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Bayesian Networks and Their Applications in Modelling Resilience and Regime Shifts

Bayesian Networks and Their Applications in Modelling Resilience and Regime Shifts

Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.

Abstract

Understanding the dynamics that regulate ecological resilience is becoming increasingly important in today’s world, as ecosystems are facing multiple pressures on global, regional, and local scales. If pressures exceed a threshold, this may trigger a regime shift, where a system undergoes a step change to another state that can last for substantial periods of time. Recent applications of Bayesian networks (BNs) have shown promise in revealing network structures of complex systems, and such understanding shows great promise for the understanding of mechanisms underlying the resilience of complex systems. In this talk, we present two case studies to document the potential of Bayesian Networks in the study of complex systems:

1. Bayesian networks as a novel tool to enhance the interpretability and predictive power of ecological models

In recent years, the use of Bayesian networks (BN) has seen successful applications in molecular biology and ecology, where it was able to recover known links in the respective systems it was applied to. While this is invaluable in ecology, an unexplored application of BNs would be utilizing it as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well-documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN).

2. A novel integration of Dynamic Bayesian Networks and Non-additive modelling to study ecological resilience

To date, two distinct approaches have emerged in the study of ecological resilience. On one hand, network-based approaches have successfully revealed ecological network structures of complex systems. On the other hand, novel non-additive modelling frameworks have been developed and allowed for the direct quantification of ecological resilience. So far, these two approaches have been largely segregated. However, connecting these two fields may offer novel insight into the study of ecological resilience. Here, we propose a novel 2-step modelling process to study ecological resilience and regime shifts: (1) we apply the Integrated Resilience Assessment (IRA) framework proposed by Vasilakopoulos et al. (2017) to quantify and approximate the ecological resilience of ecosystems under study; and (2) we apply a dynamic Bayesian Gaussian Mixture (BGMD) Bayesian network model to reveal the network structure with a changepoint process to take the temporal structure into account.

Presentation Video

About the Presenter

Edwin Hui is a Ph.D. student from the University of St Andrews, where his research focuses on developing computational models to study resilience and regime shifts across complex systems. He is interested in applying a variety of statistical and computational tools to address ecological questions and study complex systems theory. Throughout his Ph.D., he aims to develop novel computational approaches to study complex systems across disciplines, ranging from ecological to macroeconomic systems.

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