Nine-years effect of harvesting and mechanical site preparation on bryophyte decomposition and carbon stocks in a boreal forested peatland
Bibliographic record
Abstract
The boreal forest holds the world's largest soil carbon (C) reservoir. A large portion of it is contained in a thick organic layer originating from the slow decay of bryophytes. Because a thick organic layer slows down tree growth, reduces forest productivity, and thereby reduces the potential wood supply, silvicultural treatments that aim to maintain or restore forest productivity after harvesting often involve mechanical site preparation. However, while these treatments can increase growth and C storage in trees, they can also lead to accelerated decomposition of the soil organic matter, reducing C storage. In this study, we assessed the nine-years effect of two silvicultural treatments on soil C dynamics in forested peatlands of northwestern Quebec, compared to unharvested controls: (1) cut with protection of regeneration and soils (CPRS; low soil disturbance, also called careful logging around advanced growth (CLAAG)), (2) CPRS followed by mechanical site preparation (CPRS + MSP, plowing; severe soil disturbance). The mass loss rate of three bryophytes (Pleurozium schreberi, Sphagnum capillifolium, and Sphagnum fuscum) was measured over two growing seasons together with soil organic carbon (SOC) stocks. We also studied the different effects of temperature, water table level, depth, and type of soil layer on mosses decomposition.We observed a significant influence of silvicultural treatments, bryophyte species, and soil layer type (fibric, mesic, humic and mineral) on bryophyte mass loss, which was higher in the CPRS + MSP treatment (21.6 ± 0.13 % standard error) than in control sites (9.5 ± 0.21 %); CPRS alone resulted in an intermediate mass loss of 11.6 ± 0.23 %, for Sphagnum mosses. Bryophyte mass loss was significantly higher in fibric than humic layer. SOC stocks in the uppermost organic soil layer (fibric) were lower in the CPRS + MSP group than in the control group, while the CPRS group was intermediate; however, differences were not statistically significant for the other soil layer and for total SOC. We conclude that while CPRS + MSP accelerates Sphagnum moss decomposition in the topsoil layer, it has limited impact on total soil C stocks that are detectable with stock change methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".