Climate and hydrology control apparent rates of peat accumulation across Europe
Bibliographic record
Abstract
Peat accumulates when there is a positive mass balance between plant productivity inputs and litter/peat decomposition losses. Here we examine apparent peat accumulation rates (aPAR) during the last two millennia from 28 well-dated European peatlands and find them to range between 0.005 and 0.448 cm yr-1 (mean = 0.118 cm yr-1). Our work provides important context for the commonplace assertion that peatlands accumulate at ~1mm per year. We find that relationships between aPAR and climatic variables are generally weak – summer temperature is the only significant climatic control on aPAR across our European sites. aPAR tends to be higher when water-table depth (reconstructed from testate-amoeba subfossils) is within 5-10 cm of the peatland surface. When a Generalized Additive Model and Gaussian Response Curve are fitted to the data, both methods show that the optimal water-table depth for highest aPAR is ~10 cm. aPAR is generally lower when water table depths are 25 cm, which may relate to a decrease in plant productivity and increased decomposition losses, respectively. These findings corroborate contemporary experimental studies which examined the relationship between peatland water-table depth, or the thickness of the aerobic surface layer (the ‘acrotelm’), and the rate of peat formation. Our work suggests that for European peat bogs, an average water-table depth of ~10 cm is optimal to enable rapid peat growth and therefore carbon sequestration in the long term. This has important implications for peatland restoration and rewetting strategies, in our global efforts to mitigate climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".