Improving species distribution models: the value of data on abundance

Christine Howard,James W Pearce‐Higgins,Philip A Stephens,Stephen G Willis,Richard D Gregory,Jana Mcpherson

doi:10.1111/2041-210x.12184

Abstract

Summary Species distribution models (SDMs) are important tools for forecasting the potential impacts of future environmental changes but debate remains over the most robust modelling approaches for making projections. Suggested improvements in SDMs vary from algorithmic development through to more mechanistic modelling approaches. Here, we focus on the improvements that can be gained by conditioning SDMs on more detailed data. Specifically, we use breeding bird data from across Europe to compare the relative performances of SDMs trained on presence–absence data and those trained on abundance data. Species distribution models trained on presence–absence data, with a poor to slight fit according to Cohen's kappa, show an average improvement in model performance of 0·32 (SE ± 0·12) when trained on abundance data. Even those species for which models trained on presence–absence data are classified as good to excellent show a mean improvement in Cohen's kappa score of 0·05 (SE ± 0·01) when corresponding SDMs are trained on abundance data. This improved explanatory power is most pronounced for species of high prevalence. Our results illustrate that even using coarse scale abundance data, large improvements in our ability to predict species distributions can be achieved. Furthermore, predictions from abundance models provide a greater depth of information with regard to population dynamics than their presence–absence model counterparts. Currently, despite the existence of a wide variety of abundance data sets, species distribution modellers continue to rely almost exclusively on presence–absence data to train and test SDMs. Given our findings, we advocate that, where available, abundance data rather than presence–absence data can be used to more accurately predict the ecological consequences of environmental change. Additionally, our findings highlight the importance of informative baseline data sets. We therefore recommend the move towards increased collection of abundance data, even if only coarse numerical scales of recording are possible.

Highlights

To determine the impacts of future climate and habitat changes on species, ecologists increasingly use species distribution models (SDMs) to quantify speciesenvironment relationships (Guisan & Thuiller 2005)
There is no indication of the magnitude of improvements in SDMs that could be gained through using abundance rather than presence-absence data
Measures of model calibration showed improved performance in the models trained on abundance data, when compared with models trained on presence-absence data

Summary

Introduction

To determine the impacts of future climate and habitat changes on species, ecologists increasingly use species distribution models (SDMs) to quantify speciesenvironment relationships (Guisan & Thuiller 2005). The relative value of presence-only and presence-absence data has been widely discussed (Brotons et al 2004; Elith et al 2006; Pearson et al 2006), a third, more detailed form of data is available for many taxa in some regions: abundance data. This may either be an index of abundance, for example based on frequency of reporting rates (Harrison & Cherry 1997) , or an estimate of true population size ,such as derived from surveys accounting for detectability (Renwick et al 2011). There is no indication of the magnitude of improvements in SDMs that could be gained through using abundance rather than presence-absence data

Methods

Results

Discussion

Conclusion