Soil nitrogen dynamics within profiles of a managed moist temperate forest chronosequence consistent with long‐term harvesting‐induced losses
Bibliographic record
Abstract
Abstract This study investigates whether clear‐cut forest harvesting leads to alterations in the decadal‐scale biogeochemical nitrogen (N) cycles of moist temperate forest ecosystems. Using a harvested temperate red spruce (Picea rubens Sarg.) forest chronosequence in Nova Scotia, Canada, representing <1 to >80 year old postharvest conditions, alongside a reference old‐growth (125+ year old) site with no documented history of disturbance, we examine harvesting‐related changes in soil N pools and fluxes. Specifically, we quantify soil N storage with depth and age across the forest chronosequence, examine changes in physical fractions and δ15N of soil N through depth and time, and quantify gross soil N transformation rates through depth and time using a 15N isotope dilution technique. Our findings point to a large loss of total N in the soil pool, particularly within the deep soil (>20 cm) and organomineral fractions. A pulse of available mineralized N (as ammonium) was observed following harvesting (mean residence time (MRT) > 6 days), but its MRT dropped to <1 day 80 years following harvesting, in contrast to the MRT of 2–3 days observed in the reference old‐growth forest site. These mineralization patterns coupled with inferred leaching losses to groundwater are consistent with storage estimates that suggest soil N may not reaccrue for almost a century following this disturbance.
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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) | 0.000 | 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".