Assessing the biomass of zooplankton compensates for the difference between number of individuals and the accumulated body weight of the community, which helps assess aquatic ecosystem food web functions. Daphnia are crustaceans that play an intermediate role in biological interactions within food webs. The morphology and body specification of Daphnia differ during growth; hence, it is essential to apply species-specific equations to estimate biomass. We evaluated the length–weight regression equations used previously to estimate Daphnia magna biomass and conducted regression analyses using various body specifications and biomass measurements taken directly using devices such as a microbalance and microscopic camera. Biomass estimated using an equation from the Environmental Protection Agency was significantly different from the direct measurement: average biomass was lower, indicating that the equation possibly underestimated actual biomass. The biomass of D. magna had a higher multiple R² value when length was compared with width and area, and a linear regression equation was the most suitable equation for biomass estimation. Because body specifications and biomass are affected by various environmental factors, the development of accurate species-specific biomass estimation equations will contribute to obtaining fundamental data with which the biological responses of zooplankton to aquatic ecosystem changes can be assessed.

Zooplankton abundance patterns exhibit apparent seasonality depending on seasonal variations in water temperature. To analyze the abundance patterns of zooplankton communities, it is necessary to consider the environmental factors that are essential for zooplankton community succession. However, this approach is challenging due to the seasonal variability of environmental factors. In this study, all rotifer species inhabiting a water body were classified into three groups based on their abundance and frequency of occurrence, and decomposition method was used to classify them into groups that exhibit seasonal vs. non-seasonal variability. Multivariate analysis was performed on the seasonal, trend, and random components derived from the classical decomposition method of zooplankton abundance and related environmental factors. This approach provided more precise results and higher explanatory power for the correlations between rotifer communities and environmental factors, which cannot be clarified with a simple abundance-based approach. Using this approach, we analyzed the seasonality-based patterns of the abundance of rotifer species by dividing the environmental factors into those associated with seasonal and non-seasonal variabilities. Overall, the results demonstrated that the explanatory power of redundancy analysis was higher when using the three time series components than when using undecomposed abundance data.