Higher Sensitivity of Deep Soil Root Productivity to Precipitation Changes
Bibliographic record
Abstract
ABSTRACT Aim Plastic responses of root productivity to precipitation changes would facilitate plant survival under drought stress. However, whether the responses are pervasive along the soil profile remains unknown. Here, we compiled a global dataset of root productivity at different soil depths from in situ precipitation manipulation experiments, and investigated whether the root productivity responded to precipitation changes uniformly or nonuniformly along the soil profile across diverse biomes. Location Global. Time Period Studies published during 2000–2022. Major Taxa Studied Mature woody plants and herbaceous. Methods Meta‐analysis was conducted to evaluate the responses of root productivity at different soil depths to precipitation changes. The partial least squares path model (PLS‐PM) was used to explore the pathways how various biotic and abiotic factors regulate these responses at different soil depths and across different vegetation types. Results Root productivity responded divergently to decreased precipitation between surface and deep soil. Decreased precipitation significantly reduced root productivity in surface soil (effect size: −0.19 ± 0.05), but significantly increased root productivity in deep soil (effect size: 0.18 ± 0.13), particularly under long‐term (> 3 years) and severe (> 50% reduction in precipitation) drought treatments. Increased precipitation stimulates root productivity in all soil layers, with increasing stimulation towards deep soil depth. Main Conclusions This study highlights higher sensitivity of deep soil root productivity to precipitation changes, providing crucial insights into potential shifts in ecosystem belowground structure and functioning in a more extreme climate regime.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".