A JSDM with zero-inflation to increase the ecological relevance of analyses of species distribution data

Deciphering the mechanisms that explain species distributions has been a long-term goal in ecology and is critical in predicting the responses of ecological communities to environmental change. To this end, Joint Species Distribution Models (JSDM) assess the effects of abiotic factors on these communities and produce species association networks that describe statistical dependencies between species abundances. Here we propose a novel JSDM, called ZIPLN-network, that directly models count data while accounting for zero-inflation. We illustrate the utility of the ZIPLN-network approach using data from tropical freshwater fish communities. By dealing with the zero-inflation that is a near-universal feature of species occurrence data sets, our approach overcomes a previous roadblock in analyses of species distribution data, and makes it possible to extract more of the ecological signal. A further advantage of this method is that it increases our understanding of ecological mechanisms that structure species distributions in a landscape, and in doing so helps formulate testable hypotheses.

The data and code used for this paper can be found in the git repository jeannetous/JSDM_with_zero_inflation_for_increased_ecological_relevance_in_species_distribution_analysis