Precipitation frequency alters peatland ecosystem structure and <scp>CO</scp><sub>2</sub> exchange: Contrasting effects on moss, sedge, and shrub communities
Bibliographic record
Abstract
Abstract Climate projections forecast a redistribution of seasonal precipitation for much of the globe into fewer, larger events spaced between longer dry periods, with negligible changes in seasonal rainfall totals. This intensification of the rainfall regime is expected to alter near‐surface water availability, which will affect plant performance and carbon uptake. This could be especially important in peatland systems, where large stores of carbon are tightly coupled to water surpluses limiting decomposition. Here, we examined the role of precipitation frequency on vegetation growth and carbon dioxide (CO2) balances for communities dominated by a Sphagnum moss, a sedge, and an ericaceous shrub in a cool temperate poor fen. Field plots and laboratory monoliths received one of three rainfall frequency treatments, ranging from one event every three days to one event every 14 days, while total rain delivered in a two‐week cycle and the entire season to each treatment remained the same. Separating incident rain into fewer but larger events increased vascular cover in all peatland communities: vascular plant cover increased 6× in the moss‐dominated plots, nearly doubled in the sedge plots, and tripled in the shrub plots in Low‐Frequency relative to High‐Frequency treatments. Gross ecosystem productivity was lowest in moss communities receiving low‐frequency rain, but higher in sedge and shrub communities under the same conditions. Net ecosystem exchange followed this pattern: fewer events with longer dry periods increased CO2 flux to the atmosphere from the moss while vascular plant‐dominated communities became more of a sink for CO2. Results of this study suggest that changes to rainfall frequency already occurring and predicted to continue will lead to increased vascular plant cover in peatlands and will impact their carbon‐sink function.
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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".