Date of this Version
Preventive Veterinary Medicine 141 (2017), pp. 33–37.
Population density is a key driver of disease dynamics in wildlife populations. Accurate disease risk assessment and determination of management impacts on wildlife populations requires an ability to estimate population density alongside management actions. A common management technique for controlling wildlife populations to monitor and mitigate disease transmission risk is trapping (e.g., box traps, corral traps, drop nets). Although abundance can be estimated from trapping actions using a variety of analytical approaches, inference is limited by the spatial extent to which a trap attracts animals on the landscape. If the “area of influence” were known, abundance estimates could be converted to densities. In addition to being an important predictor of contact rate and thus disease spread, density is more informative because it is comparable across sites of different sizes. The goal of our study is to demonstrate the importance of determining the area sampled by traps (area of influence) so that density can be estimated from management-based trapping designs which do not employ a trapping grid. To provide one example of how area of influence could be calculated alongside management, we conducted a small pilot study on wild pigs (Sus scrofa) using two removal methods 1) trapping followed by 2) aerial gunning, at three sites in northeast Texas in 2015. We estimated abundance from trapping data with a removal model. We calculated empirical densities as aerial counts divided by the area searched by air (based on aerial flight tracks). We inferred the area of influence of traps by assuming consistent densities across the larger spatial scale and then solving for area impacted by the traps. Based on our pilot study we estimated the area of influence for corral traps in late summer in Texas to be ~8.6 km2. Future work showing the effects of behavioral and environmental factors on area of influence will help mangers obtain estimates of density from management data, and determine conditions where trap-attraction is strongest. The ability to estimate density alongside population control activities will improve risk assessment and response operations against disease outbreaks.