Ecological niche modeling of diploid flowering rush ( <i>Butomus umbellatus</i> L.) in the United States
Bibliographic record
Abstract
Flowering rush (Butomus umbellatus L.) is an invasive perennial monocot found along the United States (U.S.).-Canadian border which can grow into large monotypic mats that can cause water use issues.Currently, there are two known cytotypes, diploid and triploid, within the invaded range; however, basic ecological research is lacking on the diploid cytotype.Ecological niche modelling (ENM) was done on three known populations of the diploid cytotype, alongside a global site model, to determine site-specific abiotic influences and potential suitability within the U.S. The ENM was constructed using climatic and soil variables from public sources with resultant models compared to currently known populations of flowering rush throughout the U.S. Diploid flowering rush populations and the global site models were highly reliant on precipitation in the driest month (27-39% model contribution) and one site was highly reliant on precipitation seasonality (38% model contribution).Diploid flowering rush populations in this study displayed different responses towards abiotic factors; however, seasonal signaling of precipitation patterns are highly important.Furthermore, diploid flowering rush is predicted to invade numerous areas with less than suitable habitat which should warrant further monitoring to prevent further spread. KEY POLICY HIGHLIGHTS1. Diploid flowering rush is an invasive perennial monocot currently invading the U.S. and was found to have high suitability within most of the U.S. which may allow potential range expansion within major river watersheds.2. Flowering rush displays plasticity to the environment it invades and is highly dependent on seasonal signaling for most life processes.3. Ecological niche modelling shows promising results for use as site-by-site evaluation tool for populations of diploid flowering rush and could be used to help identify abiotic influences within invaded sites or sites with potential invasion.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".