This guidance describes how Bayesian network models can combine data with expert knowledge (ecological, physical or mātauranga Māori), to bridge the data gaps and support decision making. It includes a proof-of-concept example, where we developed a digital tool to compare outcomes on seabed health and scallop abundance from different management scenarios. The management objectives – fisheries, sediment and nutrient inputs, and restoration of seabed habitat – were identified with iwi and local stakeholders.
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This digital tool enables users to compare outcomes on seabed health and scallop abundance from different management scenarios for fisheries, sediment and nutrient inputs, and restoration of seabed habitat. Please note that this tool is not currently active as it requires a software license. The model that sits behind this tool can be adapted for other environmental management scenarios to support decision-making.
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This presentation describes how the tool was developed using expert knowledge, and how application of the Bayesian network model with a structured decision-making framework enables stakeholders and managers to make informed management decisions.
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This pilot research explored whether systems mapping could be useful for EBM, focused on the issue of scallop decline in Tasman and Golden Bays. Section 5 of the report provides a summary, and outlines recommendations for potential applications of this process.
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