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Laboratory trials conducted over the past decade at U.S. Geological Survey National Wildlife Health Center indicate that wild populations of prairie dogs (Cynomys spp.) display different degrees of susceptibility to experimental challenge with fully virulent Yersinia pestis, the causative agent of plague. We evaluated patterns in prairie dog susceptibility to plague to determine whether the historical occurrence of plague at location of capture was related to survival times of prairie dogs challenged with Y. pestis. We found that black‐tailed prairie dogs (Cynomys ludovicianus) from South Dakota (captured prior to the detection of plague in the state), Gunnison's prairie dogs (Cynomys gunnisoni) from Colorado, and Utah prairie dogs (Cynomys parvidens) from Utah were most susceptible to plague. Though the susceptibility of black‐tailed prairie dogs in South Dakota compared with western locations supports our hypothesis regarding historical exposure, both Colorado and Utah prairie dogs have a long history of exposure to plague. It is possible that for these populations, genetic isolation/bottle necks have made them more susceptible to plague outbreaks. We conduct meta‐analyses of the responses of prairie dogs (Cynomys spp.) from different populations across the western United States to plague challenge. We hypothesized that historical exposure to plague would affect the susceptibility of prairie dog populations to plague. We conclude that though historical exposure may play a role that connectivity of populations is also likely to affect a population's response to plague.

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark—recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture—recapture data have produced methods estimating abundance and density of animals from spatially explicit capture—recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture—recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km 2 ) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km 2 (95% CI 2.3—5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1—11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182—451) to 529 (95% CI 245—870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km 2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture—recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

The disease white‐nose syndrome (WNS) was first recognized in upstate New York in 2006 and has since spread across much of the United States (U.S.), causing severe mortality in several North American bat species. To aid in the identification and monitoring of at‐risk bat populations, we evaluate factors associated with the presence of the causative fungal agent of WNS, Pseudogymnoascus destructans (Pd), in the continental United States. We obtained Pd samples through hibernaculum surveys conducted from 2013 to 2020, with all samples analyzed at the U.S. Geological Survey National Wildlife Health Center. Using generalized additive models, we estimated the likelihood of Pd presence under three different hypotheses: human‐mediated, species‐mediated, and hibernaculum type. In addition to hypothesis‐related predictor variables, a subset of models included a smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since study onset to account for spatial and temporal autocorrelation. Under all hypotheses, models indicated probability of Pd detection is best described by the smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since onset of this study. After accounting for spatial and temporal autocorrelations, only hibernaculum type significantly affected Pd presence, with mines and culverts/tunnels less likely to contain Pd compared with caves. Reduced likelihood of Pd presence in mines and culverts/tunnels bodes well for bats of the western and southern United States, where use of these hibernaculum types is more common. While our findings can help guide monitoring and management efforts, the potential for long‐distance dispersal combined with variation in community composition and hibernation ecology between the western and eastern United States necessitates the continued monitoring of Pd presence.

Rabbit hemorrhagic disease virus 2 (RHDV2), recently detected in the western United States, has the potential to cause mass mortality events in wild rabbit and hare populations. Currently, few management strategies exist other than vaccination. We developed a spatially explicit model of RHDV2 for a population of riparian brush rabbits (Sylvilagus bachmani riparius), a subspecies of brush rabbit classified as endangered in the United States, on a subsection of the San Joaquin River National Wildlife Refuge. The goal of our model was to provide guidance regarding vaccination strategies for an endangered rabbit species. Our model predicts that increased interactions between rabbits (a proxy for landscape connectivity) and disease transmission rates among susceptible hosts (individual brush rabbits and conspecifics) have the greatest influence on the outcome of a potential vaccination campaign. Our model projects that across a range of parameter estimates (given an RHDV2 incursion), the median estimated population size with a 0%–10% vaccination rate after 1 year is 538 rabbits (95% Confidence Interval [C.I.] 69–1235), approximately 36% of the expected size of the study population of 1470 rabbits without an RHDV2 introduction. With a 10%–20%, 20%–30%, or 30%–40% vaccination rate, the median estimated population size increased to 628 rabbits (95% C.I. 130–1298), 723 rabbits (95% C.I. 198–1317), and 774 rabbits (95% C.I. 228–1410), respectively. These estimates represent 43%, 49%, and 53% of the expected population size without an RHDV2 introduction. Overall, a 1% increase in vaccination rate was associated with a six rabbit (95% C.I. 5–7) increase in total remaining population size. This result is dependent on assumptions regarding environmental transmission, home range size (and contact rates of rabbits). Given the relatively short lifespan of rabbits and the potential need for boosters, vaccination programs are most likely to be successful for small target populations where relatively high vaccination rates can be maintained. We developed a model of RHDV2 transmission dynamics and riparian brush rabbit population dynamics to evaluate the potential effectiveness of a vaccination strategy for this population. The model includes the potential effects of nontarget rabbit species as carriers and reservoirs of the disease, as well as environmental transmission. This model was used to evaluate the potential for the strategy to be effective, and a vaccination campaign was recently applied to this population, demonstrating the usefulness of the model in a practical application.

There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows \"apples-to-apples\" comparisons of surveillance efficiencies between units where heterogeneous samples were collected.

Wildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre‐epidemic, early outbreak, mid‐epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.

White‐nose syndrome (WNS) has caused dramatic declines of several cave‐hibernating bat species in North America since 2006, which has increased the activity of non‐susceptible species in some geographic areas or during times of night formerly occupied by susceptible species—indicative of disease‐mediated competitive release (DMCR). Yet, this pattern has not been evaluated across multiple bat assemblages simultaneously or across multiple years since WNS onset. We evaluated whether WNS altered spatial and temporal niche partitioning in bat assemblages at four locations in the eastern United States using long‐term datasets of bat acoustic activity collected before and after WNS arrival. Activity of WNS‐susceptible bat species decreased by 79–98% from pre‐WNS levels across the four study locations, but only one of our four study sites provided strong evidence supporting the DMCR hypothesis in bats post‐WNS. These results suggest that DMCR is likely dependent on the relative difference in activity by susceptible and non‐susceptible species groups pre‐WNS and the relative decline of susceptible species post‐WNS allowing for competitive release, as well as the amount of time that had elapsed post‐WNS. Our findings challenge the generality of WNS‐mediated competitive release between susceptible and non‐susceptible species and further highlight declining activity of some non‐susceptible species, especially Lasiurus borealis, across three of four locations in the eastern United States. These results underscore the broader need for conservation efforts to address the multiple potential interacting drivers of bat declines on both WNS‐susceptible and non‐susceptible species.

Wind energy generation holds the potential to adversely affect wildlife populations. Species-wide effects are difficult to study and few, if any, studies examine effects of wind energy generation on any species across its entire range. One species that may be affected by wind energy generation is the endangered Indiana bat ( ), which is found in the eastern and midwestern United States. In addition to mortality from wind energy generation, the species also faces range-wide threats from the emerging infectious fungal disease, white-nose syndrome (WNS). White-nose syndrome, caused by , disturbs hibernating bats leading to high levels of mortality. We used a spatially explicit full-annual-cycle model to investigate how wind turbine mortality and WNS may singly and then together affect population dynamics of this species. In the simulation, wind turbine mortality impacted the metapopulation dynamics of the species by causing extirpation of some of the smaller winter colonies. In general, effects of wind turbines were localized and focused on specific spatial subpopulations. Conversely, WNS had a depressive effect on the species across its range. Wind turbine mortality interacted with WNS and together these stressors had a larger impact than would be expected from either alone, principally because these stressors together act to reduce species abundance across the spectrum of population sizes. Our findings illustrate the importance of not only prioritizing the protection of large winter colonies as is currently done, but also of protecting metapopulation dynamics and migratory connectivity.

Wild waterfowl are primary reservoirs of avian influenza viruses (AIV). However the role of sea ducks in the ecology of avian influenza, and how that role differs from freshwater ducks, has not been examined. We obtained and analyzed sera from North Atlantic sea ducks and determined the seroprevalence in those populations. We also tested swab samples from North Atlantic sea ducks for the presence of AIV. We found relatively high serological prevalence (61%) in these sea duck populations but low virus prevalence (0.3%). Using these data we estimated that an antibody half-life of 141 weeks (3.2 years) would be required to attain these prevalences. These findings are much different than what is known in freshwater waterfowl and have implications for surveillance efforts, AIV in marine environments, and the roles of sea ducks and other long-lived waterfowl in avian influenza ecology.

Reliable knowledge of the status and trend of carnivore populations is critical to their conservation and management. Methods for monitoring carnivores, however, are challenging to conduct across large spatial scales. In the Northern Rocky Mountains, wildlife managers need a time- and cost-efficient method for monitoring gray wolf (Canis lupus) populations. Montana Fish, Wildlife and Parks (MFWP) conducts annual telephone surveys of >50,000 deer and elk hunters. We explored how survey data on hunters' sightings of wolves could be used to estimate the occupancy and distribution of wolf packs and predict their abundance in Montana for 2007-2009. We assessed model utility by comparing our predictions to MFWP minimum known number of wolf packs. We minimized false positive detections by identifying a patch as occupied if 2-25 wolves were detected by ≥3 hunters. Overall, estimates of the occupancy and distribution of wolf packs were generally consistent with known distributions. Our predictions of the total area occupied increased from 2007 to 2009 and predicted numbers of wolf packs were approximately 1.34—1.46 times the MFWP minimum counts for each year of the survey. Our results indicate that multi-season occupancy models based on public sightings can be used to monitor populations and changes in the spatial distribution of territorial carnivores across large areas where alternative methods may be limited by personnel, time, accessibility, and budget constraints.