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‐western Y ukon, 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 how AGS integrate 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. AGS show a strong diurnal pattern in FCM levels, 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 that FCM levels decreased as the active season progressed; however, specific patterns differed among sites. We hypothesized that the early season peak in FCM levels followed by the general decline was due to brief, intense early season breeding, followed by the necessity of AGS to increase mass in preparation for hibernation. Although we found no clear, single explanation for the different FCM patterns among sites, we hypothesized that differences in seasonal climate and adverse weather may be major factors affecting FCM levels. The environment was markedly different between years, with 2008 being colder and wetter than 2009 and this was associated with AGS in 2008 having much higher FCM levels in general than 2009. We found that population density and visibility may also contribute. In conclusion, AGS live 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".