Divergent apparent temperature sensitivity of terrestrial ecosystem respiration
Bibliographic record
Abstract
Recent studies revealed convergent temperature sensitivity of ecosystem respiration (Re) within aquatic ecosystems and between terrestrial and aquatic ecosystems. We do not know yet whether various terrestrial ecosystems have consistent or divergent temperature sensitivity. Here, we synthesized 163 eddy covariance flux sites across the world and examined the global variation of the apparent activation energy (Ea), which characterizes the apparent temperature sensitivity of and its interannual variability (IAV) as well as their controlling factors. We used carbon fluxes and meteorological data across FLUXNET sites to calculate mean annual temperature, temperature range, precipitation, global radiation, potential radiation, gross primary productivity and Re by averaging the daily values over the years in each site. Furthermore, we analyzed the sites with >8 years data to examine the IAV of Ea and calculated the standard deviation of Ea across years at each site to characterize IAV. The results showed a widely global variation of Ea, with significantly lower values in the tropical and subtropical areas than in temperate and boreal areas, and significantly higher values in grasslands and wetlands than that in deciduous broadleaf forests and evergreen forests. Globally, spatial variations of Ea were explained by changes in temperature and an index of water availability with differing contribution of each explaining variable among climate zones and biomes. IAV and the corresponding coefficient of variation of Ea decreased with increasing latitude, but increased with radiation and corresponding mean annual temperature. The revealed patterns in the spatial and temporal variations of Ea and its controlling factors indicate divergent temperature sensitivity of Re, which could help to improve our predictive understanding of Re in response to climate change.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".