Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn
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Bibliographic record
Abstract
Abstract Aim To investigate the importance of autumn phenology in controlling interannual variability of forest net ecosystem productivity ( NEP ) and to derive new phenological metrics to explain the interannual variability of NEP . Location N orth A merica and E urope. Method Flux data from nine deciduous broadleaf forests ( DBF ) and 13 evergreen needleleaf forests ( ENF ) across N orth A merica and E urope (212 site‐years) were used to explore the relationships between the yearly anomalies of annual NEP and several carbon flux based phenological indicators, including the onset/end of the growing season, onset/end of the carbon uptake period, the spring lag (time interval between the onset of growing season and carbon uptake period) and the autumn lag (time interval between the end of the carbon uptake period and the growing season). Meteorological variables, including global shortwave radiation, air temperature, soil temperature, soil water content and precipitation, were also used to explain the phenological variations. Results We found that interannual variability of NEP can be largely explained by autumn phenology, i.e. the autumn lag. While variation in neither annual gross primary productivity ( GPP ) nor in annual ecosystem respiration ( R e ) alone could explain this variability, the negative relationship between annual NEP and autumn lag was due to a larger R e / GPP ratio in years with a prolonged autumn lag. For DBF sites, a longer autumn lag coincided with a significant decrease in annual GPP but showed no correlation with annual R e . However, annual GPP was insensitive to a longer autumn lag in ENF sites but annual R e increased significantly. Main conclusions These results demonstrate that autumn phenology plays a more direct role than spring phenology in regulating interannual variability of annual NEP . In particular, the importance of respiration may be potentially underestimated in deriving phenological indicators.
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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 | 0.000 | 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 it