Local Depletion of Click Beetle Populations by Pheromone Traps Is Weather and Species Dependent
Bibliographic record
Abstract
Several Agriotes click beetle species are important pests of vegetables and field crops. Monitoring for beetles is generally done with pheromone-baited traps maintained in permanent locations. Since dispersal is mostly by walking, such traps may deplete populations around them, leading to underestimations of populations relative to nontrapped areas, and of concomitant risk of wireworm damage to nearby crops. We placed sets of five pitfall traps in field headland areas in 2015-2017, of which two were baited with Agriotes obscurus (L) or Agriotes lineatus (L) (Coleoptera: Elateridae) pheromone. Of these, one was maintained in a permanent location, while the other moved among the remaining positions. Traps were checked weekly over the emergence period. For A. obscurus, fixed and moving traps initially collected similar numbers, but the latter collected significantly more later in the season, indicating depletion around fixed traps. Depletion was most pronounced after a period of cold weather, and around the peak swarming period. Depletion observed for A. lineatus was not statistically significant. This indicates pheromone-baited traps used for walking insects can underestimate populations, but depletion rates vary with species and temperature and should be accounted for when traps are used to develop action thresholds or time control strategies.
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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 | 0.001 |
| 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".