Length of Multiple-Funnel Traps Affects Catches of Some Bark and Wood Boring Beetles in a Slash Pine Stand in Northern Florida
Bibliographic record
Abstract
The multiple-funnel trap has gained broad acceptance for catching bark and ambrosia beetles since the trap was developed more than 25 years ago (Coleoptera: Scolytidae) (Lindgren 1983). The trap consists of black plastic funnels aligned vertically over each other, allowing for intercepted beetles to fall through the funnels into a wet or dry collection cup located on the bottom funnel. Currently, there are 2 national programs in the USA that use baited multiple-funnel traps for detecting exotic species: the Cooperative Agricultural Pest Survey (CAPS) and the Early Detection and Rapid Response program (EDRR) (USDA APHIS 2007; Rabaglia et al. 2008). Multiple-funnel traps are available in several sizes or lengths, expressed by the number of funnels (4-, 8-, 12or 16-unit) (Contech Inc., Delta, BC; Synergy Semiochemicals Corp., Burnaby, BC). The general expectation is that longer multiple-funnel traps catch more beetles. In support of that position, Hoover et al. (2000) found that catches of the striped ambrosia beetle, Trypodendron lineatum (Olivier) (Coleoptera: Scolytidae), in traps baited with the pheromone lineatin, increased as the length of traps were increased from 4 to 16 units. Haack & Lawrence (1997) found that catches of Tomicus piniperda (L.) were higher in 12and 16unit traps than in 8-unit ones. The objective of our study was to verify that long multiple-funnel traps (16-unit) catch more bark and wood boring beetles than short traps (8unit) in a slash pine (Pinus elliottii Engelm.) stand in northern Florida. We focused our study on common southern species attracted to the binary combination of ethanol and (–)-α-pinene used in the national programs (Miller 2006; Miller & Rabaglia 2009). We conducted 1 trapping experiment in a mature slash pine stand on the Osceola National Forest near Olustee, FL for 9 weeks in 2001 (29 Aug-8 Nov). PheroTech Inc. (now Contech) supplied separate lures for releasing ethanol and (–)-α-pinene at rates of approximately 0.6 and 2 g/d, respectively, as well as 8unit and 16-unit multiple-funnel traps. Traps were set in 6 blocks of 2 traps per block with all traps set 10-15 m apart. There were 2 treatments: (1) 8-unit; and (2) 16-unit multiple-funnel traps. One trap of each treatment type was randomly assigned to a position within each block. All traps were baited with ethanol and (–)-α-pinene. Each trap was suspended between trees by rope such that the bottom of each was 0.2-0.5 m above ground level. No trap was within 2 m of any tree. Collection cups contained approximately 150 mL of pink propylene glycol solution (Peak RV and Marine Antifreeze, Old World Industries Inc., Northbrook, IL). Using SYSTAT ver. 11.00.01 (SYSTAT Inc., Point Richmond, CA), we conducted two-sided t tests on data transformed by ln(y + 1) to remove heteroscedasticity (Pepper et al. 1997). Catches of Arhopalus rusticus nubilus (LeConte) (Cerambycidae) in 16-unit traps were 143% greater than those in 8-unit traps (Table 1).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".