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.

中文翻译：

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假设驱动的民族生物学的方法论进展

民族生物学作为一门学科已经越来越多地发展到采用受理论启发和假设驱动的方法来研究当地人为什么以及如何选择与他们互动并用于谋生的植物和动物。然而，测试复杂的假设或民族生物学假设网络具有挑战性，特别是对于由于物种系统发育相关性或参与者之间的社会关系联系而具有非独立观察的数据集。此外，为了充分考虑当地生态知识的动态，重要的是包括知识的空间明确分布、知识的变化以及知识的传播和使用。为了促进使用解​​决这些限制的高级统计建模方法，我们综合了民族生物学中假设驱动研究的方法论进展，同时强调了民族生物学文献中需要更多的数字而不是表格和更多的表格。我们提出了进行广义线性混合效应建模、结构方程建模、系统发育广义最小二乘法、社会网络分析、物种分布建模和预测建模的民族生物学动机。对于提议的民族生物学家定量工具箱的每个元素，我们展示了实际应用以及广泛实施的脚本。由于这些统计建模方法很少在大多数民族生物学项目中教授，但对学术界或工业界的职业至关重要，因此推广以这些先进方法为重点的讲习班和短期课程至关重要。