Methodological advances for hypothesis-driven ethnobiology.

Area of Science:

• Ethnobiology
• Quantitative Ecology
• Computational Social Science

Background:

• Ethnobiology increasingly uses theory-driven approaches to understand human-environment interactions.
• Traditional methods struggle with non-independent data (phylogenetics, social links) and knowledge dynamics (spatial, temporal, transmission).

Purpose of the Study:

• To synthesize advanced statistical modeling techniques for hypothesis-driven ethnobiology.
• To address limitations in analyzing complex datasets and local ecological knowledge dynamics.
• To promote the integration of quantitative methods within ethnobiological research.

Main Methods:

• Overview of generalized linear mixed-effect modeling, structural equation modeling, phylogenetic generalized least squares, social network analysis, species distribution modeling, and predictive modeling.
• Emphasis on practical applications and implementation scripts.
• Advocacy for visual data representation (figures, tables) over extensive text.

Main Results:

• Identified key statistical models applicable to ethnobiological research challenges.
• Provided practical guidance and scripts for implementing advanced quantitative methods.
• Highlighted the necessity of integrating quantitative approaches with qualitative data.

Conclusions:

• Advanced statistical modeling is essential for robust hypothesis testing in ethnobiology.
• Training and workshops are critical to equip researchers with these vital quantitative skills.
• Integrating quantitative and qualitative methods will foster interdisciplinary collaboration and advance ethnobiology.