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Chronic wasting disease (CWD) is a fatal prion disease affecting cervids (deer, elk, moose). Current methods to monitor individual disease state include highly invasive antemortem rectal biopsy or postmortem brain biopsy. Efficient, sensitive, and selective antemortem and postmortem testing of populations would increase knowledge of the dynamics of CWD epizootics as well as provide a means to track CWD progression into previously unaffected areas. Here, we analyzed the presence of CWD prions in skin samples from two easily accessed locations (ear and belly) from 30 deceased white-tailed deer ( Odocoileus viginianus ). The skin samples were enzymatically digested and analyzed by real-time quaking-induced conversion (RT-QuIC). The diagnostic sensitivity of the ear and belly skin samples were both 95%, and the diagnostic specificity of the ear and belly skin were both 100%. Additionally, the location of the skin biopsy on the ear does not affect specificity or sensitivity. These results demonstrate the efficacy of CWD diagnosis with skin biopsies using RT-QuIC. This method could be useful for large scale antemortem population testing.

Wildlife management in the United States of America (US) is primarily delegated to the individual states wherein state wildlife agencies manage wildlife populations to achieve multiple and sometimes conflicting objectives. White-tailed deer ( Odocoileus virginianus ) are an important species in the Midwestern US whose populations are primarily managed through recreational hunting. Managers aim to adjust populations by altering the harvest of antlerless (usually female) animals by changing the number of harvest permits available, hunting season lengths, or applying incentive programs like earn-a-buck, where a hunter must harvest an antlerless deer before they may harvest an antlered deer. We estimated the effect on antlerless deer harvest from changes in these regulations and changes in the number of licensed hunters across eight states in the Midwest. We used a Bayesian hierarchical model to estimate individual state and regional (i.e., across all states) effects. We found that increasing antlerless harvest permits increased antlerless harvest; however, this effect plateaued as the number of available permits increased. Providing unlimited harvest permits increased harvest, but the same increases were achieved by minimally increasing the number of limited harvest permits. Increasing the length of hunting season had a generally positive effect on antlerless harvest but the effect was non-linear and state dependent. The earn-a-buck incentive program resulted in the largest estimated increase in harvest. Finally, the number of licensed deer hunters in a state had a strong positive effect on the number of antlerless deer harvested. Our findings show that commonly applied changes in harvest regulations have a weak effect on the number of antlerless deer harvested, highlighting the challenges facing deer managers in the Midwestern US.

Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age‐specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age‐specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age‐period survival for white‐tailed deer Odocoileus virginianus in Wisconsin, USA and Columbian sharp‐tailed grouse Tympanuchus phasianellus columbianus in Colorado, USA. We found that models that included shape constraints for the age effects in the hazard curves using CGAMs outperformed models that did not include explicit functional constraints. We demonstrate a data‐driven and easily extendable approach to survival analysis by showing its utility to obtain hazard rates and survival probabilities, accounting for heterogeneity across ages and over time, for two very different species. We show how integration of ecological theory using constrained generalized additive models, with empirical statistical methods, enhances survival analyses.

White‐tailed deer (Odocoileus virginianus; hereafter, deer) have been widely studied regarding their breeding ecology and responses to hunting pressures. However, variations in defining the breeding season—its duration and timing—across studies have created uncertainty about whether regional differences in deer breeding ecology stem from ecological factors or methodological inconsistencies. This study aims to clarify the peak breeding season timing and the movement patterns of males during this period, particularly in relation to hunting seasons. Understanding how age and the timing of hunting seasons impact movement and breeding behaviors is important for wildlife managers, as these factors can affect harvest success. This study took place in southwest Wisconsin, using GPS data collected from 188 collared male deer between 15 October and 1 December from 2017 to 2020. Based on generalized linear mixed models, 2‐year‐old males exhibited higher hourly movement rates than other ages, and the opening weekend of the firearm hunting season had no significant effect on movement rates. In contrast, the variance in daily movement rate differed significantly between yearlings and older ages, with males 3 years and older displaying the highest variance. This suggests that older males may alternate more frequently between high‐movement mate searching and lower‐movement mate tending, potentially enhancing reproductive success. Similarly, 2‐year‐old males had larger daily ranges than both older and younger ages. Changepoint analysis of daily movement rates determined that the peak breeding season occurred between 23 October and 12 November, with little variation among ages and alternative metrics. Our findings indicate that male movement rates and ranges can reflect deer reproductive efforts and vary by age, which has important implications for reproductive success and disease transmission risk. This study investigates the peak breeding season and movement patterns of white‐tailed deer (WTD) in southwestern Wisconsin using GPS data from 188 collared males. Results indicate that two‐year‐olds have higher movement rates, while older males exhibit greater variability, suggesting reproductive advantages. The peak breeding season is identified as October 23 to November 12, highlighting the relevance of male movement patterns in disease risk and wildlife management decisions.

Background Dispersal is a fundamental process to animal population dynamics and gene flow. In white-tailed deer (WTD; Odocoileus virginianus ), dispersal also presents an increasingly relevant risk for the spread of infectious diseases. Across their wide range, WTD dispersal is believed to be driven by a suite of landscape and host behavioral factors, but these can vary by region, season, and sex. Our objectives were to (1) identify dispersal events in Wisconsin WTD and determine drivers of dispersal rates and distances, and (2) determine how landscape features (e.g., rivers, roads) structure deer dispersal paths. Methods We developed an algorithmic approach to detect dispersal events from GPS collar data for 590 juvenile, yearling, and adult WTD. We used statistical models to identify host and landscape drivers of dispersal rates and distances, including the role of agricultural land use, the traversability of the landscape, and potential interactions between deer. We then performed a step selection analysis to determine how landscape features such as agricultural land use, elevation, rivers, and roads affected deer dispersal paths. Results Dispersal predominantly occurred in juvenile males, of which 64.2% dispersed, with dispersal events uncommon in other sex and age classes. Juvenile male dispersal probability was positively associated with the proportion of the natal range that was classified as agricultural land use, but only during the spring. Dispersal distances were typically short (median 5.77 km, range: 1.3–68.3 km), especially in the fall. Further, dispersal distances were positively associated with agricultural land use in potential dispersal paths but negatively associated with the number of proximate deer in the natal range. Lastly, we found that, during dispersal, juvenile males typically avoided agricultural land use but selected for areas near rivers and streams. Conclusion Land use—particularly agricultural—was a key driver of dispersal rates, distances, and paths in Wisconsin WTD. In addition, our results support the importance of deer social environments in shaping dispersal behavior. Our findings reinforce knowledge of dispersal ecology in WTD and how landscape factors—including major rivers, roads, and land-use patterns—structure host gene flow and potential pathogen transmission.

Chronic wasting disease (CWD) is a fatal neurodegenerative disease affecting free-ranging and captive cervids that now occurs in 24 U.S. states and two Canadian provinces. Despite the potential threat of CWD to deer populations, little is known about the rates of infection and mortality caused by this disease. We used epidemiological models to estimate the force of infection and disease-associated mortality for white-tailed deer in the Wisconsin and Illinois CWD outbreaks. Models were based on age-prevalence data corrected for bias in aging deer using the tooth wear and replacement method. Both male and female deer in the Illinois outbreak had higher corrected age-specific prevalence with slightly higher female infection than deer in the Wisconsin outbreak. Corrected ages produced more complex models with different infection and mortality parameters than those based on apparent prevalence. We found that adult male deer have a more than threefold higher risk of CWD infection than female deer. Males also had higher disease mortality than female deer. As a result, CWD prevalence was twofold higher in adult males than females. We also evaluated the potential impacts of alternative contact structures on transmission dynamics in Wisconsin deer. Results suggested that transmission of CWD among male deer during the nonbreeding season may be a potential mechanism for producing higher rates of infection and prevalence characteristically found in males. However, alternatives based on high environmental transmission and transmission from females to males during the breeding season may also play a role.

Establishment and spread of infectious diseases are controlled by the frequency of contacts among hosts. Although managers can estimate transmission coefficients from the relationship between disease prevalence and age or time, they may wish to quantify or compare contact rates before a disease is established or while it is at very low prevalence. Our objectives were to quantify direct and indirect contacts rates among white-tailed deer (Odocoileus virginianus) and to compare these measures of contact rate with simpler measures of joint space use. We deployed Global Positioning System (GPS) collars on 23 deer near Carbondale, Illinois, USA, from 2002 to 2005. We used location data from the GPS collars to measure pairwise rates of direct and indirect contact, based on a range of proximity criteria and time lags, as well as volume of intersection (VI) of kernel utilization distributions. We analyzed contact rates at a given distance criterion and time lag using mixed-model logistic regression. Direct contact rates increased with increasing VI and were higher in autumn–spring than in summer. After accounting for VI, the estimated odds of direct contact during autumn–spring periods were 5.0–22.1-fold greater (depending on the proximity criterion) for pairs of deer in the same social group than for between-group pairs, but for direct contacts during summer the within:between-group odds ratio did not differ significantly from 1. Indirect contact rates also increased with VI, but the effects of both season and pair-type were much smaller than for direct contacts and differed little as the time lag increased from 1–30 days. These results indicate that simple measures of joint space use are insufficient indices of direct contact because group membership can substantially increase contacts at a given level of joint space use. With indirect transmission, however, group membership had a much smaller influence after accounting for VI. Relationships between contact rates and season, VI, and pair-type were generally robust to changes in the proximity criterion defining a contact, and patterns of indirect contacts were affected little by the choice of time lag from 1–30 days. The use of GPS collars provides a framework for testing hypotheses about the form of contact networks among large mammals and comparing potential direct and indirect contact rates across gradients of ecological factors, such as population density or landscape configuration.

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause‐specific mortality provide an example of implicit use of expert knowledge when causes‐of‐death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause‐specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause‐of‐death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event‐time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause‐of‐death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause‐of‐death assignment in modeling of cause‐specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause‐specific survival data for white‐tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause‐of‐death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference. We describe a new statistical method for time‐to‐event analyses, which leverages expert knowledge to account for uncertainty in event outcomes. We demonstrate its superior performance over traditional methods via simulation, and apply it to estimation of cause‐specific mortality of white‐tailed deer.

Exurban development is nonmetropolitan, residential development characterized by a human population density and average property size intermediate between suburban and rural areas. Although growth in exurban areas is outpacing that of urban, suburban, or rural landscapes, studies of deer (Odocoileus spp.) ecology in exurban areas are nonexistent. During 2003–2005, we studied space use (i.e., seasonal home-range and core-area size and habitat use relative to human dwellings) and survival of 43 female white-tailed deer (O. virginianus) in an exurban setting near Carbondale, Illinois. Deer had larger home ranges than most suburban deer populations and generally smaller home ranges than rural deer populations. When we analytically controlled for habitat use, deer exhibited a subtle avoidance of human dwellings, especially during the fawning season. The annual survival rate was among the highest reported in the literature at 0.872 (SE = 0.048). Only 5 deer (cause-specific mortality rate = 0.091) were harvested by hunters, indicating major obstacles for wildlife managers when attempting to manage deer in exurban areas using traditional hunter harvest.

In temperate and northern ecosystems where there are pronounced seasonal patterns in weather and available energy, there are corresponding patterns of body condition among white-tailed deer (Odocoileus virginianus). Body condition of white-tailed deer can affect survival and reproduction, which has large repercussions for state-level natural resource agencies that allocate hunting permits. In this study, we investigated how variation in winter weather, spring phenology, habitat composition, and browse quantity affected white-tailed deer body condition across a large spatial scale. Several body condition indicators (e.g., carcass mass, heart fat, antler size) were measured by hunters for 795 deer during September–December 2016–2018 in Wisconsin, USA. Winter severity in the previous year was an unreliable predictor of fall body condition of deer when winters were considered mild or moderate. The timing of spring green-up had a consistent effect on the body condition of all age and sex classes of deer. Earlier spring green-up resulted in heavier fawns and larger antlers among adult males. Region and spring green-up interacted to affect the heart fat of adult females. Earlier springs resulted in adult females in northern and central Wisconsin having a higher probability of heavy heart fat, whereas spring green-up had no effect on adult female heart fat in southern Wisconsin. Effects of habitat differed by age and sex class of deer, and by the body condition metric being evaluated, indicating that there are important physiological differences among age and sex classes of deer that are affected by the environment. Our study demonstrates that the hunting public can contribute large-scale, cost-effective, and quality data to deer monitoring and research projects. It is important that natural resource agencies be able to identify and recruit highly engaged members of the hunting public to ensure project success. The timing of spring green-up can have lasting effects on deer health that can be consistently observed the following fall, which is in contrast to the effects of winter severity that did not appear to persist when previous winters were mild or moderate. We encourage managers in northern or temperate regions to consider measures of spring green-up timing in conjunction with traditional winter severity when making deer population management decisions, such as antlerless tag allocation.