Economic and geographic drivers of wildlife consumption in rural Africa

Abstract

The harvest of wildlife for human consumption is valued at several billion dollars annually and provides an essential source of meat for hundreds of millions of rural people living in poverty. This harvest is also considered among the greatest threats to biodiversity throughout Africa, Asia, and Latin America. Economic development is often proposed as an essential first step to win-win solutions for poverty alleviation and biodiversity conservation by breaking rural reliance on wildlife. However, increases in wealth may accelerate consumption and extend the scale and efficiency of wildlife harvest. Our ability to assess the likelihood of these two contrasting outcomes and to design approaches that simultaneously consider poverty and biodiversity loss is impeded by a weak understanding of the direction and shape of their interaction. Here, we present results of economic and wildlife use surveys conducted in 2,000 households from 96 settlements in Ghana, Cameroon, Tanzania, and Madagascar. We examine the individual and interactive roles of wealth, relative food prices, market access, and opportunity costs of time spent hunting on household rates of wildlife consumption. Despite great differences in biogeographic, social, and economic aspects of our study sites, we found a consistent relationship between wealth and wildlife consumption. Wealthier households consume more bushmeat in settlements nearer urban areas, but the opposite pattern is observed in more isolated settlements. Wildlife hunting and consumption increase when alternative livelihoods collapse, but this safety net is an option only for those people living near harvestable wildlife.

Fig. 1.

Map of the location of the four countries and eight regions sampled in this study. Black stars show the location of the capital of each country, and white patches identify the approximate locations of households included in our sampling.

Fig. 2.

Household wealth is only weakly linked to wildlife consumption (slope = −0.06; r = −0.04) for the complete dataset of 2,000 households and 96 settlements (A), but it is significantly and negatively related to consumption (r = −0.71, P < 0.001) for the 500 most rural households (B) and positively related to consumption (r = 0.56, P < 0.001) for the 500 most urban households (C). Values shown are means ± SD for each graph.

Fig. 3.

Distance of human settlements from harvestable wildlife populations in Ghana, Tanzania, Madagascar, and Cameroon was a strong predictor of the amount of bushmeat that households in those communities consume annually (r = −0.48, P < 0.001, n = 96 settlements) (A). Distance from wildlife also was positively related (r = 0.78, P < 0.001, n = 52 settlements) to the price that consumers paid for bushmeat in Ghana and Tanzania. Values shown are means ± SE (B). Geographic distances are shown here as raw values, but they were rank-transformed for each country for statistical analysis.

Fig. 4.

Relative distance of settlements from urban areas in Ghana and Tanzania strongly predicted whether harvested wildlife would be consumed within the hunter's home community or exported to external markets (r = 0.72, P < 0.001, n = 47 hunters and 762 harvested animals). Geographic distances are shown here as raw values, but they were rank-transformed for each country for statistical analysis.

Fig. 5.

Time allocation of 47 hunters surveyed weekly in Ghana and Tanzania was tied closely to employment or commitment to farming-related activities, with hunting levels peaking during periods when farms were fallow (P < 0.01 for ANOVAs of hunting and farming across the four time periods). Sleep was excluded from time budget calculations. Average daily bushmeat consumption in households provisioned by hunters included in this analysis declined by 68% (±17) during months of peak farming activity.