Data for: Red foxes enhance long-term tree growth near the Arctic treeline
Bibliographic record
Abstract
Recent climate warming is expected to increase tree growth and productivity, substantially altering ecological function and boundaries in northern ecosystems. Temperature and precipitation largely determine the range and growth of trees in any biome, yet variations in microsite conditions can also influence tree growth on a finer scale. By altering essential resources and habitat conditions, terrestrial organisms could modify Subarctic tree growth. Red foxes (Vulpes vulpes) are found in most terrestrial ecosystems and are considered ecosystem engineers by enriching soil nutrients and plant composition through denning. Added soil nutrients from prey remains, feces, and urine could benefit tree growth in Subarctic regions by alleviating soil nutrient limitations. We examined growth in white spruce (Picea glauca) trees growing on eight red fox dens and paired control sites near Churchill, Manitoba, Canada, at the Arctic treeline. Radial growth was 55% higher for trees on dens than on control sites between 1897 and 2017, despite similarities in tree ages, densities, and regional climate across all sites. By promoting tree growth near the treeline, red foxes may influence the position of the Arctic treeline. Although the impacts on tree growth largely depend on the spatial distribution of dens and predator activity in the boreal forest, predators can create distinct microhabitats across the landscape via ecosystem engineering processes, leading to increased vegetation productivity, persisting over many decades.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".