Biophysical Impacts of Historical Disturbances, Restoration Strategies, and Vegetation Types in a Peatland Ecosystem
Bibliographic record
Abstract
Abstract Rewetting of disturbed peatlands is an important restoration strategy for climate change mitigation. Previous work primarily focuses on the biogeochemical processes altered by rewetting and few studies have investigated the biophysical impacts, which can diminish or amplify biogeochemical effects beyond the ecosystem scale. We used a paired flux tower approach in a restored peatland to collect year‐round eddy covariance data to assess the biophysical impacts of disturbance and management practices. The first site was actively rewetted and is characterized by Sphagnum and white beak‐rush with patches of open water. The second site represents a disturbed ecosystem, which underwent natural regeneration and is dominated by scrub pine, Sphagnum, and low shrubs. We found that the actively restored site had higher net radiation compared to the second site due to more surface water ponding; however, the higher aerodynamic conductance at the passively restored site contributed to enhanced daytime turbulent fluxes, and hence, both sites had similar aerodynamic temperatures during the daytime. The actively restored site experienced warmer nighttime and seasonal aerodynamic temperature as much of the excess radiation during the day was stored in the water column and released at night. To achieve restoration goals, higher water tables are now maintained throughout large sections of the bog. The study implies that water table manipulation has the potential to minimize greenhouse gas emissions from the bog, thereby allowing the biophysical impacts of peatland restoration to enhance the biogeochemical benefits. Therefore, it is important to consider both biophysical and biogeochemical changes in peatland restoration management.
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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 | 0.001 | 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".