Tracing gender variation in traditional knowledge: participatory tools to promote conservation in a Quilombola community in Brazil.

Background: Based on participatory research tools and analysis with a gender focus, we aim to identify the knowledge associated with native plants of the Atlantic Forest in one Quilombola community whose territory is juxtaposed with a protected area, in South Brazil.

Methods: Through the perception of the residents of the Quilombola community of São Roque, we classified the availability, harvesting intensity, abundance, and importance of fourteen plants native to the Atlantic Forest found in their territory. These fourteen plants were selected after initial interviews with a free listing of plants done with all adults (44 people), followed by plant collection and identification. A participatory workshop was built with the community to collect data through three activities: four-cell tool, environment matrix, and importance matrix. To identify the gender nuances in the knowledge within this community, all activities were separated into two groups based on the gender of the 22 participants (9 women and 13 men) and the researchers.

Results: The species Pau-pra-tudo (Picrasma crenata), Quina (Coutarea hexandra), and Cipó-milome (Aristolochia triangularis) were similarly classified as important by both groups, which indicates the cultural and environmental relevance associated with them regardless of gender. The perceptions of other species were expressed differently between the groups, showing the variance of the ecological knowledge and the relationship between the sociocultural contexts of gender and the knowledge manifested. The final part of the workshop was a lecture given by two community experts about herbal medicines based on forest species found in the territory.

Conclusions: Based on the multiple forms of results recorded in the workshop, we discuss the demand for inclusion of the traditional community in land management plans of environmental agencies, highlighting how individual characteristics, such as gender, can fill gaps in data about local biodiversity.