Parturition timing has long been a topic of interest in ungulate research. However, few studies have examined parturition timing at fine scale (e.g., <1 day). Predator activity and environmental conditions can vary considerably with diel timing, which may result in selective pressure for parturition to occur during diel times that maximize the likelihood of neonate survival. We monitored parturition events and early-life survival of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) in Utah, USA to better understand diel timing of parturition in temperate ungulates. Diel timing of parturition was moderately synchronous among conspecifics and influenced by environmental variables on the date of parturition. For elk, parturition events were most common during the morning crepuscular period and generally occurred later (i.e., closer to 12:00) when a relatively large proportion of the moon was illuminated. For mule deer, parturition events were most common during the diurnal period and generally occurred later (i.e., closer to 15:00) on cold, wet dates. Diel timing of parturition did not influence neonate survival, but larger datasets may be required to verify the apparent lack of influence. Although additional work could evaluate alternative variables that might affect parturition timing, our data provide an improved and finer scale understanding of reproductive ecology and phenology in ungulates.

To assess the susceptibility of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) to SARS-CoV-2, we performed experimental infections in both species. Elk did not shed infectious virus but mounted low-level serologic responses. Mule deer shed and transmitted virus and mounted pronounced serologic responses and thus could play a role in SARS-CoV-2 epidemiology.

BACKGROUND: Information on reproduction of harvested species such as mule deer (Odocoileus hemionus) is vital for conservation and management. Furthermore, parturition in ungulates may be detected using patterns of movement logged by GPS transmitters. Several movement-based methods have been developed to detect parturition in ungulates including the Peterson method, behavioral change point analysis (BCPA), rolling minimum convex polygons (rMCP), individual-based method (IBM), and population-based method (PBM). Our objectives were to (1) test the accuracy and the precision of each previously described method and (2) develop an improved method optimized for mule deer that incorporated aspects of the other methods.
METHODS: We determined parturition timing and status for female mule deer fitted with GPS collars and implanted with vaginal implant transmitters (VITs). We used movement patterns before and after parturition to set movement thresholds for each movement-based method. Following model training, we used location and birth date data from an external dataset to test the effectiveness of each movement-based method. Additionally, we developed a novel method for detecting parturition called the analysis of parturition indicators (API). We used two regression analyses to determine the accuracy and precision of estimates generated by each method.
RESULTS: The six methods we employed varied in accuracy, with the API, rMCP, and BCPA being most accurate. Precision also varied among methods, with the API, rMCP, and PBM generating the most precise estimates of parturition dates. The API and the rMCP performed similarly and better overall than any of the other existing methods.
CONCLUSIONS: We found that movement-based methods could be used to accurately and precisely detect parturition in mule deer. Further, we determined that the API and rMCP methods had the greatest overall success at detecting parturition in mule deer. The relative success of the API and rMCP may be attributed to the fact that both methods use home range size to detect parturition and are validated using known parturition dates of collared deer. We present the API as an efficient method of estimating birth status and timing of parturition of mule deer fitted with GPS transmitters, as well as affirm the effectiveness of a previously developed method, rMCP.

Growing evidence supports the hypothesis that temperate herbivores surf the green wave of emerging plants during spring migration. Despite the importance of autumn migration, few studies have conceptualized resource tracking of temperate herbivores during this critical season. We adapted the frost wave hypothesis (FWH), which posits that animals pace their autumn migration to reduce exposure to snow but increase acquisition of forage. We tested the FWH in a population of mule deer in Wyoming, USA by tracking the autumn migrations of n = 163 mule deer that moved 15-288 km from summer to winter range. Migrating deer experienced similar amounts of snow but 1.4-2.1 times more residual forage than if they had naïve knowledge of when or how fast to migrate. Importantly, deer balanced exposure to snow and forage in a spatial manner. At the fine scale, deer avoided snow near their mountainous summer ranges and became more risk prone to snow near winter range. Aligning with their higher tolerance of snow and lingering behavior to acquire residual forage, deer increased stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration completed. Our findings support the prediction that mule deer pace their autumn migration with the onset of snow and residual forage, but refine the FWH to include movement behavior en route that is spatially dynamic.

Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short-distance local movement state and a long-distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long-distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long-distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long-distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long-distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.

Increasing resource extraction and human activity are reshaping species\' spatial distributions in human-altered landscape and consequently shaping the dynamics of interspecific interactions, such as between predators and prey. To evaluate the effects of industrial features and human activity on the occurrence of wolves (Canis lupus), we used wildlife detection data collected in 2014 from an array of 122 remote wildlife camera traps in Alberta\'s Rocky Mountains and foothills near Hinton, Canada. Using generalized linear models, we compared the occurrence frequency of wolves at camera sites to natural land cover, industrial disturbance (forestry and oil/gas exploration), human activity (motorized and non-motorized), and prey availability (moose, Alces alces; elk, Cervus elaphus; mule deer, Odocoileus hemionus; and white-tailed deer, Odocoileus virginianus). Industrial block features (well sites and cutblocks) and prey (elk or mule deer) availability interacted to influence wolf occurrence, but models including motorized and non-motorized human activity were not strongly supported. Wolves occurred infrequently at sites with high densities of well sites and cutblocks, except when elk or mule deer were frequently detected. Our results suggest that wolves risk using industrial block features when prey occur frequently to increase predation opportunities, but otherwise avoid them due to risk of human encounters. Effective management of wolves in anthropogenically altered landscapes thus requires the simultaneous consideration of industrial block features and populations of elk and mule deer.

Wildlife population dynamics are modulated by abiotic and biotic factors, typically climate, resource availability, density-dependent effects, and predator-prey interactions. Understanding whether and how human-caused disturbances shape these ecological processes is helpful for the conservation and management of wildlife and their habitats within increasingly human-dominated landscapes. However, many jurisdictions lack either long-term longitudinal data on wildlife populations or measures of the interplay between human-mediated disturbance, climate, and predator density. Here, we use a 50-year time series (1962-2012) on mule deer (Odocoileus hemionus) demographics, seasonal weather, predator density, and oil and gas development patterns from the North Dakota Badlands, USA, to investigate long-term effects of landscape-level disturbance on mule deer fawn fall recruitment, which has declined precipitously over the last number of decades. Mule deer fawn fall recruitment in this study represents the number of fawns per female (fawn:female ratio) that survive through the summer to October. We used this fawn recruitment index to evaluate the composite effects of interannual extreme weather conditions, energy development, and predator density. We found that density-dependent effects and harsh seasonal weather were the main drivers of fawn fall recruitment in the North Dakota Badlands. These effects were further shaped by the interaction between harsh seasonal weather and predator density (i.e., lower fawn fall recruitment when harsh weather was combined with higher predator density). Additionally, we found that fawn fall recruitment was modulated by interactions between seasonal weather and energy development (i.e., lower fawn fall recruitment when harsh weather was combined with higher density of active oil and gas wells). Interestingly, we found that the combined effect of predator density and energy development was not interactive but rather additive. Our analysis demonstrates how energy development may modulate fluctuations in mule deer fawn fall recruitment concurrent with biotic (density-dependency, habitat, predation, woody vegetation encroachment) and abiotic (harsh seasonal weather) drivers. Density-dependent patterns emerge, presumably due to limited quality habitat, being the primary factor influencing fall fawn recruitment in mule deer. Secondarily, stochastic weather events periodically cause dramatic declines in recruitment. And finally, the additive effects of human disturbance and predation can induce fluctuations in fawn fall recruitment. Here we make the case for using long-term datasets for setting long-term wildlife management goals that decision makers and the public can understand and support.

Fences have recently been recognized as one of the most prominent linear infrastructures on earth. As animals traverse fenced landscapes, they adjust movement behaviours to optimize resource access while minimizing energetic costs of coping with fences. Examining individual responses is key for connecting localized fence effects with population dynamics. We investigated the multi-scale effects of fencing on animal movements, space use and survival of 61 pronghorn and 96 mule deer on a gradient of fence density in Wyoming, USA. Taking advantage of the recently developed Barrier Behaviour Analysis, we classified individual movement responses upon encountering fences (i.e. barrier behaviours). We adopted the reaction norm framework to jointly quantify individual plasticity and behavioural types of barrier behaviours, as well as behaviour syndromes between barrier behaviours and animal space use. We also assessed whether barrier behaviours affect individual survival. Our results highlighted a high-level individual plasticity encompassing differences in the degree and direction of barrier behaviours for both pronghorn and mule deer. Additionally, these individual differences were greater at higher fence densities. For mule deer, fence density determined the correlation between barrier behaviours and space use and was negatively associated with individual survival. However, these relationships were not statistically significant for pronghorn. By integrating approaches from movement ecology and behavioural ecology with the emerging field of fence ecology, this study provides new evidence that an extraordinarily widespread linear infrastructure uniquely impacts animals at the individual level. Managing landscape for lower fence densities may help prevent irreversible behavioural shifts for wide-ranging animals in fenced landscapes.

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