Oral Presentation Society for Freshwater Science 2025 Annual Meeting

Comparison of molecular and morphological approaches for biodiversity detection in the Great Lakes Introduced Species Sentinel Network (GLISSNet) (118648)

Sarah Brown 1 , John Darling 2 , Jenna Hanlon 3 , Lana Fanberg 4 , Chelsea Hatzenbuhler 5 , Andrew Chang 6 , Monaca Noble 6 , Clinton Arriola 6 , Katrina Pagenkopp-Lohan 6 , Paula Pappalardo 6 , Emmett Haggard 7 , Jon Geller 7 , Greg Ruiz 6
  1. Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, U.S.
  2. Environmental Protection Agency, Durham, North Carolina, U.S.
  3. Oak Ridge Associated Universities, Oak Ridge, Tennessee, U.S.
  4. University of Wisconsin-Superior, Lake Superior Research Institute, Superior, Wisconsin, U.S.
  5. Great Lakes Toxicological & Ecological Division, Environmental Protection Agency, Duluth, Minnesota, U.S.
  6. Smithsonian Environmental Research Center, Edgewater, Maryland, U.S.
  7. Moss Landing Marine Laboratory, Moss Landing, California, U.S.

The Great Lakes Introduced Species Sentinel Network (GLISSNet) is a long-term monitoring program that aims to establish sentinel sites with standardized methods for the long-term monitoring of biodiversity and early detection of non-indigenous species (NIS) in the Great Lakes. The first sentinel site, established in 2021 in Duluth-Superior Harbor, experiences some of the highest rates of ship traffic and ballast water discharge in the Great Lakes, making it an essential location for early NIS detection efforts. Here, we compare the efficacy of molecular and morphological methods in detecting NIS during the first year of sampling in Duluth-Superior Harbor. Samples for morphological analysis and DNA metabarcoding were collected from 50 sites in the St. Louis River Estuary using three different methods, including two types of benthic invertebrate sampling devices as well as the collection of surface water zooplankton communities. Trends in the total number of species and NIS detected varied between molecular and morphological analyses as well as between different types of sample collection methods. While DNA metabarcoding enabled the detection of cryptic (i.e., morphologically indistinct) and rare NIS that were not identified morphologically, morphological identification of NIS enabled the detection of species for which reference sequence data is limited. These results suggest the complementary use of molecular and morphological methods for the effective identification of non-indigenous species. We describe the standardization of molecular methods for ongoing monitoring in Duluth Harbor and for additional sentinel sites in the Great Lakes. We also explore the potential utility of these methods for similar surveillance and monitoring programs in other large lake systems and address the general effectiveness of DNA metabarcoding for assessment of temporal changes in biodiversity and reporting detection of species of concern.