Plant cover changes drive soil carbon pool responses in High Arctic dry heath exposed to decades of experimentally increased summer rain and nutrient addition
Bibliographic record
Abstract
With accelerating climate change, higher summer rainfall and warmer soils are expected for High Arctic ecosystems . Yet, how increased rainfall and soil nutrient availability will affect plant composition and ecosystem carbon (C) storage in these arid, low-productivity ecosystems remains unclear. We utilised a long-term experiment in dry shrub heath tundra in Zackenberg, NE Greenland, in which nitrogen and phosphorus availability was increased and precipitation doubled experimentally every summer for 25 years. We determined soil and vegetation C pools, plant cover and leaf chemistry, and ecosystem CO2 fluxes in peak growing season. Watering increased the cover of graminoids and all plants by 78% and 18%, respectively, which likely drove a moderate 6% increase in upper soil C stocks. Soil respiration was consistently stimulated in watered plots, confirming high sensitivity of soil microbes to moisture in dry tundra, but also stimulation of microbial activity by increased plant inputs. We suggest that belowground processes linked to root growth, root exudation, and/or microbial turnover of organic matter are important in driving the C pool changes. Our results show that increased summer rainfall can lead to greening and enhanced soil C pool in High Arctic dry heaths, potentially providing moderate negative feedback to climate 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) | 0.001 | 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".