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Aquatic Assemblages from Edna Metabarcoding 1 Casting a Broader

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Aquatic Assemblages from Edna Metabarcoding 1 Casting a Broader 1 Running Head: Aquatic assemblages from eDNA metabarcoding 2 Casting a broader net: Using microfluidic metagenomics to capture aquatic biodiversity data from 3 diverse taxonomic targets 4 Laura L. Hauck1†, Kevin A. Weitemier2†, Brooke E. Penaluna1, Tiffany Garcia2, and Richard Cronn1* 5 1U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, 6 Corvallis, OR 97331, email: [email protected] 7 2Oregon State University, Department of Fisheries and Wildlife, 104 Nash Hall, Corvallis, OR 97331 8 Tel: 1 (541) 737-7291 Fax: 1 (541) 750-7329 *Author for correspondence 9 †L. Hauck and K. Weitemier should be considered joint first author. 10 11 Abstract 12 Environmental DNA (eDNA) assays for single- and multi-species detection show promise for providing 13 standardized assessment methods for diverse taxa, but techniques for evaluating multiple taxonomically- 14 divergent assemblages are in their infancy. We evaluated whether microfluidic multiplex metabarcoding and 15 high-throughput sequencing could identify diverse aquatic and riparian assemblages from 48 taxon-general and 16 taxon-specific metabarcode primers. eDNA screening was paired with electrofishing along a stream continuum 17 to evaluate congruence between methods. A fish hatchery located midway along the stream continuum 18 provided a dispersal barrier, and a point source for non-native White Sturgeon (Acipencer transmontanus). 19 Microfluidic metabarcoding detected all 13 species observed by electrofishing, with overall accuracy of 86%. 20 Taxon-specific barcoding primers were more successful than taxon-general universal metabarcoding primers at 21 classifying sequences to species. Both types of markers detected a transition from downstream sites dominated 22 by multiple fish species, to upstream sites dominated by a single species; however, we failed to detect a 23 transition in amphibian population structure. White Sturgeon was only detected at the hatchery outflow, 24 indicating eDNA transport was not detectable at ~2.4 km. Overall, we identified 878 predicted taxa, with most 25 sequences (49.8%) derived from fish (Actinopteri, Petromyzontidae), Oomycetes (21.4%), Arthropoda (classes 26 Insecta, Decapoda; 16.6%), and Apicomplexan parasites (3.83%). Taxa accounting for ~1% or less of 27 sequences included freshwater red algae, diatoms, amphibians, and beaver. Our work shows that microfluidic 28 metabarcoding can survey multiple phyla per assay, providing fine discrimination required to resolve closely- 29 related species, and enable data-driven prioritization for multiple forest health objectives. 30 Introduction 31 Land management agencies survey and monitor many biota to meet diverse management objectives. These 32 activities typically involve multiple agencies and objectives that address questions focusing on shared 33 geography. Adjacent riparian areas can be migration corridors for amphibians or water-dependent mammals, 34 while catchments may harbor pathogens that affect wildlife (e.g., bat white-nose syndrome [Flory et al., 2012]; 35 amphibian chytridiomycosis [Olson et al., 2013]), forest health (sudden oak death from Phytophthora [Hansen 36 et al., 2012]), or human community health (Tiedemann, 2000). These overlapping management concerns on a Aquatic assemblages from eDNA metabarcoding 2 37 common catchment require multiple teams with technical expertise to address basic questions of species 38 detection. Typically, biotic surveys are conducted with limited cross-taxon integration due to the difficulty of 39 coordinating across disciplines, jurisdictions (state, federal), and agencies (US Fish and Wildlife Service, US 40 Forest Service, Bureau of Land Management, US Geological Survey, and others). 41 Environmental DNA (eDNA) analysis has emerged as a powerful method for detecting aquatic and riparian 42 species, one with the capacity to bridge diverse disciplines and inform multiple management objectives. 43 Originally developed for characterizing microbial (Venter et al., 2004) and fungal (Anderson and Cairney, 44 2004) communities, eDNA analysis has expanded to include diverse eukaryotes, including plants (Willerslev et 45 al., 2003), invertebrates (Hajibabaei et al., 2011; Thomsen, Kielgast, Iversen, Wiuf, et al., 2012), and 46 vertebrates (Andersen et al., 2012; Thomsen, Kielgast, Iversen, Møller, et al., 2012). Methods for eDNA 47 analysis have evolved from assays targeting one to a few well-characterized taxa (e.g., qPCR, digital PCR; 48 Nathan et al., 2014), to “metabarcoding” assays that identify scores of taxonomic targets per sample (Deiner et 49 al., 2016; Thomsen, Kielgast, Iversen, Møller, et al., 2012; Valentini et al., 2016; Wilcox et al., 2018). 50 Metabarcoding approaches have been shown to provide detection accuracies equivalent or better than 51 traditional sampling methods (Deiner et al., 2016; Thomsen, Kielgast, Iversen, Møller, et al., 2012; Valentini et 52 al., 2016), and a much larger taxonomic spectrum per assay. Metabarcoding represents a technological leap, 53 but it is not without limitations, such as the challenge of independently validating unexpected (false) positives 54 and negatives, the difficulty in detecting rare species, or the difficulty of identifying taxa to the species-level 55 using universal DNA metabarcoding genes (Deiner et al., 2016). From the perspective of land management 56 agencies, metabarcoding assays based on single markers share a limitation in that they address a fraction of the 57 broad spectrum management questions asked by land managers. 58 Incorporating multiple barcoding and metabarcoding gene targets into a single assay offers independent 59 observations for taxon presence/absence, and more accurate biodiversity estimates across highly-diverse taxa 60 (Drummond et al., 2015; Elbrecht et al., 2017; Evans et al., 2017; Gibson et al., 2014; Stat et al., 2017). Here, 61 we evaluate the performance of microfluidic PCR in combination with eDNA metabarcoding to 62 simultaneously evaluate up to 48 loci in 2,304 nanoliter-scale PCR reactions per array. High level multiplexing 63 allows simultaneous screening with taxon-general and taxon-specific genes in one assay, providing multiple 64 markers for taxonomic inference (e.g., phylum to species), and a mechanism to independently validate 65 presence and absence observations (Brown et al., 2016). 66 We tested eDNA samples collected from a stream continuum ~11 km in length. This continuum includes an 67 impassable barrier to hatchery fish, which creates a break in the distribution of hatchery and wild stocks for 68 select species. Multiple primer sets targeting taxon-general and taxon-specific mitochondrial genes were 69 designed for the detection of fish (salmonids, sculpins, lamprey, sturgeon), amphibians (frogs, salamanders), 70 invertebrates (crayfish, mayflies, stoneflies), and oomycete (Phytophthora, Saprolegnia) and fungal 71 (Batrachochytrium, Pseudogymnoascus) pathogens. PCR products ranging from ~150-406 bp in length were 72 amplified using the microfluidic Fluidigm Access Array, and products were sequenced by Illumina massively 73 parallel sequencing. Presence/absence detection by eDNA is directly compared to electrofishing to evaluate the 74 accuracy and specificity of eDNA microfluidic metabarcoding as a qualitative and semi-quantitative proxy for 75 species-level field identification. Aquatic assemblages from eDNA metabarcoding 3 76 Materials and Methods 77 Study Site 78 We surveyed five sites in Fall Creek, a tributary to the Alsea River in the Coast Range of Oregon, USA (Figure 79 1). Annual precipitation at Fall Creek averages 1856 mm/yr (2001-2010 estimate; Wang et al., 2016), with 2/3 80 of annual precipitation falling November–February. Stream temperatures range from 5-17°C year-round. The 81 Oregon Hatchery Research Center (OHRC) is located on Fall Creek, and OHRC maintains a physical barrier to 82 fish passage that completely blocks upstream migration of Chinook Salmon (Oncorhynchus tshawytscha 83 Walbaum), partially blocks passage of hatchery Rainbow Trout (O. mykiss Walbaum, including the steelhead 84 life history) and Coho Salmon (O. kisutch Walbaum), and allows migration of wild Coastal Cutthroat Trout (O. 85 clarkii clarkii Richardson), Rainbow Trout, Coho Salmon, and smaller native fish. Two sampling sites on Fall 86 Creek were located downstream of the OHRC (sites 1, 2), one site was at the OHRC effluent outflow (site 3), 87 and two sites were located upstream of the hatchery (sites 4, 5). 88 The OHRC provides experimental opportunities to evaluate the impact of eDNA point sources on downstream 89 detection. At the time of our sampling in 2017, the hatchery was rearing ~5,000 Rainbow Trout and ~15,000 90 Chinook Salmon (D.L.G. Noakes, personal communication). OHRC also maintains eight captive White 91 Sturgeon (Acipencer transmontanus Richardson), creating a point source for a phylogenetically distinctive 92 species not naturally found in this catchment. 93 Traditional Backpack Electrofishing Survey 94 Backpack electrofishing (Cossel et al., 2012) for fish, amphibians, and crayfish was conducted at sites below 95 (sites 1, 2) and above (sites 4, 5) the OHRC within a 15 day period (20-July-2017 to 04-August-2017). Site 3 96 (hatchery outflow) was not evaluated by electrofishing. Some species show high morphological similarity and 97 can be misidentified in the field. These include Riffle and Reticulate sculpin (Cottus gulosus Girard; C. 98 perplexus Gilbert & Evermann), Longnose
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