Physiological acclimation of elk during population restoration in the Missouri Ozarks, USA.

Conservation translocations-the intentional movement of animals to restore populations-have increased over the past 30 years to halt and reverse species declines and losses. However, there are many challenges translocated animals face that should be considered for restoration programs to be successful. Understanding how long it takes for translocated animals to acclimate to these challenges and their new landscape is a critical component of post-release population management. Physiological measures such as hormone responses are increasingly used to assess animal responses and acclimation to disturbances including translocation. We determined the physiological acclimation period of elk (Cervus canadensis) translocated to the Missouri Ozarks, USA, as part of a restoration effort. From 2011 to 2013, we translocated 108 GPS-radio-collared elk from Kentucky, USA, to Missouri, USA, and collected faecal samples for glucocorticoid metabolite extraction to use as an indicator of physiological acclimation. We modelled the response of population-wide faecal glucocorticoid metabolites (fGCMs) across the initial 9 years of the restoration in response to days following release and additional site-specific covariates. Presence of white-tailed deer (Odocoileus virginianus) hunts and monthly precipitation levels were positively and negatively associated with fGCM levels, respectively. Concurrent with influences from site-specific conditions on the release landscape, fGCM levels declined following release. We identified a breakpoint in fGCM decline at ~42 days following translocation releases suggesting elk acclimated physiologically relatively quickly compared to other species. The fast physiological acclimation by Missouri elk suggests effective use of temporary post-release management efforts. Determining how quickly animals acclimate following translocations allows researchers to tailor post-release management plans to each species' needs, thus maximizing the success of future translocation efforts while minimizing costs.

A rare 300 kilometer dispersal by an adult male white-tailed deer.

Despite the key roles that dispersal plays in individual animal fitness and meta-population gene flow, it remains one of the least understood behaviors in many species. In large mammalian herbivores, dispersals might span long distances and thereby influence landscape-level ecological processes, such as infectious disease spread. Here, we describe and analyze an exceptional long-distance dispersal by an adult white-tailed deer (Odocoileus virginianus) in the central United States. We also conducted a literature survey to compare the dispersal to previous studies. This dispersal was remarkable for its length, duration, and the life history stage of the dispersing individual. Dispersal is typical of juvenile deer seeking to establish postnatal home ranges, but this dispersal was undertaken by an adult male (age = 3.5). This individual dispersed ~300 km over a 22-day period by moving, on average, 13.6 km/day and achieving a straight-line distance of ~215 km, which was ~174 km longer than any other distance recorded for an adult male deer in our literature survey. During the dispersal, which occurred during the hunting season, the individual crossed a major river seven times, an interstate highway, a railroad, and eight state highways. Movements during the dispersal were faster (mean = 568.1 m/h) and more directional than those during stationary home range periods before and after the dispersal (mean = 56.9 m/h). Likewise, movements during the dispersal were faster (mean = 847.8 m/h) and more directional at night than during the day (mean = 166.4 m/h), when the individual frequently sheltered in forest cover. This natural history event highlights the unpredictable nature of dispersal and has important implications for landscape-level processes such as chronic wasting disease transmission in cervids. More broadly, our study underscores how integrating natural history observations with modern technology holds promise for understanding potentially high impact but rarely recorded ecological events.

Size matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework.

Epidemiological surveillance for many important wildlife diseases relies on samples obtained from hunter-harvested animals. Statistical methods used to calculate sample size requirements assume that the target population is randomly sampled, and therefore the samples are representative of the population. But hunter-harvested samples may not be representative of the population due to disease distribution heterogeneities (e.g. spatial clustering of infected individuals), and harvest-related non-random processes like regulations, hunter selectivity, variable land access, and uneven hunter distribution. Consequently, sample sizes necessary for detection of disease are underestimated and disease detection probabilities are overestimated, resulting in erroneous inferences about disease presence and distribution. We have developed a modeling framework to support the design of efficient disease surveillance programs for wildlife populations. The constituent agent-based models can incorporate real-world heterogeneities associated with disease distribution, harvest, and harvest-based sampling, and can be used to determine population-specific sample sizes necessary for prompt detection of important wildlife diseases like chronic wasting disease and bovine tuberculosis. The modeling framework and its application has been described in detail by Belsare et al. [1]. Here we describe how model scenarios were developed and implemented, and how model outputs were analyzed. The main objectives of this methods paper are to provide users the opportunity to a) assess the reproducibility of the published model results, b) gain an in-depth understanding of model analysis, and c) facilitate adaptation of this modeling framework to other regions and other wildlife disease systems.•The two agent-based models, MOOvPOP and MOOvPOPsurveillance, incorporate real-world heterogeneities underpinned by host characteristics, disease spread dynamics, and sampling biases in hunter-harvested deer.•The modeling framework facilitates iterative analysis of locally relevant disease surveillance scenarios, thereby facilitating sample size calculations for prompt and reliable detection of important wildlife diseases.•Insights gained from modeling studies can be used to inform the design of effective wildlife disease surveillance strategies.

An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer.

Epidemiological surveillance for important wildlife diseases often relies on samples obtained from hunter-harvested animals. A problem, however, is that although convenient and cost-effective, hunter-harvest samples are not representative of the population due to heterogeneities in disease distribution and biased sampling. We developed an agent-based modeling framework that i) simulates a deer population in a user-generated landscape, and ii) uses a snapshot of the in silico deer population to simulate disease prevalence and distribution, harvest effort and sampling as per user-specified parameters. This framework can incorporate real-world heterogeneities in disease distribution, hunter harvest and harvest-based sampling, and therefore can be useful in informing wildlife disease surveillance strategies, specifically to determine population-specific sample sizes necessary for prompt detection of disease. Application of this framework is illustrated using the example of chronic wasting disease (CWD) surveillance in Missouri's white-tailed deer (Odocoileus virginianus) population. We show how confidence in detecting CWD is grossly overestimated under the unrealistic, but standard, assumptions that sampling effort and disease are randomly and independently distributed. We then provide adjusted sample size recommendations based on more realistic assumptions. Wildlife agencies can use these open-access models to design their CWD surveillance. Furthermore, these models can be readily adapted to other regions and other wildlife disease systems.

Getting in Front of Chronic Wasting Disease: Model-Informed Proactive Approach for Managing an Emerging Wildlife Disease.

Continuing geographic spread of chronic wasting disease (CWD) poses a serious threat to the sustainable future of cervids and hunting in North America. Moreover, CWD has been detected in captive cervids in South Korea and, in recent years, in free-ranging reindeer in Europe (Norway). Management of this disease is limited by logistical, financial, and sociopolitical considerations, and current strategies primarily focus on reducing host densities through hunter harvest and targeted culling. The success of such strategies in mitigating the spread and prevalence of CWD only upon detection is questionable. Here, we propose a proactive approach that emphasizes pre-emptive management through purposeful integration of virtual experiments (simulating alternate interventions as model scenarios) with the aim of evaluating their effectiveness. Here, we have used a published agent-based model that links white-tailed deer demography and behavior with CWD transmission dynamics to first derive a CWD outbreak trajectory and then use the trajectory to highlight issues associated with different phases of the CWD outbreak (pre-establishment/transition/endemic). Specifically, we highlight the practical constraints on surveillance in the pre-establishment phase and recommend that agencies use a realistic detection threshold for their CWD surveillance programs. We further demonstrate that many disease introductions are "dead ends" not leading to a full epidemic due to high stochasticity and harvesting in the pre-establishment phase of CWD. Model evaluated pre-emptive (pre-detection) harvest strategies could increase the resilience of the deer population to CWD spread and establishment. We conclude it is important to adaptively position CWD management ahead of, rather than behind, the CWD front.