Using Bayesian network models to bridge the gap between ecology and management
This guidance explains how Bayesian network models can combine data with expert knowledge (ecological, physical or Mātauranga Māori), to bridge data gaps and support decision making.
How to use Bayesian network models to develop robust policies
- Strive for ‘satisfactory’ outcomes across a range of future scenarios – rather than ‘optimal’ outcomes that maximise the immediate perceived ‘value’ of an action but have sub-optimal outcomes over the long-term.
- Focus not only on the potential drivers of a tipping point but on identifying actions that can change how an ecosystem responds to those drivers – ie, resilience-enhancing actions such as restoration of key habitats/species, or fishing at levels where recruitment is likely to be successful under changing environmental conditions.
- Adapt to changes that occur in the ecosystem over time
This guidance includes a case study of using the Bayesian network tool for seabed health and scallop fisheries.