Oral Presentation Society for Freshwater Science 2025 Annual Meeting

The Great Lakes Introduced Species Sentinel Network (GLISSNet): Toward standardized molecular methods for long-term non-native species surveillance (117006)

John A Darling 1 2 , Sarah Brown 3 , Jenna Hanlon 4 , Lana Fanberg 5 , Chelsea Hatzenbuhler 6 , Andrew Chang 7 , Monaca Noble 7 , Clinton Arriola 7 , Katrina Pagenkopp-Lohan 7 , Paula Pappalardo 7 , Emmett Haggard 8 , Jonathan Geller 8 , Greg Ruiz 7
  1. United States Environmental Protection Agency, Research Triangle Park, NORTH CAROLINA, United States
  2. Watershed and Ecosystem Characterization Division, US Environmental Protection Agency, , Research Triangle Park, NC, USA
  3. Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
  4. Oak Ridge Associated Universities, Oak Ridge, TN, USA
  5. Lake Superior Research Institute, University of Wisconsin-Superior, Superior, WI, USA
  6. Great Lakes Toxicological & Ecological Division, US Environmental Protection Agency, Duluth, MN, USA
  7. Smithsonian Environmental Research Center, , Smithsionian Institution, Edgewater, MD, USA
  8. Moss Landing Marine Laboratory, San Jose State University, Moss Landing, CA, USA

The Laurentian Great Lakes of the United States and Canada are among the most invaded aquatic systems in the world. They have also been the beneficiary of multiple efforts at various levels of governance to reduce risks of novel invasions and the spread of existing non-indigenous species (NIS). Understanding the degree to which these management tools mitigate invasion risk requires a sensitive, standardized, long-term monitoring program capable of detecting NIS across a network of sites throughout the Great Lakes. Here we describe the establishment of a “sentinel sites” model for biodiversity monitoring in the Great Lakes, with the primary aim of better understanding patterns of NIS introduction and spread. Borrowing from previous experience in marine systems, our model employs multiple sampling methods and incorporates DNA metabarcoding approaches based primarily on the cytochrome C oxidase subunit I (COI) locus to maximize the likelihood of detecting NIS, even when rare. We summarize results from the first several years of monitoring at our initial sentinel site in Duluth Harbor (Lake Superior), examining the importance of sentinel site location, the utility of different sampling approaches for biodiversity capture, the complementarity of morphological and molecular analysis for describing taxa, and the effectiveness of different metabarcoding loci for detecting NIS. Based on these results, we describe a standardized approach for using DNA metabarcoding to monitor changes in lake biodiversity and track introduction and spread of non-native taxa. We discuss the benefits of a standardized approach for assessing site-specific rates of NIS introduction and we explore the potential for adopting these standard methods to establish an effective long-term monitoring network across the Great Lakes. We also address the challenges associated with reporting NIS observations based on DNA metabarcoding data and we identify approaches to reduce uncertainties in taxonomic assignments and better communicate confidence in positive detections of species of concern.