Maximizing Bark and Ambrosia Beetle (Coleoptera: Curculionidae) Catches in Trapping Surveys for Longhorn and Jewel Beetles
Bibliographic record
Abstract
Bark and ambrosia beetles are commonly moved among continents within timber and fresh wood-packaging materials. Routine visual inspections of imported commodities are often complemented with baited traps set up in natural areas surrounding entry points. Given that these activities can be expensive, trapping protocols that attract multiple species simultaneously are needed. Here we investigated whether trapping protocols commonly used to detect longhorn beetles (Coleoptera: Cerambycidae) and jewel beetles (Coleoptera: Buprestidae) can be exploited also for detecting bark and ambrosia beetles. In factorial experiments conducted in 2016 both in Italy (seminatural and reforested forests) and Canada (mixed forest) we tested the effect of trap color (green vs purple), trap height (understory vs canopy), and attractive blend (hardwood-blend developed for broadleaf-associated wood-boring beetles vs ethanol in Italy; hardwood-blend vs softwood-blend developed for conifer-associated wood-boring beetles, in Canada) separately on bark beetles and ambrosia beetles, as well as on individual bark and ambrosia beetle species. Trap color affected catch of ambrosia beetles more so than bark beetles, with purple traps generally more attractive than green traps. Trap height affected both beetle groups, with understory traps generally performing better than canopy traps. Hardwood-blend and ethanol performed almost equally in attracting ambrosia beetles in Italy, whereas hardwood-blend and softwood-blend were more attractive to broadleaf-associated species and conifer-associated species, respectively, in Canada. In general, we showed that trapping variables suitable for generic surveillance of longhorn and jewel beetles may also be exploited for survey of bark and ambrosia beetles, but trapping protocols must be adjusted depending on the forest type.
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) | 0.001 | 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".