N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

• DOI: 10.1111/ecog.06540
• Publication Date: 2023-04-06T08:09:31Z
• Authors: Antoine Adde; Pierre‐Louis Rey; Philipp Brun; Nathan Külling; Fabian Fopp; Florian Altermatt; Olivier Broennimann; Anthony Lehmann; Blaise Petitpierre; Niklaus E. Zimmer...; Loïc Pellissier; Antoine Guisan
• Keywords: 10127 Institute of Evolutionary Biology and Environmental Studies; info:eu-repo/classification/ddc/333.7-333.9; 1105 Ecology, Evolution, Behavior and Systematics; info:eu-repo/classification/ddc/550; Ecology; Behavior and Systematics; Evolution; 570 Life sciences; biology; 590 Animals (Zoology); 15. Life on land

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developedN‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments.N‐SDMwas built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales.N‐SDMallows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied inN‐SDMincludes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach.N‐SDMis designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and runningN‐SDMis openly available on the GitHub repositoryhttps://github.com/N‐SDM/N‐SDM.