Tous, J. | bioRxiv Science

bioRxiv2026

Catching the effects of biotic interactions on community data: partial correlations outperform marginal ones with proper abiotic modelling.

A major goal of community ecology lies in the deciphering of the processes underlying species distribution. A widespread approach to this question is to identify patterns in species community data and relate them to possible processes. Joint Species Distribution Models (JS-DMs) offer one way to do so through the infernece of association networks that describe patterns of statistical correlations and dependencies between species, but it is unclear what processes can explain the presence of such correlations. While it has now been established that there is no equivalence between JSDM-inferred associations and biotic interactions, the later remain one possible explanation, among others, for the former. However, to our knowledge, there is no specific study of the statistical patterns induced by different types of interactions or of the conditions under which they may or may not appear as statistical correlations / dependencies in species communities. To explore these questions, we propose a "virtual ecologist" approach that consists in simulating community data based on abiotic and biotic processes with the VirtualCom model that emulates the effects of environmental processes and of competition and facilitation interactions. Then, we study to what extent JSDMs retrieve correlations between species that match the simulated interactions. We show that these interactions are better identified when using JSDMs that model partial correlations between species rather than marginal ones. We further demonstrate how critical it is to correctly model abiotic effects in order to identify biotic ones and that the "correct modelling" of these effects depend on the type of interactions at stake.

ecology↗

bioRxiv2025

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

ecology↗