Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
Mirasbekov Y. Zhumakhanova A. Zhantuyakova A. Sarkytbayev K. Malashenkov D.V. Baishulakova A. Dashkova V. Davidson T.A. Vorobjev I.A. Jeppesen E. Barteneva N.S.
December 2021Nature Research
Scientific Reports
2021#11Issue 1
A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.
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School of Sciences and Humanities, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
National Laboratory Astana, Nur-Sultan, 010000, Kazakhstan
Department of General Ecology and Hydrobiology, Lomonosov Moscow State University, Moscow, 119991, Russian Federation
School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Bioscience, Aarhus University, Silkeborg, 8600, Denmark
Institute of Marine Sciences, Middle East Technical University, Mersin, 33731, Turkey
Limnology Laboratory, Department of Biological Sciences and Centre for Ecosystem Research and Implementation, Middle East Technical University, Ankara, 06800, Turkey
Sino-Danish Centre for Education and Research, Beijing, 100049, China
The Environmental Research and Efficiency Cluster (EREC), Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
University of British Columbia, Vancouver, Canada
School of Sciences and Humanities
National Laboratory Astana
Department of General Ecology and Hydrobiology
School of Engineering and Digital Sciences
Department of Bioscience
Institute of Marine Sciences
Limnology Laboratory
Sino-Danish Centre for Education and Research
The Environmental Research and Efficiency Cluster (EREC)
University of British Columbia
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