Epsilon Open Archive

Abstract

In 2012 Sweden implemented a collaborative governance regime for managing moose (Alces aloes). This was guided by the awareness that decentralization and stakeholder participation can help to reduce conflicts, foster systematic learning, and handle complexity. However, previous research has highlighted that there are no blueprint approaches to the governance and management of natural resources. In this case, diverse multi-use landscapes, ever-changing ungulate populations, and other external stressors (e.g. climate change, wildlife diseases) can create challenges for collaborative institutions. Adaptive capacity is therefore needed as it allows a system and the actors involved to react successfully to social-ecological changes and to develop even in times of no imminent change or risk. Using Swedish moose management as an example of a multi-level governance system, this research assesses the critical determinants of adaptive capacity across levels. We developed and applied a psychometric approach to measure actors' perceived adaptive capacity on two levels in the management system. A web-based survey was sent to Moose Management Groups (n = 765, response rate = 81 %) and Moose Management Units (n = 1,380, response rate = 71 %). Using structural equation modelling, we assessed the relative importance of governance aspects, different types of social capital, as well as human and financial capital on actors' perceived adaptive capacity. Linking and bridging social capital in the system had significant impacts on both levels. Actors felt more prepared to handle future challenges in moose management when they perceived benefits through collaborations with levels below and expressed social trust in authorities and the management level above. Besides those similarities between the two levels, fairness was a more important determinant of actors' perceived adaptive capacity on the lower management level. These results can contribute to a future improvement of the collaborative governance setting by finessing strategic interventions on different levels. Furthermore, our results illustrate the importance of scale when assessing the adaptive capacity of a system.