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Both natural and anthropogenic forces can play a substantial role in the demographic history and current structure of a wildlife population. Species with strict habitat requirements are especially susceptible to these impacts. Mountain goats (Oreamnos americanus) in Alaska are of particular interest in this regard due to their influence on alpine ecosystems, importance to human cultures, and enigmatic history in some areas. Here, we used genetic tools to examine the population structure and demographic history of mountain goats in Alaska. We genotyped 816 mountain goats at 18 microsatellites, identified the number of genetically distinct subpopulations, and assessed their genetic diversity. We used Bayesian methods to investigate demographic history relative to the known geologic and human history of Alaska, and we simulated human-mediated translocation events onto islands to address the hypothesis that Baranof Island harbored an extant population prior to an early 20th-century introduction. We showed that Alaska has 4 genetically distinct subpopulations of mountain goats. The main demographic split between Southcentral and Southeast Alaska occurred following the retreat of ice after the Last Glacial Maximum. Simulations of translocation events largely aligned with expected genetic diversity patterns of current subpopulations except for Baranof Island which showed greater diversity than the simulation, consistent with the hypothesis of an endemic population prior to the translocation. This study highlights the value of considering both natural and anthropogenic forces when assessing the biogeographic history of a species and provides new insights about the complex demographic history and biogeography of mountain goats in Alaska. [ABSTRACT FROM AUTHOR]

Potential negative artificial selection on horn size is a concern for many harvested ungulates. The mountain goat (Oreamnos americanus) has distinct black horns, but targeting animals based on horn size in the field can be challenging. We analyzed over 23,000 horn records that included base circumference and total length, from which we also derived horn volume, from mountain goats harvested in Alaska, British Columbia, and the Northwest Territories from 1980 to 2016. We tested 3 potential drivers of horn size variation: geographical location, environmental conditions, and artificial selection. We found no support for a latitudinal effect with surprisingly little variation across the sampling distribution. The Pacific Decadal Oscillation had the largest effect outside age in the model, suggesting a role of climate in shaping variation. Mountain goats harvested closer to roads had larger horns, indicating that ease of access might allow hunters to be more selective, though the effect size was small. Our findings reinforce the value of accurate and complete record keeping on horn size, age, and sex of harvested animals, and highlight the importance of explicitly considering climate and accessibility when devising management strategies for the mountain goat. [ABSTRACT FROM AUTHOR]

Understanding patterns of animal space use and range fidelity has important implications for species and habitat conservation. For species that live in highly seasonal environments, such as mountain goats (Oreamnos americanus), spatial use patterns are expected to vary in relation to seasonal changes in environmental conditions and sex‐ or age‐specific selection pressures. To address hypotheses about sex, age, and seasonality influence on space‐use ecology, we collected GPS location data from 263 radio‐collared mountain goats (males, n = 140; females, n = 123) in coastal Alaska during 2005–2016. Location data were analyzed to derive seasonal and sex‐specific fixed‐kernel home range estimates and to quantify the degree of seasonal range and utilization distribution overlap. Overall, we determined that home range size was smallest during winter, expanded coincident with the onset of green‐up and parturition, and was largest during summer. Home range size of males and females did not differ significantly during winter, but females had larger home ranges than males during summer, a relationship that was switched during the mating season. Pairwise comparisons involving individual females across subsequent years indicated home ranges were significantly smaller during years when they gave birth to offspring. Mountain goats exhibited a strong degree of range fidelity, and 99% (n = 138) of individual animals returned to their previous year's seasonal range with an average annual Bhattacharyya's affinity utilization distribution overlap index of 68%. Similarity of seasonal home range utilization distributions varied in relation to sex and season in some respects. Home range overlap was highest during the summer vegetation growing season, particularly among females. These findings advance our understanding about how environmental variation and sex‐ and age‐related reproductive constraints influence space use and range fidelity among alpine ungulates. Documentation of the high degree of range fidelity among mountain goats has important conservation implications in landscapes increasingly altered by anthropogenic activities. [ABSTRACT FROM AUTHOR]

Acquisition of field data and analytical methods needed for conservation and management of wildlife populations represent significant challenges, particularly for species that inhabit landscapes that are difficult to access or species that persist in small, isolated populations. In such instances, integrating diverse and complementary data streams, such as genetic and non‐genetic data, can advance our understanding of population dynamics and associated management implications. We examined how genetic and morphologic data can be used to articulate population structure of a low‐density, peninsular population of mountain goats (Oreamnos americanus) on the Cleveland Peninsula, Alaska, USA, and surrounding areas, 2005–2018. We then use a population demographic modeling approach to examine how the use of population structure information influences sustainable harvest quotas, as compared to a panmictic, null model. Specifically, we conducted extensive field sampling of genetic (n = 446) and morphologic (i.e., horn length, n = 371) data to characterize population structure. We conducted demographic analyses and examined harvest modeling scenarios using a sex‐ and age‐specific matrix population modeling approach. Genetic and morphologic data analyses suggested peninsular subpopulations were demographically isolated, relative to surrounding mainland populations. Specifically, genetic structuring was evident and followed an isolation‐by‐distance, stepping‐stone pattern indicating limited interchange, low effective population sizes, and reduced genetic diversity along a peninsular extremity to mainland gradient. Harvest modeling indicated that overharvest would likely occur if the panmictic, null model was used to guide harvest because the smallest genetically defined population at the peninsular extremity was too small to permit any level of sustainable harvest. Our analyses illustrate the importance of using genetic and morphologic data, in combination with demographic modeling, to quantitatively delineate population boundaries and dynamics for ensuring viability of small, isolated populations. © 2020 The Wildlife Society. We integrated population genetic data and demographic modeling to facilitate conservation of small, peninsular mountain goat populations in coastal Alaska. Our findings illustrate how population genetic data can represent a key resource for delineating populations and, when integrated in a rigorous and quantitative harvest modeling framework, can significantly advance our ability to sustainably manage populations. [ABSTRACT FROM AUTHOR]

Winter recreation and tourism continue to expand worldwide, and where these activities overlap with valuable wildlife habitat, there is greater potential for conservation concerns. Wildlife populations can be particularly vulnerable to disturbance in alpine habitats as helicopters and snowmachines are increasingly used to access remote backcountry terrain. Brown bears (Ursus arctos) have adapted hibernation strategies to survive this period when resources and energy reserves are limited, and disturbance could negatively impact fitness and survival. To help identify areas of potential conflict between helicopter skiing and denning brown bears in Alaska, we developed a model to predict alpine denning habitat and an associated data-based framework for mitigating disturbance activities. Following den emergence in spring, we conducted three annual aerial surveys (2015–2017) and used locations from three GPS-collared bears (2008–2014) to identify 89 brown bear dens above the forest line. We evaluated brown bear den site selection of land cover, terrain, and climate factors using resource selection function (RSF) models. Our top model supported the hypothesis that bears selected dens based on terrain and climate factors that maximized thermal efficiency. Brown bears selected den sites characterized by steep slopes at moderate elevations in smooth, well-drained topographies that promoted vegetation and deep snow. We used the RSF model to map relative probability of den selection and found 85% of dens occurred within terrain predicted as prime denning habitat. Brown bear exposure to helicopter disturbance was evident as moderate to high intensities of helicopter flight tracking data overlapped prime denning habitat, and we quantified where the risk of these impact was greatest. We also documented evidence of late season den abandonment due to disturbance from helicopter skiing. The results from this study provide valuable insights into bear denning habitat requirements in subalpine and alpine landscapes. Our quantitative framework can be used to support conservation planning for winter recreation industries operating in habitats occupied by denning brown bears. [ABSTRACT FROM AUTHOR]

Minimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection‐corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape‐scale distance sampling surveys with local‐scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south‐central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52–0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise (CV̅ = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split‐panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. © 2019 The Wildlife Society. We demonstrate how data collected under 2 different survey protocols, distance sampling and minimum counts, can be jointly analyzed in an integrated modeling framework. The ability to formally link datasets through analysis provides a critical link between legacy data and more recently developed, rigorous approaches. [ABSTRACT FROM AUTHOR]

Ecological theory predicts that individual survival should vary between sex and age categories due to differences in allocation of nutritional resources for growth and reproductive activities. During periods of environmental stress, such relationships may be exacerbated, and affect sex and age classes differently. We evaluated support for hypotheses about the relative roles of sex, age, and winter and summer climate on the probability of mountain goat (Oreamnos americanus) survival in coastal Alaska. Specifically, we used knownfates analyses (Program MARK) to model the effects of age, sex, and climatic variation on survival using data collected from 279 radio-marked mountain goats (118 M, 161 F) in 9 separate study areas during 1977-2008. Models including age, sex, winter snowfall, and average daily summer temperature (during Jul-Aug) best explained variation in survival probability of mountain goats. Specifically, our findings revealed that old animals (9+yr) have lower survival than younger animals. In addition, males tended to have lower survival than females, though differences only existed among prime-aged adult (5-8 yr) and old (9+yr) age classes. Winter climate exerted the strongest effects on mountain goat survival; summer climate, however, was significant and principally influenced survival during the following winter via indirect effects. Furthermore, old animals were more sensitive to the effects of winter conditions than young or prime-aged animals. These findings detail how climate interacts with sex and age characteristics to affect mountain goat survival. Critically, we provide baseline survival rate statistics across various age, sex, and climate scenarios. These data will assist conservation and management of mountain goats by enabling detailed, model-based demographic forecasting of human and/or climate-based population impacts. [ABSTRACT FROM AUTHOR]

Snow affects the nutritional ecology of northern ungulates during winter through burial of important winter forages. We used nonlinear regression analyses to model snow-burial dynamics of blueberry (Vaccinium spp.) browse biomass, a key winter food item of Sitka black-tailed deer (Odocoileus hemionus sitchensis) in southeastern Alaska, USA. During November 2003-March 2004 we collected data from 546 individually marked twigs located on 100 plants of differing sizes and architectures across a range of snow depths. In general, browse biomass became buried and unavailable to deer at snow depths substantially lower than prewinter twig heights. Plant architecture and plant height were related to the probability of a twig being buried. Probability of twig burial was higher on plants with lateral than on those with erect architectures. Twig height also affected the probability of burial by snow but the relationship was complex. For twigs located on erect plants, probability of burial was greatest for twigs near the bottom and top of the plant due to ground-up burial and bending of flexible apex stems, respectively. We used estimated nonlinear equations to model blueberry browse availability in a simulated upland old-growth habitat patch subject to a range of snow depths. We then compared subsequent estimates of deer winter nutritional carrying capacity for this habitat patch to findings derived using an alternative, simple linear (ground-up) model of winter-browse burial by snow. Comparisons indicated that ground-up models of browse burial overestimated browse availability and nutritional carrying capacity for most snow depths. Our findings demonstrate the importance of applying detailed snow-burial models when characterizing nutritional landscape of northern ungulates during winter. [ABSTRACT FROM AUTHOR]

Presents information on a study of behavioral and ecologic effects of differential predation pressure on moose (Alces alces gigas) in the eastern Talkeetna Mountains, Alaska, during 1996-1998. Materials and methods; Results; Discussion.

Reports on several instances of wolf predation on mustelids in Alaska, which involved wolverines and an American marten. Details of observations on wolf predation; Analysis of the behavior of the wolves; Examination of the possible role of wolves in influencing mesocarnivore communities in northern environments.

Human disturbance and climate change can impact populations by disrupting movement corridors and reducing important habitat. Characterizing how animals respond to such environmental changes is valuable for conservation as many species can experience reduced genetic diversity when deleterious habitat change occurs, leading to an increased likelihood of extirpation. Mountain goats (Oreamnos americanus) exemplify this conservation challenge; their geographically isolated habitat can inhibit gene flow, making them susceptible to population declines in the face of anthropogenic-induced change. To facilitate biologically informed population management of mountain goats in Glacier Bay National Park, Alaska, we characterized the fine-scale genetic population structure and examined how future climate change could impact population density. Our results indicated that three genetically distinct subpopulations exist in Glacier Bay and that the genealogical history is reflective of the historic landscape patterns. Climate modeling predicted that demographic productivity was likely to be reduced for all subpopulations; additionally, we found that climate change likely degrades the suitability of movement corridors that facilitate gene flow between subpopulations, ultimately increasing the cost of travel. Understanding such fine-scale patterns are key to managing subpopulations, particularly with impending changes to the landscape. [ABSTRACT FROM AUTHOR]

Snow is a major, climate-sensitive feature of the Earth's surface and catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat (Oreamnos americanus). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23-65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals., (© 2024. The Author(s).)

Landscape heterogeneity plays an integral role in shaping ecological and evolutionary processes. Despite links between the two disciplines, ecologists and population geneticists have taken different approaches to evaluating habitat selection, animal movement, and gene flow across the landscape. Ecologists commonly use statistical models such as resource selection functions (RSFs) to identify habitat features disproportionately selected by animals, whereas population genetic approaches model genetic differentiation according to the distribution of habitat variables. We combined ecological and genetic approaches by using RSFs to predict genetic relatedness across a heterogeneous landscape. We constructed sex- and season-specific resistance surfaces based on RSFs estimated using data from 102 GPS (global positioning system) radio-collared mountain goats (Oreamnos americanus) in southeast Alaska, USA. Based on mountain goat ecology, we hypothesized that summer and male surfaces would be the best predictors of relatedness. All individuals were genotyped at 22 microsatellite loci, which we used to estimate genetic relatedness. Summer resistance surfaces derived from RSFs were the best predictors of genetic relatedness, and winter models the poorest. Mountain goats generally selected for areas close to escape terrain and with a high heat load (a metric related to vegetative productivity and snow depth), while avoiding valleys. Male- and female-specific surfaces were similar, except for winter, for which male habitat selection better predicted genetic relatedness. The null models of isolation-by-distance and barrier only outperformed the winter models. This study merges high-resolution individual locations through GPS telemetry and genetic data, that can be used to validate and parameterize landscape genetics models, and further elucidates the relationship between landscape heterogeneity and genetic differentiation.