Response and adaptation of terrestrial ecosystem processes to climate warming
Bibliographic record
Abstract
Terrestrial ecosystems are characterized by a series of spatiotemporally continuous, multiple scaled, and mutually connected processes. Since most of these ecological processes are regulated by temperature, climate warming will profoundly impact terrestrial ecosystems at global scale. Recently, how key processes in terrestrial ecosystems respond and/or adapt to climate warming has become a fundamental question in global change ecology. Here, we reviewed the recent research progress related to such question. This review focuses on key ecosystem processes, such as plant ecophysiological processes, phenology, community dynamics, productivity and carbon allocation, decomposition of litter and soil organic carbon, nutrient cycling, and carbon-nitrogen coupling. Based on a literature review, we propose perspectives for future research to tackle fundamental questions, such as the predictability of plant traits on ecosystem processes, coupling between biogeochemical cycles, mechanisms driving ecosystem responses to extreme climate and asymmetric warming, and ecological forecasting with models. We finally suggest more research efforts on warming adaptation rather than response on China's specific ecosystems, and on the integration of experiments, observations, and models for coordinating studies across scales.
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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 | 0.000 | 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".