Foxes fertilize the subarctic forest and modify vegetation through denning
Bibliographic record
Abstract
Ecosystem engineers modify habitats through processes other than trophic interactions, such as by regulating soil nutrients, and can influence resource availability and quality for other organisms. Predator-mediated elemental cycling may be especially important in determining plant diversity and growth in ecosystems where soil fertility and primary productivity are low. Red foxes (Vulpes vulpes L.), top predators in the Subarctic, could engineer local ecosystems through denning, which could create biogeochemical hotspots of nutrients due to continual input of feces, urine and prey remains. We examined soil and vegetation characteristics on red fox dens and paired control sites in woodland habitats near the Arctic treeline in Manitoba, Canada. The organic soil layer on den sites had 81% more inorganic nitrogen and 250% more extractable phosphorus than in control areas. Denning also increased soil respiration and pH in the organic layer, suggesting improved soil quality and nutrient availability for plants. By enriching nutrients and disturbing soils through digging, den sites had a higher plant species ß-diversity and a greater cover of erect woody shrubs (Salix spp.), grasses (Leymus mollis (Trinius) Pilger) and weedy ephemerals compared to control sites, resulting in a regional increase in plant species richness. Our research highlights the importance of considering impacts of predators other than through their consumption of prey, and provides insight into the role of red foxes in modifying plant diversity and productivity in the Subarctic.
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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 | 0.001 | 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".