The use of Bayesian networks to guide investments in flow and catchment restoration for impaired river ecosystems
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1. The provision of environmental flows and the removal of barriers are regarded as a high priority for restoration where changes to flow regimes are a significant cause of degradation for riverine ecosystems. Nevertheless, alteration of flow regimes is often accompanied by changes in catchment and riparian land use, which also can have major impacts on river health via local habitat degradation or modification of stream energy regimes. 2. Determining the relative importance of flow, land use and other impacts and where to focus effort on restoration are difficult challenges. As a consequence, flow, catchment and riparian restoration efforts are often addressed in isolation. River managers need a means to assess which flow and catchment interventions are most likely to succeed and, importantly, which are cost effective. 3. Bayesian networks can be used as a robust decision support tool for considering the influence of multiple stressors on aquatic ecosystems and the relative benefits of various restoration options. We provide simple illustrative examples of how Bayesian networks (BN) can address specific river restoration goals and assist with the prioritisation of flow and catchment restoration options. This includes the use of cost functions to assist decision makers in their choice of potential management interventions. 4. A Bayesian network approach facilitates the development of conceptual models of likely cause and effect relationships between flow regime, land use and river health, and provides an interactive tool to explore the relative benefits of various mitigation options. When combined with information on monetary costs of intervention, the BN approach could also be used to determine the most effective use of available funds for river restoration.
Natural Resource Management
Environmental Rehabilitation (excl. Bioremediation)