Methodological advances for hypothesis‐driven ethnobiology. (30th May 2021)

• Record Type: Journal Article
• Authors: Gaoue, Orou G.; Moutouama, Jacob K.; Coe, Michael A.; Bond, Matthew O.; Green, Elizabeth; Sero, Nadejda B.; Bezeng, Bezeng S.; Yessoufou, Kowiyou
• Is Part Of: Biological reviews. Volume 96:Number 5(2021)
• Page Start: 2281
• Page End: 2303
• Publication Date: 2021-05-30
• Subjects: community phylogenetics -- ethnobiology -- theory‐driven ethnobotany -- quantitative ethnobotany -- statistical modelling -- structural equation modelling -- social network analysis -- phylogenetic generalized least squares; Biology -- Periodicals; 570
• DOI: 10.1111/brv.12752
• Languages: English
• ISSNs: 1464-7931

Abstract

ABSTRACT: Ethnobiology as a discipline has evolved increasingly to embrace theory‐inspired and hypothesis‐driven approaches to study why and how local people choose plants and animals they interact with and use for their livelihood. However, testing complex hypotheses or a network of ethnobiological hypotheses is challenging, particularly for data sets with non‐independent observations due to species phylogenetic relatedness or socio‐relational links between participants. Further, to account fully for the dynamics of local ecological knowledge, it is important to include the spatially explicit distribution of knowledge, changes in knowledge, and knowledge transmission and use. To promote the use of advanced statistical modelling approaches that address these limitations, we synthesize methodological advances for hypothesis‐driven research in ethnobiology while highlighting the need for more figures than tables and more tables than text in ethnobiological literature. We present the ethnobiological motivations for conducting generalized linear mixed‐effect modelling, structural equation modelling, phylogenetic generalized least squares, social network analysis, species distribution modelling, and predictive modelling. For each element of the proposed ethnobiologists quantitative toolbox, we present practical applications along with scripts for a widespread implementation. Because these statistical modelling approaches are rarely taught in most ethnobiological programs but are essential for careers in academia or industry, it is critical to promote workshops and short courses focused on these advanced methods. By embracing these quantitative modelling techniques without sacrificing qualitative approaches which provide essential context, ethnobiology will progress further towards an expansive interaction with other disciplines.