Coordination among leaf and fine root traits across a strong natural soil fertility gradient
Bibliographic record
Abstract
Abstract Unravelling how fundamental axes of trait variation correlate among leaves and roots and relate to nutrient availability is crucial for understanding plant distribution. While the leaf trait variation axis is linked to nutrient availability gradients, the response of root trait variation to the same gradients yields inconsistent results. We studied leaf and root trait variation among 23 co-occurring plant species along a 2-million year soil chronosequence to assess how leaf and root traits coordinate and how this resulting joint axis of variation relates to soil fertility. Mycorrhizal association types primarily structured the axes of leaf and root trait variation. However, when considering species abundance, soil nutrient availability was an important driver of trait distribution. Leaves that support rapid growth in younger, fertile soils were associated with roots of larger diameter and arbuscular mycorrhizal colonization. In contrast, leaves that favour nutrient conservation in nutrient-impoverished soil were associated with greater root hair length and phosphorus-mobilizing root exudates. At the species level, the signals deviated from the community-wide results presented above, highlighting the challenge of generalizing a specific set of root trait values that consistently meet the requirements of leaves supporting either rapid growth or survival.
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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 | 0.000 |
| 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) | 0.001 | 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".