Behavioral and Demographic Responses of Mule Deer to Energy Development on Winter Range

Applied winter biology: threats, conservation and management of biological resources during winter in cold climate regions

Winter at high latitudes is characterized by low temperatures, dampened light levels and short photoperiods which shape ecological and evolutionary outcomes from cells to populations to ecosystems. Advances in our understanding of winter biological processes (spanning physiology, behaviour and ecology) highlight that biodiversity threats (e.g. climate change driven shifts in reproductive windows) may interact with winter conditions, leading to greater ecological impacts. As such, conservation and management strategies that consider winter processes and their consequences on biological mechanisms may lead to greater resilience of high altitude and latitude ecosystems. Here, we use well-established threat and action taxonomies produced by the International Union of Conservation of Nature-Conservation Measures Partnership (IUCN-CMP) to synthesize current threats to biota that emerge during, or as the result of, winter processes then discuss targeted management approaches for winter-based conservation. We demonstrate the importance of considering winter when identifying threats to biodiversity and deciding on appropriate management strategies across species and ecosystems. We confirm our expectation that threats are prevalent during the winter and are especially important considering the physiologically challenging conditions that winter presents. Moreover, our findings emphasize that climate change and winter-related constraints on organisms will intersect with other stressors to potentially magnify threats and further complicate management. Though conservation and management practices are less commonly considered during the winter season, we identified several potential or already realized applications relevant to winter that could be beneficial. Many of the examples are quite recent, suggesting a potential turning point for applied winter biology. This growing body of literature is promising but we submit that more research is needed to identify and address threats to wintering biota for targeted and proactive conservation. We suggest that management decisions consider the importance of winter and incorporate winter specific strategies for holistic and mechanistic conservation and resource management.

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Genomic correlates for migratory direction in a free-ranging cervid

Animal migrations are some of the most ubiquitous and one of the most threatened ecological processes globally. A wide range of migratory behaviours occur in nature, and this behaviour is not uniform among and within species, where even individuals in the same population can exhibit differences. While the environment largely drives migratory behaviour, it is necessary to understand the genetic mechanisms influencing migration to elucidate the potential of migratory species to cope with novel conditions and adapt to environmental change. In this study, we identified genes associated with a migratory trait by undertaking pooled genome-wide scans on a natural population of migrating mule deer. We identified genomic regions associated with variation in migratory direction, including FITM1, a gene linked to the formation of lipids, and DPPA3, a gene linked to epigenetic modifications of the maternal line. Such a genetic basis for a migratory trait contributes to the adaptive potential of the species and might affect the flexibility of individuals to change their behaviour in the face of changes in their environment.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Some Memories Never Fade: Inferring Multi-Scale Memory Effects on Habitat Selection of a Migratory Ungulate Using Step-Selection Functions

Understanding how animals use information about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store information about experienced landscapes, however, remains an understudied topic in movement ecology. By studying how species select for familiar locations, visited recently and in the past, we can gain insight to how they store and use local information in multiple memory types. In this study, we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado, United States to investigate the influence of spatial experience over different time scales on seasonal range habitat selection. We inferred the influence of short and long-term memory from the contribution to habitat selection of previous space use within the same season and during the prior year, respectively. We fit step-selection functions to GPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection, inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals, models incorporating covariates representing both recent and past experience and environmental covariates performed best. In the top model, locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-term memory in driving seasonal range habitat selection. Further, the influence of previous space uses was stronger in the summer relative to winter, which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-term information about seasonal ranges, which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly, these findings provide novel insight into how species store and use information over different time scales.