Ecological controls on net ecosystem productivity of a mesic arctic tundra under current and future climates
Bibliographic record
Abstract
[1] Changes in arctic C stocks with climate are thought to be caused by rising net primary productivity (NPP) during longer and warmer growing seasons, offset by rising heterotrophic respiration (Rh) in warmer and deeper soil active layers. In this study, we used the process model ecosys to test hypotheses for these changes with CO2 and energy fluxes measured by eddy covariance over a mesic shrub tundra at Daring Lake, Canada, under varying growing seasons. These tests corroborated substantial rises in NPP, smaller rises in Rh, and, hence, rises in net ecosystem productivity (NEP) from 17 to 45 g C m−2 yr−1 (net C sink), modeled with higher Ta and longer growing seasons. However, NEP was found to decline briefly during midsummer warming events (Ta > 20°C). A model run under climate change predicted for Daring Lake indicated that rises in NPP would exceed those in Rh during the first 100 years, causing NEP to rise. Rises in NPP were driven by more rapid net N mineralization from more rapid Rh in warming soils. However, greater declines in NEP were modeled during more frequent and intense midsummer warming events as climate change progressed. Consequently, average annual NEP (± interannual variability) rose from 30 (±13) g C m−2 yr−1 under current climate to 57 (±40) g C m−2 yr−1 after 90 years but declined to 44 (±51) g C m−2 yr−1 after 150 years, indicating that gains in tundra NEP under climate change may not be indefinite.
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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 | 0.001 |
| 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 | 0.001 | 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".