Projected near‐future flooding and warming increase graminoid biomass in a high‐latitude coastal wetland
Bibliographic record
Abstract
Abstract With rapid climate warming, some coastal high‐latitude ecosystems are experiencing more frequent tidal floods. Yet little is known about tundra plant‐community responses to flooding, and whether Arctic warming may modulate such responses. In a 2‐year, full‐factorial field experiment in coastal tundra wetlands of the Yukon‐Kuskokwim (Y‐K) Delta (western Alaska), we simulated periodic tidal flood events at two severities under both ambient and warmed summer conditions and measured above‐ground plant‐community responses. Low‐severity flooding represented overbank flooding 1 day per month, which is consistent with projections in the next 5 years. High‐severity flooding represented a more impactful flooding regime (three consecutive days per month) that is projected to occur in the next 10 years. Our warming treatment (+1°C) also represented a change projected in the next 10 years. Regardless of temperature, high‐severity flooding increased graminoid biomass by >45%, in turn increasing live plant‐community biomass by >18%. Low‐severity flooding had similar, though weaker, effects. Flooding had overall negative effects on both forb and shrub biomass, though shrub responses were weaker. Only during the second summer, warming increased graminoid biomass by 20% and tended to increase shrub biomass, regardless of flooding. Concurrently, warming enhanced standing‐dead graminoid biomass by 20%, while high‐severity flooding decreased it by 15%. Therefore, wet tundra that was both flooded and warmed had the greatest proportion of graminoids and total live biomass, but standing‐dead biomass comparable to that of unmanipulated wet tundra. Synthesis . While our manipulations simulated flooding and warming regimes expected in the wetlands of the Y‐K Delta over the same, near‐future (5‐to‐10 years) time frame, flooding had stronger effects than warming. What is striking is the rate at which graminoid increases occurred, becoming apparent after only two monthly flood events in the first experimental year. Flooding‐induced decreases in standing‐dead biomass suggests that the incorporation of dead plant material into the litter layer might be facilitated by tidal floods. These rapid increases in plant biomass and potentially biomass turnover, especially of graminoids, which are characterized by high‐quality litter, may have major implications for carbon and nutrient cycling of more frequently flooded coastal ecosystems in a warmer Arctic.
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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".