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The social networks is one factor determining the flow of information within communities and as such may be important in determining successful implementation of community based management. We mapped the social network used for communication of knowledge and information related to natural resource extraction among villagers in a coastal seascape in Kenya. We further identified subgroups and examined their interrelations while measuring to what extent personal attributes such as occupation can explain observed group structure. Finally, we compared the local ecological knowledge held by villagers of different occupations with the structure of the communication network to map how well this structure can explain distribution of ecological knowledge among them. Results show that communication occurs primarily between fishermen who use the same gear type, which may inhibit exchange of ecological knowledge within the community. This may partly explain why the community has been unsuccessful in regulating resource extraction, especially since potentially influential groups of nonfishermen have a limited communication with the various fisher groups. Analysis of network structure also shows that groups most central, and hence potentially most influential, are dominated in numbers by migrant deep sea fishermen, hypothetically less motivated to initiate collective action for resource management. Hence, we conclude that a lack of collective action to remedy an unsustainable situation may be attributed to various different but distinct aspects of the specific structure of the social network. [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]