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

Abstract

1. 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.2. 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.3. 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).

Hub‐and‐spoke social networks among Indonesian cocoa farmers homogenise farming practices

“…The potential of ERGMs for the study of social‐ecological systems and networks of smallholder farmers has been demonstrated for example by Barnes et al. (2022) and Hermans et al. (2017).…”

“…"simple" contagion) implies that opinion leaders and everyone else in the network, would be influential in direct proportion to how many social ties they have.To test whether the observed patterns of fertiliser use within villages can be explained by simple additive peer-to-peer (dyadic) contagion, we analyse the networks with heterogeneous fertiliser outcomes with autologistic actor attribute models (ALAAMs), which belong to the family of exponential random graph models(Daraganova & Robins, 2012). The potential of ERGMs for the study of socialecological systems and networks of smallholder farmers has been demonstrated for example byBarnes et al (2022) andHermans et al (2017). ALAAMs are used for cross-sectional network data to test, by Markov Chain Monte Carlo maximum likelihood estimations, if certain network patterns can explain nodal attributes.…”

Downscaling global reference points to assess the sustainability of local fisheries

“…We conducted fieldwork in Ahus Island, a coastal island of approximately 950 people, who are highly dependent on reef resources (Barnes et al 2022), in Manus Province, Papua New Guinea (Fig. 1).…”

Institutional Approaches for Studying System-Oriented Networks

“…Temporal applications of the SEN can be challenging as the system tends to be large spatially, thus making it difficult to collect consistent data over multiple periods [55]. However, some researchers have successfully applied time series models of the SEN. For example, Barnes et al [60] collected data over multiple time periods and used a temporal exponential random graph model (TERGM) to study the social and environmental changes of a fishing community that was largely responsible for managing its own common-pool marine resources in Papua New Guinea. To highlight the challenge of longitudinal network studies, it should be noted that Barnes and colleagues [60] began their research in 2002 and collected data periodically until 2018, thus reflecting a sustained period of engagement in the community.…”