Humid, warm and treed ecosystems show longer time-lag of vegetation response to climate
Bibliographic record
Abstract
Climate-induced temperature rise and shifting precipitation patterns across diverse global ecosystems impact vegetation growth. Due to the diverse nature of terrestrial ecosystems and their climates, interactions between climate and vegetation vary spatially and temporally. Most studies focus on simultaneous interactions, overlooking the legacy effects of climate on vegetation physiology and growth. In this research, we use satellite-observed Solar-Induced Fluorescence (SIF) and Enhanced Vegetation Index (EVI) as the indicators of vegetation photosynthesis and greenness to assess the time-lag effect in vegetation response to climate from May 2018 to Dec 2021. Specifically, we examine the relationship between SIF, EVI, and concurrent or antecedent climate variables containing precipitation, soil moisture, and temperature. Additionally, we compare different time-lags of these climate variables under distinct environmental conditions to understand how climatic conditions influence them. Our findings reveal that arid and cold climates exhibit more concurrent climate-vegetation interactions than other ecosystems. In contrast, humid ecosystems with high mean annual temperature and precipitation show a substantial time-lag response of vegetation to climate, for up to six months. Given the significance of time-lag effects in global climate-vegetation interactions, acknowledging these effects is paramount for improving our understanding of vegetation dynamics in a changing climate.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".