Transferability of trait‐based species distribution models

Peter A Vesk,Karel Mokany,Will C Neal,William K Morris,Laura J Pollock

doi:10.1111/ecog.05179

Peter A Vesk, Karel Mokany + Show 3 more

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https://doi.org/10.1111/ecog.05179

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Abstract

The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait‐SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south‐eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.

Highlights

Improving our understanding of where species occur and why, are core aims in both fundamental and applied ecology
In Pollock et al (2012), we found strong trait–environment relationships with these trait-SDMs, in particular: heavierseeded species were more likely to occur in sandy soils; low specific leaf area (SLA) species were more likely to occur in sites with higher rock cover
We have demonstrated a predictive framework for using traitSDMs to transfer knowledge from reference taxa to target taxa and from one reference region to other target regions, along with ways to measure and visualise the performance of such transferability

Summary

Introduction

Improving our understanding of where species occur and why, are core aims in both fundamental and applied ecology. One approach to addressing this priority area has been through species distribution modelling (SDM), which has been the focus of substantial research over the past two decades (Elith and Leathwick 2009). The vast majority of SDM work is correlative, based on fitting statistical models to datasets of species occurrences and making predictions from these models. This approach is commonly used to predict to new situations (space, time and environments), but the ability of these models to be transferred to new conditions is rarely tested. Mechanistic models are more explicit about including process (Briscoe et al 2019), and may include climate dependent phenology

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