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Research Commodity

Persistence of marine fish environmental Deoxyribonucleic acid and the influence of sunlight

• Elizabeth A. Andruszkiewicz,
• Lauren M. Sassoubre,
• Alexandria B. Boehm

ten

• Published: September 15, 2017
• https://doi.org/ten.1371/periodical.pone.0185043

Figures

Abstract

Harnessing data encoded in environmental DNA (eDNA) in marine waters has the potential to revolutionize marine biomonitoring. Whether using organism-specific quantitative PCR assays or metabarcoding in conjunction with amplicon sequencing, scientists have illustrated that realistic organism censuses can be inferred from eDNA. The next stride is establishing means to link information obtained from eDNA analyses to bodily organism abundance. This is only possible by understanding the processes that control eDNA concentrations. The nowadays written report uses mesocosm experiments to report the persistence of eDNA in marine waters and explore the function of sunlight in modulating eDNA persistence. Nosotros seeded solute-permeable dialysis bags with water containing indigenous eDNA and suspended them in a large tank containing seawater. Numberless were subjected to two treatments: half the numberless were suspended near the water surface where they received high doses of sunlight, and half at depth where they received lower doses of sunlight. Bags were destructively sampled over the course of 87 hours. eDNA was extracted from water samples and used as template for a Scomber japonicus qPCR assay and a marine fish-specific 12S rRNA PCR assay. The latter was subsequently sequenced using a metabarcoding approach. S. japonicus eDNA, as measured by qPCR, exhibited kickoff order decay with a rate constant ~0.01 hr ^-1 with no difference in decay rate constants between the 2 experimental treatments. eDNA metabarcoding identified 190 organizational taxonomic units (OTUs) assigned to varying taxonomic ranks. In that location was no difference in marine fish communities every bit measured by eDNA metabarcoding betwixt the two experimental treatments, but at that place was an consequence of time. Given the differences in UVA and UVB fluence received by the ii experimental treatments, we conclude that sunlight is not the main driver of fish eDNA decay in the experiments. However, in that location are clearly temporal effects that need to be considered when interpreting information obtained using eDNA approaches.

Citation: Andruszkiewicz EA, Sassoubre LM, Boehm AB (2017) Persistence of marine fish environmental Dna and the influence of sunlight. PLoS Ane 12(9): e0185043. https://doi.org/10.1371/journal.pone.0185043

Editor: Hideyuki Doi, Academy of Hyogo, Nihon

Received: June 26, 2017; Accepted: September four, 2017; Published: September 15, 2017

Copyright: © 2022 Andruszkiewicz et al. This is an open admission article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available from the Dryad database at http://dx.doi.org/10.5061/dryad.d76d1.

Funding: This work was supported by a souvenir from the Seaver Institute to Woods Establish for the Environs at Stanford University. This publication was adult under STAR Fellowship Aid Agreement no. F15E211913419 awarded past the U.S. Environmental Protection Bureau (EPA). It has not been formally reviewed past EPA. The views expressed in this publication are solely those of Elizabeth Andruszkiewicz and co-authors, and EPA does non endorse any products or commercial services mentioned in this publication. The funders had no function in written report design, data drove and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests be.

Introduction

Marine biodiversity is threatened past stressors including climatic change, ascension sea surface temperature, body of water acidification, overfishing, habitat loss, and nutrient, plastic, and pollution [ane–9]. Biomonitoring, monitoring of organism abundance and diverseness, is traditionally conducted using visual counts by divers or remote operated vehicles (ROVs), trawl nets, fishing, or tagging of individuals [10,11]. These traditional methods can disturb habitats and damage organisms [eleven–14] and resultant datasets are spatially and temporally thin [xv]. Researchers are exploring the use of a less-invasive method of biomonitoring which entails collecting water samples to capture extra-organismal, environmental Deoxyribonucleic acid (eDNA) that has been shed from organisms and remains suspended in the water cavalcade [16–38]. eDNA from macroorganisms is in the course of scales, tissue, mucus, blood, carrion, gametes, or any other secretion [35,37]. eDNA tin be free-floating or bound to particles, with preliminary studies demonstrating the poly-disperse nature of eDNA [39–41]. Molecular methods are used to interrogate the eDNA. Quantitative PCR (qPCR) can be used to discover and quantify eDNA from specific organisms, or alternatively, eDNA metabarcoding tin can be used to obtain a census of organisms. In the eDNA metabarcoding arroyo, "universal" primers targeting a gene of interest are used in conjunction with adjacent generation sequencing (NGS) [36]. Due to the ease of collecting h2o samples for eDNA analysis, temporally and spatially dense biological datasets are possible [37].

Numerous studies have demonstrated the apply of eDNA for detecting macroorganisms in water [18,42–44]. However, at that place is uncertainty as to how these data relate to bodily organism counts and their locations. The concentration of eDNA in water is controlled by eDNA sources (i.due east., shedding) and sinks (i.e., disuse) as well equally its send (i.e., advection, dispersion, settling, resuspension) [45]. A better understanding of these unlike processes will allow a link to be made between eDNA concentrations and organism abundance, and potentially spring the spatial and temporal range of where and when an organism shed the eDNA that was captured in the water sample.

The focus of the present report is eDNA disuse in marine waters. Decay is defined every bit the disappearance of eDNA due to concrete, chemical, or biological processes, and does not include settling under the influence of gravity. eDNA decay is expected to depend on a broad variety of factors. eDNA decay may depend on whether it is actress-cellular or cellular, or if it is particle-leap or gratuitous-floating. It may also depend on abiotic factors such as sunlight, water temperature, pH, and salinity [46], and biotic factors such as grazers or enzymes in the water column [46,47].

eDNA has been studied extensively in soils, sediments, and ice cores, and more than commonly has been used to investigate microbial communities rather than macroorganism communities [48,49]. In those matrices, eDNA persistence depends on a diverseness of factors including soil type, depth of eDNA in the matrix, and soil chemical science, simply it is accustomed that eDNA can persist on the order of years in soils and sediments [32]. Contempo persistence studies of macroorganism eDNA from fish and amphibians in freshwater indicate eDNA can persist from 4 to 52 days [44–47,50–54]. Withal, there are few studies that accost the persistence of eDNA in marine waters, where abiotic and biotic stressors are expected to differ from freshwaters and sediments [37]. Furthermore, there are no studies that investigate how information on macroorganism presence/absence obtained from eDNA metabarcoding changes over time as a water parcel ages.

The nowadays report investigates the decay of eDNA in oceanic waters. Nosotros employ seawater from Monterey Bay mixed with aquarium water containing ethnic eDNA deployed in solute-permeable dialysis bags. Experiments were conducted under two different sunlight exposures to investigate the role of sunlight in controlling eDNA persistence. We chose to investigate sunlight specifically considering it varies with depth in the water cavalcade every bit do fish populations, making it an of import parameter for interpreting fish eDNA concentrations measured in marine waters. There is also a lack of studies investigating the effect of sunlight on eDNA disuse in marine waters. Nosotros rail changes in fish eDNA in the experiments using both qPCR and eDNA metabarcoding. Results provide insight into the persistence of eDNA and the effect of sunlight on the decay of eDNA in marine waters.

Materials and methods

Experimental design

Experiments were contained within dialysis bags (6–viii kDa molecular weight cutoff respective to nine–12 base pairs of double stranded Dna, 120 mm diameter Spectra/Por i RC Tubing, Spectrum Laboratories Inc., Rancho Domingo, CA) that were deployed in an outdoor seawater tank owned by the Monterey Bay Aquarium in Monterey, CA. The dialysis bags immune passage of h2o and dissolved constituents through (therefore simulating more environmentally relevant conditions), simply did not permit molecules or particles in or out of the bag larger than the pore size (i.e., target DNA). Maraccini et al. [55] determined the percent of transmittance of light (280–700 nm wavelength) through the dialysis numberless. The tank held ~10,000 50 of water sourced from Monterey Bay, but that had passed through a nominal pressure sand filter removing particles larger than ~20 μm and an aeration tower (hereafter this water is referred to as processed Monterey Bay seawater). The tank had walls surrounding it resulting in shading at certain times of day (Fig 1). Water flowed through the tank throughout the experiment at 0.0871 m^three/min. The average temperature of the water over the course of the experiment (from data collected every minute at the closest sensor to the tank, provided by Monterey Bay Aquarium staff) was 16.eight°C (standard departure: 0.46°C; range: xv.8°C—17.viii°C).

Dialysis numberless were filled with 500 ml of a mix of eighty% processed Monterey Bay seawater and 20% water from a tank located at the Tuna Research Conservation Center (TRCC) at Hopkins Marine Laboratory of Stanford Academy in Pacific Grove, CA. The h2o from the TRCC tank (future referred to as TRCC tank water) was candy Monterey Bay seawater that had flowed through aquaria holding tuna (Thunnus), Pacific Chub Mackerel (Scomber japonicus), and Pacific Sardines (Sardinops sagax) earlier information technology was sampled. These 3 species are as well native to Monterey Bay. Nosotros opted to mix these two waters together to seed the dialysis bags at the first of the experiment in lodge to ensure we would take: (1) sufficient eDNA from Due south. japonicus in club to utilise a species-specific qPCR analysis for quantification of decay rate constants, and (2) sufficient eDNA from a variety of marine macroorganisms to detect changes in marine fish communities over time via eDNA metabarcoding.

We filled 45 dialysis numberless with the 80/twenty mixture (hereafter referred to every bit T₀ water). 23 bags were deployed at the surface of the h2o in the tank and 22 at the bottom of the tank. To concur all other variables constant and only test the event of sunlight, nosotros used the same tank for both treatments. We were express by the depth of the tank and could non append the bags deep enough for consummate darkness. Nosotros deemed for differences in UV exposure at depth in our assay. Bags were deployed secured to a polyvinylchloride pipe frame with cipher-ties used in a previous written report [56] (Fig i). The rig at the surface had buoys to keep the frame floating and the center of the bags was v cm beneath the h2o surface (futurity referred to as surface). The second rig had no buoys and sank to the bottom of the tank, resulting in the dialysis numberless being suspended seventy cm beneath the water surface (hereafter referred to every bit depth).

We destructively sampled triplicate bags (quadruplicate for some time points) to represent biological replicates both at the surface and depth approximately every 12 hours for 4 days (Table 1). The length of the experiment was called based by considering previous decay charge per unit constants reported by others [45]. Experiments commenced on the evening of 16 October 2022 and ended the forenoon of 20 October 2015. Over the course of the four days, there were 2 rain events (resulting in <0.01 inches pelting total) (overnight 18/19 October 2022 and early on morn xix Oct 2015, S1 Tabular array).

The absorbance of the ambience tank water was measured in triplicate using Uvikon XL UV-Vis Spectrophotometer (BioTek Instruments, Winooski, VT). The reference solution was deionized water and we used the average of the three wavelength scans. We used the Elementary Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) model to obtain the intensity of light (280 nm– 700 nm) incident on the surface of the water for every 30 minutes on 18 October 2022 (midpoint of the written report), which was used as a representative day for the whole experiment (S1 Text, S2 Table, S1 Fig). The SMARTS model does not account for cloud comprehend; the weather on 18 October 2022 was partly cloudy with scattered clouds for the majority of the 24-hour interval. The UVA+UVB (280–400 nm) and UVB (280–320 nm) range light intensity incident on the h2o in the bags was adamant for bags deployed at the surface and at depth as described elsewhere [55]. In cursory, we deemed for the absorbance of the water and the dialysis pocketbook membrane material in the calculations, but did not account for potential cloud cover or shading from the walls of the tank. However, both treatments (surface and depth) experienced the same cloud cover weather over the course of the experiment.

H2o filtration

We transported the sampled dialysis bags on ice to the laboratory for filtering. Each sample (northward = 48) was vacuum-filtered through a 0.22 μm pore size (47 mm diameter) track-etched polycarbonate filters (Nucleopore Track-Etch Membrane, Whatman, GE Healthcare Bio-Sciences, Pittsburgh, PA) using 250 ml disposable belittling test filter funnels filled twice (ThermoScientific, Waltham, MA) to capture eDNA. Some samples (13 of 48) clogged the filters before all 500 ml could laissez passer through, resulting in <500 ml of water filtered (S3 Table). Filtration blanks were created (northward = 7) by filtering 50 ml of molecular-biological science-form water (Sigma Aldrich, St. Louis, MO) in the aforementioned manner as water samples to check for contamination during filtration. Filters were immediately placed in sterile five ml sterile polypropylene ship tubes, and stored at -20°C for the length of the experiment, and then transported back to Stanford University and stored at -fourscore°C until extraction within 6 months of collection.

Laboratory environment

Deoxyribonucleic acid extraction and molecular work was performed at Stanford University. Benchtops were cleaned with 10% bleach for x minutes and and then wiped with 70% ethanol. Benchtops were wiped with RNASE AWAY before beginning molecular work. Pipettes were wiped with RNASE AWAY and UV-irradiated for at to the lowest degree 10 minutes before use. Deoxyribonucleic acid extractions were performed on one bench, PCR preparation was performed in a designated Dna-costless hood, PCR amplification was performed in a separate room in the laboratory, and post-PCR work was performed in yet another separate room.

DNA extraction and inhibition testing

DNA was extracted from the archived filters in 4 sets, adding in an extraction blank (northward = iv, extraction reagents added to an empty five ml tube with no filter) for each set in addition to the experimental samples (north = 48) and filtration blanks (n = 7). Samples were randomized prior to extraction. We extracted DNA from each filter using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) with a modified lysis step (S1 Text). To increase DNA yield, nosotros performed 2 elutions of fifty μL each for a total excerpt volume of 100 μL. We immediately quantified full DNA using a QUBIT fluorometer two.0 (Life Technologies, One thousand Island, NY) and stored extracts at -twenty^o C until distension inside 6 months of extraction. DNA extracts were then used for the post-obit analyses: qPCR analysis using Southward. japonicus analysis and mitochondrial 12S rRNA amplicon metabarcoding.

Before PCR or qPCR amplification, we tested a subset of Deoxyribonucleic acid extracts for the presence of PCR inhibitors using serial dilutions (meet S1 Text for methods and S2 Text for results) [57]. Based on the results of each inhibition test, we did not dilute DNA extracts for the Due south. japonicus qPCR distension, but nosotros did dilute extracts one:10 before the conventional PCR distension used for eDNA metabarcoding.

Scomber japonicus qPCR assay

We used a recently published qPCR primer set and probe for Pacific Chub Mackerel (S. japonicus) targeting the cytochrome c oxidase subunit I (COI) gene [45]. The primers/probe sequences were: F five' GCTGAACAGTTTATCCTCCCCTCG 3', R 5' CCCAAGGATTGAGGAAACACCTGCTAG 3', P 5'-FAM-TGGGAACCTGGCACACGCCGGG-BHQ. Each DNA extract was amplified in the following 20 μL reaction: Taqman Universal Mastermix 2 (1x), 0.2 mg/ml bovine serum album (BSA), forwards and contrary primer (0.6 μM), probe (0.ane μM), 2 μL of Deoxyribonucleic acid extract, and molecular-biology-grade water (Sigma-Aldrich, St. Louis, MO). Cycle temperature parameters are given in Sassoubre et al. [45]; the initial step is 95°C for x min, followed by 40 cycles of 95°C for 15 southward and threescore°C for ane min. The cycle quantification (Ct) threshold was set to 0.01. Each PCR plate included 3 no template controls (NTCs) with molecular grade water added to the reaction in lieu of DNA extract.

We used standards constructed from genomic Deoxyribonucleic acid (gDNA) extracted from South. japonicus tissue using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA). Nosotros quantified DNA extracted using a QUBIT fluorometer 2.0 (Life Technologies, Grand Island, NY). We ran standards in triplicate along with samples in each PCR plate at the post-obit concentrations: 200 pg per reaction, twenty pg per reaction, two pg per reaction, 0.two pg per reaction, and 0.02 pg per reaction. Standard curve data were pooled and used together to create a regression of DNA concentration per reaction versus Ct to calculate concentrations of unknown samples. The concentrations of unknowns were converted from mass Dna per reaction to mass Dna per book of water filtered (S3 Table) using dimensional assay. Nosotros set the limit of quantification at the lowest concentration of a known standard that all three triplicates were consistently assigned a Ct value. Environmental samples with Ct values assigned higher than the average Ct value for the lowest reliably amplified standard were accounted as below the limit of quantification and removed from further analysis.

Data analyses for qPCR results

Disappearance of S. japonicus eDNA in the dialysis numberless was modeled using a beginning club decay model: dC/dt = -kC, where C is the concentration of S. japonicus eDNA in mass per volume of h2o filtered, t is time, and one thousand is the first society decay rate constant in units of 1/time. To calculate the first guild disuse rate constant and its standard mistake (SE), nosotros fit a straight line to ln(C/C ₀ ) versus time using linear regression in R [58]. We used the average of the concentrations of the T₀ samples as C₀. We performed a z-test with α = 0.05 to test the null hypothesis that the S. japonicus eDNA charge per unit constant derived from samples at the surface is the same as the rate abiding derived from samples nerveless at depth. The z-statistic was generated with the equation . If |z| > 1.96, the null hypothesis was rejected.

eDNA metabarcoding

In addition to the dialysis bag and source water (T₀) samples (northward = 48), filtration blanks (n = seven), and extraction blanks (n = 4), we added two different positive controls in triplicate (due north = six) to the eDNA metabarcoding analysis. The two positive controls used were (i) genomic DNA extracted from swordfish tissue (Xiphias gladius) and (ii) a mock community with equal mass per volume of DNA from 9 species of bony fishes (S4 Table). The mock community, and the methods used to create information technology, is described in more than detail elsewhere [59].

We used a two-pace PCR method [threescore] to dilate a fish-specific fragment of the 12S rRNA gene in the extracted eDNA as well every bit add a unique tag to each sample. The method is described in detail in Andruszkiewicz et al. [61]. Briefly, Dna extract (diluted ane:ten, run into "Inhibition testing") from each sample (northward = 65 from 48 ecology samples, half-dozen positive controls, 7 filter blanks, and 4 extraction blanks) was amplified with the published fish-specific primers targeting a hypervariable region of the mitochondrial Dna 12S rRNA gene [62]. The primer sequences were F-five' GTCGGTAAAACTCGTGCCAGC and R-five' CATAGTGGGGTATCTAATCCCAGTTTG, amplifying a ca 170 bp region. Thermal conditions for the first PCR amplification were 95°C for five min followed by 40 cycles of 95°C for fifteen s, 55°C for 30 s and 72°C for 30 due south. Each excerpt was amplified in triplicate using eight-strip PCR tubes with private caps to prevent cross contamination. A no template control (NTC) using molecular-biology-grade water in lieu of DNA template was added (i per extract as each has its own tagged primers) to monitor for contamination. The replicate products from the first PCR generated from each extract were pooled, visualized on a gel, dewdrop-cleaned using the Agencourt AMPure XP bead organisation (Beckman Coulter, United states of america), and then used as template in a PCR that used the aforementioned primers listed higher up merely with the add-on of vi bp tags on the 5' ends of both the forrard and reverse primer (S5 Table). Thermal conditions for the second PCR distension were 95°C for 5 min followed by twenty cycles of 95°C for fifteen due south, 57°C for xxx s and 72°C for 30 southward. Products from the second PCR were again pooled, visualized, bead-cleaned, and quantified using a QUBIT fluorometer two.0 (Life Technologies, Grand Island, NY). Samples not showing amplification on the gel visualization after the second PCR amplification were not carried on to library preparation, with the exception of the negative controls (filter blanks, NTC, and extraction blanks), which were carried over despite the fact that none of them showed distension based on gel-visualization. In total 44 of the 48 experimental samples, the 6 positive controls, the vii filter blanks, the iv extraction blanks, and 1 representative NTC (all of the NTCs combined) were prepared for sequencing (due north = 62).

The tagged products from the second PCR amplification were combined into 3 pools by adding 50 ng of DNA from each amplicon. Then 250 ng of each of those 3 pools was used as input into the KAPA Hyper Prep Kit (KAPA Biosystems, Wilmington, MA) to create 3 libraries; each library had a unique Nextflex DNA barcode (BIOO Scientific, Austin, TX) ligated on during library preparation. The 3 libraries were then combined with an equal mass of eDNA (100 ng per library) into a unmarried tube. The last concentration of the 3 combined libraries was 10.eight ng/μL. The size and concentration of the combined libraries was confirmed using a Bioanalyzer with High Sensitivity DNA assay (Agilent Technologies, Santa Clara, CA) before sequencing on an Illumina MiSeq platform at the Stanford Functional Genomics Facility (2x250 paired-end sequencing with a 20% Phi-X spike-in control).

Bioinformatics and statistical testing

Bioinformatic analyses were conducted within a Unix shell script described in detail elsewhere [sixty]. Briefly, we merged paired end reads, quality filtered, demultiplexed tagged reads, removed primers, and clustered sequences into OTUs with a cluster radius of i (S1 Text). A representative sequence from each OTU was compared to sequences deposited in the National Center for Biotechnology Data (NCBI) nucleotide (nt) database (downloaded iv Jan 2017) using Nail+ (2.2.31+) [63] to annotate OTUs. The following parameters were used: percent identity = 97%, word size = 30, e value = 1e-20. The authors who developed the MiFish-U primers as well as other published marine metabarcoding studies [62,64] use a 97% identity cut-off, justifying this option. We used the "taxize" packet in R [65] to summarize the BLAST+ results and comment OTUs to the entry with the lowest e-value and highest percent identity to assign taxonomy. If multiple entries in the nt database matched an OTU with the same percent identity and e-value, we used the lowest mutual taxonomic rank to comment the OTU. We removed reads from OTUs annotated as non-vertebrates and non-marine vertebrates. Experimental samples and positive controls were so rarefied to 40,000 reads using the "rrarefy" role in the R package vegan [66] in order to account for unequal sequencing depths; this number appears to be sufficient based on the rarefaction bend (S2 Fig).

We modeled the presence of genera identified using eDNA metabarcoding using a binary logistic generalized estimating equation (GEE). A GEE was required because genera presence was a repeated measure over time. The GEE was used to test two null hypotheses: (1) the odds of the 8 genera being present does non change over time, regardless of sampling depth; and (2) the depth treatment (surface versus depth) does not affect the rate of genera disappearance [67]. The eight genera were defined equally clusters and the model produced estimates of population-averaged trends rather than trends for whatsoever one genus. If a genus was present in at to the lowest degree one of the biological replicate samples at a given time point, we defined is as nowadays; otherwise, information technology was absent-minded. We implemented the GEE in R using the gee bundle [68] and set the correlation structure as exchangeable. Sampling depth (binary), time (continuous), and the interaction between sampling depth and fourth dimension were predictors in the model. Fourth dimension was converted to units of days to facilitate interpretation of the model coefficients. The assumption of the logit varying linearly over time was evaluated graphically. Robust standard errors and z-values were used to evaluate statistical significance of model coefficients, with p < 0.05 defined as significant.

We used a Mantel examination (implemented using the vegan packet in R) to investigate the relationship between vertebrate marine customs composition and time. The similarity matrix for the vertebrate customs was synthetic using the Jaccard distance betwixt each sample; the temporal distance matrix was calculated using the difference between sampling times. Finally, we investigated if community limerick at each fourth dimension was affected by sampling depth using an ANOSIM (implemented using the vegan package in R) with the Jaccard altitude matrix and sampling depth as the factor. We considered results statistically significant if p <0.05.

Results

Scomber japonicus eDNA disuse

The Southward. japonicus qPCR assay had an average efficiency of 92.seven%, and a limit of quantification of 0.1 pg/μL of DNA extract. The average Ct for the lowest reliably amplified standard (0.2 pg per reaction) was 38.15. All extraction blanks, filter blanks, and NTCs had undetermined Ct values indicating no contamination. C₀ was 0.63 pg DNA/ml seawater. South. japonicus eDNA suspended at the surface of the tank had a decay rate constant of 0.039 +/- 0.0031 hr ^-1 (unadjusted R² = 0.89) and Southward. japonicus eDNA suspended at depth had a decay rate constant of 0.038 +/- 0.0029 60 minutes ^-1 (unadjusted Rⁱⁱ = 0.90) (Fig 2). The null hypothesis that the bags at surface and depth have the aforementioned decay rate constant was not rejected using a z-test with α = 0.05 (|z| = 0.28). Based on our calculations (S1 Text, S1 Fig), the bags at the surface received 0.031 W/thou²/day UVB and 3.8 W/chiliadⁱⁱ/mean solar day UVA+UVB radiation, and the numberless at depth received 0.0093 Due west/m²/day UVB and 1.6 W/1000²/mean solar day UVA+UVB radiation.

Fig 2. S. japonicus eDNA concentration (pg/ml seawater) as a part of time (hours) at surface (solid squares) and depth (open up circles).

Mistake bars stand for standard difference of triplicate qPCR reactions; triplicate samples shown as carve up symbols at each fourth dimension point (four replicates for T6 surface and T7 depth, i replicate for T7 surface). Some error confined are small and hidden by overlapping symbols.

https://doi.org/ten.1371/journal.pone.0185043.g002

eDNA metabarcoding

The MiSeq run produced 14,928,120 reads beyond environmental samples and positive controls, of which 92.57% had a Q score ≥30. After merging paired finish reads, quality filtering, removing tags and adapters, and removing singletons, iii,962,266 reads remained (median per sample: 85,578; range per sample: 44–345,762) in the 44 experimental samples. These reads clustered into 544 OTUs, of which 233 were annotated and 311 were not. Of the annotated OTUs, 23 OTUs were annotated to not-vertebrates (e.chiliad., Saccharophagus degradans) or non-marine vertebrates (east.grand., Canis lupis or Man sapiens). Subsequently removing OTUs annotated to not-vertebrates or non-marine vertebrates, iii,961,832 sequences remained in the experimental samples (median per sample: 85,544; range per sample: 36–345,756) comprising 521 OTUs. Due to diff sequencing depths, samples were rarefied to forty,000 reads. 39 of the 44 sequenced experimental samples had >forty,000 reads. The 5 experimental samples with less than forty,000 reads (T7-DA, T7-DC, T7-DD, T7-SA, T7-SB, run into S3 Table for naming convention) were removed from subsequent analyses.

Negative controls (filter blanks, extraction blanks, representative NTC) had a full of 6,574 sequencing reads earlier rarefaction (median: 84; range: 15–four,682). The majority of reads in the negative controls (86%) were annotated to C. lupis. After removing reads from OTUs assigned to non-vertebrates or non-marine vertebrates, only 534 reads remained in all of the negative controls (median per sample: 36; range per sample: 6–123). Considering of the 3 orders of magnitude deviation in number of reads in experimental samples and negative control samples after removing non-vertebrates and non-marine vertebrates, we conclude that our reads from our negative controls can be considered negligible.

The two positive controls (swordfish tissue and mock community) had simply 1 read out of 240,000 (6 x 40,000) that was non from the gDNA used to make the controls (S4 Table). For the mock customs, although equal mass of each of the 9 taxa DNA extracts was added (S4 Table), the relative proportions of sequencing reads assigned to each taxon were not equal (range: 0% for Paralichthys to 30% for Seriola; not including not annotated OTUs). Based on these results, nosotros chose to examine the eDNA metabarcoding data in a binary (presence/absence of OTU) rather than quantitative style.

Later on rarefaction and removal of data from positive controls, the 39 experimental samples had i,560,000 reads (39 x 40,000) assigned to 464 OTUs (190 annotated, 274 not annotated). The 190 annotated OTUs were assigned to unlike taxonomic ranks: 47 received species level annotation, 140 received genus level annotation, and three received subfamily level annotation. Several OTUs were assigned to the same taxon; the 190 OTUs include annotations from i unique subfamily, 8 unique genera, and 12 unique species level annotations (S6 Table). For example, 86 unique OTUs were all assigned to the genus Scomber. The 190 annotated OTUs include 99% of rarefied reads, pregnant that only one% of reads were assigned to the 274 OTUs that were not annotated.

Four OTUs account for the majority of rarefied sequencing reads in the experimental samples. Together, they represent >97% of rarefied reads across all time points (range per sample: 90.3% - 99.9%). The four OTUs were amid those receiving an annotation and were assigned to Scomber, Sardinops, Sebastes, and Sebastes, respectively (Table 2). The two OTUs annotated to Sebastes are singled-out (i.e., they were separated every bit distinct during the OTU clustering process), but annotated by the software to the same genus. They may stand for different species.

We examined how the detection of dissimilar genera varied over the time course of the experiment (see S2 Text and S3 Fig for the aforementioned analysis at the species level). This necessitated just considering OTUs annotated to at least the genus level (which meant nosotros excluded 0.91% of the rarified reads, either assigned to the subfamily level or non annotated based on our criteria). In that location were 8 unique genera identified in our experiment over all time points. Some of these genera are present at each time indicate (because surface and depth together, Sardinops, Scomber) while some are present at a subset of time points (Fig 3). The number of time points that each genus was detected at scales with the number of reads assigned to that genus at T₀ (Spearman rho: n = 8, rho = 0.92, two-tailed p = 0.0014; Fig 3). All 8 genera were detected at T_ane despite only 4 genera being detected at T₀.

Fig three. Genera identified as nowadays using eDNA metabarcoding over the grade of the experiment.

Solid squares bespeak presence of the genus in at to the lowest degree i biological replicate from surface samples; open circles point presence of the genus in at least 1 biological replicate from depth samples.

https://doi.org/x.1371/journal.pone.0185043.g003

We used a GEE to investigate whether the presence of genera depended on the time since the beginning of the experiment, sampling depth of the experiment, or their interaction (whether the touch of time differed past sampling depth). Nosotros found no evidence to suggest that detection of genera depended on sampling depth at T₀ (β = 0.054, p > 0.05). The interaction term between depth and time was not significant (β = -0.13, p > 0.05), which means that sampling depth did not statistically touch the rate of genera disappearance. Presence of genera was negatively associated with time (β = -0.35, p < 0.05); the corresponding odds ratios were 0.lxx for surface and 0.62 for depth, indicating that the odds of genera beingness nowadays decreased with an increase in fourth dimension (Table 3). We also included a GEE model for the presence of species during the experiment, which produced the aforementioned results (S2 Text, S7 Tabular array).

We used a Mantel test to explore the association between fish customs composition, as inferred from eDNA metabarcoding, and time. Fish community composition was more similar the closer samples were collected in fourth dimension (r = 0.32, p = 0.001). When separated past surface samples and depth samples, the associations remained statistically meaning (surface: r = 0.37, p = 0.001; depth: r = 0.38, p = 0.002). We found that there was no significant difference betwixt community composition at a specific time between sampling depths using ANOSIM (R = 0.031, p = 0.thirteen).

Discussion

The concentration of Southward. japonicus eDNA, every bit measured by qPCR, declined over the elapsing of the experiment. Disuse was first order with a rate constant of ~0.01 h^-one in both surface and depth treatments. This charge per unit constant is of the same society of magnitude every bit that obtained past Sassoubre et al. [45], despite the difference in experimental design. Sassoubre et al. [45] quantified disuse in a shaded, airtight, batch system, whereas the nowadays study quantified decay in a sunlit, open system where solutes could freely diffuse across dialysis membranes. The accordance of our decay rate constants and those of Sassoubre et al. [45], as well as the lack of difference between disuse charge per unit constants at surface and depth, advise that sunlight was not important in controlling eDNA decay. This is farther supported by the eDNA metabarcoding data; the odds of detecting genera were not affected by the depth of the experimental handling and customs composition at a specific time point did not vary with depth. Only i other study has looked at persistence of fish eDNA in marine water and quantified decay rates [69] merely does not include any information on the mechanisms of disuse.

The outcome of sunlight on fish and amphibian eDNA decay in freshwater systems has been investigated in previous studies, but no clear result has been determined [51,52,54]. Strickler et al. [52] investigated bullfrog eDNA disuse in freshwater and establish no effect of sunlight. Pilliod et al. [51] found that salamander eDNA rust-covered faster in sunlit than in shaded freshwater but could not carve up water temperature effects from sunlight effects. Finally, Merkes et al. [54] determined that there was no correlation betwixt UV index and persistence in a study using silver bother eDNA in freshwater. No studies accept investigated the touch of sunlight on decay of fish eDNA in marine waters. Over the course of our experiment, the surface handling received >3 times more UVB energy and >2 times more than UVA+UVB free energy than the deep treatment. The lack of difference in decay rate constants despite the departure in UV energy implies that stressors other than sunlight probable contribute eDNA disuse including leaner, grazers and enzymes [46,47,53,seventy,71]. Future research should investigate these other mechanisms as well equally the threshold at which eDNA decay would be impacted by UVA+UVB exposure for applications in other locations (well-nigh the equator for example) where solar intensity may be stronger.

The eDNA metabarcoding-derived demography of fish was not stable over the grade of the experiment. Fish community composition varied with time and the odds of detecting specific genera decreased with time. These results suggest that information on fish community composition gleaned from eDNA metabarcoding approaches may depend on how much time has elapsed since fish shed eDNA. This is likely due to the eDNA decaying over fourth dimension, which affects its ability to exist amplified by the primers and later sequenced. These results, in combination with the decay observed in Due south. japonicus eDNA by qPCR, indicate that information obtained from fish eDNA may be best interpreted when additional information on eDNA age (fourth dimension elapsed since eDNA shedding) is available. Further piece of work to elucidate how diverse physical, chemic and biological processes affect the shedding, disuse, advection and dispersion of eDNA in the ocean is needed to link eDNA measurements (from qPCR and metabarcoding) to actual fish numbers.

Our findings underscore that eDNA metabarcoding data obtained using the methods outlined herein should be interpreted in terms of presence/absence rather than quantitatively. The equal-mass of Dna from various fish in the mock community command did non yield equal proportion of reads assigned to each taxon in the mock community. Though we cannot speculate on why this occurs, some considerations are primer biases, PCR biases, and different sources of the eDNA in the sample [18,26,32,37,72,73]. Previous studies have shown correlation between biomass or population numbers and organism-specific eDNA concentrations as measured past qPCR [70,74–78], mostly in freshwater systems, and more recently a few studies advise links betwixt eDNA metabarcoding reads and fish abundance in aquatic systems [79,fourscore]. However, other studies have not found a link between organism affluence and eDNA concentrations [81,82]. Nosotros decided non to compare the results for Scomber measured past qPCR and NGS in this paper considering (1) the assays targeted dissimilar genes and (two) qPCR yields a concentration while NGS yields a proportion. We admit the need for further research on linking qPCR concentrations and number of reads from eDNA metabarcoding. Our results indicate more research is needed to fully sympathise eDNA affluence and the connection to fish affluence, including time since shedding from an organism.

The nowadays study used open, sunlit experimental systems to investigate the persistence of fish eDNA from Monterey Bay, California. Nosotros showed that over 86 hours, the data inferred from fish eDNA changes, in some cases essentially. This indicates that the time elapsed since eDNA was shed past organisms will be an important variable in linking data from eDNA to actual fish counts. Further work on establishing a modeling framework for interpreting information inferred from eDNA is needed.

Supporting information

S1 Fig. Solar intensity versus time for 18 October 2015.

Pinnacle panels are at 5 cm beneath water surface (black squares), bottom panels are at seventy cm below water surface (open circles). Left panels only account for UVB solar intensity (Due west/m²), right panels account for UVA+UVB solar intensity (W/m²).

https://doi.org/10.1371/periodical.pone.0185043.s003

(TIFF)

S3 Fig. Species establish in eDNA metabarcoding rarefied reads over time.

Solid squares indicate presence of the species in at least 1 biological replicate from surface samples; open up circles indicate presence of the species in at least 1 biological replicate from depth samples.

https://doi.org/10.1371/journal.pone.0185043.s005

(TIFF)

S1 Table. Atmospheric condition over the form of the experiment.

Obtained from the National Climatic Information Center's Climate Information Online (www.ncdc.noaa.gov); weather station located at Monterey Airport (36.588°North, -121.85°E), approximately 5 miles southeast of experiment tank. Time is in Pacific Standard Time (PST). Air temperature is measured in °C. Atmospheric precipitation is measured in cm; T indicates "trace" precipitation, which means precipitation has been detected but insufficient for meaningful measurement.

https://doi.org/x.1371/journal.pone.0185043.s006

(XLSX)

S4 Tabular array. Limerick of mock customs used as positive control and eDNA metabarcoding results of positive controls.

List of 9 taxa from which extracts of gDNA were added in equal mass (200 ng) to the mock community. eDNA metabarcoding results of rarefied sequencing reads for the three replicates of the mock customs and the 3 replicates of the swordfish gDNA extract included as positive controls. Colors betoken the ix taxa included in the mock community and the different taxon found in the eDNA metabarcoding results corresponding to the 9 original taxa.

https://doi.org/10.1371/journal.pone.0185043.s009

(XLSX)

S7 Table. Results of GEE model on presence of 12 species over time.

Dependent variable is presence of species. Sampling depth is a binary variable with values of 0 for surface (v cm below water surface) or i for depth (seventy cm below water surface). Time is a continuous variable and is measured in days since the offset of the experiment. Sampling depth:time is the interaction term. * indicates p > 0.05.

https://doi.org/10.1371/journal.pone.0185043.s012

(XLSX)

Acknowledgments

We thank Raylan Willis for his assistance with fix up of the experiment and filtration of samples, Karl Mayer and Andy Johnson from the Monterey Bay Aquarium for access to the tank used in the experiment and supplemental information, Wiley Jennings for his assistance with statistical analyses, and Ryan Kelly for providing comments on a previous version of the manuscript.

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Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185043

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