<scp>A</scp>rctic ground squirrels <i><scp>U</scp>rocitellus parryii</i> as drivers and indicators of change in northern ecosystems
Bibliographic record
Abstract
Abstract Global warming and increasing human activity are altering northern ecosystems. In these strongly seasonal environments, small herbivorous mammals may have a significant role in determining the trajectory of ecosystem transitions from one state to another. Arctic ground squirrels Urocitellus parryii are a key component of northern terrestrial food webs and are considered ecosystem engineers, exerting a large impact on their habitat through bioturbation. We review and synthesize diverse information about current and past distribution and density of arctic ground squirrels, their physiology and ecological interactions with other species. Factors that appear to affect the distribution and abundance of arctic ground squirrels include increasing temperatures, changes in flooding probability, permafrost thaw, shifting phenology, habitat change, new predators and invasive diseases. Increases in the distribution and density of arctic ground squirrels in northern latitudes and high altitudes could accelerate ecosystem change through facilitation of disturbance‐tolerant species, while decreases in southern and milder climates could remove an important agent of disturbance and prey item. Despite their pervasive ecological influence throughout most of their range, arctic ground squirrels are underrepresented in ecological research, based on a comparison of the number of publications about arctic ground squirrels with the number about other species of the same genus, and about other arctic herbivores. The widespread distribution of arctic ground squirrels, along with their potential to exacerbate and alter trajectories of ecosystem change under global warming, makes them a valuable indicator of ecosystem change and therefore a candidate for increased monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".