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Fundamental insights on when social network data are most critical for conservation planning
Author(s) -
Rhodes Jonathan R.,
Guerrero Angela M.,
Bodin Örjan,
Chadès Iadine
Publication year - 2020
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/cobi.13500
Subject(s) - social network (sociolinguistics) , social network analysis , environmental resource management , business , computer science , geography , economics , social media , world wide web
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.