Chronic wasting disease (CWD) is a prion disease affecting cervid species, both free-ranging and captive populations. As the geographic range continues to expand and disease prevalence continues to increase, CWD will have an impact on cervid populations, local economies, and ecosystem health. Mitigation of this “wicked” disease will require input from many different stakeholders including hunters, landowners, research biologists, wildlife managers, and others, working together. The NC1209 (North American interdisciplinary chronic wasting disease research consortium) is composed of scientists from different disciplines involved with investigating and managing CWD. Leveraging this broad breadth of expertise, the Consortium has created a state-of-the-science review of five key aspects of CWD, including current diagnostic capabilities for detecting prions, requirements for validating these diagnostics, the role of environmental transmission in CWD dynamics, and potential zoonotic risks associated with CWD. The goal of this review is to increase stakeholders’, managers’, and decision-makers’ understanding of this disease informed by current scientific knowledge.

Epizootic hemorrhagic disease (EHD) is the most significant source of viral disease–related mortality in white-tailed deer (Odocoileus virginianus) in the US. Deer mortality from EHD has increased in the state of Michigan, US, since 2006, with the largest outbreak occurring in 2012. The 2012 outbreak provided an opportunity to evaluate how this disease affected EHD-related mortality in deer populations at a spatial scale typical of that expected for the greatest disease risk. Our objectives were to quantify the population impacts and spatial extent of EHD associated with areas of disease risk for deer populations and to determine how populations recovered over time following localized EHD impacts. We estimated the annual local abundance of deer for 5 yr immediately following a recent EHD outbreak. Because proximity to wetlands may affect EHD occurrence, we surveyed deer at varying distances (about 1 km and 5 km) from a riparian corridor to determine spatial variation in population impacts. Further, we assessed differences in deer abundance for sites affected and unaffected by EHD. Abundance estimates were lower along transects near the riparian corridor only in the affected area, reflecting EHD mortality associated with wetlands. The only change in abundance over time was a significant increase in the riparian strata in the EHD-affected site.

Diseases spread and persist according to complex interactions among hosts, pathogens, and their shared environments. In actively managed wildlife diseases, unanticipated disease spread and persistence can challenge mitigation efforts, necessitating modeling approaches that elucidate and integrate multiple mechanisms. Dynamic occupancy models (DOMs) can predict and forecast species occurrence patterns; however, they have not been extended to explain the complex spread and persistence patterns of chronic wasting disease (CWD). We used a DOM to predict and forecast the persistence, and localized and long‐distance spread of CWD in white‐tailed deer (Odocoileus virginianus) according to distinct ecological relationships that we hypothesized were important. We estimated large differences between the probabilities of disease persistence and localized and long‐distance CWD spread in Wisconsin, USA. Chronic wasting disease was more likely to persist in areas with low clay content in soils, to spread locally as the amount of localized disease increased, and to occur in areas far removed from prior positives in places with a higher proportion of agriculture. Across all landscape compositions, the probability of occurrence decayed by distance from prior positives in a manner consistent with transient movement distances of white‐tailed deer. Finally, a multi‐mechanism DOM was able to produce spatially accurate predictions and forecasts of disease occurrence using only data collected during the first 2 days of the firearm harvest season. Thus, our multi‐mechanism DOM offers advantages over simple linear models that can improve our understanding of the complex ecology driving CWD dynamics and lead to better surveillance and management strategies moving forward.

Aerial surveys are a critical tool for inference of wildlife populations, yet are limited by current available methods to account for imperfect detection without marking animals.N‐mixture models use count data from replicate surveys for estimation of wildlife abundance while accounting for imperfect detection. This statistical framework was recently developed but is rarely applied to aerial surveys of terrestrial wildlife. We applied anN‐mixture model incorporating temporary emigration to a novel aerial survey design to estimate the availability, detection probability, and abundance of an unmarked population of white‐tailed deer (Odocoileus virginianus) in Michigan, USA, using surveys in 2014 and 2016. We assessed the selection of the sample unit size for theN‐mixture model in a post hoc sensitivity analysis and compared all estimates to a hierarchical distance sampling (HDS) approach. Hierarchical distance sampling is an expansion of conventional distance sampling (CDS), a standard method applied to aerial survey designs for wildlife, where HDS allows for modeling spatial variation in abundance and detection probability using environmental covariates and can account for changing population demographics. We foundN‐mixture models produced similar parameter estimates to HDS with both models inferring a population increase between years, which was consistent with independently available estimates for this population (2014N‐mixture = 402 [CI: 303.8–515.27], and HDS = 391 [CI: 297.67–504.63]; 2016N‐mixture = 732 [CI: 572.89–924.22], and HDS = 708 [CI: 552.45–893.89]). Estimates from both models were sensitive to the sample unit size selected in an open population framework. We recommend using ecological (e.g., home range size) and statistical (e.g., sample size) considerations when selecting a sample unit size that reflects the movement ecology of a species and evaluating the sensitivity of parameters to sample unit size. In our study,N‐mixture models and HDS models afforded a practical field approach and analytical methodology that resulted in precise estimation in a population of unmarked deer over time. When used with aerial surveys in terrestrial systems where distance sampling methods may be difficult to implement,N‐mixture models can address observer error and estimate populations for conservation and management goals without marking individual animals.