Characterising Spectral Sensitivity and the Role of Sunlight Intensity in Japanese Beetle ( <i>Popillia japonica</i> ) Surveillance and Monitoring (Coleoptera: Scarabaeidae)
Bibliographic record
Abstract
ABSTRACT The Japanese beetle (JB) Popillia japonica (Coleoptera: Scarabaeidae) has been a significant invasive pest for over a century in North America. Several studies have reported that plants under direct sunlight are preferred and trap colour affects the number of beetles captured, indicating that visual stimuli influence JB behaviour. Despite this, the influence of visual cues on trap efficiency remains poorly understood, and its visual system has not been characterised. To address this knowledge gap, we conducted a field trial using panels to manipulate sun exposure of traps to test the effect of sunlight intensity on JB trap performance. Our results indicate that JB flight activity is reduced under lower sunlight conditions and that visual cues influence trap performance. Fully shaded traps consistently captured fewer beetles when cloud cover was below 85%. To better understand JB colour perception, we investigated its genome for visual opsin genes and analysed its photoreceptor sensitivity with an electroretinogram. We found evidence for a UV‐green dichromatic visual system, lacking the orange‐sensitive photoreceptor found in some Coleoptera. This may explain inconsistencies in trap colour preference reported in the literature and indicates that JB may not have been able to differentiate some of the colour treatments tested in published studies. To optimise JB trap capture, future studies should evaluate UV‐reflecting traps, control for spectral reflectance intensity, and investigate whether JB avoids low‐light conditions due to environmental risks or visual limitations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".