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Biology subjects

Young, K. B.

Publications and source records attributed to Young, K. B..

2 recordsLinked to original sources

Unravelling the complex biogeographic and anthropogenic history of Alaska's mountain goats

AimWe used genetic tools to examine the population structure of mountain goats in Alaska, USA and assessed the demographic history of this species in relation to the natural and anthropogenic forces. LocationAlaska, USA TaxonNorth American mountain goat (Oreamnos americanus) MethodsSamples were collected between 2006 - 2020 from harvested animals and live captures. We genotyped 816 mountain goats at 18 microsatellite loci and identified the number of genetically distinct subpopulations across the state and assessed their genetic diversity. We used Bayesian computation software to investigate the demographic history relative to the known biogeographic history of the state. We also simulated island translocation events and compared simulations to empirical data to address the hypothesis that Baranof Island was a cryptic refugia. ResultsWe showed that Alaska has four genetically distinct subpopulations of mountain goats with some additional genetic structure within those subpopulations. The main split of mountain goats between Southcentral and Southeast Alaska occurred [~]14,000 years ago. Simulations of translocation events largely aligned with the current populations observed today except for Baranof Island which showed greater diversity than the translocation simulation. Main ConclusionsThe distribution and genetic structure of mountain goats in Alaska reflects a combination of natural and anthropogenic forces. A rapid northerly expansion through an ice-free corridor in combination with the isolated nature of the landscape led to low diversity and isolation 14,000 years ago in Southcentral Alaska and higher diversity in Southeast Alaska. Two of the three islands where mountain goat translocations have occurred match genetically with their source population, while Baranof Island appears to have a divergent population, consistent with the hypothesis of an endemic or cryptic population prior to the translocation event. This study highlights the value of considering both the natural and anthropogenic forces when assessing the biogeographic history of a species.

evolutionary biology↗

Quantifying the effects of recent glacial history and future climate change on a unique population of mountain goats

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, especially habitat specialists, 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 landscape 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 the population density of these mountain goats. We used DNA samples to estimate diversity and depict the genealogical history. Climate response models allowed us to simulate changes to suitable habitat and predict how this might influence future population structure. Our results indicated that three genetically distinct subpopulations exist in Glacier Bay and that the population structure 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.

evolutionary biology↗