Red foxes increase white spruce seed production at its northern range limit
Bibliographic record
Abstract
The northern boreal forest treeline is usually considered to be the result of climate limiting tree reproduction. Although climate also influences the cycling of nutrients, the effects of nutrient availability on the treeline have largely been ignored. Various animal activities can alter nutrient cycling, creating microsites of highly fertile soil. Red foxes ( Vulpes vulpes ) act as ecosystem engineers in the nutrient-poor northern boreal woodlands by concentrating soil nutrients through their denning activities, increasing white spruce ( Picea glauca ) tree growth. Here, we examined how denning activity affects reproduction in white spruce by determining the number cones per tree, seeds per cone (with or without endosperm, i.e., filled or not) and their viability. Overall, seed production in the study region was low, with an average of 3.7 filled seeds and 0.73 viable seeds per cone. Denning activity affected all stages of seed production, with trees on dens producing an average of two times more cones, three times more total seeds, four times more filled seeds, and 18 times more viable seeds than trees growing away from dens. While cone counts per tree were a good predictor of filled seeds per tree, they were less reliable at predicting viable seeds per tree. Additionally, the number of cones on a tree does not predict the number of filled seeds in a cone, making it difficult for granivores like squirrels and birds to assess food availability from cone abundance. Overall, these results suggest that nutrient availability can play a crucial role in tree reproduction at the treeline and denning activities can create hotspots of seed production and seed viability, acting as a potential food source for granivores and locations for tree reproduction.
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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) | 0.002 | 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".