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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.

Item Type:	Article
Authors/Creators:	Waldock, C and Stuart-Smith, RD and Albouy, C and Cheung, WWL and Edgar, GJ and Mouillot, D and Tjiputra, J and Pellissier, L
Keywords:	abundance-based species distribution model, biodiversity modelling, population density, random forest, species abundance model, species distribution model, systematic conservation planning
Journal or Publication Title:	Ecography
Publisher:	Blackwell Munksgaard
ISSN:	0906-7590
DOI / ID Number:	https://doi.org/10.1111/ecog.05694
Copyright Information:	Copyright (2021) The Authors. This is an open access article under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License, (https://creativecommons.org/licenses/by/3.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Related URLs:	Google Scholar: Stuart-Smith, RD UTAS Profile: Stuart-Smith, RD Google Scholar: Edgar, GJ UTAS Profile: Edgar, GJ
Item Statistics:	View statistics for this item

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