Vogelmann, Christian; Barco, Andrea; Knust, Jean-Michel; Stibor, Herwig (2024): Unraveling Zooplankton Diversity in a Pre-Alpine Lake: A Comparative Analysis of ZooScan and DNA Metabarcoding Methods. Water, 16 (3): 411. ISSN 2073-4441
water-16-00411-v2.pdf
The publication is available under the license Creative Commons Attribution.
Download (1MB)
Abstract
Zooplankton, integral to aquatic ecosystems, face diverse environmental influences. To comprehend their dynamics, critical for ecological insights and fisheries management, traditional morphological analysis proves laborious. Recent advances include automated systems like ZooScan and DNA metabarcoding. This study examines two methods on the same samples to identify similarities and dependencies between them, potentially reducing the required workload and enhancing the quality of the results. Ten Lake Starnberg vertical tows in September 2021 provided zooplankton samples preserved in ethanol. Subsamples underwent ZooScan morphological identification and subsequent DNA metabarcoding. High concordance between ZooScan counts and DNA reads (86.8%) was observed, while biomass calculations from body length (major axis) and equivalent spherical diameter (ESD) showed slightly lower agreement (78.1% and 79.6%, respectively). Linear regression analysis revealed a correlation between counts and DNA reads (r2 = 0.59). This study underscores the complementary strengths and limitations of ZooScan and DNA metabarcoding for zooplankton analysis. ZooScan aids biomass estimation and morphological differentiation, whereas DNA metabarcoding offers superior taxonomic resolution and low-abundance taxon detection. Combining both methods on the same sample enhances understanding and facilitates future advanced analyses.
Doc-Type: | Article (LMU) |
---|---|
Organisational unit (Faculties): | 19 Biology > Department Biology II |
DFG subject classification of scientific disciplines: | Life sciences |
Date Deposited: | 14. Feb 2024 07:19 |
Last Modified: | 14. Feb 2024 07:19 |
URI: | https://oa-fund.ub.uni-muenchen.de/id/eprint/1110 |
DFG: | Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491502892 |