Assessment of Lake Balkhash phytoplankton community structure using FlowCam imaging flow cytometer Yu-Mi Kim, Veronika Dashkova, Dmitry V Malashenkov, Ivan Vorobjev, Natasha S Barteneva National Laboratory Astana, Nazarbayev University, Department of Hydrobiology, M.V. Lomonosov Moscow State University, School of Science and Technology, Nazarbayev University, Program in Molecular and Cellular Medicine, Boston Children’s Hospital and Harvard Medical School

Introduction FIG 1. Sampling collection sites on Lake Balkhash Lake Balkhash, located in southeastern Kazakhstan, is a unique endorheic lake consisting of freshwater in the western part and brackish water in the eastern part. The lake covers the vast territory of 16,400 km2 situated both in unpopulated reserve areas and densely populated industrial regions such as the town of Balkhash. The varying degree of anthropogenic impact and salt content variation create a large spatial heterogeneity gradient along the lake generating ecological niches with diverse biological communities. The aim of the present study was to assess phytoplankton community structure along the environmental gradient using a FlowCam imaging cytometer. This is the first time the method of flow imaging cytometry was applied to characterization of lakes in Kazakhstan. FIG 1. Sampling collection sites on Lake Balkhash

Methods Water samples were collected lake surface layer within 2 time periods, May to September 2016, and July 2017, from 13 spatially heterogenic sites with varying levels of salinity and anthropogenic load. Water parameters, such as water temperature, pH, conductivity, salinity, dissolved oxygen, content of biogenic elements, (nitrites, nitrates, ammonium and phosphates) and heavy metals were recorded for each sampling site. Phytoplankton composition and abundance were assessed using FlowCAM (Imaging Fluid Technologies, Yarmouth ME, USA) imaging flow cytometer. Classification of phytoplankton classes based on morphology was accomplished by VisualSpreadSheet software (Fluid Imaging Technologies, USA) followed by manual sorting. Relationships between phytoplankton community and environmental parameters were examined using Redundancy analysis (RDA) using Canoco 5 statistical software (Ter Braak & Smilauer, 2012). FIG 2: Image of FlowCAM VS series imaging flow cytometer station used for analysis of phytoplankton communities

Results B A FIG 3. Relative abundance of phytoplankton groups observed in samples collected in September 2016. A – Proportion of nanoplankton (4-20 um) and mesoplankton (20-200 um) size classes in samples from different locations. B – Proportion of mesoplankton species in samples from different locations. E1-E6 samples correspond to eastern part of the lake; C1-C3 samples correspond to sites near the town of Balkhash; W1-W4 samples correspond to western part of the lake

B C A FIG 4: FlowCam image libraries of common phytoplankton species from representative sites collected in July 2017 using trigger mode and 10x objective lens. A—Sample from area near town of Balkhash; B—Sample from eastern part of lake; C—Sample from western part of the lake;

FIG 5: Correlation triplots based on RDA of phytoplankton abundance (displaying 12 best fitted groups) and environmental variables (displaying variables with contribution of more than 5%). Quantitative environmental variables are indicated by red arrows, phytoplankton groups are indicated by dark blue arrow, samples are indicated by symbols. Significant environmental parameters (Monte Carlo permutation test = 0.05) are marked by *. Conducted RDA ordination analysis accounted for 54.5% of phytoplankton variability significantly explained by water temperature (p=0.009) and NO2 (p = 0.036) concentrations. Samples are grouped by site location: sites located near the City, East, or West banks of Balkhash. Projection of a sample symbol onto an environmental vector approximates the variable’s explanatory % for that sample.

Conclusion Phytoplankton communities of Lake Balkhash consisted of the following major groups: nanoplankton (4-20 um size), colonial cyanobacteria, colonial picocyanobacteria, filamentous cyanobacteria, dinoflagellates, photosynthetic euglenids, large and small centric diatoms, large and small pennate diatoms, green algae (Oocystis spp. Coenococcus spp. Sphaerocystis spp. and Pandorina spp. ). Nanoplankton dominated nearly all sites contributing up to 95% in the total phytoplankton abundance, followed by colonial cyanobacteria, small pennate diatoms, and large and small centric diatoms. RDA showed strong and moderate positive correlations between increasing NO2 concentration and abundance of large and small pennate diatoms. Increasing temperature was positively correlated with abundance of filamentous cyanobacteria and Chaetoceros sp., and negatively correlated with abundance of colonial cyanobacteria. FlowCAM imaging cytometer, when used in tandem with light microscopy, is potentially capable of determining the general characteristics of phytoplankton community structure in situ and could be an important addition to biological monitoring systems on lakes.

Acknowledgements I would like to thank Dr. Natasha Barteneva, Veronika Dashkova, Ivan Vorobjev, and Dmitry V Malashenkov, for helping me with this project, the Harvard Life Sciences Research Department for helping me secure this opportunity, and Nazarbayev University for hosting me over the summer