Investigating the mechanism of Arctic fox (Vulpes lagopus) ecosystem engineering on dry heath communities in subarctic tundra
Bibliographic record
Abstract
Arctic terrestrial ecosystems are some of the most extreme environments on earth, with a polar climate and landscapes recently carved by glacial retreat. Soil nutrient availability is a limiting factor for tundra productivity and under these conditions, the impacts of consumer-driven nutrient cycling can be magnified. Arctic foxes (Vulpes lagopus) are predators that act as ecosystem engineers in arctic and alpine tundra heath by altering the environment of their den sites, which exhibit increased soil nutrients, verdant atypical vegetation, and deeper snow. These fox dens are biogeochemical hotspots in otherwise nutrient-limited ecosystems and sources of cascading effects across trophic levels. It has been long hypothesized that these dens are so biotically productive because Arctic foxes increase the deposition rate of limiting nutrients by concentrating prey-derived nitrogen (N) and phosphorus (P) in the soil and this hypothesis has been descriptively supported but, the mechanism had not been confirmed with experimentation. To test the ability of the nutrients concentrated by Arctic foxes to cause the ecosystem effects observed on fox dens, I examined a long-term field experiment in Wapusk National Park. Vegetation plots received N and P additions (estimated from fox activity) and/or snow fencing on the windward side to increase snow depth. I investigated how the species composition of plot plant communities changed over 5 years, the treatment effects on plant productivity, intraspecific changes in prostrate shrub leaf metabolism, the response of resident insect communities, and the space use by collared lemmings. I found that nutrient addition facilitates the invasion of tall grass that can accumulate deep snow cover in the winter, deep snow can magnify some effects of increased nutrients, and the nutrient/snow combination can shape plant communities and create preferred lemming habitat. My thesis demonstrates how N and P deposition, increased to a rate within the ability of Arctic foxes, can drive the development of the atypical vegetation and cause the cascading ecosystem effects described at Arctic fox dens. Thus, I support the ability of Arctic foxes to engineer unique den habitat in tundra ecosystems.
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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 | 0.000 | 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) | 0.000 | 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 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".