Seasonal dynamics of pelagic zooplankton structure in the Kondopoga Bay of Lake Onega

We analyzed the seasonal changes in zooplankton of the pelagial of the Kondopoga Bay of Lake Onega. The analysis was based on data from net houls during the growing season from 1989 to 2021 in three areas, differing in morphometrical characteristics, trophic conditions and thermal regime. Seasonal changes in the structure of zooplankton communities and the dominant complex were characterized. The average annual trajectories of changes in the abundance ratio (ind./m2) of the main zooplankton groups were obtained using the moving average method. Seasonal succession of zooplankton is mainly associated with changes in the thermal regime and trophic conditions. The thermal regime determines the timing of the onset and end of seasonal phases associated with the dominant complex and the ratio of the main groups of zooplankton. Trophic conditions determine the quantitative characteristics of communities. Due to the different scales of influence of these factors in the studied areas, seasonal changes in zooplankton are characterized by spatial heterogeneity. In the shallow top part, which is experiencing long-term anthropogenic stress by wastewater from the Kondopoga Pulp and Paper Mill, the period with predominance of cladocerans lasts longer than in the pelagic areas of deep-water areas, and the total number of zooplankton is higher. Deep-sea pelagic areas are characterized by a high proportion of copepods during the growing season. The difference between the central part of the Kondopoga Bay and the oligotrophic Big Onega is associated with a higher proportion of rotifers and cladocerans during the growing season. Despite the pronounced stability of the plankton system of Lake Onega, anomalies in the seasonal succession of communities associated with an unusual structure for the observed period are sporadically observed. The obtained results contribute to the study of seasonal processes in the zooplankton of Lake Onega and can be used to model ecosystem dynamics.