Host culling as an adaptive management tool for chronic wasting disease in white-tailed deer: a modelling study

Abstract

Emerging wildlife diseases pose a significant threat to natural and human systems. Because of real or perceived risks of delayed actions, disease management strategies such as culling are often implemented before thorough scientific knowledge of disease dynamics is available. Adaptive management is a valuable approach in addressing the uncertainty and complexity associated with wildlife disease problems and can be facilitated by using a formal model.We developed a multi-state computer simulation model using age, sex, infection-stage, and seasonality as a tool for scientific learning and managing chronic wasting disease (CWD) in white-tailed deer Odocoileus virginianus. Our matrix model used disease transmission parameters based on data collected through disease management activities. We used this model to evaluate management issues on density- (DD) and frequency-dependent (FD) transmission, time since disease introduction, and deer culling on the demographics, epizootiology, and management of CWD.Both DD and FD models fit the Wisconsin data for a harvested white-tailed deer population, but FD was slightly better. Time since disease introduction was estimated as 36 (95% CI, 24-50) and 188 (41->200) years for DD and FD transmission, respectively. Deer harvest using intermediate to high non-selective rates can be used to reduce uncertainty between DD and FD transmission and improve our prediction of long-term epidemic patterns and host population impacts. A higher harvest rate allows earlier detection of these differences, but substantially reduces deer abundance.Results showed that CWD has spread slowly within Wisconsin deer populations, and therefore, epidemics and disease management are expected to last for decades. Non-hunted deer populations can develop and sustain a high level of infection, generating a substantial risk of disease spread. In contrast, CWD prevalence remains lower in hunted deer populations, but at a higher prevalence the disease competes with recreational hunting to reduce deer abundance.Synthesis and applications. Uncertainty about density- or frequency-dependent transmission hinders predictions about the long-term impacts of chronic wasting disease on cervid populations and the development of appropriate management strategies. An adaptive management strategy using computer modelling coupled with experimental management and monitoring can be used to test model predictions, identify the likely mode of disease transmission, and evaluate the risks of alternative management responses.

Figure 1

Prevalence and deer density dynamics (deer mi⁻²) for a non‐hunted resource‐limited and resource‐unlimited deer population under density‐ and frequency‐dependent transmission. Lines show the predicted trajectories for the mean, upper, and lower confidence limits for the estimated transmission coefficients. Note the substantial overlap of these lines for the prevalence in the density‐dependent transmission model (upper left panel) and for deer‐density in the resource‐unlimited population under frequency‐dependent transmission (lower right panel). A colour version of this figure is provided in Supporting Information Fig. S2.

Figure 2

Prevalence and deer density dynamics (deer mi⁻²) for a deer population managed using a harvest strategy under density‐ and frequency‐dependent transmission. Lines show the predicted trajectories for the mean, upper, and lower confidence intervals for the estimated transmission coefficients. Note different time‐scales for density‐ and frequency‐dependent transmission models.

Figure 3

Prevalence and deer density dynamics (deer mi⁻²) after CWD detection in south‐central Wisconsin under harvest and culling strategies. Lines show the predicted trajectories for the mean, upper, and lower confidence intervals for the estimated transmission coefficients. Note different time‐scales for density‐ and frequency‐dependent transmission models.

Figure 4

Prevalence, deer density (deer mi⁻²), and antlerless harvest‐rate for a deer population managed via fixed and density‐dependent harvest (maximum harvest rate equals 48% and 26% for antlered and antlerless deer, respectively) under density‐ and frequency‐dependent transmission models. Note different time‐scales for density‐ and frequency‐dependent transmission models.