Publication details for Professor Stephen G WillisHoward, Christine, Stephens, Philip A., Pearce-Higgins, James W., Gregory, Richard D. & Willis, Stephen G. (2014). Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution 5(6): 506-513.
- Publication type: Journal Article
- ISSN/ISBN: 2041-210X
- DOI: 10.1111/2041-210X.12184
- Keywords: Species distribution modelling, Ordinal abundance data, Presence-absence data, Random forests, Model performance.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.
SDMs 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.