Multi-colored traps can enhance monitoring programs for native and non-native longhorn beetles in forest ecosystems
Bibliographic record
Abstract
Longhorn beetles (Coleoptera: Cerambycidae) are one of the most diverse families of beetles worldwide and they play critical roles in forest environments. Monitoring longhorn beetles is essential for both conservation and pest management, and baited traps are widely used for this purpose. Longhorn beetle species vary in their visual ecology and are attracted to different trap colors. A way to optimize trapping efficiency could be to combine multiple colors on a single trap, so to create a trap that captures multiple species at once. To test this approach, we carried out seven trapping experiments in Europe and North America, comparing the effectiveness of a multi-colored trap against single-colored black, red, white, and yellow traps at whole family, subfamily, and species level. At most sites, multi-colored traps captured significantly more species and individuals than black, red, and/or yellow traps. At the subfamily level, at most sites, multi-colored traps were equally or more effective than single-colored traps for Cerambycinae and Lamiinae. For Lepturinae, multi-colored traps were generally significantly more effective than black or red traps, but significantly less effective than white traps. Responses varied among species. Overall, our study suggests that the use of multi-colored traps can improve monitoring programs for longhorn beetles, supporting both faunistic surveys and early detection efforts targeting non-native species. • Different longhorn beetle species are attracted to different trap colors. • Combining multiple colors on the same trap can increase monitoring efficiency. • Multi-colored traps matched or outperformed single-colored traps in most cases. • Effectiveness of multi-colored traps varied across subfamily and species. • Multi-colored traps are a promising tool for monitoring longhorn beetles.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".