A temperature threshold to identify the driving climate forces of the respiratory process in terrestrial ecosystems
Bibliographic record
Abstract
Abstract. Terrestrial ecosystem respiration (Re) is the major source of CO2 release and constitutes the second largest carbon flux between the biosphere and atmosphere. Therefore, climate-driven changes of Re may greatly impact on future atmospheric CO2 concentration. The aim of this study was to derive an air temperature threshold for identifying the driving climate forces of the respiratory process in terrestrial ecosystems within different temperature zones. For this purpose, a global dataset of 647 site-years of ecosystem flux data collected at 152 sites has been examined. Our analysis revealed an ecosystem threshold of mean annual air temperature (MAT) of 11 ± 2.3 °C. In ecosystems with the MAT below this threshold, the maximum Re rates were primarily dependent on temperature and respiration was mainly a temperature-driven process. On the contrary, in ecosystems with the MAT greater than 11 ± 2.3 °C, in addition to temperature, other driving forces, such as water availability and surface heat flux, became significant drivers of the maximum Re rates and respiration was a multi-factor-driven process. The information derived from this study highlight the key role of temperature as main controlling factor of the maximum Re rates on a large fraction of the terrestrial biosphere, while other driving forces reduce the maximum Re rates and temperature sensitivity of the respiratory process. These findings are particularly relevant under the current scenario of rapid global warming, given that the potential climate-induced changes in ecosystem respiration may lead to substantial anomalies in the seasonality and magnitude of the terrestrial carbon budget.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".