Deer slow down litter decomposition by reducing litter quality in a temperate forest
Bibliographic record
Abstract
Litter decomposition is a key process that allows the recycling of nutrients within ecosystems. In temperate forests, the role of large herbivores in litter decomposition remains a subject of debate. To address this question, we used two litterbag experiments in a quasiexperimental situation resulting from the introduction of Sitka black-tailed deer Odocoileus hemionus sitkensis on forested islands of Haida Gwaii (Canada). We investigated the two main pathways by which deer could modify litter decomposition: change in litter quality and modification of decomposer communities. We found that deer presence significantly reduced litter mass loss after 1 yr, mainly through a reduction in litter quality. This mass loss reflected a 30 and 28% lower loss of carbon (C) and nitrogen (N), respectively. The presence of deer also reduced the ability of decomposers to break down carbon, but not nitrogen. Indeed, litter placed on an island with deer lost 5% less carbon after 1 yr of decomposition than did litter decomposing on an island without deer. This loss in ability to decompose litter in the presence of deer was outweighed by the differences in mass loss associated with the effect of deer on litter quality. Additional effects of feces deposition by deer on the decomposition process were also significant but minor. These results suggest that the effects dramatic continental-scale increases in deer populations may have on broad-scale patterns of C and N cycling deserve closer attention.
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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".