TY - JOUR
T1 - Sticky Pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions
AU - Geissmann, Quentin
AU - Abram, Paul K.
AU - Wu, Di
AU - Haney, Cara H.
AU - Carrillo, Juli
N1 - Publisher Copyright:
© 2022 Geissmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/7
Y1 - 2022/7
N2 - AInUth:ePflaecaeseocfosnefivrmertehaetanlvlhireoandminegnletavel lcsraisreersepthreastetnhtreedactoernreicntslye:ct 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 wAheUre:,Pwlehaesne,naotnedthwathaiscpherinsstyelec;tsitawliecrseshcoaupldtunroetbde.uFsiersdtf,owreemvpahlaidsiast:ed 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.
AB - AInUth:ePflaecaeseocfosnefivrmertehaetanlvlhireoandminegnletavel lcsraisreersepthreastetnhtreedactoernreicntslye:ct 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 wAheUre:,Pwlehaesne,naotnedthwathaiscpherinsstyelec;tsitawliecrseshcoaupldtunroetbde.uFsiersdtf,owreemvpahlaidsiast:ed 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.
KW - Agriculture
KW - Animals
KW - Biodiversity
KW - Drosophila melanogaster
KW - Insecta
KW - Wasps
UR - http://www.scopus.com/inward/record.url?scp=85134361477&partnerID=8YFLogxK
U2 - 10.1371/journal.pbio.3001689
DO - 10.1371/journal.pbio.3001689
M3 - Journal article
C2 - 35797311
AN - SCOPUS:85134361477
SN - 1544-9173
VL - 20
JO - PLoS Biology
JF - PLoS Biology
IS - 7
M1 - e3001689
ER -