Effects of Sublethal Doses of Herbicides on the Competitive Interactions Between 2 Nontarget Plants, <i>Centaurea cyanus</i> L. and <i>Silene noctiflora</i> L.
Bibliographic record
Abstract
Plant competitive interactions influence the effect of herbicides, and the effect of competitive interactions on plant responses may be important to include in the ecological risk assessment of herbicides. In the present study the effect of competitive interactions and sublethal doses of 2 herbicides on plant species was investigated in competition experiments and fitted to empirical competition models. Two nontarget species commonly found in agroecosystems (Centaurea cyanus L. and Silene noctiflora L.) and 2 herbicides (glyphosate and metsulfuron methyl) were used in separate experiments. Plants were sprayed at the 6- to 8-leaf stage. Effects of herbicide treatments and plant density were modeled by generalization of a discrete hyperbolic competition model. The 10% effective dose (ED10) was calculated for C. cyanus. All experiments showed that as density increased, plants were negatively affected. Furthermore, in all cases, C. cyanus remained a better competitor than S. noctiflora. Nevertheless, the density of S. noctiflora (competitor) was an influential element in determining the ED10 of C. cyanus measured at the mature stage. With herbicide exposure, the competitive interactions were further altered; C. cyanus was less affected by glyphosate when S. noctiflora increased to high density. In contrast, at the young stage, conspecific density was important in determining the sensitivity of C. cyanus to metsulfuron methyl, whereas the density of the competitor S. noctiflora had a limited influence. Overall, the results demonstrate the importance of integrating the effect of herbicide and species interactions measured at the reproductive stage into the ecological risk assessments of pesticides. Environ Toxicol Chem 2019;38:2053-2064. © 2019 SETAC.
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 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".