Effect of Host Volatile Release Rate and Racemic Fuscumol on Trap Catch of <i>Hylurgus ligniperda</i>, <i>Hylastes ater</i> (Coleoptera: Curculionidae), and <i>Arhopalus ferus</i> (Coleoptera: Cerambycidae)
Bibliographic record
Abstract
Early detection of bark- and wood-boring beetles is critical to support the eradication of recently established populations in novel regions. Flight intercept traps baited with semiochemical lures are essential for surveillance and population monitoring of introduced insects. We present laboratory and field data to test potential improvements in trap sensitivity to detect Hylastes ater (Paykull), Hylurgus ligniperda (Fabricius) (Coleoptera:Scolytinae), and Arhopalus ferus (Mulsant) (Coleoptera:Cerambycidae). We evaluated the effects of type and permeability of plastic material on release rates of host-plant volatiles and the addition of the pheromone racemic E-fuscumol on beetle species catch within an exotic Pinus radiata (D. Don) plantation forest. We trapped 228,278 H. ligniperda, 91 H. ater, and 2,504 A. ferus over two experiments. Our results show strong attraction of H. ligniperda to host-plant volatiles with a positive relationship between increased release rates and trap catch. H. ater follows this trend, with attraction to ethanol and (-)-β-pinene. H. ligniperda was more strongly attracted to host volatiles than H. ater, the average ratio between baited and unbaited traps being 51.25:1 and 3.75:1, respectively for each species. There was no effect of host-plant volatiles or release rate on A. ferus catch, but a weak attraction by A. ferus to racemic E-fuscumol. Overall, we demonstrate species-specific differences in Coleoptera attraction to host-plant volatile release rates and semiochemical blends. Results demonstrate mechanisms to improve trap catch of H. ligniperda and H. ater. Racemic E-fuscumol shows some promise for A. ferus, but further investigation is required to determine whether chirality affects attraction.
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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".