Unraveling the complex biogeographic and anthropogenic history of mountain goats (<i>Oreamnos americanus</i>) in Alaska
Bibliographic record
Abstract
Abstract 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.
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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 | 0.000 | 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".