A quantitative review of abundance‐based species distribution models

Conor Waldock,Graham J Edgar,David Mouillot,William W L Cheung,Camille Albouy,Rick D Stuart‐Smith,Loïc Pellissier,Jerry Tjiputra

doi:10.1111/ecog.05694

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.

Highlights

Environmental change alters the occurrence and local abundance patterns of species (Antão et al 2020b, Hastings et al 2020, Lenoir et al 2020, Román-Palacios and Wiens 2020)
We show that abundance-based species distribution models have great potential – additional to occurrence-based models – to generate insights in spatial ecology and biogeography and to improve systematic conservation planning outcomes
For generalised linear models (GLMs) and generalised additive models (GAMs), we used a range of error distributions rather than determining a priori the most appropriate error distribution for each species. This follows previous species abundance model comparisons (Potts and Elith 2006), which assumed that incorrect model specification leads to poor predictive ability, and we focused our comparison of model performance on predictive ability

Summary

Introduction

Environmental change alters the occurrence and local abundance patterns of species (Antão et al 2020b, Hastings et al 2020, Lenoir et al 2020, Román-Palacios and Wiens 2020). Modelling species occurrence has helped predict the distribution and erosion Oikos. Provide limited opportunities to understand local abundance changes that accompany species distribution shifts (Lenoir and Svenning 2013, Bates et al 2015, Hastings et al 2020). Abundance trends can act as an early warning signal of population collapse (Clements et al 2017, Ceballos et al 2020), but occurrence patterns may not change until after local population depletion (Hastings et al 2020). Abundance-based species distribution models remain underdeveloped relative to occurrence-based models

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