Arctic foxes as ecosystem engineers: increased soil nutrients lead to increased plant productivity on fox dens
Bibliographic record
Abstract
Top predators can provide fundamental ecosystem services such as nutrient cycling, and their impact can be even greater in environments with low nutrients and productivity, such as Arctic tundra. We estimated the effects of Arctic fox (Vulpes lagopus) denning on soil nutrient dynamics and vegetation production near Churchill, Manitoba in June and August 2014. Soils from fox dens contained higher nutrient levels in June (71% more inorganic nitrogen, 1195% more extractable phosphorous) and in August (242% more inorganic nitrogen, 191% more extractable phosphorous) than adjacent control sites. Inorganic nitrogen levels decreased from June to August on both dens and controls, whereas extractable phosphorous increased. Pup production the previous year, which should enhance nutrient deposition (from urine, feces, and decomposing prey), did not affect soil nutrient concentrations, suggesting the impact of Arctic foxes persists >1 year. Dens supported 2.8 times greater vegetation biomass in August, but δ(15)N values in sea lyme grass (Leymus mollis) were unaffected by denning. By concentrating nutrients on dens Arctic foxes enhance nutrient cycling as an ecosystem service and thus engineer Arctic ecosystems on local scales. The enhanced productivity in patches on the landscape could subsequently affect plant diversity and the dispersion of herbivores on the tundra.
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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) | 0.001 | 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".