Methodological advances for hypothesis‐driven ethnobiology

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...

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Vydané v:Biological reviews of the Cambridge Philosophical Society Ročník 96; číslo 5; s. 2281 - 2303
Hlavní autori: Gaoue, Orou G., Moutouama, Jacob K., Coe, Michael A., Bond, Matthew O., Green, Elizabeth, Sero, Nadejda B., Bezeng, Bezeng S., Yessoufou, Kowiyou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.10.2021
Predmet:
community phylogenetics
Ethnobiology
Geographical distribution
Hypotheses
Mathematical models
Multivariate statistical analysis
Network analysis
phylogenetic generalized least squares
Phylogenetics
Phylogeny
Prediction models
quantitative ethnobotany
social network analysis
Social networks
Social organization
statistical modelling
Statistical models
Structural equation modeling
structural equation modelling
theory‐driven ethnobotany
ISSN:1464-7931, 1469-185X, 1469-185X
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Shrnutí: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.
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ISSN:1464-7931
1469-185X
1469-185X
DOI:10.1111/brv.12752