Consumption of cereal leaf beetle, <i>Oulema melanopus</i>, by generalist predators in wheat fields detected by molecular analysis
Bibliographic record
Abstract
Abstract The cereal leaf beetle (CLB), Oulema melanopus L. (Coleoptera: Chrysomelidae), is a major pest of cereal crops that has recently been reported in western Canada. We developed a set of primers to detect CLB DNA in the gut of six common predator taxa in wheat fields: lady beetles (20 positives of 143 individuals), nabid bugs (73 positives of 206 individuals), and wolf spiders (2 positives of 25 individuals). Nabis americoferus Carayon (Hemiptera: Nabidae) and Coccinella septempunctata L. (Coleoptera: Coccinellidae) were the most abundant predators in cereal fields, with 0.35 and 0.05 proportion of samples positive for CLB DNA, respectively. The prey DNA half‐lives were used to adjust the estimates for N. americoferus to 0.22, due to its longer DNA detectability relative to C. septempunctata. Overall, Hippodamia parenthesis (Say) (Coleoptera: Coccinellidae) had the highest proportion of positives at 0.43. There was a positive association between CLB abundance and proportion of N. americoferus and C. septempunctata positives for CLB DNA. This study highlights the contribution of generalist predators to CLB mortality and their important role in integrated management for CLB. Furthermore, we provide a molecular tool that can be used to identify predators of CLB and predation frequency in agricultural fields.
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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 | 0.001 | 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".