Mountain‐top and valley‐bottom experiences: the stress axis as an integrator of environmental variability in arctic ground squirrel populations
Bibliographic record
Abstract
Abstract Environmental variation in mountainous regions can impose major differences in demography and physiology on animal populations that occupy a large elevation range. This variation can be both predictable and unpredictable. In the south‐westernYukon, arctic ground squirrel (AGS) populations occur all the way from the forested valley bottoms to the alpine meadows and, in so doing, experience a wide range of predation risk, forage quality and exposure to weather variables. To assess howAGSintegrate these factors and respond to them, we measured fecal glucocorticoid metabolites (FCM) as an index of the stress axis on four sites at different elevations. We first validated the enzyme immunoassay with a hormonal challenge protocol and a simulated predator stressor.AGSshow a strong diurnal pattern inFCMlevels, with peaks at mid‐day and a marked increase in response to adrenocorticotropic hormone and the simulated predator and a decline in response to dexamethasone. The lag time between the challenge and its reflection in the feces was 4–12 h. Using this method in our field studies, we found thatFCMlevels decreased as the active season progressed; however, specific patterns differed among sites. We hypothesized that the early season peak inFCMlevels followed by the general decline was due to brief, intense early season breeding, followed by the necessity ofAGSto increase mass in preparation for hibernation. Although we found no clear, single explanation for the differentFCMpatterns among sites, we hypothesized that differences in seasonal climate and adverse weather may be major factors affectingFCMlevels. The environment was markedly different between years, with 2008 being colder and wetter than 2009 and this was associated withAGSin 2008 having much higherFCMlevels in general than 2009. We found that population density and visibility may also contribute. In conclusion,AGSlive in a mosaic of habitats and each population is faced with a variety of environmental stressors; how they cope and respond to these stressors may not depend on a single factor but the complete aggregate of these stressors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".