Plant functional trait dissimilarity drives plant mixture effects on fine root biomass and trait variations
Bibliographic record
Abstract
Fine roots are critical for soil resource uptake, yet how declining plant diversity alters fine root biomass (RB) and traits remains unclear. We tested how plant functional trait dissimilarity in shade tolerance, drought tolerance, growth rate, and nitrogen-fixation ability drives variation in root attributes. Using a global meta-analysis, we examined how functional dissimilarity affects RB and community-weighted mean rooting depth (WRD), root length density (RLD), and specific root length (SRL). Shade-tolerant dissimilarity reduced RB and SRL but increased RLD. Drought-tolerant dissimilarity decreased RLD, growth-rate dissimilarity increased SRL, and nitrogen-fixation dissimilarity increased both RB and RLD. Over time, drought-tolerant dissimilarity increasingly suppressed RLD, growth-rate dissimilarity switched from negative to positive effects on RB but from weak positive to negative effects on RLD, and nitrogen-fixation dissimilarity showed progressively stronger positive effects on RB and RLD. With increasing soil depth, shade-tolerant dissimilarity effects intensified (more negative on RB, more positive on RLD), growth-rate dissimilarity effects shifted from positive to negative on RB, and nitrogen-fixation dissimilarity effects on RLD weakened. Climate did not modulate these responses. These findings highlight that functional trait dissimilarity mediates diversity effects on belowground processes and offer insights for enhancing root productivity and ecosystem carbon sequestration.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".