标题：Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom
摘要：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.
