A quantitative review of abundance‐based species distribution models

Type Article
Date 2022-01
Language English
Author(s) Waldock ConorORCID1, 2, Stuart‐smith Rick D.3, Albouy CamilleORCID4, Cheung William W. L.5, Edgar Graham J.3, Mouillot David6, Tjiputra Jerry7, Pellissier Loïc1, 2
Affiliation(s) 1 : Landscape Ecology, Inst. of Terrestrial Ecosystems, ETH Zürich Zürich, Switzerland
2 : Swiss Federal Research Inst. WSL Birmensdorf, Switzerland
3 : Inst. for Marine and Antarctic Studies, Univ. of Tasmania Hobart TAS, Australia
4 : IFREMER, Unité Écologie et Modèles pour l'Halieutique Nantes, France
5 : Inst. for the Oceans and Fisheries, Univ. of British Columbia Vancouver BC ,Canada
6 : MARBEC, Univ. de Montpellier, CNRS, Ifremer, IRD Montpellier ,France
7 : NORCE Climate, Bjerknes Centre for Climate Research Bergen ,Norway
Source Ecography (0906-7590) (Wiley), 2022-01 , Vol. 2022 , N. 1 , P. e05694 (18p.)
DOI 10.1111/ecog.05694
WOS© Times Cited 36
Keyword(s) abundance-based species distribution model, biodiversity modelling, population density, random forest, species abundance model, species distribution model, systematic conservation planning
Abstract

The contributions of species to ecosystem functions or services depend not only on their presence but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance-based species distribution models fitted to 800 000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found a large amount of variation in the performance of abundance-based models. While many models performed poorly, a subset of models consistently reconstructed range-wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but our results indicate extrapolated predictions of abundance under changing climate have a much greater uncertainty. Our synthesis provides a road map for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation.

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How to cite 

Waldock Conor, Stuart‐smith Rick D., Albouy Camille, Cheung William W. L., Edgar Graham J., Mouillot David, Tjiputra Jerry, Pellissier Loïc (2022). A quantitative review of abundance‐based species distribution models. Ecography, 2022(1), e05694 (18p.). Publisher's official version : https://doi.org/10.1111/ecog.05694 , Open Access version : https://archimer.ifremer.fr/doc/00740/85251/