Long-term assessment of relationships between changing environmental conditions and the physiology of southern Beaufort Sea polar bears (Ursus maritimus)

Climate change is influencing polar bear (Ursus maritimus) habitat, diet, and behavior but the effects of these changes on their physiology is not well understood. Blood-based biomarkers are used to assess the physiologic health of individuals but their usefulness for evaluating population health, especially as it relates to changing environmental conditions, has rarely been explored. We describe links between environmental conditions and physiologic functions of southern Beaufort Sea polar bears using data from blood samples collected from 1984 to 2018, a period marked by extensive environmental change. We evaluated associations between 13 physiologic biomarkers and circumpolar (Arctic oscillation index) and regional (wind patterns and ice-free days) environmental metrics and seasonal and demographic co-variates (age, sex, season, and year) known to affect polar bear ecology. We observed signs of dysregulation of water balance in polar bears following years with a lower annual Arctic oscillation index. In addition, liver enzyme values increased over time, which is suggestive of potential hepatocyte damage as the Arctic has warmed. Biomarkers of immune function increased with regional-scale wind patterns and the number of ice-free days over the Beaufort Sea continental shelf and were lower in years with a lower winter Arctic oscillation index, suggesting an increased allocation of energetic resources for immune processes under these conditions. We propose that the variation in polar bear immune and metabolic function is likely indicative of physiologic plasticity, a response that allows polar bears to remain in homeostasis even as they experience changes in nutrition and habitat in response to changing environments.

A phenology of fear: Investigating scale and seasonality in predator-prey games between wolves and white-tailed deer

Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale-sensitive inferences in predator-prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white-tailed deer and canid predators (coyotes and wolves), we used a large and year-round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk-response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade-offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a "phenology of fear." Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra-annual variation in predator-prey interactions may be common in seasonal environments.

A call to action: Standardizing white-tailed deer harvest data in the Midwestern United States and implications for quantitative analysis and disease management

Recreational hunting has been the dominant game management and conservation mechanism in the United States for the past century. However, there are numerous modern-day issues that reduce the viability and efficacy of hunting-based management, such as fewer hunters, overabundant wildlife populations, limited access, and emerging infectious diseases in wildlife. Quantifying the drivers of recreational harvest by hunters could inform potential management actions to address these issues, but this is seldom comprehensively accomplished because data collection practices limit some analytical applications (e.g., differing spatial scales of harvest regulations and harvest data). Additionally, managing large-scale issues, such as infectious diseases, requires collaborations across management agencies, which is challenging or impossible if data are not standardized. Here we discuss modern issues with the prevailing wildlife management framework in the United States from an analytical point of view with a case study of white-tailed deer (Odocoileus virginianus) in the Midwest. We have four aims: (1) describe the interrelated processes that comprise hunting and suggest improvements to current data collections systems, (2) summarize data collection systems employed by state wildlife management agencies in the Midwestern United States and discuss potential for large-scale data standardization, (3) assess how aims 1 and 2 influence managing infectious diseases in hunted wildlife, and (4) suggest actionable steps to help guide data collection standards and management practices. To achieve these goals, Wisconsin Department of Natural Resources disseminated a questionnaire to state wildlife agencies (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin), and we report and compare their harvest management structures, data collection practices, and responses to chronic wasting disease. We hope our “call to action” encourages re-evaluation, coordination, and improvement of harvest and management data collection practices with the goal of improving the analytical potential of these data. A deeper understanding of the strengths and deficiencies of our current management systems in relation to harvest and management data collection methods could benefit the future development of comprehensive and collaborative management and research initiatives (e.g., adaptive management) for wildlife and their diseases.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

A recovery network leads to the natural recolonization of an archipelago and a potential trailing edge refuge

Rapid environmental change is reshaping ecosystems and driving species loss globally. Carnivore populations have declined and retracted rapidly and have been the target of numerous translocation projects. Success, however, is complicated when these efforts occur in novel ecosystems. Identifying refuges, locations that are resistant to environmental change, within a translocation framework should improve population recovery and persistence. American martens (Martes americana) are the most frequently translocated carnivore in North America. As elsewhere, martens were extirpated across much of the Great Lakes region by the 1930s and, despite multiple translocations beginning in the 1950s, martens remain of regional conservation concern. Surprisingly, martens were rediscovered in 2014 on the Apostle Islands of Lake Superior after a putative absence of >40 yr. To identify the source of martens to the islands and understand connectivity of the reintroduction network, we collected genetic data on martens from the archipelago and from all regional reintroduction sites. In total, we genotyped 483 individual martens, 43 of which inhabited the Apostle Islands (densities 0.42-1.46 km^-2 ). Coalescent analyses supported the contemporary recolonization of the Apostle Islands with progenitors likely originating from Michigan, which were sourced from Ontario. We also identified movements by a first-order relative between the Apostle Islands and the recovery network. We detected some regional gene flow, but in an unexpected direction: individuals moving from the islands to the mainland. Our findings suggest that the Apostle Islands were naturally recolonized by progeny of translocated individuals and now act as a source back to the reintroduction sites on the mainland. We suggest that the Apostle Islands, given its protection from disturbance, complex forest structure, and reduced carnivore competition, will act as a potential refuge for marten along their trailing range boundary and a central node for regional recovery. Our work reveals that translocations, even those occurring along southern range boundaries, can create recovery networks that function like natural metapopulations. Identifying refuges, locations that are resistant to environmental change, within these recovery networks can further improve species recovery, even within novel environments. Future translocation planning should a priori identify potential refuges and sources to improve short-term recovery and long-term persistence.

Large-scale patterns of seed removal by small mammals differ between areas of low- versus high-wolf occupancy

Because most tree species recruit from seeds, seed predation by small-mammal granivores may be important for determining plant distribution and regeneration in forests. Despite the importance of seed predation, large-scale patterns of small-mammal granivory are often highly variable and thus difficult to predict. We hypothesize distributions of apex predators can create large-scale variation in the distribution and abundance of mesopredators that consume small mammals, creating predictable areas of high and low granivory. For example, because gray wolf (Canis lupus) territories are characterized by relatively less use by coyotes (C. latrans) and greater use by foxes (Vulpes vulpes, Urocyon cinereoargentus) that consume a greater proportion of small mammals, wolf territories may be areas of reduced small-mammal granivory. Using large-scale, multiyear field trials at 22 sites with high- and low-wolf occupancy in northern Wisconsin, we evaluated whether removal of seeds of four tree species was lower in wolf territories. Consistent with the hypothesized consequences of wolf occupancy, seed removal of three species was more than 25% lower in high-wolf-occupancy areas across 2 years and small-mammal abundance was more than 40% lower in high-wolf areas during one of two study years. These significant results, in conjunction with evidence of seed consumption in situ and the absence of significant habitat differences between high- and low-wolf areas, suggest that top-down effects of wolves on small-mammal granivory and seed survival may occur. Understanding how interactions among carnivores create spatial patterns in interactions among lower trophic levels may allow for more accurate predictions of large-scale patterns in seed survival and forest composition.

A landscape of overlapping risks for wolf-human conflict in Wisconsin, USA

Managing risk requires an adequate understanding of risk-factors that influence the likelihood of a particular event occurring in time and space. Risk maps can be valuable tools for natural resource managers, allowing them to better understand spatial characteristics of risk. Risk maps can also support risk-avoidance efforts by identifying which areas are relatively riskier than others. However, risks, such as human-carnivore conflict, can be diverse, multi-faceted, and overlapping in space. Yet, efforts to describe risk typically focus on only one aspect of risk. We examined wolf complaints investigated in Wisconsin, USA for the period of 1999-2011. We described the spatial patterns of four types of wolf-human conflict: livestock depredation, depredation on hunting hounds, depredation on non-hound dogs, and human health and safety concerns (HHSC). Using predictive landscape models and discriminant functions analysis, we visualized the landscape of risk as a continuous surface of overlapping risks. Each type of conflict had its own unique landscape signature; however, the probability of any type of conflict increased closer to the center of wolf pack territories and with increased forest cover. Hunting hound depredations tended to occur in areas considered to be highly suitable wolf habitat, while livestock depredations occurred more regularly in marginal wolf habitat. HHSC and non-hound dog depredations were less predictable spatially but tended to occur in areas with low housing density adjacent to large wildland areas. Similar to other research evaluating the risk of human-carnivore conflict, our data suggests that human-carnivore conflict is most likely to occur where humans or human property and large carnivores co-occur. However, identifying areas of co-occurrence is only marginally valuable from a conservation standpoint and could be described using spatially-explicit human and carnivore data without complex analytical approaches. These results challenge our traditional understanding of risk and the standard approach used in describing risk. We suggest that a more comprehensive understanding of the risk of human-carnivore conflict can be achieved by examining the spatial and non-spatial factors influencing risk within areas of co-occurrence and by describing the landscape of risk as a continuous surface of multiple overlapping risks.

Resource limitations and competitive interactions affect carnivore community composition at different ecological scales in a temperate island system

Selective pressures (i.e. resource limitation and competitive interaction) that drive the composition of ecological communities vary, and often operate on different ecological scales (ecological variables across varying spatial scales) than observed patterns. We studied the drivers of distribution and abundance of the American marten (Martes americana) and the carnivore community at three ecological scales on a Great Lakes island archipelago using camera traps. We found different drivers appeared important at each ecological scale and studying any of the three scales alone would give a biased understanding of the process driving the system. Island biogeography (resource limitation) was most important for carnivore richness, with higher richness on larger islands and lower richness as distance from the mainland increased. Marten presence on individual islands appeared to be driven by island size (resource limitation) and human avoidance (competitive interaction). Marten abundance at camera trap sites was driven by the cascading effect of coyotes (Canis latrans) on fishers (Pekania pennanti) (competitive interaction). Incorporating three ecological scales gave novel insights into the varying effects of resource limitation and competitive interaction processes. Our data suggests that ecological communities are structured through multiple competing ecological forces, and effective management and conservation relies on our ability to understand ecological forces operating at multiple ecological scales.

Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.

Effects of territorial status and life history on Sandhill Crane (Antigone canadensis) population dynamics in south-central Wisconsin, USA

Population growth rate in long-lived bird species is often most sensitive to changes in adult survival. Sandhill Cranes (Antigone canadensis (Linnaeus, 1758)) have long life spans, small broods, and delayed first reproduction. Only territorial adult Sandhill Cranes participate in breeding, and territory acquisition reflects the interplay between the availability of suitable territories and the variation in mortality of adult birds occupying those territories. We estimated vital rates of a population at equilibrium using long-term resightings data (2000–2014; n = 451 marked individuals) in a multistate mark–resight model and used a stage-structured projection matrix to assess how strongly territorial adult survival affects population growth rate. Elasticity analysis indicated territorial birds surviving and retaining territories had a 2.58 times greater impact on population growth compared with the next most important transition rate (survival of nonterritorial adults remaining nonterritorial). Knowing how changes in vital rates of various stage classes will differentially impact population growth rate allows for targeted management actions including encouraging growth in recovering populations, assessing opportunity for recreational harvest, or maintaining populations at a desired level. This study also highlights the value of collecting demographic data for all population segments, from which one can derive reproductive output or growth rate.

Hunter selection for larger and older male bobcats affects annual harvest demography

Wildlife researchers often rely on demographic data collected from harvested animals to estimate population dynamics. But demographic data from harvested animals may be non-representative if hunters/trappers have the ability and motivation to preferentially select for certain physical traits. Hunter preference is well demonstrated for ungulates, but less so for other wildlife species such as furbearers. We used data from bobcats harvested in Wisconsin (1983-2014) to determine if harvest method and demographics (mass, male:female sex ratio and age) have changed over time, and if bobcat hunters/trappers exhibited selection. Each trait of harvested bobcats that we tested changed over time, and because these selected traits were interrelated, we inferred that harvest selection for larger size biased harvests in favour of older, male bobcats. The selection of older, male bobcats appears primarily driven by hound hunters (hereafter hunters) compared to trappers, with hunters more frequently creating taxidermy mounts from their harvested bobcats. We found an increase in the proportion of bobcats that were harvested by hunting compared to trapping over time, and this was associated with increased selectivity and substantial changes in the characteristics of harvested bobcats. Selection by hunters may bias population models that are based on the demography of harvested bobcats, and accounting for biases that may occur, including from different harvest methods, is critical when using harvest-dependent data.

A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin

Population estimation is essential for the conservation and management of fish and wildlife, but accurate estimates are often difficult or expensive to obtain for cryptic species across large geographical scales. Accurate statistical models with manageable financial costs and field efforts are needed for hunted populations and using age-at-harvest data may be the most practical foundation for these models. Several rigorous statistical approaches that use age-at-harvest and other data to accurately estimate populations have recently been developed, but these are often dependent on (a) accurate prior knowledge about demographic parameters of the population, (b) auxiliary data, and (c) initial population size. We developed a two-stage state-space Bayesian model for a black bear (Ursus americanus) population with age-at-harvest data, but little demographic data and no auxiliary data available, to create a statewide population estimate and test the sensitivity of the model to bias in the prior distributions of parameters and initial population size. The posterior abundance estimate from our model was similar to an independent capture-recapture estimate from tetracycline sampling and the population trend was similar to the catch-per-unit-effort for the state. Our model was also robust to bias in the prior distributions for all parameters, including initial population size, except for reporting rate. Our state-space model created a precise estimate of the black bear population in Wisconsin based on age-at-harvest data and potentially improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size.

Compensatory mortality in a recovering top carnivore: wolves in Wisconsin, USA (1979-2013)

Populations of large terrestrial carnivores are in various stages of recovery worldwide and the question of whether there is compensation in mortality sources is relevant to conservation. Here, we show variation in Wisconsin wolf survival from 1979 to 2013 by jointly estimating the hazard of wolves' radio-telemetry ending (endpoint) and endpoint cause. In previous analyses, wolves lost to radio-telemetry follow-up (collar loss) were censored from analysis, thereby assuming collar loss was unconfounded with mortality. Our approach allowed us to explicitly estimate hazard due to collar loss and did not require censoring these records from analysis. We found mean annual survival was 76% and mean annual causes of mortality were illegal killing (9.4%), natural and unknown causes (9.5%), and other human-caused mortality such as hunting, vehicle collisions and lethal control (5.1%). Illegal killing and natural mortality were highest during winter, causing wolf survival to decrease relative to summer. Mortality was highest during early recovery and lowest during a period of sustained population growth. Wolves again experienced higher risk of human-caused mortality relative to natural mortality as wolves expanded into areas with more human activity. We detected partial compensation in human- and natural-caused mortality since 2004 as the population saturated more available habitat. Prior to 2004, we detected additivity in mortality sources. Assessments of wolf survival and cause of mortality rates and the finding of partial compensation in mortality sources will inform wolf conservation and management efforts by identifying sources and sinks, finding areas of conservation need, and assessing management zone delineation.

Benefits and drawbacks of determining reproductive histories for black bears (Ursus americanus) from cementum annuli techniques

Recruitment is difficult to estimate but is essential for determining population trend. Recruitment in bears can be estimated from patterns in width of cementum annuli that indicate years with cubs. We evaluated reproductive history estimates from cementum annuli of 19 101 black bears (Ursus americanus Pallas, 1780) collected over 20 years to determine the benefits and drawbacks of this technique for management agencies. The technique only worked to estimate reproductive histories for 25% of submitted samples, and 49% of samples with estimates contained uncertain litters. Whether uncertain litters were counted or not caused significant variation in estimates of age at first litter, number of litters per female, and interbirth intervals. Hence, naive treatment of uncertain litters may bias analyses. A data set we optimized to reduce bias showed that litters per female ranged from 0 to 12, mean interbirth interval was 2.07 years, and both increased as females aged. Large samples of teeth collected from harvested bears over multiple decades potentially provides a wealth of information on reproductive parameters at a minimal cost compared with intensive field studies, but until uncertain litters are understood mechanistically and can be better quantified, reproductive estimates from this technique should be interpreted with caution.

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

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.

Are the numbers adding up? Exploiting discrepancies among complementary population models

Large carnivores are difficult to monitor because they tend to be sparsely distributed, sensitive to human activity, and associated with complex life histories. Consequently, understanding population trend and viability requires conservationists to cope with uncertainty and bias in population data. Joint analysis of combined data sets using multiple models (i.e., integrated population model) can improve inference about mechanisms (e.g., habitat heterogeneity and food distribution) affecting population dynamics. However, unobserved or unobservable processes can also introduce bias and can be difficult to quantify. We developed a Bayesian hierarchical modeling approach for inference on an integrated population model that reconciles annual population counts with recruitment and survival data (i.e., demographic processes). Our modeling framework is flexible and enables a realistic form of population dynamics by fitting separate density-dependent responses for each demographic process. Discrepancies estimated from shared parameters among different model components represent unobserved additions (i.e., recruitment or immigration) or removals (i.e., death or emigration) when annual population counts are reliable. In a case study of gray wolves in Wisconsin (1980–2011), concordant with policy changes, we estimated that a discrepancy of 0% (1980–1995), −2% (1996–2002), and 4% (2003–2011) in the annual mortality rate was needed to explain annual growth rate. Additional mortality in 2003–2011 may reflect density-dependent mechanisms, changes in illegal killing with shifts in wolf management, and nonindependent censoring in survival data. Integrated population models provide insights into unobserved or unobservable processes by quantifying discrepancies among data sets. Our modeling approach is generalizable to many population analysis needs and allows for identifying dynamic differences due to external drivers, such as management or policy changes.

Evaluating spatial overlap and relatedness of white-tailed deer in a chronic wasting disease management zone

Wildlife disease transmission, at a local scale, can occur from interactions between infected and susceptible conspecifics or from a contaminated environment. Thus, the degree of spatial overlap and rate of contact among deer is likely to impact both direct and indirect transmission of infectious diseases such chronic wasting disease (CWD) or bovine tuberculosis. We identified a strong relationship between degree of spatial overlap (volume of intersection) and genetic relatedness for female white-tailed deer in Wisconsin's area of highest CWD prevalence. We used volume of intersection as a surrogate for contact rates between deer and concluded that related deer are more likely to have contact, which may drive disease transmission dynamics. In addition, we found that age of deer influences overlap, with fawns exhibiting the highest degree of overlap with other deer. Our results further support the finding that female social groups have higher contact among related deer which can result in transmission of infectious diseases. We suggest that control of large social groups comprised of closely related deer may be an effective strategy in slowing the transmission of infectious pathogens, and CWD in particular.