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A novel method for the rapid enumeration of planktonic salmon lice in a mixed zooplankton assemblage using fluorescence
Author(s) -
Thompson Cameron R. S.,
Bron James,
Bui Samantha,
Dalvin Sussie,
Fordyce Mark John,
á Norði Gunnvør,
SkernMauritzen Rasmus
Publication year - 2022
Publication title -
aquaculture research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.646
H-Index - 89
eISSN - 1365-2109
pISSN - 1355-557X
DOI - 10.1111/are.15750
Subject(s) - enumeration , plankton , zooplankton , biology , fluorescence , fluorescence microscope , sampling (signal processing) , zoology , fishery , ecology , mathematics , physics , combinatorics , filter (signal processing) , quantum mechanics , computer science , computer vision
The relative rarity of the planktonic larval stages of salmon lice in comparison to other animals captured in a zooplankton assemblage is an obstacle to estimating their abundance and distribution. Due to the labour intensiveness of standard plankton sorting approaches, the planktonic stages of salmon lice remain understudied and unmonitored despite their importance to the spread of the parasite between salmon farms and to wild salmonids. Alternative methods of identification have been investigated and in a previous study a fluorescence signal was identified. Using filters to target that signal with fluorescence microscopy (excitation/emission wavelengths of 470/525 nm), the salmon louse has a fluorescence intensity 2.4 times greater than non‐target animals, which distinguishes it from the zooplankton assemblage and enables rapid enumeration. Here, we present a novel method for the enumeration of planktonic salmon lice larvae, nauplius and copepodid stages, in a mixed zooplankton sample using fluorescence‐aided microscopy. Performance of the method was evaluated with a blind trial which found a median accuracy of 81.8% and a mean sample processing time of 31 min. Compared with previously published findings, the novel method provides satisfactory accuracy and enumeration that is more than 20 times faster than traditional light microscopy approaches. Factors influencing the performance of the method are identified and recommendations are made for targeted sampling and automated enumeration.