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|Estimating Salmonid Genetic Diversity from Pacific Northwest Streams using Multi-gene eDNA Metabarcoding
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|Oregon State University
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|Western Division/WA-BC Chapter
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The use of environmental DNA (eDNA) is rapidly advancing as a method for quantifying species presence in a wide variety of ecosystems. As the technology advances, particularly with the use of metabarcoding and high-throughput sequencing, eDNA is increasingly used to infer population abundance estimates and indices of population health. Here, we use multi-gene multi-species sequence data from eDNA to examine mitochondrial genetic diversity of four salmonids (Rainbow Trout, Coastal Cutthroat Trout, Chinook Salmon, and Coho Salmon) across 16 locations in 8 watersheds in Oregon and northern California. Our multi-gene panel included three targets commonly used in population genetic (ND2), phylogenetic (COI) and eDNA (12S ribosomal DNA) screening. Following a codon-informed denoising process to reduce false haplotypes, the mitochondrial locus ND2 showed the greatest within-species haplotype diversity compared to COI or 12S rDNA. For Cutthroat Trout, the greatest diversity was found among streams of coastal watersheds. Sites within the Willamette, Umpqua, and Rogue River watersheds either did not share haplotypes, or had highly divergent allele frequencies. The number and frequency distribution of haplotypes detected at ND2 (21) show congruence with a recent survey of ND2 sequences from 88 coastal Cutthroat Trout individuals. For Rainbow Trout, the highest genetic diversity was found within the Deschutes River watershed and among small streams in coastal watersheds, with lower diversity in the Umpqua River watershed. Coho Salmon were only observed in coastal streams and the Klamath River watershed, with diversity among the coastal streams greatly exceeding the Klamath. Chinook Salmon were only observed in the Willamette watershed, with multiple haplotypes observed at each locus. The characterization of diversity at multiple genetic loci from eDNA samples expands the knowledge gained from this technique, and provides additional information for data-driven prioritization of management and conservation actions for multiple aquatic species.