4.6 Article

Sticky Pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions

期刊

PLOS BIOLOGY
卷 20, 期 7, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pbio.3001689

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资金

  1. International Human Frontier Science Program Organization [LT000325/2019]
  2. Agriculture and Agri-Food Canada's Canadian Agricultural Partnership - AgriScience Program
  3. Agriculture and Agri-Food Canada
  4. Seeding Food Innovation grant from George Weston Ltd.
  5. Canada Research Chair award

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In the face of environmental crises threatening insect biodiversity, new technology is needed to monitor the identity and ecology of insect species. The Sticky Pi is an autonomous trap that captures images of insects and accurately identifies them. This tool provides biologists with unique data that has implications for biodiversity monitoring, pest control, and various fields of study.
In the face of severe environmental crises that threaten insect biodiversity, new technologies are imperative to monitor both the identity and ecology of insect species. Traditionally, insect surveys rely on manual collection of traps, which provide abundance data but mask the large intra- and interday variations in insect activity, an important facet of their ecology. Although laboratory studies have shown that circadian processes are central to insects' biological functions, from feeding to reproduction, we lack the high-frequency monitoring tools to study insect circadian biology in the field. To address these issues, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every 20 minutes. Using custom deep learning algorithms, we automatically and accurately scored where, when, and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology, with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.

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