Long‐term experimentally deepened snow decreases growing‐season respiration in a low‐ and high‐arctic tundra ecosystem
Bibliographic record
Abstract
Abstract Tundra soils store large amounts of carbon (C) that could be released through enhanced ecosystem respiration (ER) as the arctic warms. Over time, this may change the quantity and quality of available soil C pools, which in‐turn may feedback and regulate ER responses to climate warming. Therefore, short‐term increases in ER rates due to experimental warming may not be sustained over longer periods, as observed in other studies. One important aspect, which is often overlooked, is how climatic changes affecting ER in one season may carry‐over and determine ER in following seasons. Using snow fences, we increased snow depth and thereby winter soil temperatures in a high‐arctic site in Svalbard (78°N) and a low‐arctic site in the Northwest Territories, Canada (64°N), for 5 and 9 years, respectively. Deepened snow enhanced winter ER while having negligible effect on growing‐season soil temperatures and soil moisture. Growing‐season ER at the high‐arctic site was not affected by the snow treatment after 2 years. However, surprisingly, the deepened snow treatments significantly reduced growing‐season ER rates after 5 years at the high‐arctic site and after 8–9 years at the low‐arctic site. We speculate that the reduction in ER rates, that became apparent only after several years of experimental manipulation, may, at least in part, be due to prolonged depletion of labile C substrate as a result of warmer soils over multiple cold seasons. Long‐term changes in winter climate may therefore significantly influence annual net C balance not just because of increased wintertime C loss but also because of “legacy” effects on ER rates during the following growing seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 teacher head, 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".