Species' population dynamics are tied to neonatal survival. White-tailed deer (Odocoileus virginianus) fawn survival varies according to spatially explicit patterns of natural (e.g., starvation, predation) and human-caused mortalities (e.g., vehicle collision). Our objective was to compare fawn survival under different, though representative, ecological conditions in Wisconsin USA. We identified 2 ecologically distinct study areas: the northern forest (NF) and the eastern farmland (EF). Beginning in May (2011-2013), we fitted fawns in both areas with radio-collars and tracked their survival daily until 31 August of the capture year. We obtained daily weather data for each study area to model weather effects on survival. We captured 89 (NF), and 139 (EF) fawns, and observed 42 (NF) and 43 (EF) mortalities. Predation mortality was higher than other mortality causes in the NF, and mortality due to natural causes other than predation was higher for fawns in the EF. Female fawns had higher survival than males, and fawns in 2011 in the NF had lower survival than fawns in 2012 or 2013. During the first 40 days of life, occurrence of precipitation associated with a threefold increase in daily hazard of death in the EF, but effects of daily low temperatures were trivial. In the NF, precipitation had little effect, but a decrease in daily low temperature by 0.568C increased the daily hazard of mortality by 5%. Because risks facing fawns vary with ecological context, understanding specific factors that affect fawn survival is important for predicting local outcomes of white-tailed deer management.

Although most wildlife professionals agree that science should inform wildlife management decisions, disconnect still exists between researchers and managers. If researchers are not striving to incorporate their findings into management decisions, support for research programs by managers can wane. If managers are not using research findings to inform management decisions, those decisions may be less effective or more vulnerable to legal challenges. Both of these situations can have negative consequences for wildlife conservation. We outline a collaborative research‐management approach to bridging the gap between wildlife managers and researchers. We describe differences in perspectives, perceptions, and priorities between managers and researchers; outline how and why the divide between researchers and managers has likely occurred and continues to grow; and present specific strategies and recommendations to foster stronger collaborations between managers and researchers. We advocate increased synergy between managers and researchers based on a shared vision of conservation and a collaborative structure that rewards researchers and managers. Most importantly, we suggest that relationships and communication between managers and researchers must be established early in research development and decision‐making processes, fostering the trust needed for collaboration. Institutions and agencies can facilitate these relationships by creating opportunities and incentives for integrating collaborative research into management decisions. We suggest this approach will strengthen ties between researchers and managers, increase relevance of research to management decisions, promote effectiveness of management decisions, reduce legal challenges, and ultimately produce positive, tangible, and lasting effects on wildlife conservation. © 2019 The Wildlife Society.

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.

Landscape features can alter the transfer phase of dispersal and dispersal‐mediated disease transmission and gene flow. The transfer phase is poorly understood, but improved understanding of landscape effects on dispersal distance and direction would allow better prediction and mitigation of disease spread and improved delineation of management zones. To investigate how ecological settings influence dispersal in white‐tailed deer (Odocoileus virginianus), we captured and radio‐collared 409 juvenile male deer from 2 study areas in Wisconsin, USA, one dominated by public forest and another by row‐crop agriculture. Dispersal directions were non‐directed in the heavily forested study area, but there was a southeastern bias in the farmland study area. Individual dispersal distances were not related to forest cover, and study area average and maximum distances differed from expected, based on published relationships between forest cover and population‐average dispersal distance. Roads, rivers, and cities were semipermeable barriers to dispersal, but effects of barriers differed with respect to study area, suggesting that natural and anthropogenic features influence dispersal‐mediated disease transmission and gene flow. Our results suggest that dispersal models should consider movement barriers in more developed landscapes, and barriers can also be used to inform designation of biologically meaningful management units. © 2017 The Wildlife Society.

The performance of 2 popular methods that use age-at-harvest data to estimate abundance of white-tailed deer is contingent on assumptions about variation in estimates of subadult (1.5 yr old) and adult (!2.5 yr old) male harvest rates. Auxiliary data (e.g., estimates of survival or harvest rates from radiocollared animals) can be used to relax some assumptions, but unless these population parameters exhibit limited temporal or spatial variation, these auxiliary data may not improve accuracy. Unfortunately maintaining sufficient sample sizes of radiocollared deer for parameter estimation in every wildlife management unit (WMU) is not feasible for most state agencies. We monitored the fates of 397 subadult and 225 adult male white-tailed deer across 4 WMUs from 2002 to 2008 using radio telemetry. We investigated spatial and temporal variation in harvest rates and investigated covariates related to the patterns observed. We found that most variation in harvest rates was explained spatially and that adult harvest rates (0.36-0.69) were more variable among study areas than subadult harvest rates (0.26-0.42). We found that hunter effort during the archery and firearms season best explained variation in harvest rates of adult males among WMUs, whereas hunter effort during only the firearms season best explained harvest rates for subadult males. From a population estimation perspective, it is advantageous that most variation was spatial and explained by a readily obtained covariate (hunter effort). However, harvest rates may vary if hunting regulations or hunter behavior change, requiring additional field studies to obtain accurate estimates of harvest rates. ß 2011 The Wildlife Society.