<p>Communities across the world need resilience to adapt to changing circumstances. In this paper, we blend focus group work and network analysis to assess adaptive capacities and potential resilience, in a peri-urban community in Hobart, Tasmania, Australia. We used focus groups to gather information about core social and environmental features of the community and then constructed group mental models (a diagrammatic representation of how the community works). We asked participants to identify drivers (presses and pulses) of change, and to use the mental models to show how externally imposed changes might impact core features. To complement this qualitative data, network analysis was used to quantitatively identify core features, based on centrality, and to test for redundancies—assessing the likelihood of the system remaining resilient if a core feature were removed. The empirical results highlight the significance of networks’ contributions to local resilience and provide new pathways for conceptualizing change in social-ecological systems. Future research based on this case study could be scaled up and applied in many situations to improve our understanding of ways to maintain resilience at multiple geographic scales.</p>

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Network analysis can provide useful insights for building resilience in social-ecological systems

  • Paul Doehring,
  • Vanessa M. Adams,
  • Natalie Stoeckl

摘要

Communities across the world need resilience to adapt to changing circumstances. In this paper, we blend focus group work and network analysis to assess adaptive capacities and potential resilience, in a peri-urban community in Hobart, Tasmania, Australia. We used focus groups to gather information about core social and environmental features of the community and then constructed group mental models (a diagrammatic representation of how the community works). We asked participants to identify drivers (presses and pulses) of change, and to use the mental models to show how externally imposed changes might impact core features. To complement this qualitative data, network analysis was used to quantitatively identify core features, based on centrality, and to test for redundancies—assessing the likelihood of the system remaining resilient if a core feature were removed. The empirical results highlight the significance of networks’ contributions to local resilience and provide new pathways for conceptualizing change in social-ecological systems. Future research based on this case study could be scaled up and applied in many situations to improve our understanding of ways to maintain resilience at multiple geographic scales.