A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

Matthias F Biber,Christian Hof,Alke Voskamp,Aidin Niamir,Thomas Hickler

doi:10.1111/jbi.13696

Abstract

AbstractAimPredicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used.LocationGlobal land, excluding Antarctica.TaxonAmphibians, birds and mammals.MethodsWe fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning.ResultsUnder current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type.Main conclusionsBoth model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.

Highlights

One of the current major challenges in biogeography is to understand and predict the potential impacts of global change on the distribution of biological diversity
Comparing the similarity in future predictions among model type, model algorithm and general circulation models (GCMs), we found that predictions with different model types (i.e. species distribution models (S‐SDMs) vs. macroecological models (MEMs)) are least similar, followed by predictions with different model algorithms (i.e. Generalized Additive Models (GAMs) vs. GBM)
While future predictions of species richness showed a relatively high correlation between S‐SDMs and MEMs, we found considerable divergence especially when focusing on the predicted changes

Summary

Introduction

One of the current major challenges in biogeography is to understand and predict the potential impacts of global change on the distribution of biological diversity. Changes in biological systems in response to climate change are frequently documented, including shifts in species distribution (Chen, Hill, Ohlemüller, Roy, & Thomas, 2011). Such changes in species distributions result in changes of biodiversity patterns, such as the geographical variation in species richness. EBVs function as an interface between raw data and indicators and are meant to provide robust and coordinated data about biodiversity change on a global scale in order to inform policy makers (Brummitt et al, 2017; Geijzendorffer et al, 2016). Field studies and monitoring schemes provide data for a wide range of EBVs, but are often limited in spatial or temporal coverage, whereas large‐scale data sources, such as the Global Biodiversity Information Facility (GBIF), are inherently biased (Meyer, Kreft, Guralnick, & Jetz, 2015) and not representative on a global scale (Proença et al, 2017)

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