John C Kilgo | Search USGS

USGS2026

Integrating fecal DNA and telemetry to estimate wildlife densities in anthropogenic landscapes

Density estimation is central to wildlife management efforts but can be challenging in anthropogenically-dominated landscapes due to small parcel sizes, access restrictions, and limited green space. Reliable density estimates are especially important for managing white-tailed deer ( Odocoileus virginianus ), whose high abundance in urbanized areas increases the need for accurate population assessments to guide management and reduce negative human–wildlife interactions. Herein, we evaluated (a) the use of a plot-based spatial capture-recapture sampling design to estimate deer density in developed landscapes and (b) if integrating telemetry data improved the precision or biological inferences of density estimates. We conducted fecal sampling at 2 study areas reflecting different characteristics of the urbanization gradient in Durham and eastern Orange counties, North Carolina: (1) rural (i.e., forested, large parcels) and (2) suburban (i.e., small parcels, limited continuous access, diversity of landscape features). We collected 223 fecal samples during a 3-week sampling period in July and September 2022, resulting in 157 individual deer detected (112 F:45 M). Rural densities were estimated at 54 deer/km 2 (95% CI = 35–84) and suburban densities at 75 deer/km 2 (95% CI = 48–117). Integrating telemetry data allowed estimation of sex-specific space use and densities, which were not possible from fecal data alone; however, precision of total density estimates was similar across integrated and fecal-only analyses. Overall, our results highlighted key trade-offs inherent to plot-based spatial capture-recapture in suburban landscapes, most notably low recapture rates, accessibility challenges to maintain optimal trap spacing, and the importance of considering auxiliary data streams as part of the study design.

North Carolina↗

USGS2017

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

Background The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. Methods We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. Results We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. Conclusions The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Movement Ecology↗