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.

The bushmeat and food security nexus: A global account of the contributions, conundrums and ethical collisions

“…Yet, in spite of their popularity and intuitive appeal, these proposed win-win approaches have suffered many practical failures, have generally faltered in achieving their desired outcomes and have been the target of considerable backlash (Agrawal & Gibson, 1999;Barrett & Arcese, 1995;Christensen, 2004;Gibson & Marks, 1995;McShane & Newby, 2004;McShane et al, 2011;Songorwa, Buhrs, & Hughey, 2000;Wells & McShane, 2004). The central stumbling blocks have included erroneous assumptions, poor planning, inadequate or short-term funding, the unilateral application of projects with little consideration of socio-economic factors driving overexploitation, the inability to provide real benefits at the household-level, as well as limited monitoring of outcomes and adaptive management strategies (Nyaki, Gray, Lepczyk, Skibins, & Rentsch, 2014;Roe & Elliott, 2006;Wicander & Coad, 2015).…”

Mapping recreational fishers’ informal learning of scientific information using a fuzzy cognitive mapping approach to mental modelling

“…Indegree, outdegree, centrality and density were calculated using formulae from previous studies (see Ozesmi & Ozesmi ; Nyaki et al . ). Outdegree is the cumulative strength of relationships exiting a variable (arrows pointing away from a variable).…”

Managing hunting under uncertainty: from one-off ecological indicators to resilience approaches in assessing the sustainability of bushmeat hunting

“…Further, the emergence of market-based economies and the commercialization of bushmeat in urban centers have increased demand for bushmeat through commodification and exportation ). Other drivers include disease control, wildfires, weather, crop scarcity, cultural preferences, lack of environment education, and crop destruction (Nyaki et al 2014). Some of these drivers operate at a local scale, but other operate at national, regional, and even international scales.…”

“…The strength of using FCM in this context is the ability to extract, combine, and represent group knowledge in a sensitive situation for comparison between or among groups. Nyaki et al (2014) used FCM to understand the drivers of bushmeat trade in four Tanzanian villages bordering Serengeti National Park.…”