Monitoring zooplankton diversity and community dynamics is essential for understanding ecological processes within freshwater ecosystems. Environmental DNA (eDNA) has been increasingly employed in this field due to its efficiency and accuracy. However, its potential applications in freshwater ecosystems require further validation. In this study, we evaluated the performance of 18S rRNA and COI primers for freshwater zooplankton diversity monitoring and systematically compared the selected primers with the microscopy method in Qiandao Lake, China. Our results indicated that the COI primer marker (mlCOIintF/jgHCO2198) was more suitable for freshwater zooplankton diversity monitoring than 18S rRNA. The eDNA method identified a total of 102 species, whereas the microscopy method detected 111 species. Non-metric multidimensional scaling (NMDS) analysis and nonparametric multivariate statistical tests revealed that both abundance and biomass species compositions determined by microscopy differed significantly from those based on eDNA reads. Both methods detected significant seasonal changes in zooplankton community species composition, while eDNA provided a comprehensive view of the complex interactions within the community. Both methods indicate that rotifers are the primary group driving seasonal changes in the zooplankton community. The eDNA identified more environmental factors associated with seasonal changes in zooplankton communities than microscopy, including dissolved oxygen (DO), nephelometric turbidity unit (NTU), ammonia nitrogen (NH3-N), and total phosphorus (TP). The eDNA reads of rotifer and crustacean plankton increased linearly with their morphological abundance but not with biomass. Furthermore, combining morphological abundance and biomass as predictor variables for eDNA reads moderately enhanced the explanatory power compared to using them individually. Although eDNA cannot yet replace morphological methods, its efficiency and sensitivity make it a valuable complementary tool for zooplankton monitoring, with considerable potential for future applications.
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