Effects of timber harvesting on the genetic potential for carbon and nitrogen cycling in five North American forest ecozones
Bibliographic record
Abstract
Forest ecosystems are critical to global biogeochemical cycles but under pressure from harvesting and climate change. We investigated the effects of organic matter (OM) removal during forest harvesting on the genetic potential of soil communities for biomass decomposition and nitrogen cycling in five ecozones across North America. We analyzed 107 samples, representing four treatments with varied levels of OM removal, at Long-Term Soil Productivity Study sites. Samples were collected more than ten years after harvesting and replanting and were analyzed via shotgun metagenomics. High-quality short reads totaling 1.2 Tbp were compared to the Carbohydrate Active Enzyme (CAZy) database and a custom database of nitrogen cycle genes. Gene profile variation was mostly explained by ecozone and soil layer. Eleven CAZy and nine nitrogen cycle gene families were associated with particular soil layers across all ecozones. Treatment effects on gene profiles were mainly due to harvesting, and only rarely to the extent of OM removal. Harvesting generally decreased the relative abundance of CAZy genes while increasing that of nitrogen cycle genes, although these effects varied among ecozones. Our results suggest that ecozone-specific nutrient availability modulates the sensitivity of the carbon and nitrogen cycles to harvesting with possible consequences for long-term forest sustainability.
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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 | 0.001 | 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) | 0.000 | 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".