Monitoring of marine protected areas (MPAs) is critical for marine ecosystem management, yet current protocols rely on SCUBA-based visual surveys that are costly and time consuming, limiting their scope and effectiveness. Environmental DNA (eDNA) metabarcoding is a promising alternative for marine ecosystem monitoring, but more direct comparisons to visual surveys are needed to understand the strengths and limitations of each approach. This study compares fish communities inside and outside the Scorpion State Marine Reserve off Santa Cruz Island, CA using eDNA metabarcoding and underwater visual census surveys. Results from eDNA captured 76% (19/25) of fish species and 95% (19/20) of fish genera observed during pairwise underwater visual census. Species missed by eDNA were due to the inability of MiFish 12S barcodes to differentiate species of rockfishes (Sebastes, n=4) or low site occupancy rates of crevice-dwelling Lythrypnus gobies. However, eDNA detected an additional 23 fish species not recorded in paired visual surveys, but previously reported from prior visual surveys, highlighting the sensitivity of eDNA. Significant variation in eDNA signatures by location (50 m) and site (~1000 m) demonstrates the sensitivity of eDNA to address key questions such as community composition inside and outside MPAs. Results demonstrate the utility of eDNA metabarcoding for monitoring marine ecosystems, providing an important complementary tool to visual methods.

Materials and Methods

Sample collection

We conducted our study at Scorpion State Marine Reserve within the Channel Islands National Park and National Marine Sanctuary. To determine the degree to which eDNA could capture documented differences inside and outside this MPA, we sampled three sites: 1) inside the MPA (34.05223 N , 119.58253 W) 2) outside but adjacent (<0.5km) to the MPA (“edge site”; 34.04415 N, 119.54245 W), and 3) 2.3 km outside the MPA boundary (“outside site”; 34.03837 N, 119.5253 W; Fig 1). At each of these three sites, we sampled directly along a 100 m fixed transect used by the Kelp Forest Monitoring Program for visual monitoring, using a GPS to ensure transects overlapped [4]. We collected three replicate 1 L water samples from three locations on each transect, totaling nine spatially structured replicates per site. Due to fieldwork logistical challenges, each site was sampled on a different day with a maximum of 72 hours between sampling events.

Fig 1. Map of Scorpion State Marine Reserve off Santa Cruz Island, CA, USA. The map was generated using the free and open source software QGIS version 3.0.

We collected seawater samples from 10 m below the surface and 1 m above the benthos using a 4 L Niskin bottle deployed from the UCLA RV Kodiak [25]. From each Niskin deployment, we transferred a single liter of seawater to an enteral feeding pouch and conducted gravity filtration through a sterile 0.22 µm Sterivex cartridge (MilliporeSigma, Burlington, MA, USA) in the field (Miya et al., 2016). Additionally, we processed three field blanks as a negative control that consisted of 1 L of distilled water following the method above. Finally, we dried Sterivex filters using a 3 mL syringe and then capped and stored the filters at -20˚C for DNA laboratory work back at UCLA (Miya et al., 2015).

DNA extraction and library preparation

We extracted eDNA from the Sterivex cartridge using the DNAeasy Tissue and Blood Kit (Qiagen Inc., Germantown, MD) following modifications of Spens et al. (2017). We PCR amplified the extracted eDNA using the MiFish Universal Teleost 12S primer (Miya et al., 2015) with Nextera modifications following PCR and the library preparation methods of Curd et al. (2019) (See S1 Appendix for supplemental methods). All PCRs included a negative control where molecular grade water replaced the DNA extraction. For positive controls, we used DNA extractions of grass carp (Ctenopharyngodon idella, Cyprinidae) and Atlantic salmon (Salmo salar, Salmonidae), both non-native to California. Libraries were sequenced on a MiSeq PE 2x300bp at the Technology Center for Genomics & Bioinformatics (University of California- Los Angeles, CA, USA), using Reagent Kit V3 with 20% PhiX added to all sequencing runs.

Bioinformatics

To determine community composition, we used the Anacapa Toolkit (version: 1) to conduct quality control, amplicon sequence variant (ASV) parsing, and taxonomic assignment using user-generated custom reference databases [28]. The Anacapa Toolkit sequence QC and ASV parsing module relies on cutadapt (version: 1.16) [29], FastX-toolkit (version: 0.0.13) [30], and DADA2 (version 1.6) [31] as dependencies and the Anacapa classifier modules relies on Bowtie2 (version 2.3.5)[32] and a modified version of BLCA [33] as dependencies. We processed sequences using the default parameters and assigned taxonomy using two CRUX-generated reference databases. We first assigned taxonomy using the FishCARD California fish specific reference database [34]. Second, we used the CRUX-generated 12S reference database supplemented with FishCARD reference sequences to assign taxonomy using all available 12S reference barcodes to identify any non-fish taxa. We note that CRUX relies on ecoPCR (version: 1.0.1) [35], blastn (version: 2.6.0) [36], and Entrez-qiime (version: 2.0) [37] as dependencies.

Raw ASV community table was decontaminated following Kelly et al. (2018) and McKnight et al. (2019) (See S1 Appendix). We chose a site occupancy cutoff score of 84% which corresponded with the minimum occupancy rate observed for three detections out of nine PCR replicates at a given location sampled. We then transformed all read counts into an eDNA index for beta-diversity statistics [16]. All non-fish species (mammals and birds) were removed prior to final analyses.

eDNA data analysis

To test for alpha diversity differences, we compared total species richness for each site using an Analysis of Variance (ANOVA) and subsequent Levine’s test for equality of variance [39].

To determine whether our eDNA sampling design was sufficient to fully capture fish community diversity, we created species rarefaction curves using the iNext package (version 2.0.2) [40]. We then compared species coverage estimates between each site, with and without site occupancy modeling, and using all three 1 L replicates taken at three locations along a 100 m transect (n=9) as well as only three 1 L biological replicates (n=3). We ran a piecewise regression analysis to identify breakpoints in the rate of species diversity found per sample collected using the R packaged segmented (version 1.3) [41].

To test for differences among fish communities, we calculated Bray-Curtis similarity distances on the eDNA index scores between all samples (See S2 Appendix for Supplemental Results) [23]. Specifically, we tested for the difference in community similarity variance between our three sites using an adonis PEMANOVA (vegan version: 2.4.2)[39], followed by a companion multivariate homogeneity of group dispersions test (BETADISPER) [39]. Both the PERMANOVA and BETADISPER were run using the following model: eDNA Index ~ Site + Location. We also visualized community beta diversity using non-metric multidimensional scaling (NMDS) [39]. To further investigate which species were driving eDNA community differences among sites, we conducted constrained analysis of principle components (CAP) [39].

Visual underwater census methods

To assess fish communities using underwater visual census techniques, SCUBA divers from the Kelp Forest Monitoring Program followed standard survey protocols following Kushner et al. (2013). These protocols include survey types: visual fish transects, roving diver fish counts, and 1 m² quadrats. The visual fish transects targeted 13 indicator species of fish on visual fish transects recording the counts of adults and juveniles. This protocol consists of performing 2 m x 3 m x 50 m transects along the 100 m permanent transect. During roving diver fish count surveys all positively identified species are recorded. This protocol consists of 3-6 divers counting all fish species observed during a 30 minute time period, covering as much of the 2000 m² of bottom and entire water column as possible. The 1 m² quadrat records three small demersal species of fish. All visual surveys occurred along a permanent 100 m transect at each site and were conducted within two weeks of eDNA sampling (See S1 Appendix).

Comparison of eDNA and visual underwater census methods

We compared species detected by eDNA and underwater visual census approaches across corresponding transects at each site. We identified core taxa that were shared across all sites for eDNA and visual survey methods. In addition, we identified species that eDNA methods failed to detect but were observed in visual census surveys and vice versa. Given the few numbers of sites (n=3), we were unable to robustly compare abundance estimates between methods.

See https://github.com/zjgold/Scorpion-SMR-eDNA-Metabarcoding 

See https://www.authorea.com/users/330161/articles/457029-fishcard-fish-12s-california-current-specific-reference-database-for-enhanced-metabarcoding-efforts?commit=37fe31b2da0c2b30fb72b3088bb6eedc7460e913 for reference database