is combined with a subrange, fine‐grain SDM. N‐SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest. Here, we review developments, applications and effectiveness of N‐SDMs. We present and discuss existing methods and tools to fit N‐SDMs, and assess when N‐SDMs are not needed. We highlight strengths and weaknesses of N‐SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead t...