Abstract
The backwater lowland habitats of large rivers, like the Mississippi River in North America, present complex and often inaccessible environments for traditional capture-based fish biodiversity sampling. Our knowledge of the assemblages of the fishes that occupy such habitats is often incomplete, and this can compromise management efforts. We employed environmental DNA (eDNA) metabarcoding methods to sample a Mississippi River bottom wetland system to assess the ichthyofaunal diversity and the assemblage structure across habitat types, and we compared our results with capture-based survey records for the same habitats. We collected water samples in the spring and fall of 2022 from slough, ditch, shallow lake, and bayou habitats that varied in depth, vegetation, seasonal variability, and connectivity to the Mississippi River channel. We detected a diverse array of fish species that included 51 taxa. Nearly all the species previously documented in the habitats were detected using eDNA metabarcoding, and we increased the number of documented species by more than a third. Most of the species were ubiquitous across the range of habitats, but there was also a substantial assemblage structure, with some species exhibiting clear habitat specificity. Fall sampling was limited to the deeper bayou habitats where seasonal variation between the spring and fall was minimal. eDNA meta barcode sampling was demonstrated to be effective at detecting invasive species as well as uncommon species, which included several species of conservation concern.
Recommended Citation
E. J. Ludwig et al., "Biodiversity Assessment of a Mississippi River Backwater Complex using Edna Metabarcoding," Diversity, vol. 16, no. 8, article no. 495, MDPI, Aug 2024.
The definitive version is available at https://doi.org/10.3390/d16080495
Department(s)
Biological Sciences
Publication Status
Open Access
Keywords and Phrases
backwater; biodiversity; biological monitoring; DNA; environmental; fish; fresh water; invasive species; lowland; metabarcoding
International Standard Serial Number (ISSN)
1424-2818
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Aug 2024
