No time to dye: dye-induced light differences mediate growth rates among invasive macrophytes
Bibliographic record
Abstract
Invasive, submerged macrophytes negatively alter aquatic ecosystems and biodiversity through disruption of ecological structure and functioning. These plants are especially challenging and costly to control, with relatively few successful eradications. We examine the efficacy of dye treatments to control three invasive, submerged macrophyte species: Elodea canadensis Michx., Elodea nuttallii (Planchon) H. St. John and Lagarosiphon major (Ridley). Using an experimental mesocosm approach, growth rates of each species were monitored in relation to five light treatment groups: light, 1, 2, 3 dye dosage, and complete darkness (range: 270 to 0 molm -2 s -1 ). Dye presence did not negate growth in any of the tested species, but the effects of treatments on invasive macrophyte growth rates differed across species. In dyed conditions, E. canadensis exhibited significantly greater increases in length compared to E. nuttallii and L. major, whilst E. nuttallii and L. major were lower and statistically similar. However, L. major significantly increased length relative to Elodea spp. in dark conditions. Similarly, for biomass changes, Elodea spp. gained significantly more biomass than L. major under light and dyed conditions, but not in the dark. Our findings suggest that the tested dye concentrations are not sufficient to halt the growth of these plants. However, under certain conditions, they could potentially help to reduce densities of invasive macrophytes by slowing growth rates and reducing biomass in select species. Differential responses to light could also help explain species replacement dynamics under varying environmental contexts. Overall, while further empirical research is required, management actions that reduce light could help control aquatic macrophytes in combination with other actions, but could also simultaneously mediate shifts in community assembly.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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; both teacher heads agree on what is shown here.
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".