Deer browsing effects on temperate forest soil nitrogen cycling shift from positive to negative across fertility gradients
Bibliographic record
Abstract
Herbivores impact soil biogeochemical processes, often increasing nutrient cycling rates under high nutrient availability and decreasing nutrient cycling rates under low nutrient availability. These patterns are far from universal, and interactions between habitat fertility and herbivore effects are under continuing investigation. By sampling inside and outside a network of deer exclosures, we determined deer browsing effects on temperate forest soil nitrogen (N) and carbon (C) cycling along a gradient of soil and litter C–N ratios across our network of sites. Deer browsing increased net N mineralization rates in high nutrient environments and decreased N mineralization rates in low nutrient environments, whereas browsing decreased CO2 respiration rates in high nutrient environments and increased CO2 respiration rates in low nutrient environments. Differences in deer browsing effects on soil processes could be explained by plant responses to herbivory across gradients of resource availability. To our knowledge, our study is one of the first to show that deer browsing can have significant effects on net N mineralization and C respiration in temperate forest soils and that the direction and magnitude of deer browsing effects on soil N and C cycling vary across fertility gradients.
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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".