Root and shoot phenology, architecture, and organ properties: an integrated trait network among 44 herbaceous wetland species
Bibliographic record
Abstract
Integrating traits across above- and belowground organs offers comprehensive insights into plant ecology, but their various functions also increase model complexity. This study aimed to illuminate the interspecific pattern of whole-plant trait correlations through a network lens, including a detailed analysis of the root system. Using a network algorithm that allows individual traits to belong to multiple modules, we characterize interrelations among 19 traits, spanning both shoot and root phenology, architecture, morphology, and tissue properties of 44 species, mostly herbaceous monocots from Northern Ontario wetlands, grown in a common garden. The resulting trait network shows three distinct yet partially overlapping modules. Two major trait modules indicate constraints of plant size and form, and resource economics, respectively. These modules highlight the interdependence between shoot size, root architecture and porosity, and a shoot-root coordination in phenology and dry-matter content. A third module depicts leaf biomechanical adaptations specific to wetland graminoids. All three modules overlap on shoot height, suggesting multifaceted constraints of plant stature. In the network, individual-level traits showed significantly higher centrality than tissue-level traits do, demonstrating a hierarchical trait integration. The presented whole-plant, integrated network suggests that trait covariation is essentially function-driven rather than organ-specific.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".