Local-scale dynamics and local drivers of bushmeat trade.

Abstract

Bushmeat management policies are often developed outside the communities in which they are to be implemented. These policies are also routinely designed to be applied uniformly across communities with little regard for variation in social or ecological conditions. We used fuzzy-logic cognitive mapping, a form of participatory modeling, to compare the assumptions driving externally generated bushmeat management policies with perceptions of bushmeat trade dynamics collected from local community members who admitted to being recently engaged in bushmeat trading (e.g., hunters, sellers, consumers). Data were collected during 9 workshops in 4 Tanzanian villages bordering Serengeti National Park. Specifically, we evaluated 9 community-generated models for the presence of the central factors that comprise and drive the bushmeat trade and whether or not models included the same core concepts, relationships, and logical chains of reasoning on which bushmeat conservation policies are commonly based. Across local communities, there was agreement about the most central factors important to understanding the bushmeat trade (e.g., animal recruitment, low income, and scarcity of food crops). These matched policy assumptions. However, the factors perceived to drive social-ecological bushmeat trade dynamics were more diverse and varied considerably across communities (e.g., presence or absence of collaborative law enforcement, increasing human population, market demand, cultural preference). Sensitive conservation issues, such as the bushmeat trade, that require cooperation between communities and outside conservation organizations can benefit from participatory modeling approaches that make local-scale dynamics and conservation policy assumptions explicit. Further, communities' and conservation organizations' perceptions need to be aligned. This can improve success by allowing context appropriate policies to be developed, monitored, and appropriately adapted as new evidence is generated.