Zooplankton are essential components of Great Lakes ecosystems, playing pivotal roles in nutrient cycling and trophic dynamics. Current taxonomic assessment strategies for these taxa are either underdeveloped or resource-intensive, which impedes biodiversity monitoring in the face of rapid ecological change. This study employs a novel approach utilizing a three-marker metabarcoding technique for zooplankton biodiversity assessment to enhance taxonomic recovery while minimizing sequencing costs. Bulk zooplankton samples were collected using mesh nets from 96 stations across the Great Lakes. DNA was PCR amplified using 2 overlapping COI fragments and one 18S fragment, which were pooled prior to sequencing. Sequence reads were de-multiplexed by marker and assigned taxonomy through the RDP classifier implemented in dada2 against a reference database constructed from sequence data in the Barcode of Life Database (BOLD) and the SILVA database for COI and 18S markers respectively. The metabarcoding method proved more sensitive in detecting rare and cryptic species compared to traditional morphological identification. These findings highlight the potential of multi-marker metabarcoding as a standard practice in aquatic monitoring projects, facilitating the tracking of taxonomic changes and the detection of species invasions.

Authors:

Joseph Connolly, Cornell University  (jkc238@cornell.edu)

Elizabeth Whitmore-Stolar, Cornell University  (eaw237@cornell.edu)

Chris Marshall, Cornell University  (ccm243@cornell.edu)

James Watkins, Cornell University  (jmw237@cornell.edu)

Michael Pfrender, University of Notre dame  (mpfrende@nd.edu)