Assessing the accuracy of distance- and interview-based measures of hunting pressure

Abstract

In the Amazon Rainforest, the sustainability of hunting is difficult to model. Accurate sustainability models for hunting of mammal populations require a spatially explicit measure of hunting pressure. Because field-based measures of hunting pressure are time and labor-intensive, distance-based proxies for hunting pressure are frequently used. In this study, we tested accuracy of distance-based parameters in predicting measured hunting pressure obtained through interviews for a riverine community in the Peruvian Amazon. We examined the spatial accuracy of the interviews and investigated the minimum requirements for spatial assessment of hunting pressure based on interviews. Results illustrate that hunter-reported animal kill locations were accurate to within a mean of 1 km. Interview effort results showed that approximately 4 months of interviews capturing at least 50% of hunts are necessary to obtain a complete measure of hunting pressure across the landscape. Generalized linear models identified a novel spatially explicit approach that explained 59% of the deviance in measured hunting pressure. Our model was based on distances from locations that are easily obtained through remotely sensed imagery, participatory mapping, and terrain characteristics, highlighting that biologically relevant and spatially explicit estimates of hunting pressure can be obtained without lengthy field-based methods. Hunting pressure across a landscape can be accurately predicted from remote sensing, participatory mapping, and terrain characteristics.