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Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge

Citation

Bond, Matthew; Gaoue, Orou (2020), Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge, Dryad, Dataset, https://doi.org/10.5061/dryad.cfxpnvx3q

Abstract

Human subsistence societies have thrived in environmental extremes while maintaining biodiversity through social learning of ecological knowledge, such as techniques to prepare food and medicine from local resources. However, there is limited understanding of which processes shape social learning patterns and configuration in ecological knowledge networks, or how these processes apply to resource management and biological conservation. In this study, we test the hypothesis that the prestige (rarity or exclusivity) of knowledge shapes social learning networks. In addition, we test whether people tend to select who to learn from based on prestige (knowledge or reputation), and homophily (e.g., people of the same age or gender). We used interviews to assess five types of medicinal plant knowledge and how 303 people share this knowledge across four villages in Solomon Islands. We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. We found that prestige predicts five hypothesized network configurations and all three hypothesized learning patterns, while homophily predicts one of three hypothesized network configurations and five of the seven hypothesized learning patterns. These results compare the strength of different prestige and homophily effects on social learning and show how cultural practices such as intermarriage can affect certain aspects of prestige and homophily. By advancing our understanding of how prestige and homophily affect ecological knowledge networks, we identify which social learning patterns have the largest effects on biocultural conservation of ecological knowledge.

Usage Notes

These files contain the adjacency matrices and nodal attributes used for explonential random graph models (ERGMs) used in M.O. Bond and O.G. Gaoue. 2020. Prestige and Homophily Predict Network Structure for Social Learning of Medicinal Plant Knowledge. PLOS ONE (in press).

Funding

National Science Foundation, Award: Graduate Research Fellowship

American Philosophical Society, Award: Lewis & Clark Fund for Exploration & Field Research

Garden Club of America, Award: Anne S. Chatham Fellowship in Medicinal Botany

National Science Foundation, Award: #1513354