Fundamental insights on when social network data are most critical for conservation planning
© 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.
| Veröffentlicht in: | Conservation biology : the journal of the Society for Conservation Biology. - 1999. - 34(2020), 6 vom: 19. Dez., Seite 1463-1472 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | Conservation biology : the journal of the Society for Conservation Biology |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't análisis de redes sociales artificial intelligence distribución de las especies inteligencia artificial programación estocástica dinámica social network analysis species distributions stochastic dynamic programing mehr... |
| Zusammenfassung: | © 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. 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 |
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| Beschreibung: | Date Completed 26.02.2021 Date Revised 26.02.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1523-1739 |
| DOI: | 10.1111/cobi.13500 |