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昆虫的色觉(缨翅目)

Colour vision in thrips (Thysanoptera).

机构信息

Bio-Protection Research Centre, Lincoln University, Lincoln, New Zealand.

Better Border Biosecurity (B3, B3nz.org.nz), New Zealand.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2022 Oct 24;377(1862):20210282. doi: 10.1098/rstb.2021.0282. Epub 2022 Sep 5.

DOI:10.1098/rstb.2021.0282
PMID:36058245
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9441234/
Abstract

Insects are an astonishingly successful and diverse group, occupying the gamut of habitats and lifestyle niches. They represent the vast majority of described species and total terrestrial animal biomass on the planet. Their success is in part owed to their sophisticated visual systems, including colour vision, which drive a variety of complex behaviours. However, the majority of research on insect vision has focused on only a few model organisms including flies, honeybees and butterflies. Especially understudied are phytophagous insects, such as diminutive thrips (Thysanoptera), in spite of their damage to agriculture. Thrips display robust yet variable colour-specific responses despite their miniaturized eyes, but little is known about the physiological and ecological basis of their visual systems. Here, we review the known visual behavioural information about thrips and the few physiological studies regarding their eyes. Eye structure, spectral sensitivity, opsin genes and the presence of putative colour filters in certain ommatidia strongly imply dynamic visual capabilities. Finally, we discuss the major gaps in knowledge that remain for a better understanding of the visual system of thrips and why bridging these gaps is important for expanding new possibilities for applied pest management strategies for these tiny insects. This article is part of the theme issue 'Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods'.

摘要

昆虫是一类惊人的成功且多样的生物,占据了各种栖息地和生活方式的生态位。它们代表了地球上绝大多数已描述的物种和总陆地动物生物量。它们的成功部分归功于其复杂的视觉系统,包括色觉,这驱动了各种复杂的行为。然而,大多数关于昆虫视觉的研究只集中在少数几个模式生物上,包括苍蝇、蜜蜂和蝴蝶。尤其是植食性昆虫,如微小的蓟马(缨翅目),尽管它们会对农业造成损害,但却很少受到研究。蓟马的眼睛虽小,但却表现出强大而多变的颜色特异性反应,但我们对其视觉系统的生理和生态基础知之甚少。在这里,我们回顾了关于蓟马的已知视觉行为信息,以及少数关于其眼睛的生理学研究。眼睛结构、光谱灵敏度、视蛋白基因以及某些小眼点中存在的假定颜色过滤器强烈暗示了其具有动态视觉能力。最后,我们讨论了在更好地理解蓟马的视觉系统方面仍然存在的主要知识差距,以及为什么弥合这些差距对于拓展这些微小昆虫的应用害虫管理策略的新可能性很重要。本文是主题为“理解色觉:节肢动物的分子、生理、神经元和行为研究”的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/ae2b3d076127/rstb20210282f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/e3e63caff523/rstb20210282f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/d10c9cbb59c0/rstb20210282f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/8eefa71cc626/rstb20210282f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/5c9fe203349e/rstb20210282f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/ae2b3d076127/rstb20210282f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/e3e63caff523/rstb20210282f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/d10c9cbb59c0/rstb20210282f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/8eefa71cc626/rstb20210282f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/5c9fe203349e/rstb20210282f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4c/9441234/ae2b3d076127/rstb20210282f05.jpg

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本文引用的文献

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Quantifying biologically essential aspects of environmental light.量化环境光的生物学重要方面。
J R Soc Interface. 2021 Apr;18(177):20210184. doi: 10.1098/rsif.2021.0184. Epub 2021 Apr 28.
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Haze of glue determines preference of western flower thrips (Frankliniella occidentalis) for yellow or blue traps.胶雾决定了西方花蓟马(Frankliniella occidentalis)对黄色或蓝色诱捕器的偏好。
人工照明会影响暗黑花蝽(半翅目:花蝽科)对西花蓟马(缨翅目:蓟马科)的捕食性能。
Environ Entomol. 2025 Jun 18;54(3):421-431. doi: 10.1093/ee/nvaf024.
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Ips typographus vision system: a comprehensive study.云杉八齿小蠹视觉系统:一项综合研究。
J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2025 Jan;211(1):101-112. doi: 10.1007/s00359-024-01717-2. Epub 2024 Sep 27.
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Real-Time Detection and Classification of on Fruit Crops with Smartphone-Based Deep Learning System: Preliminary Results.基于智能手机的深度学习系统对水果作物上的[具体内容缺失]进行实时检测与分类:初步结果
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