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Evaluating the use of local ecological knowledge to monitor hunted tropical-forest wildlife over large spatial scales

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Article number15
<mark>Journal publication date</mark>2015
<mark>Journal</mark>Ecology and Society
Issue number3
Number of pages26
Publication StatusPublished
<mark>Original language</mark>English


Monitoring the distribution and abundance of hunted wildlife is critical to achieving sustainable resource use, yet adequate data are sparse for most tropical regions. Conventional methods for monitoring hunted forest-vertebrate species require intensive in situ survey effort, which severely constrains spatial and temporal replication. Integrating local ecological knowledge (LEK) into monitoring and management is appealing because it can be cost-effective, enhance community participation, and provide novel insights into sustainable resource use. We develop a technique to monitor population depletion of hunted forest wildlife in the Brazilian Amazon, based on the local ecological knowledge of rural hunters. We performed rapid interview surveys to estimate the landscape-scale depletion of ten large-bodied vertebrate species around 161 Amazonian riverine settlements. We assessed the explanatory and predictive power of settlement and landscape characteristics and were able to develop robust estimates of local faunal depletion. By identifying species-specific drivers of depletion and using secondary data on human population density, land form, and physical accessibility, we then estimated landscape- and regional-scale depletion. White-lipped peccary (Tayassu pecari), for example, were estimated to be absent from 17% of their putative range in Brazil’s largest state (Amazonas), despite 98% of the original forest cover remaining intact. We found evidence that bushmeat consumption in small urban centers has far-reaching impacts on some forest species, including severe depletion well over 100 km from urban centers. We conclude that LEK-based approaches require further field validation, but have significant potential for community-based participatory monitoring as well as cost-effective, large-scale monitoring of threatened forest species.