The effects of clearcut harvesting on moss chloroplast lipidome and adaptation to light stress during boreal forest regeneration
Bibliographic record
Abstract
Moss plays an important role in boreal forest ecosystems as an understory bryophyte species. Clearcut harvesting is a common boreal forest regeneration method that can expose understory vegetation to abiotic stressors impeding their recovery following post-harvest conditions. Very little is known concerning how moss remodel their chloroplast lipidome to enhance photosynthetic performance for successful acclimation to light and water stress during boreal forest regeneration following clearcut harvesting. The chloroplast lipidome and photosynthetic performance of Sphagnum sp. and three feathermoss species (Pleurozium schreberi, Hylocomium splendens, and Ptilium crista-castrensis) from a boreal black spruce (Picea mariana) forest were assessed using liquid chromatography-mass spectrometry (LC-MS), photospectrometry, and light response curves. We observed an overall increase in monogalactosyldiacylglycerol (MGDG) and sulfoquinovosyldiacylglycerol (SQDG) and decrease in digalactosyldiacylglycerol (DGDG) and phosphatidylglycerol (PG). In addition, unsaturation of the chloroplast lipidome occurred concomitant with photoprotection by carotenoid pigments to enhance the efficiency and photosynthetic capacity in moss exposed to light and water stress following clearcut harvesting. This appears to be a successful acclimation strategy used by moss to circumvent light stress during boreal forest regeneration following clearcut harvesting. These findings could be of significance in the development of boreal forest management strategies following resource harvesting.
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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".