Geology topics

David Stainbrook

Publications and source records attributed to David Stainbrook.

4 recordsLinked to original sources

USGS2026

Intraspecific contact among white-tailed deer: A literature review and chronic wasting disease case study

White-tailed deer ( Odocoileus virginianus ) are a valuable game mammal in the eastern United States necessitating detailed understanding of disease transmission. We conducted a literature review on intraspecific contact (i.e., interactions wherein disease transmission may occur) among deer. From 69 studies, we identified five themes underlying research on intraspecific deer contact: physical touch, social groups, spatial overlap, association rates, and social networks. Visual observations determined physical touch to be infrequent (< 2 touches/h) and indicated deer social groups were dependent on spatial dynamics of parturition and dispersal; most females remained with matriarchal family groups while males dispersed and formed bachelor groups. Assessed using global positioning system (GPS) monitoring, spatial overlap and association rates (i.e., instances of deer in close spatial–temporal proximity) were higher in correspondence to within-group social dynamics, and between-group scores were correspondingly low. Social network analyses indicated between-group transmission may be driven by socially dominant males, often termed super-spreaders (i.e., hosts infecting disproportionately high numbers of healthy individuals). We investigated these themes via a case study of deer infected with chronic wasting disease (CWD) in southcentral Pennsylvania, United States. We assessed spatial overlap and association rates using GPS monitoring data from 180 deer. Our results supported findings in the literature, showing strong correlations among spatial overlap, association rates, and correlated movements. Further, CWD-infected deer exhibited similar association rates to deer in which CWD was not detected. Our literature review and case study indicate direct transmission of CWD and other diseases is likely greatest within social groups following seasonal behavioral dynamics and that between-group transmission is likely driven by males via dispersal and mating interactions. Our results may be used to inform population management models with future work focused on high resolution spatial assessments of transmission in localized areas.

Pennsylvania↗

USGS2026

Statewide agent-based model for management of chronic wasting disease in white-tailed deer: PAOvCWD

Chronic wasting disease (CWD) is an always-fatal disease infecting wild cervids globally. Ecologically and economically important, CWD presents a challenge for managing white-tailed deer ( Odocoileus virginianus ). We built an agent-based model to simulate CWD transmission and assess potential management actions that could slow disease spread: PAOvCWD . We developed PAOvCWD using contact rates and other behavioral and ecological metrics estimated from deer monitored in Pennsylvania, USA. We programmed potential management responses (e.g., culling, altered hunter harvest) for all 22 Pennsylvania wildlife management units and validated the efficacy of PAOvCWD using a deer population in south-central Pennsylvania infected with CWD for > 10 years. To support applications of our model, we developed a user-friendly R pipeline that allows implementation with relatively minor modifications. Our pipeline includes four steps: • Steps 1 and 2 generate regional percent forest cover rasters and curate population-level demographics. • Step 3 initializes landscapes and agents using our PAOvPOP model. • Step 4 assesses CWD transmission and management responses using our PAOvCWD model.

Pennsylvania↗

USGS2024

Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types

Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer ( Odocoileus virginianus ) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.

Ecological Modeling↗

USGS2018

Heterogeneity of a landscape influences size of home range in a North American cervid

In the northeastern United States, chronic wasting disease has recently been detected in white-tailed deer ( Odocoileus virginianus ) populations, and understanding the relationship between landscape configuration and home range may improve disease surveillance and containment efforts. The objectives of our study were to compare size of home range for deer occupying a continuum of forested landscapes and to investigate relationships between size of home range and measures of landscape configuration. We used a movement-based kernel density estimator to estimate home range at five spatial scales among deer across study areas. We developed 7 linear regression models that used measures of the configuration of the forested landscape to explain size of home range. We observed differences in size of home range between sexes among areas that differed based on landscape configuration. We documented size of home range changed with various metrics that identifying connectivity of forested patches. Generally, size of home range increased with an increasing proportion of homogenous forest. Our results suggest that deer in our region occupy a landscape at hierarchically-nested scales that is controlled by the connectivity of the forested landscape across local or broad geographical regions.

Pennsylvania↗