Evolutionary history and root trait coordination predict nutrient strategy in tropical legume trees
Bibliographic record
Abstract
Plants express diverse nutrient use and acquisition traits, but it is unclear how trait combinations at the species level are constrained by phylogeny, trait coordination, or trade-offs in resource investment. One trait - nitrogen (N) fixation - is assumed to correlate with other traits and used to define plant functional groups, despite potential confounding effects of phylogeny. We quantified growth, carbon metabolism, fixation rate, root phosphatase activity (RPA), mycorrhizal colonization, and leaf and root morphology/chemistry across 22 species of fixing and nonfixing tropical Fabaceae trees under common conditions. Belowground trait variation was high even among closely related species, and most traits displayed a phylogenetic signal, including N-fixation rate and nodule biomass. Across species, we observed strong positive correlations between physiological traits such as RPA and root respiration. RPA increased ~ fourfold per unit increase in fixation, supporting the debated hypothesis that N-fixers 'trade' N for phosphatases to enhance phosphorus acquisition. Specific root length and root N differed between functional groups, though for other traits, apparent differences became nonsignificant after accounting for phylogenetic nonindependence. We conclude that evolutionary history, trait coordination, and fixation ability contribute to nutrient trait expression at the species level, and recommend explicitly considering phylogeny in analyses of functional groupings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".