‘Bunkering down’: How one community is tightening social-ecological network structures in the face of global change

  • Michele L. Barnes
  • , Lorien Jasny
  • , Andrew Bauman
  • , Jon Ben
  • , Ramiro Berardo
  • , Örjan Bodin
  • , Josh Eli Cinner
  • , David A. Feary
  • , Angela M. Guerrero
  • , Fraser A. Januchowski-Hartley
  • , John T. Kuange
  • , Jacqueline D. Lau
  • , Peng Wang
  • , Jessica Zamborain-Mason

Research output: Contribution to journalArticlepeer-review

Abstract

Complex networks of relationships among and between people and nature (social-ecological networks) play an important role in sustainability; yet, we have limited empirical understanding of their temporal dynamics. We empirically examine the evolution of a social-ecological network in a common-pool resource system faced with escalating social and environmental change over the past two decades. We first draw on quantitative and qualitative data collected between 2002 and 2018 in a Papua New Guinean reef fishing community to provide contextual evidence regarding the extent of social and environmental change being experienced. We then develop a temporal multilevel exponential random graph model using complete social-ecological network data, collected in 2016 and 2018, to test key hypotheses regarding how fishing households have adapted their social ties in this context of change given their relationships with reef resources (i.e. social-ecological ties). Specifically, we hypothesized that households will increasingly form tight-knit, bonding social and social-ecological network structures (H1 and H3, respectively) with similar others (H2), and that they will seek out resourceful actors with specialized knowledge that can promote learning and spur innovation (H4). Our results depict a community that is largely ‘bunkering down’ and looking inward in response to mounting risk to resource-dependent livelihoods and a breakdown in the collaborative processes that traditionally sustained them. Community members are increasingly choosing to interact with others more like themselves (H2), with friends of friends (H1), and with those connected to interdependent ecological resources (H3)—in other words, they are showing a strong, increasing preference for forming bonding social-ecological network structures and interacting with like-minded, similar others. We did not find strong support for H4. Bonding network structures may decrease the risk associated with unmonitored behaviour and help to build trust, thereby increasing the probability of sustaining cooperation over time. Yet, increasing homophily and bonding ties can stifle innovation, reducing the ability to adapt to changing conditions. It can also lead to clustering, creating fault lines in the network, which can negatively impact the community's ability to mobilize and agree on/enforce social norms, which are key for managing common resources. Read the free Plain Language Summary for this article on the Journal blog.

Original languageAmerican English
Pages (from-to)1032-1048
Number of pages17
JournalPeople and Nature
Volume4
Issue number4
DOIs
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. People and Nature published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Funding

This project was supported by the Australian Research Council through a Discovery Early Career Fellowship Grant to M.L.B. (grant no. DE190101583), the ARC Centre of Excellence for Coral Reef Studies, and the U.S. National Science Foundation (award no. 1513354 and 1620416). We thank Sarah Sutcliffe and Edith Shum for help with data processing and all of the community members who participated in this project.

FundersFunder number
National Science Foundation1513354, 1620416
Centre of Excellence for Coral Reef Studies, Australian Research Council
Australian Research CouncilDE190101583

    ASJC Scopus Subject Areas

    • Ecology, Evolution, Behavior and Systematics

    Keywords

    • adaptation
    • coastal communities
    • social capital
    • social network
    • temporal exponential random graph model
    • transformation

    Disciplines

    • Marine Biology
    • Oceanography and Atmospheric Sciences and Meteorology

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