Oral Presentation Society for Freshwater Science 2025 Annual Meeting

Metabarcoding or Morphological Metawebs? Using DNA and traditional approaches for making trait-based food web models at a continental scale. (117877)

Zacchaeus G Compson 1 , Jason Bohenek 1 , Dhroov Pandey 1 , Sanjukta Bhowmick 1 , James Junker 1 , Elinor M Lichtenberg 2 , Avery E Pearson 2 , Sean C Emmons 3 , Carla L Atkinson 4 , Arial Shogren 4 , Chelsea Smith 4 , Megan Malish 5 , Stephen Cook 6 , Michelle Busch 7 , Thomas Neeson 8 , Albert Ruhi Vidal 9 , Kyle Leathers 9 , Jacob Dorris 4 , Morgan Bucher 1 , Madison Peters 1 , Tim Poirier 10 , Roxanne MacKinnon 10 , Kaley Cave 1 , Lindsey Davis 1 , Caricia Alcantar 1 , Madison Condron 1 , Mickyle Celis 1 , Sahaj Satani 1 , Daniel C Allen 11
  1. Department of Biological Sciences, University of North Texas, Denton, TX, United States
  2. Department of Biological Sciences, University of North Texas, Denton, TX, United States
  3. Eastern Ecological Science Center, United States Geological Survey, Kearneysville, WV, United States
  4. Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States
  5. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, United States
  6. School of Biological Sciences, University of Oklahoma, Norman, OK, United States
  7. Kansas Biological Survey & Center for Ecological Research, University of Kansas, Lawrence, KS, United States
  8. Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK, United States
  9. Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, United States
  10. Department of Biology, University of New Brunswick, Fredericton, NB, Canada
  11. Department of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, United States

DNA metabarcoding has emerged as a transformative tool in biodiversity research, offering rapid, high-resolution species identification. Despite its promise, using DNA metabarcoding for generating ecological networks remains underexplored, representing a significant frontier in ecological research. This study evaluated the utility of DNA metabarcoding in generating trait-based ecological food web models, comparing its performance to traditional morphological approaches across a continental scale. Utilizing data from the National Ecological Observatory Network (NEON), we constructed heuristic food webs for 24 wadeable streams, integrating macroinvertebrate data derived from both DNA metabarcoding and morphological methods. Our objectives were to (1) compare the structure and metrics of food webs generated using DNA metabarcoding and morphological data, (2) assess their capacity to detect spatial variation across NEON sites, (3) identify environmental predictors of food web metrics, and (4) evaluate the ability of each approach to predict ecosystem functions. Key metrics, including connectance, linkage density, and trophic position, were calculated and analyzed. Results revealed that the enhanced species detection afforded by DNA metabarcoding yielded more complex and interconnected food webs with higher levels of omnivory and maximum trophic levels compared to morphological data, while food webs generated from morphological data exhibited significantly higher degrees of modularity. Interestingly, several metrics related to food web size did not differ between the two approaches, indicating similar topological structures; coupled with significant differences in modularity, degree of omnivory, linkage density, and other size-standardized metrics, this indicates that models generated from the two approaches provide different inferences about the functional aspects of food webs, including how resistant they may be to lost linkages from disturbance. This study underscores the potential of combining DNA metabarcoding with trait-based models to improve the accuracy and scalability of ecological network analysis for biodiversity monitoring, providing a foundation for future investigations into ecosystem structure, function, and resilience.