Zero-inflated Joint Species Distribution Models for improved partial-correlation network inference from community data

1. A long-term goal of community ecology has been to decipher the mechanisms that shape the spatio-temporal organization of species communities. Understanding these processes is critical to predicting the responses of ecological communities to environmental change. To this end, Joint Species Distribution Models (JSDMs) offer statistical tools to analyze community data, identify the impact of abiotic factors on them and study inter-species correlations in their distributions. In particular, the JSDM-inferred partial-correlation networks allow one to identify direct links between species that can help decipher the mechanisms that shape their joint distribution. 2. Community data based on species counts often contains numerous zeros. However, not accounting for these zeros in a data set is known to hinder parameter inference. We investigate this issue in the context of JSDMs, and ask what impact it can have on the inference of partial-correlation networks. 3. We propose a novel JSDM, the ZIPLN-network model, based on the PLN-network (Poisson log-normal network) and ZIPLN (Zero-Inflated Poisson log-normal) model, which models count data while including zero-inflation and infers a partial-correlation network. Using simulated data, we compare the results obtained by this model with existing JSDMs in terms of association network inference from abundance data containing structural zeros. We then illustrate the ZIPLN-network approach using real data from tropical freshwater fish communities. 4. Simulations show that zero-inflation can significantly bias the inference of partial-correlation networks from community data and that the ZIPLN-network model efficiently counterbalances these effects. The ZIPLN-network approach is widely applicable to community data, delivers ecologically-insightful analyses, helps distinguish amongst potential mechanisms, and aids better understanding of community assembly rules. We provide guidance for getting started with our approach.