Knowledge coproduction to improve assessments of nature’s contributions to people
Améline Vallet, Bruno Locatelli, Merelyn Valdivia-Díaz, Yésica Quispe Conde, Gerardina Matencio García, Alejandrina Ramos Criales, Francisca Valverde Huamanñahui, Santusa Ramos Criales, David Makowski, Sandra Lavorel
Sustainability science needs new approaches to produce, share, and use knowledge because there are major barriers to translating research into policy and practice. Multiple actors hold relevant knowledge for sustainability including indigenous and local people who have developed over generations knowledge, methods, and practices that biodiversity and ecosystem assessments need to capture. Despite efforts to mainstream knowledge coproduction, less than 3% of the literature on nature’s contributions to people (NCP) integrates indigenous and local knowledge (ILK). Approaches and tools to better integrate scientific and ILK knowledge systems in NCP assessments are urgently needed. To fill this gap, we conducted interviews with ILK experts from Abancay and Tamburco, Peru, and convened focus groups and workshops during which participatory mapping, a serious game, a Bayesian belief network based on ILK were introduced. We inventoried 60 medicinal plants used to treat different illnesses, and analyzed the spatial distribution of the 7 plants that contribute the most to a good quality of life, and delineated their nonmedicinal uses. Based on the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services conceptual framework, we defined dimensions of a good quality of life according to indigenous and local worldviews. Medicinal plants contributed strongly to health and household security, among other contributions. Climate change and overexploitation were the main perceived threats to medicinal plants, despite the existence of formal and customary institutions to regulate trade. Our approach was flexible enough to integrate diverse forms of knowledge, as well as qualitative and quantitative information from, for example, the Bayesian belief network.