Non-random medicinal plants selection in the Kichwa community of the Ecuadorian Amazon. (10th January 2020)

• Record Type: Journal Article
• Authors: Robles Arias, Daniela M.; Cevallos, Daniela; Gaoue, Orou G.; Fadiman, Maria G.; Hindle, Tobin
• Is Part Of: Journal of ethnopharmacology. Volume 246(2019)
• Publication Date: 2020-01-10
• Subjects: Non-random medicinal plant selection -- Kichwa -- Ecuadorian amazon -- Negative binomial model -- Theory-driven ethnobotany; Ethnopharmacology -- Periodicals; Pharmacognosy -- Periodicals; Herbs -- Periodicals; Herbs -- Periodicals; Pharmacognosy -- Periodicals; Pharmacognosie -- Périodiques; Herbes -- Périodiques; 615.1
• DOI: 10.1016/j.jep.2019.112220
• Languages: English
• ISSNs: 0378-8741

Abstract

Abstract: The non-random selection of medicinal plants theory, which predicts taxonomical biases in ethnopharmacopeias, indirectly demonstrates that traditional medicinal systems are rational and based in part on the therapeutic efficacy of plants. This theory suggests that because members of a taxonomical group share similar characteristics, some groups will be over-utilized in pharmacopeias, while other groups bereft of therapeutic potential will be under-utilized medicinally. Empirical evidence fo this theory comes from studies that used data collected at the national level which may lead to the overestimation of medicinal plant list given that some parts of the country (e.g., protected areas) can be unavailable for medicinal plant collection. Similarly, because medicinal plant importance and knowledge can be gender-specific and depends on the degree of exposure of a community, failure to account for gender and community experience can limit our understanding of non-random selection of medicinal plants. In this study, we used the negative binomial model and an examination of studentized residuals to demonstrate that a Kichwa community in the Ecuadorian Amazon over-utilized different sets of medicinal plant families depending on the gender of the informants or the experience of the community. We showed that utilizing local data instead of nationwide data reveals new over-utilized families. Seven of the nine most over-utilized medicinal plant families we found were previously reported in different biogeographical regions. The other two families are novel reports. Overall, our study proposes a novel method to uncover the intracultural heterogeneity of traditional knowledge and people non-random selection of medicinal plants at the local level. Graphical abstract: Image 1