A Second Dimension to the Leaf Economics Spectrum Predicts Edaphic Habitat Association in a Tropical Forest
Bibliographic record
Abstract
BACKGROUND: Strong patterns of habitat association are frequent among tropical forest trees and contribute to the maintenance of biodiversity. The relation of edaphic differentiation to tradeoffs among leaf functional traits is less clear, but may provide insights into mechanisms of habitat partitioning in these species rich assemblages. METHODOLOGY/PRINCIPAL FINDINGS: We quantify the leaf economics spectrum (LES) for 16 tree species within a Bornean forest characterized by highly pronounced habitat specialization. Our findings suggest that the primary axis of trait variation in light-limited, lowland tropical forests was identical to the LES and corresponds with the shade tolerance continuum. There was no separation with respect to edaphic variation along this primary axis of trait variation. However, a second orthogonal axis determined largely by foliar P concentrations resulted in a near-perfect separation of species occupying distinct soil types within the forest. CONCLUSIONS/SIGNIFICANCE: We suggest that this second axis of leaf trait variation represents a "leaf edaphic habitat spectrum" related to foliar P and potentially other nutrients closely linked to geological substrate, and may generally occur in plant communities characterized by strong edaphic resource gradients.
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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 | 0.001 | 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".