Modelling spatial and temporal changes with GIS and spatial and dynamic Bayesian networks

Yung En Chee, Lauchlin Wilkinson, Ann E. Nicholson, Pedro F. Quintana-Ascencio, John E Fauth, Dianne Hall, Kimberli J. Ponzio, Libby Rumpff

    Research output: Contribution to journalArticleResearchpeer-review

    24 Citations (Scopus)

    Abstract

    State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) d incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured. This allows key phenomena and ecological processes to be represented by hierarchies of components that include similar, repetitive structures. We demonstrate the generality and power of our approach using two models - one developed for adaptive management of eucalypt woodland restoration in south-eastern Australia, and another developed to manage the encroachment of invasive willows into marsh ecosystems in eastcentral Florida.
    Original languageEnglish
    Pages (from-to)108 - 120
    Number of pages13
    JournalEnvironmental Modelling and Software
    Volume82
    DOIs
    Publication statusPublished - 2016

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