Interactions among vegetation, climate, and herbivory control greenhouse gas fluxes in a subarctic coastal wetland
Bibliographic record
Abstract
Abstract High‐latitude ecosystems are experiencing the most rapid climate changes globally, and in many areas these changes are concurrent with shifts in patterns of herbivory. Individually, climate and herbivory are known to influence biosphere‐atmosphere greenhouse gas (GHG) exchange; however, the interactive effects of climate and herbivory in driving GHG fluxes have been poorly quantified, especially in coastal systems that support large populations of migratory waterfowl. We investigated the magnitude and the climatic and physical controls of GHG exchange within the Yukon‐Kuskokwim Delta in western Alaska across four distinct vegetation communities formed by herbivory and local microtopography. Net CO 2 flux was greatest in the ungrazed Carex meadow community (3.97 ± 0.58 [SE] µmol CO 2 m −2 s −1 ), but CH 4 flux was greatest in the grazed community (14.00 ± 6.56 nmol CH 4 m −2 s −1 ). The grazed community is also the only vegetation type where CH 4 was a larger contributor than CO 2 to overall GHG forcing. We found that vegetation community was an important predictor of CO 2 and CH 4 exchange, demonstrating that variation in regional gas exchange is best explained when the effect of grazing, determined by the difference between grazed and ungrazed communities, is included. Further, we identified an interaction between temperature and vegetation community, indicating that grazed regions could experience the greatest increases in CH 4 emissions with warming. These results suggest that future GHG fluxes could be influenced by both climate and by changes in herbivore population dynamics that expand or contract the vegetation community most responsive to future temperature 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 | 0.000 | 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) | 0.000 | 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".