Both authors contributed equally to this work.
Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)
Article first published online: 19 MAY 2014
© 2014 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Methods in Ecology and Evolution
Volume 5, Issue 5, pages 397–406, May 2014
How to Cite
Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O'Hara, R. B., Parris, K. M., Vesk, P. A., McCarthy, M. A. (2014), Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5: 397–406. doi: 10.1111/2041-210X.12180
- Issue published online: 19 MAY 2014
- Article first published online: 19 MAY 2014
- Accepted manuscript online: 15 MAR 2014 06:25AM EST
- Manuscript Accepted: 6 MAR 2014
- Manuscript Received: 24 OCT 2013
- biotic interactions;
- community assembly;
- correlated residuals;
- Eucalyptus ;
- species covariance
- A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co-occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co-occurrence data into a SDM.
- JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co-occurrence patterns into components describing shared environmental responses and residual patterns of co-occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia.
- Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co-occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities.
- Environmental and residual correlations estimated from JSDMs can help indicate whether co-occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.