Impacts of nutrient loads on the invasion potential of Butomus umbellatus L. on Ottawa National Wildlife Refuge diked wetlands
Bibliographic record
Abstract
Introduced to the Great Lakes Region from Europe before 1900, invasive Butomus umbellatus (Flowering rush) forms monotypic stands that crowd native species and cover open water systems across Great Lakes shorelines and reservoirs in the northern US.Factors contributing to invasion persistence and impacts on ecosystem function by this species are poorly understood.This study characterizes vegetation and environmental factors at the Ottawa National Wildlife Refuge, which borders Lake Erie, to understand how sediment nutrient levels in watersheds affect B. umbellatus invasion.We hypothesized that increased sediment nutrient levels are important drivers of B. umbellatus invasion success.Sediment nutrient levels, matter, water depth, and vegetation were sampled within 1m 2 plots throughout the management units of the marsh complex.Vegetation of B. umbellatus and 18 other species present were harvested or canopy characteristics measured to estimate biomass.B. umbellatus was the most abundant of all identified emergent invasive species found, occurring at 55 % of the surveyed plots.B. umbellatus rhizome bud count averaged 509 per plot, with a range of 0 -2760 buds.While sediment nutrient analysis of nitrogen and phosphorus showed heterogeneity within and across management units, nutrient levels did not predict B. umbellatus abundance.However, B. umbellatus biomass decreased with increasing community biomass.Vegetative propagule production via rhizome buds decreased with increased nutrients and increased community biomass.B. umbellatus was found to have a wide range of nitrogen and phosphorus in leaf tissue, and 2 -4 times more average phosphorus than all analyzed native species.This data will assist managers in identifying timing and approaches for controlling this invasive species and restoring wetland biodiversity.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".