Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Ecological systems consist of complex sets of interactions among species and their environment, the understanding of which has implications for predicting environmental response to perturbations such as invading species and climate change. However, the revelation of these interactions is not straightforward, nor are the interactions necessarily stable across space. Machine learning can enable the recovery of such complex, spatially varying interactions from relatively easily obtained species abundance data. Here, we describe a novel Bayesian regression and Mondrian process model (BRAMP) for reconstructing species interaction networks from observed field data. BRAMP enables robust inference of species interactions considering autocorrelation in species abundances and allowing for variation in the interactions across space. We evaluate the model on spatially explicit simulated data, produced using a trophic niche model combined with stochastic population dynamics. We compare the model’s performance against L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. Finally, we apply BRAMP to real ecological data.

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