期刊
CONSERVATION BIOLOGY
卷 27, 期 6, 页码 1355-1365出版社
WILEY
DOI: 10.1111/cobi.12124
关键词
compliance; indirect questioning; poaching; sensitive questions; UCT; uncertainty; unmatchedcount technique
资金
- Portuguese Foundation for Science and Technology (FCT) [SFRH/BD/43186/2008]
- European Commission
- Royal Society Wolfson Research Merit award
- Fundação para a Ciência e a Tecnologia [SFRH/BD/43186/2008] Funding Source: FCT
Assessing anthropogenic effects on biological diversity, identifying drivers of human behavior, and motivating behavioral change are at the core of effective conservation. Yet knowledge of people's behaviors is often limited because the true extent of natural resource exploitation is difficult to ascertain, particularly if it is illegal. To obtain estimates of rule-breaking behavior, a technique has been developed with which to ask sensitive questions. We used this technique, unmatched-count technique (UCT), to provide estimates of bushmeat poaching, to determine motivation and seasonal and spatial distribution of poaching, and to characterize poaching households in the Serengeti. We also assessed the potential for survey biases on the basis of respondent perceptions of understanding, anonymity, and discomfort. Eighteen percent of households admitted to being involved in hunting. Illegal bushmeat hunting was more likely in households with seasonal or full-time employment, lower household size, and longer household residence in the village. The majority of respondents found the UCT questions easy to understand and were comfortable answering them. Our results suggest poaching remains widespread in the Serengeti and current alternative sources of income may not be sufficiently attractive to compete with the opportunities provided by hunting. We demonstrate that the UCT is well suited to investigating noncompliance in conservation because it reduces evasive responses, resulting in more accurate estimates, and is technically simple to apply. We suggest that the UCT could be more widely used, with the trade-off being the increased complexity of data analyses and requirement for large sample sizes.
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