Soil processes dominate the long-term response of forest net primary productivity to increased temperature and atmospheric CO<sub>2</sub> concentration
Bibliographic record
Abstract
Predicting the responses of forest growth to elevated temperature (T) and atmospheric CO2 concentration ([CO2]) on decadal time scales presents a formidable challenge because of the many interacting processes involved. A key uncertainty concerns the relative importance of plant and soil processes to the overall long-term response. In this study, the plant-soil model G'DAY was used to simulate forest growth responses to T and [CO2] on different time scales for forests in cool and warm climates. An equilibrium-based graphical analysis was used to distinguish the roles played by plant and soil processes in determining the response. Doubled [CO2] caused a large initial increase (~20%) in net primary productivity (NPP), but this did not persist in the long term. By contrast, a 2°C increase in T caused a persistent long-term increase in NPP of approximately 10-15%. These responses were similar at cool and warm sites. The equilibrium analysis indicated that soil processes dominated the long-term responses predicted by the model. In particular, the predicted long-term increase in NPP under elevated T reflected an increase in predicted N mineralization and plant N uptake, assuming that a constant fraction of mineralized N is taken up by plants. The analysis highlights key uncertainties for future research.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".