Functional Traits Exert More Control on Root Carbon Exudation than Do Short-Term Light and Nitrogen Availability in Four Herbaceous Plant Species
Bibliographic record
Abstract
Root carbon exudation is a critical element of the soil carbon cycle, and how both environmental conditions and plant traits influence exudation remains uncertain. I studied relationships between environmental conditions, plant traits, and carbon exudation in four herbaceous plant species: Asclepias incarnata, Microstegium vimineum, Panicum virgatum, and Scirpus cyperinus. Mature individuals were given short-term factorial light and N treatments, and exudates were collected from 8-hour carbon-free hydroponic incubations. I measured size traits (biomass, leaf area, root length, and root volume), photosynthesis (leaf-level and whole-plant), and tissue N traits (root, stem, and leaf percent N and C:N ratio). Neither light nor N treatments affected exudation, while exudation varied with species and traits. Species alone substantially explained mass-specific exudation (estimated R2 = 0.38). Size strongly predicted both total and mass-specific exudation, interacting with species (estimated R2 = 0.52 and 0.48, respectively). Generally, larger individuals exuded more overall but less per unit mass, although larger M. vimineum plants exuded more per unit mass. Whole-plant photosynthetic rate was weakly related to total exudation (estimated R2 = 0.17), and tissue N concentration moderately predicted mass-specific exudation (estimated R2 = 0.23). Other researchers have found that high light and low nitrogen availability stimulate exudation; my results indicate that this relationship is not straightforward. Plant traits, however, significantly explained variation in exudation, including some variation across species, supporting trait-based analyses of plant species' effects on ecosystem processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".