Unravelling the complex biogeographic and anthropogenic history of Alaska’s mountain goats
Bibliographic record
Abstract
Abstract Aim We 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. Location Alaska, USA Taxon North American mountain goat ( Oreamnos americanus ) Methods Samples 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. Results We 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 Conclusions The 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".