Phenology and resource allocation strategies of diploid flowering rush (<i>Butomus umbellatus</i>) in Ohio and New York
Bibliographic record
Abstract
Abstract Flowering rush ( Butomus umbellatus L.) is an emergent perennial monocot that has invaded aquatic systems along the U.S.–Canadian border. Currently, there are two known cytotypes of flowering rush, diploid and triploid, within the invaded range. Although most studies have focused on the triploid cytotype, little information is known about diploid plants. Therefore, phenology and resource allocation were studied on the diploid cytotype of flowering rush in three study sites (Mentor Marsh, OH; Tonawanda Wildlife Management Area, NY; and Unity Island, NY) to understand seasonal resource allocation and environmental influences on growth, and to optimize management strategies. Samples were harvested once a month from May to November at each site from 2021 to 2023. Plant metrics were regressed to air temperature, water temperature, and water depth. Aboveground biomass peaked from July to September and comprised 50% to 70% of total biomass. Rhizome biomass peaked from September to November and comprised 40% to 50% of total biomass. Rhizome bulbil densities peaked from September to November at 3,000 to 16,000 rhizome bulbils m −2 . Regression analysis resulted in strong negative relationships between rhizome starch content and air temperature (r 2 = 0.52) and water temperature (r 2 = 46). Other significant, though weak, relationships were found, including a positive relationship between aboveground biomass and air temperature (r 2 = 0.17), a negative relationship between rhizome bulbil biomass and air temperature (r 2 = 0.18) and a positive relationship between leaf density and air temperature (r 2 = 0.17). Rhizomes and rhizome bulbils combined stored up to 60% of total starch, and therefore, present a unique challenge to management, as these structures cannot be reached directly with herbicides. Therefore, management should target the aboveground tissue before peak production (July) to reduce internal starch storage and aim to limit regrowth over several years.
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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".