Nitrogen aboveground turnover and soil stocks to 8 m depth in primary and selectively logged forest in southern Amazonia
Bibliographic record
Abstract
Abstract Extensive areas of Amazonia undergo selective logging, modifying forest structure and nutrient cycles. Anthropogenic‐accelerated rates of nitrogen (N) turnover could increase N loss and affect regeneration, carbon sequestration and timber production. We quantified leaf area reduction, canopy opening and downed biomass and resultant N flux from reduced impact logging (RIL) activities. We compared canopy reduction, surface soil moisture and nitrate to 8 m depth between logged gaps and intact primary forest to determine if logging activities increase subsoil nitrate. To test long‐term logging effects, we evaluated surface N stocks along a 12‐year postlogging chronosequence. At the harvest rate of 2.6 trees ha−1, total N additions in logging gaps, including leaves and wood from felled crowns (24.8 kg N ha−1) and other killed trees (41.9 kg N ha−1), accounted for over 80% of the total N addition to aboveground necromass from all logging activities (81.9 kg N ha−1). Despite this N turnover by logging, belowground nitrate storage to 8 m depth did not differ between logging gaps and primary forest at the low harvest rate and disturbance intensity of this study. Soil water depletion also did not differ between gaps and primary forest over 1 year, indicating the impact on belowground inorganic N was low. Compared with primary forest, nitrate concentrations to 8 m depth in logging gaps were only significantly higher at 60–100 cm, suggesting some N redistribution beyond the bulk of the fine roots in logging gaps. Extrapolated to the Amazon Basin scale, we provide a conservative estimate that logging damage and bole export under RIL would turn over 0.14 ± 0.07 to 0.23 ± 0.12 Tg N yr−1based on 1999–2002 selective logging rates. Greater damage during conventional selective logging would cause higher N turnover throughout the Amazon Basin than our results based on RIL.
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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".