Modelling Spatial And Temporal Changes With Gis And Spatial And Dynamic Bayesian Networks

Keywords

Adaptive management; Object-oriented; Probabilistic graphical models; State-and-transition models; Willow

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) - 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 east-central Florida.

Publication Date

8-1-2016

Publication Title

Environmental Modelling and Software

Volume

82

Number of Pages

108-120

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.envsoft.2016.04.012

Socpus ID

84967333740 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84967333740

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