Methodological advances for hypothesis‐driven ethnobiology

Ethnobiology as a discipline has evolved recently to increasingly 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 datasets with non-independent observations due to species phylogenetic relatedness or socio-relational links between participants. Further, to fully account for the dynamics of local ecological knowledge, it is important to account for the spatially explicit distribution of knowledge, the changes in knowledge, knowledge transmission and use. To promote the use of advanced statistical modeling 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 ethnobiologicalmotivations for conducting generalized linear mixed-effect modeling, structural equation modeling, phylogenetic generalized least squares, social network analysis, species distribution modeling, and predictive modeling. For each element of the proposed ethnobiologists quantitative toolbox, we present practical applications along with script for a widespread implementation. Because these statistical modeling approaches are rarely taught in most ethnobiological programs but are essential for careers in academia or industry, it is critical to promote specialized workshops organized during annual professional meetings and focused on these advanced methods. By embracing these quantitative modeling techniques without sacrificing qualitative approaches which provide essential context, ethnobiology will further progress toward an expansive interaction with other disciplines.

Methods
These datasets include portions of data from authors' research used for illustrative purpose, data from published sources.

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