Ecological knowledge is fundamentally linked to the function, diversity, evolution, and stability of biological and cultural systems (AKA socio-ecological or biocultural systems) and the ecosystem services they provide. However, there is currently limited understanding of how ecological knowledge is distributed among humans. This interdisciplinary research tests how people’s life history is related to the amount and diversity of their medicinal plant knowledge (one type of ecological knowledge). Traditionally, biocultural research has been limited to testing how individual variables, such as age, correlate with medicinal plant knowledge. This research uses structural equation modeling to quantify how medicinal plant knowledge is simultaneously related to the following variables: education, income, medical clinic access, sociocultural exposure, ecological exposure, gender, age, and tribal affiliation.
To answer our research question, data were collected from four villages in Solomon Islands using interviews with every adult (303 participants). Interviews surveyed life history and medicinal plant knowledge. Structural equation modeling was used to calculate path coefficients and test the causal links (both direct and indirect) between each variable and five types of medicinal plant knowledge (# illnesses known, # species known, # uses known, similarity of species known, and similarity of uses known).
Results/Conclusions
Results indicate that all measured variables explain how one type of ecological knowledge, medicinal plant knowledge, is distributed among individuals. Relationships between variables are often indirect, and change depending on the type of medicinal plant knowledge. We find that our life history survey data explain about 13%-82% of the variation of people’s medicinal plant knowledge, with the highest explanatory power for the number of species and uses known. Structural equation model coefficients highlight direct and indirect affects of each variable on medicinal plant knowledge, providing support for all tested biocultural hypotheses.
In conclusion, characteristics of a person’s life can significantly predict their medicinal plant knowledge. Because there are many significant interactions between characteristics, analyzing single or few variables can mask significant indirect or interactive effects. Collection and analysis of multivariate biocultural data can rigorously assess poorly-tested biocultural theories to (1) enhance understanding of which characteristics of people and cultures are associated with different kinds and amounts of ecological knowledge, and (2) pinpoint generalizable principles of how humans think about and interact with their environment.