Stacking species distribution models and adjusting bias by linking them to macroecological models

Abstract

Aim Species distribution models (SDMs) are common tools in biogeography and conservation ecology. It has been repeatedly claimed that aggregated (stacked) SDMs (S‐SDMs) will overestimate species richness. One recently suggested solution to this problem is to use macroecological models of species richness to constrain S‐SDMs. Here, we examine current practice in the development of S‐SDMs to identify methodological problems, provide tools to overcome these issues, and quantify the performance of correctly stacked S‐SDMs alongside macroecological models. Locations Barents Sea, Europe and Dutch Wadden Sea. Methods We present formal mathematical arguments demonstrating how S‐SDMs should and should not be stacked. We then compare the performance of macroecological models and correctly stacked S‐SDMs on the same data to determine if the former can be used to constrain the latter. Next, we develop a maximum‐likelihood approach to adjusting S‐SDMs and discuss how it could potentially be used in combination with macroecological models. Finally, we use this tool to quantify how S‐SDMs deviate from observed richness in four very different case studies. Results We demonstrate that stacking methods based on thresholding site‐level occurrence probabilities will almost always be biased, and that these biases will tend toward systematic overprediction of richness. Next, we show that correctly stacked S‐SDMs perform very similarly to macroecological models in that they both have a tendency to overpredict richness in species‐poor sites and underpredict it in species‐rich sites. Main conclusions Our results suggest that the perception that S‐SDMs consistently overpredict richness is driven largely by incorrect stacking methods. With these biases removed, S‐SDMs perform similarly to macroecological models, suggesting that combining the two model classes will not offer much improvement. However, if situations where coupling S‐SDMs and macroecological models would be beneficial are subsequently identified, the tools we develop would facilitate such a synthesis.

Ecology and Evolution of Melanism in Big Cats: Case Study with Black Leopards and Jaguars

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Avian SDMs: current state, challenges, and opportunities

“…Despite their frequent application, S-SDMs have been criticized, primarily because they have a high risk of overestimating species richness at species-poor locations and underestimating species richness at species-diverse sites (Calabrese et al 2014, Zurell et al 2016. These limitations are thought to be due to three factors: S-SDMs 1) often predict areas to be climatically suitable that are out of reach for some species; 2) ignore constraints of the carrying capacity that cap maximum species numbers of a given location; and 3) do not include biotic interactions (Guisan and Rahbek 2011).…”

Integrating Climate Change and Land Use Impacts to Explore Forest Conservation Policy

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