Environmental drivers of Sphagnum growth in peatlands across the Holarctic region
Bibliographic record
Abstract
The relative importance of global versus local environmental factors for growth and thus carbon uptake of the bryophyte genus Sphagnum – the main peat-former and ecosystem engineer in northern peatlands – remains unclear. 2) We measured length growth and net primary production (NPP) of two abundant Sphagnum species across 99 Holarctic peatlands. We tested the importance of previously proposed abiotic and biotic drivers for peatland carbon uptake (climate, N deposition, water table depth, and vascular plant cover) on these two responses. Employing structural equation models, we explored both indirect and direct effects of drivers on Sphagnum growth. 3) Variation in growth was large, but similar within and between peatlands. Length growth showed a stronger response to predictors than NPP. Moreover, the smaller and denser Sphagnum fuscum growing on hummocks had weaker responses to climatic variation than the larger and looser S. magellanicum growing in the wetter conditions. Growth decreased with increasing vascular plant cover within a site. Between sites, precipitation and temperature increased growth for S. magellanicum. The structural equation models indicated that indirect effects are important. For example, vascular plant cover increased with a deeper water table, increased nitrogen deposition, precipitation and temperature. These factors also influenced Sphagnum growth indirectly by affecting moss shoot density. 4) Synthesis Our results imply that in a warmer climate, S. magellanicum will increase length growth as long as precipitation is not reduced, while S. fuscum is more resistant to decreased precipitation, but also less able to take advantage of increased precipitation and temperature. Such species-specific sensitivity to climate may affect competitive outcomes in a changing environment, and potentially the future carbon sink function of peatlands.
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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) | 0.001 | 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".