Mosses are Important for Soil Carbon Sequestration in Forested Peatlands
Bibliographic record
Abstract
Nutrient-rich peat soils have previously been demonstrated to lose carbon despite higher photosynthesis and litter production compared to nutrient-poor soils, where instead carbon accumulates. To understand this phenomenon, we used a process-oriented model (CoupModel) calibrated on data from two closely located drained peat soil sites in boreal forests in Finland, Kalevansuo and Lettosuo, with different soil C/N ratios. Uncertainty-based calibrations were made using eddy-covariance data (hourly values of net ecosystem exchange) and tree growth data. The model design used two forest scenarios on drained peat soil, one nutrient-poor with dense moss cover and another with lower soil C/N ratio with sparse moss cover. Three vegetation layers were assumed: conifer trees, other vascular plants, and a bottom layer with mosses. Adding a moss layer was a new approach, because moss has a modified physiology compared to vascular plants. The soil was described by three separate soil organic carbon (SOC) pools consisting of vascular plants and moss litter origin and decomposed organic matter. Over 10 years, the model demonstrated a similar photosynthesis rate for the two scenarios, 903 and 1,034 g C m−2 yr−1, for the poor and rich site respectively, despite the different vegetation distribution. For the nutrient-rich scenario more of the photosynthesis produce accumulated as plant biomass due to more trees, while the poor site had abundant moss biomass which did not increase living aboveground biomass to the same degree. Instead, the poor site showed higher litter inputs, which compared with litter from vascular plants had low turnover rates. The model calibration showed that decomposition rate coefficients for the three SOC pools were similar for the two scenarios, but the high quantity of moss litter input with low decomposability for the nutrient poor scenario explained the major difference in the soil carbon balance. Vascular plant litter declined with time, while SOC pools originating from mosses accumulated with time. Large differences between the scenarios were obtained during dry spells where soil heterotrophic respiration doubled for the nutrient-rich scenario, where vascular plants dominated, owing to a larger water depletion by roots. Where moss vegetation dominated, the heterotrophic respiration increased by only 50% during this dry period. We suggest moss vegetation is key for carbon accumulation in the poor soil, adding large litter quantities with a resistant quality and less water depletion than vascular plants during dry conditions.
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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 | 0.001 |
| 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".