Quantifying the effects of recent glacial history and future climate change on a unique population of mountain goats
Bibliographic record
Abstract
Abstract 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.
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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 | 0.001 | 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".