Arrowhead (Sagittaria cuneata) as a bioindicator of nitrogen and phosphorus for prairie streams and wetlands
Bibliographic record
Abstract
Abstract The emergent aquatic plant, Sagittaria cuneata, is an easily-identified and commonly-found species in the Great Plains region of North America and has the potential to be a bioindicator of nitrogen (N) and phosphorus (P) because of its previously-identified leaf plasticity in response to nutrient conditions. To identify associations between leaf morphology and soil and water nutrients, we conducted: (1) a 10-week controlled experiment in which plants were grown in nutrient-enriched sediment, nutrient-enriched water, or unamended control trials, and (2) a field study where emergent leaves were collected from 15 streams of varying nutrient concentrations. Plants grown in experimentally enriched sediment were more productive than those grown in enriched water or control conditions: they produced more emergent leaves and tubers, had a larger final biomass and height, and developed emergent leaves that showed a consistent increase in size and unique change in shape over time. Emergent leaves collected from field plants also showed significant variability of leaf traits; however, this variability occurred at all scales of replication (leaf, plant, quadrat, and site), with linear mixed effects modelling indicating that random chance was likely driving this variability. Although sediment nutrients were crucial to successful growth of S. cuneata under controlled conditions, the high variability in leaf morphology under field conditions (likely due to large natural variability at the species, population, and individual scale) make leaf plasticity of S. cuneata unsuitable as a bioindicator. Our results emphasize the need to quantify within and among plant variation in leaf morphology (and to clarify sampling methods) for the many taxa of aquatic macrophytes that are phenotypically plastic and notoriously difficult to classify.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".