Harvesting surface vegetation does not impede self‐recovery of<i>Sphagnum</i>peatlands
Bibliographic record
Abstract
Ecosystem restoration frequently involves the reintroduction of plant material in the degraded ecosystem. When there are no plant nurseries or seeds available on the market, the plant material has to be harvested in the wild, in a “donor ecosystem.” A comprehensive assessment of donor ecosystem recovery is lacking, especially for Sphagnum ‐dominated donor peatlands, where all top vegetation is harvested mechanically with different practices. We aimed to evaluate (1) the regeneration of vegetation, especially of Sphagnum mosses, to determine which harvesting practices are best to enhance recovery and (2) the influence of the site hydrological conditions and meteorological variables of the first complete growing season postharvesting on peat moss regeneration. Twenty‐five donor sites covering a 17‐year chronosequence (harvested 1–17 years ago) were inventoried along with 15 associated natural reference sites located in Quebec, New Brunswick, and Alberta, Canada. All donor sites aged 10 years or more were dominated by Sphagnum mosses, though plant composition varied between donor and their associated reference sites because of the wetter conditions at harvested donor sites. Harvesting practices strongly influenced donor site recovery, showing that the skills of the practitioner are an essential ingredient. Harvesting practices minimizing donor site disturbances are recommended, such as the choice of the adequate donor site (localization, hydrologic conditions, vegetation), the use of less disruptive methods, and harvesting when the soil is deeply frozen. This study demonstrated that harvesting surface plant material for peatland restoration is not detrimental towards the recovery of near‐natural peatland ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".