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Journal Article

Visual modelling can optimise the appearance and capture efficiency of sticky traps used to manage insect pests

Alexander E. Dearden; Martyn J. Wood; Henry O. Frend; Tariq. M. Butt; William L. Allen
Journal of Pest Science · Vol. 97, Issue 1 · pp. 469-479 · 2024

Abstract

Sticky traps are one of the most important tools for monitoring and mass trapping of insect pests. Their effectiveness depends on attracting and capturing target pests efficiently. Trap colour strongly affects capture rates, but currently a principled approach to identifying optimal trap colour for a given pest and growing context is lacking. Here we propose that modelling pest colour vision enables identification of trap colours that optimise pest capture rates. We test this novel approach to trap design in field trials on Western flower thrips (WFT) Frankiniella occidentalis , an economically damaging pest of agriculture and horticulture worldwide. Prior studies have reported that WFT prefer blue and yellow sticky traps, aligning with recent evidence that WFT have trichromatic colour vision with peak sensitivities in the UV, blue and green portion of the visual spectrum and a blue-green colour opponent mechanism. Therefore, we hypothesised that a shade of blue that maximally stimulates the blue photoreceptor whilst minimally stimulating the green photoreceptor would improve sticky trap capture rates, while a shade of blue that decreased the opponent response would reduce capture rates. In three field experiments, we found strong support for this hypothesis: the optimised blue colour captured 1.3–2.6 times more WFT than current commercial trap colours. Our results also demonstrated that visual modelling can identify optimally contrasting colours for two-colour traps that further improve capture rates. This study provides a novel and principled approach to the design of visual traps that could be extended to other pest management contexts.

Bibliographic Information

JournalJournal of Pest Science
PublisherSpringer
Publication Date2024-01-01
Publication Year2024
Volume97
Issue1
Pages469-479
Document TypeJournal Article
Print ISSN1612-4758
eISSN1612-4766
DOI10.1007/s10340-023-01604-w

Access Information

NARA Access Coverage1925-01-01~Current
Journal Homepagehttps://www.springer.com/journal/10340
Publisher PageOpen Publisher Page
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