First Application of IFCB High-Frequency Imaging-in-Flow Cytometry to Investigate Bloom-Forming Filamentous Cyanobacteria in the Baltic Sea

Abstract

Cyanobacteria are an important part of phytoplankton communities, however, they are also known for forming massive blooms with potentially deleterious effects on recreational use, human and animal health, and ecosystem functioning. Emerging high-frequency imaging flow cytometry applications, such as Imaging FlowCytobot (IFCB), are crucial in furthering our understanding of the factors driving bloom dynamics, since these applications provide community composition information at frequencies impossible to attain using conventional monitoring methods. However, the proof of applicability of automated imaging applications for studying dynamics of filamentous cyanobacteria is still scarce. In this study we present the first results of IFCB applied to a Baltic Sea cyanobacterial bloom community using a continuous flow-through setup. Our main aim was to demonstrate the pros and cons of the IFCB in identifying filamentous cyanobacterial taxa and in estimating their biomass. Selected environmental parameters (water temperature, wind speed and salinity) were included, in order to demonstrate the dynamics of the system the cyanobacteria occur in and the possibilities for analyzing high-frequency phytoplankton observations against changes in the environment. In order to compare the IFCB results with conventional monitoring methods, filamentous cyanobacteria were enumerated from water samples using light microscopical analysis. Two common bloom forming filamentous cyanobacteria in the Baltic Sea, Aphanizomenon flosaquae and Dolichospermum spp. dominated the bloom, followed by an increase in Oscillatoriales abundance. The IFCB results compared well with the results of the light microscopical analysis, especially in the case of Dolichospermum. Aphanizomenon biomass varied slightly between the methods and the Oscillatoriales results deviated the most. Bloom formation was initiated as water temperature increased to over 15°C and terminated as the wind speed increased, dispersing the bloom. Community shifts were closely related to movements of the water mass. We demonstrate how using a high-frequency imaging flow cytometry application can help understand the development of cyanobacteria summer blooms.

The diurnal cycle of <i>p</i>CO<sub>2</sub> in the coastal region of the Baltic Sea

“…At the same time, surface pCO 2 was close to equilibrium with the atmosphere. In mid-July, a cyanobacteria bloom developed, as is typical for the study area and time of the year (Kraft et al, 2021). The primary production activity lowered the pCO 2 below 200 µatm.…”

“…Long-term trends of increasing alkalinity throughout the Baltic Sea have been shown to partly compensate acidification induced by rising atmospheric CO 2 (Müller et al, 2016). Within our study region, phytoplankton blooms are a recurrent phenomenon due to eutrophication (Kraft et al, 2021).…”

“…fi/uto-observations, last access: 5 November 2021). Site bathymetry and other information about the study site are given in Laakso et al (2018) and Kraft et al (2021). Our study is based on 1 year of data gathered between July 2018 and June 2019.…”

Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks

“…The instrument has an image resolution of roughly 3.5 pixels per µm. According to the manufacturer, it captures images of suspended particles in the range of 10 to 150 µm (ESD), but in practice particles ranging from ~5 µm ESD to filaments ~300 µm in length have been captured (Kraft et al, 2021). The limiting factors are camera resolution, to get identifiable images, on the lower end and a 150-µm mesh at the instrument inlet that prevents it from clogging, together with the size of the field of view.…”

“…Laney and Sosik, 2014;Harred and Campbell, 2014;Anglès et al, 2019;Fischer et al, 2020). It has also been popular in Harmful Algal Bloom (HAB) studies or as an early warning detection of rare but toxic species (Campbell et al, 2010;2013, Harred andCampbell, 2014;Henrichs et al, 2021;Kraft et al, 2021). The IFCB can produce up to tens of thousands of images per hour (Olson and Sosik, 2007), yielding real-time big data.…”