Intensive forest harvesting increases susceptibility of northern forest soils to carbon, nitrogen and phosphorus loss
Bibliographic record
Abstract
Abstract Understanding the impact of forest harvesting is critical to sustainable forest management, yet there remains much uncertainty regarding how harvesting affects soil carbon (C), nitrogen (N) and phosphorus (P) dynamics. Here, we conducted a global meta‐analysis of 808 observations from 49 studies to test the effects of harvesting on the stocks and concentrations of soil C, N and P and C:N:P ratios relative to uncut control stands. With all harvesting intensities combined, C stock was unaffected by harvesting in either the forest floor or mineral soil, while harvesting reduced forest floor [C], [N] and [P] and C:N ratio, increased the mineral soil [C] and C:N ratio, but reduced mineral soil N stock. The impacts of harvesting on forest floor C and N stocks, C:P and N:P and mineral soil [C] and [N] changed from no effects by partial, stem‐only and whole‐tree harvesting (WTH) to significantly negative effects by the harvesting coupled with fire. Stem‐only and WTH similarly reduced forest floor [P]. The negative effects of harvesting were most pronounced in conifer stands. Soil [C], [N] and C:N decreased with time since harvesting, but soil [P] did not, resulting in an increase in forest floor N:P. Synthesis and applications . Our findings highlight the importance of harvest intensity and rotation length on long‐term soil nutrient availability when managing forests. Furthermore, the lag in the recovery of phosphorus concentration following harvesting may indicate a decoupling of the phosphorus cycle from those of carbon and nitrogen, and a potential concern in managed forests.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".