Population genetic structure of the carrot weevil (<i>Listronotus oregonensis</i>) in North America
Bibliographic record
Abstract
Abstract Population genetic studies of insect pests enhance our ability to anticipate problems in agroecosystems, such as pest outbreaks, insecticide resistance, or expansions of the host range. This study focuses on geographic distance and host plant selection as potential determinants of genetic differentiation of the carrot weevil Listronotus oregonensis, a major pest of several apiaceous crops in North America. To undertake genetic studies on this species, we assembled the first complete genome sequence for L. oregonensis. Then, we used both haplotype discrimination with mitochondrial DNA (mtDNA) and a genotyping‐by‐sequencing (GBS) approach to characterize the genetic population structure. A total of 220 individuals were sampled from 17 localities in the provinces of Québec, Ontario, Nova Scotia (Canada), and the state of Ohio (USA). Our results showed significant genetic differences between distant populations across North America, indicating that geographic distance represents an important factor of differentiation for the carrot weevil. Furthermore, the GBS analysis revealed more different clusters than COI analysis between Québec and Nova Scotia populations, suggesting a recent differentiation in the latter province. In contrast, we found no clear evidence of population structure associated with the four cultivated apiaceous plants tested (carrot, parsley, celery, and celeriac) using populations from Québec. This first characterization of the genetic structure of the carrot weevil contributes to a better understanding of the gene flow of the species and helps to adapt local pest management measures to better control this agricultural pest.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".