Model development of a Bayesian Belief Network for managing inundation events for wetland fish

Lorenzo Vilizzi, Amina Price, Leah Beesley, Ben Gawne, Alison Jane King, John Koehn, Shaun Meredith, Daryl Nielsen

    Research output: Contribution to journalArticlepeer-review


    Wetlands are essential components of floodplain-river ecosystems that often suffer degradation due to river regulation. To this end, the application of environmental water is increasingly being seen as an important amelioration strategy. However, decisions regarding the delivery of water to maximise environmental benefits, including native fish population health, are complex and difficult. This paper describes the development of a Bayesian Belief Network (BBN) model as part of a Decision Support Tool for assessing inundation strategies to benefit native wetland fish. Separate, albeit closely related, BBNs were developed for three native (golden perch Macquaria ambigua, carp gudgeon Hypseleotris spp., Australian smelt Retropinna semoni) and one alien fish species (common carp Cyprinus carpio carpio). The model structure was based on a conceptualisation of the relationships between wetland habitats, hydrology and fish responses, with emphasis on the types of inundation activities undertaken by managers. Conditional probability tables for fish responses were constructed from expert opinion and the model was validated against field data. The predictive ability and sensitivity of the model reflected the inherent high variability in relationships between wetland characteristics, hydrology and fish responses, but was nonetheless able to address satisfactorily such complexities within a holistic framework. As the model was designed in conjunction with managers and evaluated by them, its application will be enhanced by on-going engagement between managers and scientists.
    Original languageEnglish
    Pages (from-to)1-14
    Number of pages14
    JournalEnvironmental Modelling and Software
    Publication statusPublished - 2013


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