In New Zealand, planning for freshwater quality and quantity management increasingly is being passed from regional government to collaborative stakeholder groups (CSGs). Comprising major water users and communities of interest, CSGs typically identify a wide variety of ecological, economic, social and cultural values related to a river watershed, values that interact in highly complex ways. Making good policy and management decisions in this context requires a sound scientific understanding of the watershed, including its hydrology, nutrient dynamics and ecology. However, detailed knowledge of economic, social and cultural aspects is also required in order to meet the wide range of objectives identified. Since the members of collaborative stakeholder groups come with various levels of knowledge about the watershed, they require tools to support their decision-making.
Bayesian Networks (BNs) are increasingly being used worldwide in this context. They represent the key components of a system, linking decisions with outcomes through cause-effect relationships. The relationships are defined probabilistically, allowing BNs to include aspects of a system for which knowledge is incomplete. BNs are highly flexible models, combining science with other forms of knowledge and representing them in an intuitive, visual way. They are useful for collaborative planning as they are highly interactive and bring transparency to policy and management decisions. I will discuss how Bayesian Networks have been used to support a collaborative planning process with robust science on the east coast of New Zealand.