A landscape‐level meta‐analysis approach to examining early survival of ungulates may elucidate patterns in survival not evident from individual studies. Despite numerous efforts, the relationship between fawn survival and habitat characteristics remains unclear and there has been no attempt to examine trends in survival across landscape types with adequate replication. In 2015–2016, we radiomarked 98 white‐tailed deer (Odocoileus virginianus) fawns in 2 study areas in Pennsylvania. By using a meta‐analysis approach, we compared fawn survival estimates from across North America using published data from 29 populations in 16 states to identify patterns in survival and cause‐specific mortality related to landscape characteristics, predator communities, and deer population density. We modeled fawn survival relative to percentage of agricultural land cover and deer density. Estimated average survival to 3–6 months of age was 0.414 ± 0.062 (SE) in contiguous forest landscapes (no agriculture) and for every 10% increase in land area in agriculture, fawn survival increased 0.049 ± 0.014. We classified cause‐specific mortality as human‐caused, natural (excluding predation), and predation according to agriculturally dominated, forested, and mixed (i.e., both agricultural and forest cover) landscapes. Predation was the greatest source of mortality in all landscapes. Landscapes with mixed forest and agricultural cover had greater proportions and rates of human‐caused mortalities, and lower proportions and rates of mortality due to predators, when compared to forested landscapes. Proportion and rate of natural deaths did not differ among landscapes. We failed to detect any relationship between fawn survival and deer density. The results highlight the need to consider multiple spatial scales when accounting for factors that influence fawn survival. Furthermore, variation in mortality sources and rates among landscapes indicate the potential for altered landscape mosaics to influence fawn survival rates. Wildlife managers can use the meta‐analysis to identify factors that will facilitate comparisons of results among studies and advance a better understanding of patterns in fawn survival. © 2018 The Wildlife Society.

Background Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

It is unknown how ungulate physiological responses to environmental perturbation influence overall population demographics. Moreover, neonatal physiological responses remain poorly studied despite the importance of neonatal survival to population growth. Glucocorticoid (GC) hormones potentially facilitate critical physiological and behavioral responses to environmental perturbations. However, elevated GC concentrations over time may compromise body condition and indirectly reduce survival. We evaluated baseline salivary cortisol (CORT; a primary GC in mammals) concentrations in 19 wild neonatal white‐tailed deer (Odocoileus virginianus) in a northern (NS) and southern (SS) area in Pennsylvania. After ranking survival models consisting of variables hypothesized to influence neonate survival (i.e. weight, sex), the probability of neonate survival was best explained by CORT concentrations, where elevated CORT concentrations were associated with reduced survival probability to 12 weeks of age. Cortisol concentrations were greater in the SS where predation rates and predator densities were lower. As the first evaluation of baseline CORT concentrations in an ungulate neonate to our knowledge, this is also the first study to demonstrate CORT concentrations are negatively associated with ungulate survival at any life stage. Glucocorticoid hormones could provide a framework in which to better understand susceptibility to mortality in neonatal white‐tailed deer.

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