Visual modelling can optimise sticky trap design for simultaneous monitoring of multiple species of insect pests
Abstract Coloured sticky traps are commonly used to monitor insect pests. Colour affects trap performance, with preferred colours often differing between species, making selection of trap colour for effective management of multiple pests challenging. Greenhouse whitefly (GWF) Trialeurodes vaporarior...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01954-8 |
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| Summary: | Abstract Coloured sticky traps are commonly used to monitor insect pests. Colour affects trap performance, with preferred colours often differing between species, making selection of trap colour for effective management of multiple pests challenging. Greenhouse whitefly (GWF) Trialeurodes vaporariorum and Western flower thrips (WFT) Frankliniella occidentalis, are major horticultural pests that often co-occur. Yellow colours are attractive to GWF, while blue is often used to target WFT, although WFT are also attracted to yellow colours. The visual mechanisms that make yellow colours attractive to either species are poorly understood. Previous experiments in WFT find that visual modelling of an opponent mechanism between short wavelength sensitive (SWS) and long wavelength sensitive (LWS) photoreceptors optimises the performance of blue sticky traps. In the current study, we assess whether an opponent response that highly stimulates LWS relative to SWS photoreceptors predicts the attractiveness of yellow sticky cards to both WFT and GWF. Our results showed that yellow sticky cards that maximize a predicted SWS:LWS opponent mechanism improves capture for both species. Further, optimising the SWS:LWS ratio allowed for simultaneous monitoring of both pest species using single colour cards. We also showed that sticky trap colour and luminance are comparable across different lab and field contexts, highlighting the broad applicability of visual modelling in pest management strategies. |
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| ISSN: | 2045-2322 |