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Most if not all environmental problems entail conflicts of interest. Yet, different actors and opposing coalitions often but certainly not always cooperate in solving these problems. Hence, processes of conflict and cooperation often work in tandem, albeit much of the scholarly literature tends to focus on either of these phenomena in isolation. Social network analysis (SNA) provides opportunities to study cooperation and conflict together. In this review, we demonstrate how SNA has increased our understanding of the promises and pitfalls of collaborative approaches in addressing environmental problems. The potential of SNA to investigate conflicts in environmental governance, however, remains largely underutilized. Furthermore, a network perspective is not restricted to the social domain. A multilevel social-ecological network perspective facilitates integration of social and environmental sciences in understanding how different patterns of resource access can trigger both cooperation and conflict. [ABSTRACT FROM AUTHOR]

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state‐dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small‐scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species‐poor sites are subsets of species‐rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small‐scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un‐nested and when social networks are likely to be centralized. Article impact statement: The value of collecting social network information for conservation planning depends on both species distributions and social network structure. [ABSTRACT FROM AUTHOR]