Rapid Characterisation of Stakeholder Networks in Three Catchments Reveals Contrasting Land-Water Management Issues

Catchments are socio-ecological systems integrating land, water and people with diverse roles and views. Characterising stakeholder networks and their levels of influence and interaction within catchments can help deliver more effective land and water management. In this study, we combined stakeholder analysis and social network methods to provide a novel stakeholder-mapping tool capable of identifying interactions among the land and water management communities across three contrasting study catchments. The overarching aim was to characterise the influence of different stakeholders involved in catchment management based on the perceptions of participants from four key stakeholder groups (Environmental Regulators, Water Industry Practitioners, the Farm Advisor Community, and Academics). A total of 43 participants identified 28 types of specific catchment management stakeholder groups with either core or peripheral importance to our three case study catchments. Participants contributed 490 individual scores relating to the perceived influence of these different stakeholder groups and categorised whether this influence was positive, negative or neutral for the management of catchment resources. Local Government, Farmers and Environmental Regulators were perceived to have the greatest level of influence. Social network analysis further determined which stakeholders were most commonly connected in all of the study catchments and hence formed the core of stakeholder networks in each catchment. Comparing outputs from the analysis of three contrasting river catchments, as well as between participants from four key stakeholder groups allowed identification of which stakeholders were more central to the catchment management networks. Such analyses could help facilitate effective communication within land and water management stakeholder networks by targeting highly connected opinion leaders or promoting peer learning via distinct catchment subgroups.