A direct comparison of ecological theories for predicting the relationship between plant traits and growth
Bibliographic record
Abstract
Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime’s CSR triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to biomass investments, leaf structure or gas exchange, respectively. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and ecophysiological traits, growth rate varied 50-fold and was best predicted by growth analysis and CSR traits, as well as total leaf area and plant height. Despite two LES traits correlating with growth, they contradicted predictions and leaf traits did not scale with root and stem characteristics. Thus, although combining leaf traits and whole-plant allocation best predicts growth, when destructive measures are not feasible, we suggest total leaf area and plant height, or easy-to-measure traits associated with the CSR classification.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".