Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Détails

ID Serval
serval:BIB_65CB21E512B5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Data integration methods to account for spatial niche truncation effects in regional projections of species distribution
Périodique
Ecological Applications
Auteur⸱e⸱s
Chevalier M., Broennimann O., Cornuault J., Guisan A.
Statut éditorial
Publié
Date de publication
2021
Peer-reviewed
Oui
Volume
31
Numéro
7
Pages
e02427
Langue
anglais
Résumé
Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence datasets (e.g. a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e. spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g. when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species’ niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically-truncated regional datasets and (2) test the performance of different methods to harness information from larger-scale datasets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real dataset to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted datasets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale datasets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g. data pooling, downscaling) performed well on both simulated and real data, others (e.g. those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g. shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modelling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.
Mots-clé
multi-scale models, Bayesian inference, data resolution, geographic extent, national inventories, citizen science data, atlas, range maps, species response curves, Poisson point process
Création de la notice
23/03/2021 10:42
Dernière modification de la notice
26/10/2021 6:38
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