Effects of Repeated Herbicide Use for Leafy Spurge (Euphorbia esula L.) Control on Rangeland Functioning
Bibliographic record
Abstract
Invasive species management poses a significant challenge to ecosystem restoration. Leafy spurge (Euphorbia esula L.) is an invasive weed in North America that can lead to declines in native plant diversity, forage productivity and have large effects on microbial communities, nutrient cycling, and overall ecosystem functioning. Herbicides are frequently used to control leafy spurge but can have non-target impacts on ecosystems and often need to be re-applied to maintain control, which may worsen effects. The objective of this study was to determine if repeated herbicide applications of a broadleaf specific herbicide (active ingredients: aminocyclopyrachlor and metsulfuron-methyl) for leafy spurge control negatively affects non-target plant species and alters microbial abundance and community structure and nutrient retention. We established an experiment in a leafy spurge infested mixed grass prairie to test the effects of three herbicide rates – never, once, and in two consecutive years – in areas both invaded and uninvaded with leafy spurge, on plant community composition and production, microbial abundance and community structure and soil carbon (C) and nitrogen (N) concentrations. With a single application we found that: leafy spurge was effectively reduced for two growing seasons but was recovering by the third, forbs and broadleaf species richness declined, and plant community composition was altered. A second application worsened these effects and significantly reduced shrubs. There was no improvement in grass production. Herbicide did not have significant effects on bacterial abundance and microbial community structure but with a second application did lead to a decline in fungal and AMF abundance and an increase in the Gram-negative stress indicator. We also saw an initial increase in inorganic N, but a reduction in water-extractable organic carbon (WEOC) with a repeated application. These effects were most likely due to reductions in leafy spurge and native forbs and shrubs. Our results show that herbicides can have detrimental effects on non-target species and the plant community, which can lead to changes in the microbial community and nutrient concentrations and that these effects can be more pronounced with a repeated application.
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 | 0.000 |
| 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) | 0.001 | 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".