A machine vision system for tracking population behavior of zooplankton in small scale experiments: a case study on salmon lice (Lepeophtheirus salmonis Krøyer, 1838) copepodite population responses to different light stimuli

Overview
TitleA machine vision system for tracking population behavior of zooplankton in small scale experiments: a case study on salmon lice (Lepeophtheirus salmonis Krøyer, 1838) copepodite population responses to different light stimuli
AuthorsKvæstad B, Nordtug T, Hagemann A
TypeJournal Article
Journal NameBiology open
VolumeN/A
IssueN/A
Year2020
Page(s)N/A
CitationKvæstad B, Nordtug T, Hagemann A. A machine vision system for tracking population behavior of zooplankton in small scale experiments: a case study on salmon lice (Lepeophtheirus salmonis Krøyer, 1838) copepodite population responses to different light stimuli. Biology open. 2020 Jan 01.

Abstract

To achieve efficient and preventive measures against salmon lice (Lepeophtheirus salmonis Krøyer, 1838) infestation, a better understanding of behavioral patterns of the planktonic life stages is key. To investigate light responses in L. salmonis copepodites, a non-intrusive experimental system was designed to measure behavioral responses in a 12.5-liter volume using machine vision technology and methodology. The experimental system successfully tracked the collective movement patterns of the sea lice population during exposure to different light stimuli emitted from alternating zones in the system. This system could further be used to study behavioural responses to different physical cues of various developmental stages of sea lice or other zooplankton.

Author Details
Additional information about authors:
Details
1Bjarne Kvæstad
2Trond Nordtug
3Andreas Hagemann
Properties
Additional details for this publication include:
Property NameValue
Journal CountryEngland
Publication TypeJournal Article
Language Abbreng
LanguageEnglish
Copyright© 2020. Published by The Company of Biologists Ltd.
DOI10.1242/bio.050724
Elocation10.1242/bio.050724
PIIbio.050724
Journal AbbreviationBiol Open
Publication Date2020 Jan 01
eISSN2046-6390
ISSN2046-6390
Publication ModelPrint-Electronic
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PMID: PMID:34004751