Local Aphid Species Infestation on Invasive Weeds Affects Virus Infection of Nearest Crops Under Different Management Systems – A Preliminary Study
Bibliographic record
Abstract
In the present study, we conducted field surveys to detect the population density of the most important invasive weed species and their associated virus vectoring aphids in crops grown under high input field (HIF) vs. low-input field (LIF) conditions, with and without fertilizers and pesticides. The most frequent invasive weed species were the annual fleabane, Erigeron annua (L.), Canadian horseweed, Erigeron canadensis (L.) and Canadian goldenrod, Solidago canadensis (L.). These species were predominantly hosts of the aphids Brachycaudus helichrysi and Aulacorthum solani under both management systems. The 13% higher coverage of E. annua under LIF conditions resulted in a 30% higher B. helichrysi abundance and ~85% higher A. solani abundance compared with HIF conditions. To reveal the incidence of virus infection in crop plants and invasive weeds, high-throughput sequencing of small RNAs was performed. Bioinformatics analysis combined with independent validation methods revealed the presence of six viruses, but with strikingly different patterns under LIF and HIF conditions. Their presence without symptoms in invasive weeds and crop plants supports the necessity of employing new approaches to those currently employed in invasive weed management. These findings also suggest that invasive weeds could serve as hosts for local aphid species and reservoirs for plant pathogenic viruses, both under low and high input management systems. In this light, as here demonstrated, viruses transmitted by local aphid species were found to differ between the management systems; hence, the importance of B. helichrysi and A. solani as virus vectors in particular clearly needs to be re-evaluated. Altogether, we can conclude that this present study is a pilot one and individual virus vectoring aphids needs to be directly tested, still it represents one of the first contributions to this particular area, and thereby paving the way for further similar applied research.
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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 | 0.001 |
| 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".