Ecological implications of single and mixed nitrogen nutrition in Arabidopsis thaliana
Bibliographic record
Abstract
BACKGROUND: Ecologists recognize that plants capture nitrogen in many chemical forms that include amino acids. Access to multiple nitrogen types in plant communities has been argued to enhance plant performance, access to nitrogen and alter ecological interactions in ways that may promote species coexistence. However, data supporting these arguments have been limited. While it is known that plants uptake amino acids from soil, long term studies that link amino acid uptake to measures of plant performance and potential reproductive effort are not typically performed. Here, a series of experiments that link uptake of nitrate, glutamine or asparagine with lifetime reproductive effort in Arabidopsis thaliana are reported. Nitrogen was offered either singly or in mixture and at a variety of combinations. Traits related to reproductive output were measured, as was the preference for each type of nitrogen. RESULTS: When plants were supplied with a single nitrogen type at concentrations from 0.1-0.9 mM, the ranking of nitrogen types was nitrate > glutamine > asparagine in terms of the relative performance of plants. When plants were supplied with two types of nitrogen in mixture at ratios between 0.1:0.9-0.9:0.1 mM, again plants performed best when nitrate was present, and poorly when amino acids were mixed. Additionally, stable isotopes revealed that plants preferentially captured nitrogen types matching the hierarchy of nitrate > glutamine > asparagine. Comparing between the two experiments revealed that mixed nitrogen nutrition was a net cost to the plants. CONCLUSIONS: Plant performance on mixed nitrogen was less than half the performance on equal amounts of any single nitrogen type. We asked: why did A. thaliana capture amino acids when doing so resulted in a net cost? We argue that available data cannot yet answer this question, but hypothesize that access to lower quality forms of nitrogen may become important when plants compete.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".