Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

Abstract

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions.

Ecological Interactions and the Netflix Problem

“…data [1,7]. Being able to predict with high enough accuracy whether two species will interact given simply two sets of attributes, or the preferences of similar species, would be of value to conservation and invasion biology, allowing us to build food webs with partial information about interactions and help us understand cascading effects caused by perturbations.…”

Deep Bayesian network architecture for Big Data mining

“…As it is shown in Figure 2, this hierarchical structure varies so as to include the one-leveled tree, 24 the strict hierarchy, 25 and the loose hierarchy. The HBN models have been used to represent the hidden cause of some observed nodes such as the discovery of unobserved factors in medical applications, 28 the human physical interaction recognition, 29 the study of species interactions in ecology, 30 and the risk assessment for mobile systems. In fact, it was asserted that the more complex is the latent variable, the simpler is the latent structure of the HBN, and vice versa.…”

A knowledge-based system for generating interaction networks from ecological data

“…The interaction network is a generic modelling construct that is commonly used in the broader domain of ecology to visualise various kinds of relationships between interacting species. Different ways of inferring specific plant-animal interaction networks from data appear in the literature, including mathematical techniques using symbolic computation and algebraic combinatorics [8], statistical techniques, including correlation analysis [9], hierarchical Bayesian models [10] and Bayesian networks [11,12], and computational methods, including machine learning [13] and network theory [14].…”