Estimating population density for disease risk assessment: The importance of understanding the area of influence of traps using wild pigs as an example

Abstract

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 ∼8.6km. 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.

Exposure of a population of invasive wild pigs to simulated toxic bait containing biomarker: implications for population reduction

“…Alternatively, spacing the bait sites at farther distances will allow for a larger geographic focus but a lower overall proportion of wild pigs will be removed. 80 Our results showed evidence that consumption of the biomarker bait varied by sex of wild pigs. Despite males and females having similarly sized home ranges, males visited ∼ 37% more bait sites and thus had more encounters with the bait.…”

“…Therefore, bait sites placed at a density of one site per ≤ 1.5 km 2 should theoretically expose all encompassed wild pigs to the bait. Alternatively, spacing the bait sites at farther distances will allow for a larger geographic focus but a lower overall proportion of wild pigs will be removed …”

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

“…Wild pigs in the drier ecoregions are predicted to move more because water resources are further away on average. Thus, ecoregion may describe numerous characteristics of a landscape, and could be a simple predictor of movement levels which is valuable for planning management programs across a large spatial scale [81, 82]. …”

“…However, it is possible that extrinsic factors such as weather could result in different movement reaction norms as a function of individual-level attributes. For example, perhaps adult males show a different reaction norm to temperature relative to females such as [81] found, where home range sizes of males were not affected by drought conditions but female home range sizes changed according to food availability and temperature constraints. Quantifying these individual-based differences in reaction norms could improve our fundamental understanding of movement capacity and lead to better prediction of how meteorological changes may impact population ecology.…”

Quantifying site-level usage and certainty of absence for an invasive species through occupancy analysis of camera-trap data

“…If the bias were the other way and camera placement was less likely to detect wild pigs than we would be underestimating the occupancy of wild pigs which would be a considerable problem. Previous work on wild pigs has shown that baiting usually attracts pigs within a * 1.7 km radius (Davis et al 2017) and up to 10 km (N. Snow unpublished data). Therefore, it is likely that pigs within the 2 km 9 2 km sites would be detected if present.…”