Fertilizer and fungicide reduce herbicide efficacy and enhance growth of invasive common tansy (Tanacetum vulgare)
Bibliographic record
Abstract
Common tansy (Tanacetum vulgare; Asteraceae) is a widespread invasive species in North America that threatens biodiversity and agricultural productivity by displacing resident vegetation. Combined with being unpalatable, it can be toxic and thus poses significant challenges for the livestock industry. Current tansy control strategies are largely chemical and rely on a suite of synthetic auxin herbicides. The need for reapplication may lead to resistance development in addition to significant biodiversity losses. Recent work suggests that invasive Asteraceae may rely on symbiotic arbuscular mycorrhizal fungi (AMF) to give them a competitive advantage. We hypothesized that suppressing AMF would reduce tansy growth and reduce reliance on more damaging herbicides. Fungicides and fertilizers, known to suppress AMF, may be potential tools for tansy suppression by reducing its competitive ability; however, both may also enhance invader growth and represent a significant risk. We conducted a two-year experiment crossing three herbicides, with varying degrees of residual control, with fungicide and fertilizer treatments to explore their effects on tansy. Despite initially reducing AMF abundances, both fertilizer and fungicide unexpectedly improved tansy growth, especially when applied with the non-residual herbicide (2,4-D), where strong control was eliminated by either treatment. This suggests that, at least at our study site, any suppression of AMF did not affect tansy strongly enough to overcome the benefits of increased nutrients and pathogen suppression. Independent of fungicide or fertilizer, all three herbicides reduced tansy biomass and increased community biomass by year two, driven by increases in grasses. The most effective herbicide (picloram), however, also caused the greatest declines in broadleaf plants, leading to significant species losses. Conversely, 2,4-D was only slightly less effective after two years, while having limited non-target effects. Non-residual herbicides, like 2-4D, may offer a better balance between tansy control and biodiversity conservation.
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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 | 0.000 |
| 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 | 0.001 |
| 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".