MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species

Abstract

We developed a set of universal PCR primers (MiFish-U/E) for metabarcoding environmental DNA (eDNA) from fishes. Primers were designed using aligned whole mitochondrial genome (mitogenome) sequences from 880 species, supplemented by partial mitogenome sequences from 160 elasmobranchs (sharks and rays). The primers target a hypervariable region of the 12S rRNA gene (163–185 bp), which contains sufficient information to identify fishes to taxonomic family, genus and species except for some closely related congeners. To test versatility of the primers across a diverse range of fishes, we sampled eDNA from four tanks in the Okinawa Churaumi Aquarium with known species compositions, prepared dual-indexed libraries and performed paired-end sequencing of the region using high-throughput next-generation sequencing technologies. Out of the 180 marine fish species contained in the four tanks with reference sequences in a custom database, we detected 168 species (93.3%) distributed across 59 families and 123 genera. These fishes are not only taxonomically diverse, ranging from sharks and rays to higher teleosts, but are also greatly varied in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. We also sampled natural seawaters around coral reefs near the aquarium and detected 93 fish species using this approach. Of the 93 species, 64 were not detected in the four aquarium tanks, rendering the total number of species detected to 232 (from 70 families and 152 genera). The metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods. It has the potential to serve as an alternative (or complementary) tool for biodiversity monitoring that revolutionizes natural resource management and ecological studies of fish communities on larger spatial and temporal scales.

1. Introduction

Environmental DNA (eDNA) in aquatic environments refers to genetic material found in the water column. In the case of multicellular organisms, eDNA originates from various sources, such as metabolic waste, damaged tissue or sloughed skin cells [1]. Ficetola et al. [2] was the first study demonstrating the use of eDNA for detecting an aquatic vertebrate species (invasive American bullfrog) from controlled environments and natural wetland, published in 2008. Subsequently, eDNA from fishes has been detected from various aquatic environments, including ponds [3–5], streams [6], rivers [7–10] and seawater [11,12]. Such ubiquitous presence of eDNA from fishes in the water column has led to the increasing use of this technique as a tool for detections of invasive [3,7–9], rare or threatened species [5,6], investigations of local fauna [10,13], or in a larger mesocosm [12] with known species composition. These pioneering studies have shown the use of eDNA to be appropriate as a non-invasive genetic monitoring tool in various fields of fish biology.

For monitoring the occurrence of a single or few fish species, short species-specific eDNA fragments (72–312 bp) have been used [3,5–9], with earlier studies detecting those species based on the presence/absence of PCR products by visually inspecting the products on an agarose gel stained with ethidium bromide [7–9]. More recently, quantitative PCR (qPCR) using probe-based chemistries has been employed for the detection of target species [3–6] owing to the method's sensitivity, specificity and potential to quantify the target DNA [6]. For example, Takahara et al. [4] estimated the biomass of common carp (Cyprinus carpio) in a natural freshwater lagoon, using the qPCR approach (real-time PCR), based on the positive relationships between eDNA concentrations and biomass in aquaria and experimental ponds.

For monitoring fish assemblages with broader taxonomic scopes, Minamoto et al. [10] designed degenerate PCR primers to amplify a short fragment of the mitochondrial cyt b gene (285 bp) with reference to those sequences from the local freshwater fish fauna. Based on PCR amplification of the fragment and subsequent subcloning and sequencing of the product, they successfully detected multiple species in eDNA from the controlled aquaria (one to five spp.) and three stations in the Yura River, central Japan (two to four spp.) [10]. Thomsen et al. [11] developed two generic and four species-specific PCR primer sets for amplifying short fragments of the cyt b gene (32–51 bp), in order to detect marine fish species from three sampling sites at a coastal zone in Denmark. Using a next-generation sequencing (NGS) platform (Roche 454 GS FLX), they detected 15 species in the amplicons, including both important commercial fishes as well as some species rarely recorded by conventional monitoring methods [11]. More recently, Kelly et al. [12] attempted to estimate the fish fauna in a large tank at the Monterey Bay Aquarium with known species composition by sequencing PCR amplicons from eDNA using an NGS platform (Illumina MiSeq). They used a set of published universal PCR primers to amplify a 106 bp fragment of the mitochondrial 12S rRNA gene [14] for metabarcoding fish species in the tank. Although they detected seven of the eight species of bony fishes present, they were able to identify those species only to taxonomic family or genus owing to the limited sequence variability within the amplicons. In addition, they failed to detect all three elasmobranchs (sharks and rays) contained in the tank [12].

These earlier studies on eDNA metabarcoding (high-throughput multispecies identification using degraded DNA extracted from an environmental sample [15]) have shown both potential and limitations. They are non-invasive and are demonstrably more efficient and cost-effective than the traditional monitoring methods, such as visual surveys, trawls and seines [11,12]. The former two studies [10,11], however, required development of PCR primers specifically designed with reference to DNA sequences from the known local fish fauna and those primers are of limited uses in future studies with little prior knowledge on the faunal composition. The latter study [12] employed PCR primers that have been developed using the computer software ‘ecoPrimers’ [14] and that are supposedly universal among vertebrates. Despite the use of universal primers, the successful detection in the aquarium tank was dependent on the taxonomic groups (e.g. no detection for ocean sunfish and all elasmobranchs), and the amplified products, if any, exhibited little sequence variability to correctly assign fish species in the same family or genus [12].

The primary objective of this study was to circumvent these problems associated with PCR primers. To achieve this goal, we: (i) developed universal primers for fish eDNA that amplify a short fragment (less than 200 bp) containing sufficient sequence variation to correctly assign fish species; (ii) tested versatility of the primers across a taxonomically and ecologically diverse range of fishes using eDNA from aquarium tanks with known species compositions; and (iii) preliminarily examined the use of the primers for detecting eDNA from fishes inhabiting natural seawater environments with unknown species composition and abundances in an open ecosystem.

The development of the universal primers (MiFish-U/E) was based on the aligned whole mitochondrial genome (mitogenome) sequences from 880 fish species, which was supplemented by partial mitogenome sequences from 160 elasmobranchs. The primers are targeted to amplify a hypervariable region of the 12S rRNA gene (163–185 bp), which contains sufficient information to unambiguously identify fishes we tested to taxonomic family, genus and species, with one exception (closely related congeners of Thunnus). We tested the versatility of those PCR primers using eDNA from four tanks in the Okinawa Churaumi Aquarium and from natural seawaters near the aquarium in the subtropical western North Pacific. Using a high-throughput Illumina MiSeq platform, we detected eDNA from 232 fish species from those seawaters, which are taxonomically diverse and are distributed across 70 families and 152 genera. In addition to eDNA, this metabarcoding approach is applicable to bulk samples (total DNA), such as those from net collections containing a diverse range of fish eggs, larvae, juveniles or damaged specimens with few diagnostic characters present for species identification.

2. Material and methods

2.1. Primer development

2.1.1. Selection of genetic marker

Mitochondrial DNA (mtDNA) was chosen as the genetic marker because copy number of mtDNA is greater than that of nuclear DNA per cell, and detection rate therefore is expected to be higher in the former, even where DNA is present at a low concentration and/or is degraded [16]. In order to select a suitable region in the mitogenome for species identification based on eDNA, 1044 whole mitogenome sequences were batch downloaded from the database MitoFish v. 2.80 [17] in a FASTA format as of 20 April 2013. After removing problematic sequences involving large-scale gene rearrangements [18], the remaining 880 sequences (electronic supplementary material, table S1) were subjected to multiple alignment using MAFFT v. 6.956 [19] with a default set of parameters. The aligned sequences were imported into Mesquite v. 2.75 [20] for visual inspection of the conservative and hypervariable regions. The search for a short hypervariable region (up to 200 bp for paired-end sequencing using the Illumina MiSeq) flanked by two conservative regions (ca 20–30 bp) across 880 species was performed on the entire set of aligned mitogenomes. The conservative and hypervariable regions were highlighted by a ‘Select’ function in Mesquite (a submenu ‘Variable among taxa’ in ‘Select Characters’) [20].

2.1.2. Primer design

To facilitate primer design based on comparisons of diverse sequences from 880 fish species, a base composition for a selected position in the conservative region was shown using a ‘Show Selection Summary Strip’ function in Mesquite [20]. The base compositions in selected characters were manually recorded in a spreadsheet for the primer design. In the primer design process, we considered a number of technical tips that enhance the primer annealing to the template without the uses of degenerate bases [21]: primers include some G/C at the 3′-ends to strengthen primer–template annealing at this position, but a string of either Gs or Cs at the 3′-end should be avoided; considering the unconventional base pairing in the T/G bond, the designed primers use G rather than A when the template is variably C or T, and T rather than C when the template is A or G; G/C contents of the primers fall between 40 and 60% with an almost identical melting temperature (T_m). T_m was calculated using a nearest-neighbour thermodynamic model implemented in OligoCalc [22].

The first universal primers for eDNA were designed on the 12S rRNA gene (for details, see Results and Discussion) and were named MiFish-U-F/R (with overhang adapter sequences for library preparation; U, F and R represent universal, forward and reverse, respectively). In addition, we had to design MiFish-E-F/R to accommodate sequence variations in the priming sites of elasmobranchs (E), with the primer designs based on newly assembled partial mitogenome sequences from 160 species (electronic supplementary material, table S2). For more accurate species assignments within closely related congeners, we also designed genus-specific primers that amplify a different mitogenomic gene (ND5) with significant variations across constituent species (e.g. MiFish-tuna).

2.1.3. Primer testing with extracted DNA

In order to test whether these newly designed PCR primers were universal or not, we first tested MiFish-U-F/R (no adapter sequences) using extracted DNA from 96 species representing all the four major lineages of fishes (Agnatha, Chondrichthyes, Actinopterygii and Sarcopterygii) placed in 47 orders and 96 different families (table 1). Double-stranded DNA concentrations from those fishes were measured with a NanoDrop Lite spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and the extracted DNA was diluted to 15 ng μl⁻¹ using Milli-Q water. PCR was carried out with 30 cycles of a 15 μl reaction volume containing 8.3 μl sterile distilled H₂O, 1.5 μl 10×PCR buffer (Takara, Otsu, Japan), 1.2 μl dNTPs (4 mM), 1.5 μl of each primer (5 μM), 0.07 μl Taq polymerase (Z Taq; Takara) and 1.0 μl template. The thermal cycle profile after an initial 2 min denaturation at 94°C was as follows: denaturation at 98°C for 5 s; annealing at 50°C for 10 s; and extension at 72°C for 10 s with the final extension at the same temperature for 5 min.

Table 1.

A list of fish species for testing MiFish-U primers (without adapter sequences) using extracted DNA diluted to 15 ng μl⁻¹, subsequently sequenced with a Sanger method.

higher classification	family	species	common name	accession no.
Class Myxini
Order Myxiniformes	Myxinidae	Eptatretus burgeri	inshore hagfish	AB938082
Class Chondrichthyes
Subclass Holocephali
Order Chimaeriformes	Chimaeridae	Chimaera phantasma	silver chimaera	AB938084
Subclass Elasmobranchii
Subdivision Selachii
Order Carcharhiniformes	Triakidae	Mustelus griseus	spotless smooth-hound	AB938092
Order Squaliformes	Squalidae	Cirrhigaleus barbifer	mandarin dogfish	AB938108
Order Pristiophoriformes	Pristiophoridae	Pristiophorus japonicus	Japanese sawshark	AB938111
Subdivision Batoidea
Order Torpediniformes	Torpedinidae	Torpedo tokionis	trapezoid torpedo	AB938112
Order Rajiformes	Rhinobatidae	Rhinobatos schlegelii	brown guitarfish	AB974648
Class Actinopterygii
Subclass Cladistia
Order Polypteriformes	Polypteridae	Polypterus senegalus	grey bichir	AB969828
Subclass Chondrostei
Order Acipenseriformes	Acipenseridae	Huso dauricus	kaluga	AB969829
Subclass Neopterygii
Order Lepisosteiformes	Lepisosteidae	Atractosteus spatula	alligator gar	AB969830
Division Teleostei
Order Osteoglossiformes	Osteoglossidae	Osteoglossum bicirrhosum	arowana	AB969831
Order Elopiformes	Megalopidae	Megalops cyprinoides	Indo-Pacific tarpon	AB969832
Order Albuliformes
Suborder Notacanthoidei	Notacanthidae	Notacanthus chemnitzi	spiny eel	AB969833
Order Anguilliformes
Suborder Anguilloidei	Anguillidae	Anguilla marmorata	giant mottled eel	AB969834
	Muraenidae	Muraena pardalis	leopard moray eel	AB969835
Order Clupeiformes
Suborder Denticipitoidei	Denticipitidae	Denticeps clupeoides	denticle herring	AB969840
Suborder Clupeoidei	Clupeidae	Sardinella lemuru	Bali sardinella	AB969841
Order Gonorynchiformes
Suborder Chanoidei	Chanidae	Chanos chanos	milkfish	AB969842
Order Cypriniformes	Cyprinidae	Gnathopogon elongatus elongatus	Tamoroko gudgeon	AB969843
Order Characiformes
Suborder Characoidei	Characidae	Exodon paradoxus	bucktooth tetra	AB969844
Order Siluriformes	Bagridae	Pseudobagrus virgatus	Gibachi bagrid catfish	AB969845
Order Gyrnnotiformes	Gymnotidae	Gymnotus carapo	banded knifefish	AB969846
Order Argentiniformes
Suborder Argentinoidei	Argentinidae	Glossanodon semifasciatus	deep-sea smelt	LC020812
Order Osmeriformes	Osmeridae	Hypomesus japonicus	Japanese smelt	AB969847
Order Salmoniformes	Salmonidae	Oncorhynchus masousubsp.	masu salmon	AB969848
Order Esociformes	Esocidae	Esox americanus	redfin pickerel	AB969849
Order Stomiiformes
Suborder Gonostomatoidei	Gonostomatidae	Sigmops longipinnis	elongated bristlemouth fish	AB969850
Order Ateleopodiformes	Ateleopodidae	Ateleopus japonicus	Pacific jellynose fish	AB969853
Order Aulopiformes
Suborder Synodontoidei	Synodontidae	Saurida macrolepis	Ma-eso lizardfish	AB938170
Order Myctophiformes	Myctophidae	Diaphus watasei	Watases lanternfish	AB938172
Order Lampriformes	Trachipteridae	Trachipterus ishikawae	slender ribbonfish	AB938162
Order Polymixiiformes	Polymixiidae	Polymixia longispina	silver eye	LC020813
Order Percopsiformes	Percopsidae	Percopsis transmontana	sand roller	AB969861
Order Gadiformes	Macrouridae	Trachyrincus murrayi	roughnose grenadier	AB969865
	Gadidae	Theragra chalcogramma	Alaska pollock	AB969867
Order Ophidiiformes
Suborder Ophidioidei	Carapidae	Carapus bermudensis	pearlfish	AB969871
Suborder Bythitioidei	Bythitidae	Cataetyx rubrirostris	rubynose brotula	AB969872
Order Lophiiformes
Suborder Ogcocephalioidei	Ogcocephalidae	Chaunax abei	Japanese sea toad	AB969874
	Melanocetidae	Melanocetus murrayi	Murray's abyssal anglerfish	LC020814
Order Mugiliformes	Mugilidae	Chelon labrosus	thicklip grey mullet	AB969954
Order Atheriniformes	Atherinidae	Hypoatherina tsurugae	Gin-iso-iwashi silverside	AB974688
Order Beloniformes	Adrianichthyidae	Oryzias latipes	Japanese rice fish	AB969878
	Belonidae	Cypselurus pinnatibarbatus japonicus	Bennett's flyingfish	AB969879
Order Cyprinodontiformes	Poeciliidae	Xiphophorus maculatus	southern platyfish	AP005982
Order Stephanoberyciformes	Melamphaidae	Scopelogadussp.	bigscale	AB969880
Order Beryciformes
Suborder Berycoidei	Berycidae	Beryx decadactylus	alfonsino	AB969882
Order Zeiformes
Suborder Zeioidei	Zeniontidae	Zenion japonicum	Japanese dory	AB969885
Order Gasterosteiformes
Suborder Gasterosteoidei	Aulorhynchidae	Aulichthys japonicus	tubenose	AB969886
Order Synbranchiformes
Suborder Synbranchoidei	Synbranchidae	Synbranchus marmoratus	marbled swamp eel	AB972265
Order Scorpaeniformes
Suborder Scorpaenoidei	Scorpaenidae	Sebastes schlegelii	Korean rockfish	AB969888
	Tetrarogidae	Paracentropogon rubripinnis	Haokoze wasp fish	AB938167
	Peristediidae	Scalicus serrulatus	Kihoubou armored searobin	AB969898
Suborder Platycephaloidei	Platycephalidae	Platycephalussp.	Magochi flathead	AB969904
Suborder Cottoidei	Cottidae	Pseudoblennius percoides	sunrise	AB969909
		Hemitripterus villosus	shaggy sculpin	AB938165
	Cyclopteridae	Eumicrotremus pacificus	Fusen-uo lampfish	AB974680
	Liparidae	Careproctus rastrinus	salmon snailfish	AB974681
Order Perciformes
Suborder Percoidei	Moronidae	Lateolabrax latus	blackfin seabass	AB938173
	Serranidae	Epinephelus akaara	Hong Kong grouper	AB974679
	Opistognathidae	Opistognathus punctatus	finespotted jawfish	AB972248
	Priacanthidae	Pristigenys niphonia	Japanese bigeye	AB972242
	Apogonidae	Siphamia majimai	striped siphonfish	LC020815
	Carangidae	Selar crumenophthalmus	bigeye scad	AB938143
	Bramidae	Taractichthys steindachneri	sickle pomfret	AB938175
	Lutjanidae	Lutjanus kasmira	common bluestripe snapper	AB938146
	Lobotidae	Lobotes surinamensis	tripletail	AB972214
	Haemulidae	Parapristipoma trilineatum	chicken grunt	AB972213
	Nemipteridae	Nemipterus bathybius	yellowbelly threadfin bream	AB972211
	Lethrinidae	Gymnocranius griseus	grey large-eye bream	AB938151
	Sparidae	Acanthopagrus schlegelii	blackhead seabream	AB972186
	Sciaenidae	Boesemania microlepis	boeseman croaker	AB972206
	Mullidae	Parupeneus ciliatus	whitesaddle goatfish	AB972204
	Chaetodontidae	Chaetodon auripes	oriental butterflyfish	AB972196
	Pentacerotidae	Evistias acutirostris	striped boarfish	AB972192
	Terapontidae	Terapon jarbua	Jarbua terapon	AB972191
	Oplegnathidae	Oplegnathus fasciatus	barred knifejaw	AB972189
	Cheilodactylidae	Goniistius zonatus	spottedtail morwong	AB938161
Suborder Labroidei	Cichlidae	Thorichthys meeki	firemouth cichlid	AB972187
	Embiotocidae	Ditrema viride	Umi-tanago surfperch	AB969918
	Labridae	Cheilio inermis	cigar wrasse	AB972174
Suborder Zoarcoidei	Stichaeidae	Stichaeus grigorjewi	Nagazuka prickleback	AB972145
Suborder Notothenioidei	Eleginopidae	Eleginops maclovinus	Patagonian blennie	AB969976
Suborder Trachinoidei	Arnmodytidae	Ammodytes personatus	Pacific sandlance	AB969933
	Uranoscopidae	Xenocephalus elongatus	bluespotted stargazer	AB969930
Suborder Blennioidei	Blenniidae	Entomacrodus striatus	reef margin blenny	AB969913
Suborder Icosteoidei	Icosteidae	Icosteus aenigmaticus	ragfish	AB972142
Suborder Gobioidei	Gobiidae	Schismatogobius roxasi	Eso-haze goby	AB972140
Suborder Acanthuroidei	Scatophagidae	Scatophagus argus	spotted scat	AB969929
Suborder Scombroidei	Gempylidae	Lepidocybium flavobrunneum	escolar	AB972115
	Scombridae	Gymnosarda unicolor	dogtooth tuna	AB972114
Suborder Stromateoidei	Stromateidae	Pampus punctatissimus	Managatsuo butterfish	AB972108
Suborder Channoidei	Channidae	Channa argus	snakehead	AB972107
Order Pleuronectiformes
Suborder Pleuronectoidei	Paralichthyidae	Paralichthys olivaceus	bastard halibut	AB972104
	Cynoglossidae	Paraplagusia japonica	black cow-tongue	AB972088
Order Tetraodontiformes
Suborder Balistoidei	Monacanthidae	Chaetodermis penicilligera	prickly leatherjacket	AB972083
Suborder Tetraodontoidei	Tetraodontidae	Arothron hispidus	white-spotted puffer	AB972076

Double-stranded PCR products were purified using Exo SAP-IT (USB, Cleveland, OH, USA) to remove redundant dNTPs and oligonucleotides from primers. Direct cycle sequencing was performed with dye-labelled terminators (BigDye terminator v. 1.1; Applied Biosystems, Foster City, CA, USA) following the manufacturer's protocol and the purified PCR products were sequenced for both strands on the ABI 3130xl Genetic Analyzer (Life Technologies, Carlsbad, CA, USA). The DNA sequences were edited and assembled using GENETYX-MAC v. 17 (Genetyx, Tokyo, Japan) and deposited in DDBJ/EMBL/GenBank databases.

2.1.4. In silico evaluation of interspecific variation

Interspecific differences within the amplified DNA sequences are required for accurate assignments of taxonomic categories. To computationally evaluate levels of interspecific variation in the target region (hereafter called ‘MiFish sequence’) across different taxonomic groups of fishes, 1361 whole mitogenome sequences were batch downloaded from MitoFish v. 2.89 [17] as of 3 September 2014. After removing duplicate sequences (e.g. multiple sequences from subspecies), uncertain taxonomic status (e.g. hybrids) and possible erroneous sequences (e.g. unable to annotate using MitoAnnotator [17]), the MiFish sequences were extracted from the remaining 1324 sequences using custom Ruby scripts (available from: http://dx.doi.org/10.5061/dryad.54v2q) and they were subjected to calculation of pairwise edit distances. The edit distance quantifies dissimilarity of sequences in bioinformatics [23] and is defined as the minimum number of single-nucleotide substitutions, insertions or deletions that are required to transform one sequence into the other. For comparisons, metabarcode sequences amplified by 12S-V5 primers [14] (forward: 5′-ACTGGGATTAGATACCCC-3′; and reverse: 5′-TAGAACAGGCTCCTCTAG-3′) (hereafter called ‘ecoPrimer sequences’) were also extracted from the 1324 sequences and their interspecific variation was evaluated as described for MiFish sequences. The ecoPrimer pair amplifies the same gene (mitochondrial 12S rRNA gene) as that of the MiFish-U/E primers, but the two primer pairs are designed to amplify two different regions adjacent to each other (12S-V5-F primer is located within MiFish-U-R primer). The ecoPrimer pair was used in a metabarcoding study of fishes by Kelly et al. [12] who attempted to estimate an artificial fish fauna using eDNA in the large tank at the Monterey Bay Aquarium.

2.2. Primer testing with environmental DNA

2.2.1. Sampling sites

In order to test the versatility of the newly designed primers for metabarcoding eDNA from fishes, we sampled seawater from four tanks in the Okinawa Churaumi Aquarium, Okinawa, Japan (26°41′39′′ N, 127°52′41^′′ E; figure 1). The aquarium was chosen because of the remarkable taxonomic diversity of fishes contained in a variety of tanks that resemble surrounding environments in the subtropical western North Pacific. The four selected tanks; Kuroshio (water volume =7500 m³), tropical fish (700 m³), deep-sea (230 m³) and mangrove (35.6 m³) tanks (figure 1a–d) harbour diverse groups of fishes (ca 250 species) from elasmobranchs (sharks and rays) to higher teleosts that vary greatly in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. In addition to these four aquarium tanks, we also sampled seawaters from coral reefs nearby the aquarium (26°42′35^′′ N, 127°52′48^′′ E; figure 1e,f) to preliminarily examine the use of the primers for metabarcoding eDNA from natural environments with unknown fish composition and abundances in an open ecosystem.

Figure 1.

(a–d) Four tanks used for water sampling in the Okinawa Churaumi Aquarium and (e,f) a sampling site in the coral reefs near the aquarium: (a) Kuroshio (water volume =7500 m³); (b) tropical fish (700 m³); (c) deep-sea (230 m³); and (d) mangrove (35.6 m³) tanks; (e,f) sampling site in Bise (arrow; 26°42′35^′′ N, 127°52′48^′′ E) and the Okinawa Churaumi Aquarium (star; 26°41′39^′′ N, 127°52′41^′′ E).

2.2.2. Water sampling and DNA extraction

All sampling and filtering equipment was exposed to a 10% bleach solution for at least 30 min before use. For water samplings in the aquarium, approximately 10 l of seawater was collected from the surface using multiple casts of an 8 l polyethylene bucket fastened to a 10 m rope. The bucket was thoroughly prewashed with tank water. The sampling was conducted between 10.00 and 13.00 before daily feeding on two consecutive days (2 and 3 June 2014). The sampled water was stored in a valve-equipped 10 l book bottle and immediately brought to the laboratory before subsequent filtering. For water samples from the coral reefs near the aquarium, 10 l of seawater was collected in a similar manner on 4 June and 7 November 2014.

One to three 2 l lots of seawater from the 10 l samples were vacuum-filtered onto 47 mm diameter glass-fibre filters (nominal pore size, 0.7 μm; Whatman, Maidstone, UK). Each filter was wrapped in commercial aluminium foil and stored in −20°C before eDNA extraction. Two litres of Milli-Q water was used as the negative control and treated identically to the eDNA samples, to monitor contamination during the filtering and subsequent DNA extraction.

DNA was extracted from the filters using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) in combination with a spin column (EZ-10; Bio Basic, Markham, Ontario, Canada). After removing the attached membrane from the spin column (EZ-10), the filter was tightly folded into a small cylindrical shape and placed in the spin column. The spin column was centrifuged at 6000g for 1 min to remove redundant seawater for DNA extraction. The column was then placed in a new 2 ml tube and subjected to lysis using proteinase K. Before lysis, Milli-Q water (400 μl), proteinase K (20 μl) and buffer AL (180 μl) were mixed and the mixed solution was gently pipetted onto the folded filter in the spin column. The column was then placed on a 56°C preheated aluminium heat block and incubated for 30 min. The spin columns were covered with commercial aluminium foil and a clean blanket for effective incubation at the specified temperature. After the incubation, the spin column was centrifuged at 6000g for 1 min to collect the DNA. In order to increase DNA yields from the filter, 300 μl of sterilized TE buffer was gently pipetted onto the folded filter and the spin column was again centrifuged at 6000g for 1 min. The collected DNA solution (ca 900 μl) was purified using the DNeasy Blood and Tissue Kit following the manufacture's protocol.

2.2.3. Paired-end library preparation and MiSeq sequencing

Two to five eDNA samples from each of the four aquarium tanks (total 14 samples; figure 1a–d) and four eDNA samples from the coral reefs (figure 1e,f) were used for multiplex PCR using two universal primer pairs (MiFish-U/E). Of these 18 eDNA samples, five samples from the Kuroshio tank were additionally used for multiplex PCR using two universal plus one genus-specific primer pairs (MiFish-U/E/tuna) for correct assignments of Thunnus species.

Prior to library preparation, work-space and equipment were sterilized, filtered pipet tips were used and separation of pre- and post-PCR was carried out to safeguard against contamination. We also employed controls to monitor contamination including PCR blanks for each experiment.

Massively parallel paired-end sequencing on the MiSeq platform (Illumina, San Diego, CA, USA) requires PCR amplicons to be flanked by: (i) primer-binding sites for sequencing; (ii) dual-index (i.e. barcode) sequences; and (iii) adapter sequences for binding to the flowcells of the MiSeq. We employed a two-step tailed PCR approach to construct the paired-end libraries (figure 2).

Figure 2.

Schematic representation of the paired-end library preparation using a two-step tailed PCR. The workflow is derived from a document ‘16S metagenomic sequencing library preparation: preparing 16S ribosomal gene amplicons for the Illumina MiSeq system’ distributed by Illumina (part no. 15044223 Rev. B) and the figure was drawn with reference to a website of the Genomics and Sequencing Center at the University of Rhode Island (http://web.uri.edu/gsc/next-generation-sequencing/).

The first-round PCR (first PCR; figure 2) amplified the target region using primers 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNN + MiFish gene-specific sequences-3′ (forward) and 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNN + MiFish gene-specific sequences-3′ (reverse). The first 33 and 34 nucleotides (nt) are partially used for primer-binding sites for sequencing and the following six random hexamers (N) are used to enhance cluster separation on the flowcells during initial base call calibrations on the MiSeq platform.

The first PCR was carried out with 35 cycles of a 12 μl reaction volume containing 6.0 μl 2×KAPA HiFi HotStart ReadyMix (including DNA polymerase, reaction buffer, dNTPs and MgCl₂ (at a final concentration of 2.5 mM)) (KAPA Biosystems, Wilmington, MA, USA), 0.7 μl of each primer (5 μM), 2.6 μl sterile distilled H₂O and 2.0 μl template. When the first PCR was multiplexed (simultaneous use of multiple primer pairs), the final concentration of each primer was 0.3 μM and sterile distilled H₂O was added up to the total reaction volume of 12.0 μl. The thermal cycle profile after an initial 3 min denaturation at 95°C was as follows: denaturation at 98°C for 20 s; annealing at 65°C for 15 s; and extension at 72°C for 15 s with the final extension at the same temperature for 5 min.

The second-round PCR (second PCR; figure 2) used the first PCR products as a template and amplified the region using primers 5′-AATGATACGGCGACCACCGAGATCTACAXXXXXXXXACACTCTTTCCC TACACGACGCTCTTCCGATCT-3′ (forward) and 5′-CAAGCAGAAGACGGCATACGAGATXXXXXX XXGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′ (reverse). The octo-X segments represent dual-index sequences (40 unique indices in total; A501–508, A701–712 and D501–508, D701–D712; Illumina); the 5′-end sequences are adapters that allow the final product to bind or hybridize to short oligos on the surface of the Illumina flowcell; and the 3′-end sequences are priming sites for the MiSeq sequencing.

The first PCR product was diluted 10 times using Milli-Q water and used as a template for the second PCR. The second PCR was carried out with 12 cycles of a 12 μl reaction volume containing 6.0 μl 2× KAPA HiFi HotStart ReadyMix, 0.7 μl each primer (5 μM), 3.6 μl sterile distilled H₂O and 1.0 μl template. Different combinations of indices (chosen from A/D501–508 for forward primers and A/D701–712 for reverse primers) were used for different templates for a massively parallel sequencing using the MiSeq platform. The thermal cycle profile after an initial 3 min denaturation at 95°C was as follows: denaturation at 98°C for 20 s; annealing and extension combined at 72°C (shuttle PCR) for 15 s with the final extension at the same temperature for 5 min.

The indexed second PCR products were pooled in equal volumes and the pooled libraries (total 100 μl) were subjected to agarose gel electrophoresis using 2% L03 (Takara). A target size of the libraries (ca 370 bp) was excised from the gel and purified using a MinElute Gel Extraction kit (Qiagen) with an elution volume of 12 μl. The library concentration was estimated using a Qubit dsDNA HS assay kit and a Qubit fluorometer (Life Technologies). Double-stranded DNA concentration of the pooled library was adjusted to 4 nM (assuming 1 bp equals 660 g mol⁻¹) using Milli-Q water and 5 μl of the 4 nM library was denatured with 5 μl of fresh 0.1 N NaOH. Including HT1 buffer (provided by the Illumina MiSeq v. 2 Reagent kit for 2×150 bp PE), the denatured library (10 μl; 2 nM) was diluted to the final concentration of 12 pM for sequencing on the MiSeq platform. A 30 μl of PhiX DNA spike-in control (12 pM) was added to improve data quality of low diversity samples such as single PCR amplicons used in this study.

2.2.4. Data pre-processing

An overall quality of the MiSeq reads was evaluated by the programs FastQC (available from http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and SUGAR [24]. After confirming a lack of technical errors in the MiSeq sequencing, low-quality tails were trimmed from each read using DynamicTrim.pl from the SolexaQA software package [25] with a cut-off threshold set at a Phred score of 10 (=10⁻¹ error rate) [26]. The tail-trimmed paired-end reads (reads 1 and 2) were assembled using the software FLASH [27] with a minimum overlap of 10 bp. The assembled reads were further filtered by custom Perl scripts in order to remove reads with either ambiguous sites (Ns) or those showing unusual lengths with reference to the expected size of the PCR amplicons (297 ± 25 bp). Finally, the software TagCleaner [28] was used to remove primer sequences with a maximum of three-base mismatches and to transform the FASTQ [29] format into FASTA.

2.2.5. Taxonomic assignment

The pre-processed reads from the above custom pipeline were dereplicated using a ‘derep_fulllength’ command in UCLUST [30], with the number of identical reads added to the header line of the FASTA formatted data file. Those sequences represented by more than or equal to 10 identical reads were subjected to the downstream analyses and the remaining under-represented sequences (with less than 10 identical reads) were subjected to pairwise alignment using a ‘usearch_global’ command in UCLUST. If the latter sequences observed from less than 10 reads showed more than or equal to 99% identity with one of the former reads (one or two nucleotide differences), they were operationally considered as identical (owing to sequencing or PCR errors and/or actual nucleotide variations in the populations) and they were added to the more than or equal to 10 reads.

The processed reads were subjected to local BLASTN searches [31] against a custom-made database. The latter was generated by downloading all whole and partial fish mitogenome sequences deposited in MitoFish [17] and whole mitogenome sequences from tetrapods deposited in NCBI Organelle Genome Resources (http://www.ncbi.nlm.nih.gov/genomes/OrganelleResource.cgitaxid=32523) to cover those tetrapods occurring in aquatic environments. In addition, the custom database was supplemented by assembling new sequences in M.M.'s laboratory (electronic supplementary material, table S3). As of 4 October 2014, the database covers approximately 4230 fish species distributed across 457 families and 1827 genera. According to the latest edition of ‘Fishes of the World’ [32], fishes comprise 515 families, 1827 genera and 27 977 species with our custom-made database covering 88.7% of the families, 40.6% of the genera and 15.1% of the species.

The top BLAST hit with a sequence identity of more than or equal to 97% and E-value threshold of 10⁻⁵ was applied to species assignments of each representative sequence. We found that this cut-off value maximally recovered the species composition from each tank, while avoiding erroneous taxonomic assignment. Reliability of the species assignments were evaluated based on a ratio of total alignment length and number of mismatch bases between the query and reference sequences. For example, if a query sequence was aligned to the top BLAST hit sequence with an alignment length of 150 bp with one mismatch present, the ratio was calculated as 150/(1+1). Value one is added to the denominator to avoid zero-divisors. This ratio was calculated for the top and second BLAST hit species, and a log of odds ratio (LOD) score between these ratios was used as the comparable indicator of the species assignment. Results from the BLAST searches were automatically tabulated, with scientific names, common names, total number of the reads and representative sequences noted in an HTML format. Moreover, biological information for each detected species is available from the hyperlink in the table, such as that of FishBase (http://fishbase.sinica.edu.tw), Barcode of Life (http://www.boldsystems.org), GBIF (http://data.gbif.org), MitoFish (http://mitofish.aori.u-tokyo.ac.jp) and NCBI (http://www.ncbi.nlm.nih.gov) for quick evaluation and credibility of the bioinformatic identification.

The above bioinformatic pipeline from data pre-processing through taxonomic assignment (including Perl scripts) is available from http://dx.doi.org/10.5061/dryad.n245j and the function will be publicly available in MitoFish (http://mitofish.aori.u-tokyo.ac.jp).

3. Results and discussion

3.1. Primer development

3.1.1. MiFish-U

We visually inspected the aligned sequences throughout the entire mitogenomes across the 880 species (electronic supplementary material, table S1) by highlighting variable and invariable sites using Mesquite [20]. After repeated inspections, we found a short hypervariable region (ca 170 bp) within the 12S rRNA gene, which was flanked by highly conservative regions (ca 20–30 bp) across the 880 species (table 2). Note that we were unable to find such a region within the barcoding region of the aligned COI gene sequences, which have been frequently used as the marker of choice also in fishes [33]. This observation is consistent with a recent argument against the use of the COI gene as a genetic marker for metabarcoding studies [34].

Table 2.

Nucleotide sequences of the universal primers (MiFish-U) and base compositions in the selected 880 fish species (see electronic supplementary material, table S1). (This forward (F) and reversal (R) primer pair amplifies the mid region of the mitochondrial 12S rRNA gene with a mean length of 172 bp (163–185 bp).)

MiFish-U-F	5′-	G	T	C	G	G	T	A	A	A	A	C	T	C	G	T	G	C	C	A	G	C	-3′
	A	20	0	1	1	0	0	786	879	879	804	0	0	0	0	0	0	0	0	880	0	0
	C	1	733	855	0	0	6	30	0	0	17	832	3	878	0	0	0	880	880	0	0	880
	G	858	0	0	879	880	0	0	1	0	3	0	0	0	880	0	880	0	0	0	880	0
	T	1	147	24	0	0	874	64	0	1	56	48	877	2	0	880	0	0	0	0	0	0

MiFish-U-R	3′-	G	T	T	T	G	A	C	C	C	T	A	A	T	C	T	A	T	G	G	G	G	T	G	A	T	A	C	-5′
	A	0	880	880	880	0	0	17	2	0	880	0	0	877	0	877	1	880	0	0	0	0	878	1	0	880	0	0
	C	880	0	0	0	880	4	0	0	0	0	0	0	2	0	0	2	0	880	880	880	863	0	859	0	0	0	0
	G	0	0	0	0	0	0	863	878	880	0	0	0	0	880	3	12	0	0	0	0	0	2	0	0	0	0	880
	T	0	0	0	0	0	876	0	0	0	0	880	880	1	0	0	865	0	0	0	0	17	0	20	880	0	880	0

The hypervariable region in the 12S rRNA gene includes multiple segments that are forming big loops in a proposed secondary structure of the molecule [35,36]. In particular, four segments of the loops were so variable in length (involving multiple insertions/deletions) that they were considered unalignable even among closely related gobioid fishes in a previous study [37]. The two highly conservative regions, on the other hand, exhibit no length variations among the 880 species and were located on the two stem regions (stem nos. 15/16 and 24/25 in [35,36]), which undergo secondary structural constraints through strong Watson–Crick base pairings [35]. Following these empirical and theoretical observations, we decided to design a new primer pair located on the two conservative regions, thereby amplifying the highly taxonomic informative hypervariable region in between.

In the initial stage of this study, we designed degenerate PCR primers to accommodate sequence variations among taxa, but found that such degenerate primers did not amplify the target eDNA when they were used with long adapter sequences in the tailed PCR (figure 2). We redesigned a new set of primers without degenerate sites (MiFish-U) using various technical methods related to construction of adequate primers (see Material and methods). The new forward (MiFish-U-F) and reverse (MiFish-U-R) primers consist of 21 and 27 bases (table 2) with G/C contents of 57% and 44% and T_m of 56.6°C and 56.5°C, respectively.

With the redesigned MiFish-U primers (without adapter sequences), we confirmed successful amplifications of the hypervariable regions using extracted DNA from 96 species representing all of the four major lineages of fishes (Agnatha, Chondrichthyes, Actinopterygii and Sarcopterygii) distributed across 47 orders and 96 different families (table 1). With these PCR products, we successfully determined their nucleotide sequences using the conventional Sanger sequencing method. All the sequence data are available from DDBJ/EMBL/GenBank databases with accession numbers shown in table 1.

3.1.2. MiFish-E

During the preliminary experiments using eDNA from the aquarium tanks, we found that only a few assembled reads from the MiSeq sequencing represented elasmobranchs (sharks and rays). The lack of elasmobranch sequences was totally unexpected, because we included a number of elasmobranchs while designing the universal primers (13 spp.; see the electronic supplementary material, table S1) and more than 100 large-sized individuals of various elasmobranchs (mostly more than 1 m in total lengths; figure 1a) were present and active in the Kuroshio tank. We suspected that absence of the elasmobranch sequences resulted from PCR bias derived from primer–template mismatches. Inspection of the newly downloaded 160 elasmobranch sequences found only a few such mismatches (table 3), with significant ones being restricted to two sites near the 5′-end of the forward primer and in a single site near the 3′-end of the reverse primer. The newly designed primers for the elasmobranchs based on these mismatches were proved effective for amplification of the region, with all the species with reference sequences being detected by the MiSeq sequencing (see below). The new forward (MiFish-E-F) and reverse (MiFish-E-R) primers were designed in an identical region to that of the universal primers, consisting of 21 and 27 bases (table 3) with G/C contents of 52% and 41% and T_m of 54.1°C and 55.2°C, respectively, and were used with MiFish-U in multiplex PCR.

Table 3.

Nucleotide sequences of the universal primers more specifically designed for the elasmobranchs (sharks and rays; MiFish-E) and base compositions in the selected 160 species (electronic supplementary material, table S2). (Nucleotide differences from MitoFish-U are highlighted with underline in bold. This forward (F) and reverse (R) primer pair amplifies the mid region of the mitochondrial 12S rRNA gene with a mean length of 182 bp (170–185 bp).)

MiFish-E-F	5′-	G	T	T	G	G	T	A	A	A	T	C	T	C	G	T	G	C	C	A	G	C	-3′
	A	4	0	0	0	0	0	70	157	157	3	0	0	0	0	0	0	0	0	158	0	0
	C	0	3	14	0	0	0	32	0	0	6	157	0	157	0	0	0	158	158	0	0	158
	G	153	0	0	157	157	0	0	0	0	1	0	0	1	158	0	158	0	0	0	158	0
	T	0	154	143	0	0	157	55	0	0	148	1	158	0	0	158	0	0	0	0	0	0

MiFish-E-R	3′-	G	T	T	T	G	A	T	C	C	T	A	A	T	C	T	A	T	G	G	G	G	T	G	A	T	A	C	-5′
	A	0	160	160	160	0	0	153	0	0	160	0	0	160	0	160	2	160	0	0	2	0	160	0	0	160	0	1
	C	160	0	0	0	160	0	0	0	0	0	0	0	0	0	0	2	0	160	160	158	8	0	159	0	0	0	0
	G	0	0	0	0	0	0	7	160	160	0	0	0	0	160	0	4	0	0	0	0	0	0	0	0	0	0	159
	T	0	0	0	0	0	160	0	0	0	0	160	160	0	0	0	152	0	0	0	0	152	0	1	160	0	160	0

3.1.3. MiFish-tuna

In addition to newly constructed pairs of the universal primers (MiFish-U/E), preliminary experiments showed that nucleotide differences in the MiFish sequences from tunas (seven species of Thunnus) were so small that the bioinformatic pipeline was unable to assign assembled reads to the correct species (see below). We visually inspected the entire mitogenome sequences from the seven species of tunas and found a region with sufficient interspecific variations among constituent species. The newly designed genus-specific forward (MiFish-tuna-F) and reverse (MiFish-tuna-R) primers amplify a portion of the ND5 gene (180 bp), consisting of 22 and 21 bases with G/C contents of 55% and 57% and T_m of 56.9°C and 57.8°C, respectively (see table 3 for primer sequences with adapters).

3.1.4. In silico evaluation of interspecific variations

The pairwise edit distances from MiFish and ecoPrimer sequences were calculated for all combinations of 1324 fish species distributed across 59 orders, 319 families and 890 genera (total₁₃₂₄C₂=875 826 pairs) and the resulting distances were sorted into between-order, family, genus and species (table 4).

Table 4.

Frequency distributions of the interspecific edit distances of the MiFish (above) and ecoPrimer (below) sequences among 1324 fish species deposited in the MitoFish database [16]. (The edit distances are sorted into between-order, family, genus and species. Only edit distances from 0 to less than or equal to 10 are shown.)

MiFish	0	≤1	≤2	≤3	≤4	≤5	≤6	≤7	≤8	≤9	≤10
order	0	0	0	0	0	0	0	0	0	0	0
family	0	3	12	12	12	13	18	28	32	52	68
genus	32	72	98	125	164	201	251	316	377	430	479
species	98	187	239	294	361	413	472	524	591	645	684

ecoPrimer	0	≤1	≤2	≤3	≤4	≤5	≤6	≤7	≤8	≤9	≤10
order	0	0	0	4	12	40	85	147	254	355	465
family	2	14	38	95	163	269	365	466	572	654	736
genus	149	296	412	521	640	732	858	931	1020	1079	1132
species	284	471	603	729	817	885	985	1044	1109	1149	1191

As expected from the size difference between MiFish and ecoPrimer sequences (average lengths 172 bp versus 106 bp), the former appears to have more variation than the latter and also outperforms the latter in unambiguously assigning each taxonomic category (table 4). In particular, MiFish sequences perform well for higher taxonomic categories; for example, all the between-order edit distances are larger than 10 in MiFish sequences, while the smallest one in ecoPrimer sequences is three (four pairs). Also, two pairs of the between-family edit distances from ecoPrimer sequences are zero, indicating that interfamilial discrimination is not feasible for these two pairs. For lower taxonomic categories such as genus and species, MiFish sequences also outperform ecoPrimer sequences in terms of unambiguous taxonomic assignments. For example, the number of pairs with smaller between-genus and species edit distances (e.g. less than or equal to 3) in MiFish sequences are 4.17 and 2.48 times lower than those in ecoPrimer sequences, respectively (table 4).

It appears that MiFish sequences still have inherent limitations to unambiguously assign lower taxonomic categories, such as genus and species. Actually, there are 32 and 98 between-genus and specific pairs with the edit distances of zero, respectively (table 4). For those taxonomic groups with no or a few nucleotide differences in MiFish sequences, we need to develop new molecular markers that contain sufficient information to discriminate constituent species. Development of the new marker for correct species assignments of tunas in this study (MiFish-tuna) represents a good example of such a case (see below).

It should also be noted that those zero distances in the intergeneric comparisons from MiFish sequences (total 32 pairs) are restricted mostly to specific groups of fishes, such as Cichlidae (cichlids; 14 pairs) and Istiophoridae (billfishes; 14 pairs), whose limited genetic divergences in mtDNA are well established (and sometimes misleading owing to gene introgression) compared with their distinct morphological divergences [38–40]. The remaining four pairs include that of Cyprinidae (carp and minnow), Engraulidae (anchovy), Mormyridae (freshwater elephantfish) and Mirapinnidae (hairyfish), all of which are under taxonomic revisions at various taxonomic categories [41–44]. Actually, a recent study [42] demonstrated that members of the latter family Mirapinnidae simply represent larval stages of the different whalefish families, indicating that current fish taxonomy is still in a state of flux.

3.2. Primer testing with eDNA from aquarium

3.2.1. Library preparation for metabarcoding

We first tested MiFish-U primers (without adapter sequences) using eDNA from the aquarium tanks in preliminary experiments and observed consistent amplifications across different samples on an agarose gel stained with ethidium bromide (results not shown). The PCR bands from those amplifications, however, were often smearing, with occasional extra bands being observed outside the expected size of the products (ca 220 bp).

Following the partial success of PCR using eDNA, we constructed MiFish-U primers for the first PCR by appending adapter sequences at their 5′-ends (figure 2; for primer sequences, see table 5). Optimal experimental conditions for the first PCR with these primers were achieved through trial and error, and we found that choice of a PCR kit (KAPA HiFi HotStart ReadyMix) and associated high-annealing temperatures (65–67°C) in the first PCR are the two most important factors contributing to successful amplifications showing distinct single PCR bands on the agarose gel.

Table 5.

A list of primers for the first and second PCR used in the paired-end library preparation for the MiSeq analyses; indices (=barcodes) are highlighted with an underline. (Note that those index sequences for the reversal primers (R) are read by MiSeq on the opposite strand and should be reverse/complement in the sample sheet for MiSeq runs.)

primer	sequence (5′–3′)
universal primers for the first PCR
MiFish-U-F	ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNGTCGGTAAAACTCGTGCCAGC
MiFish-U-R	GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNCATAGTGGGGTATCTAATCCCAGTTTG
MiFish-E-F	ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNGTTGGTAAATCTCGTGCCAGC
MiFish-E-R	GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNCATAGTGGGGTATCTAATCCTAGTTTG
taxon-specific primers for the first PCR
MiFish-tuna-ND5-F	ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNATGTCCTTCCTCCTTATCGGCTG
MiFish-tuna-ND5-R	GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNTTGCCAGTGGCAGCTACGATC
forward primers for the second PCR (A series)
2nd_PCR_F_A501	AATGATACGGCGACCACCGAGATCTACACTGAACCTTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A502	AATGATACGGCGACCACCGAGATCTACACTGCTAAGTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A503	AATGATACGGCGACCACCGAGATCTACACTGTTCTCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A504	AATGATACGGCGACCACCGAGATCTACACTAAGACACACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A505	AATGATACGGCGACCACCGAGATCTACACCTAATCGAACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A506	AATGATACGGCGACCACCGAGATCTACACCTAGAACAACACTCTTTCCCTACACGACGCTCTTCCGA
2nd_PCR_F_A507	AATGATACGGCGACCACCGAGATCTACACTAAGTTCCACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_A508	AATGATACGGCGACCACCGAGATCTACACTAGACCTAACACTCTTTCCCTACACGACGCTCTTCCGATCT
forward primers for the second PCR (D series)
2nd_PCR_F_D501	AATGATACGGCGACCACCGAGATCTACACTATAGCCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D502	AATGATACGGCGACCACCGAGATCTACACATAGAGGCACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D503	AATGATACGGCGACCACCGAGATCTACACCCTATCCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D504	AATGATACGGCGACCACCGAGATCTACACGGCTCTGAACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D505	AATGATACGGCGACCACCGAGATCTACACAGGCGAAGACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D506	AATGATACGGCGACCACCGAGATCTACACTAATCTTAACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D507	AATGATACGGCGACCACCGAGATCTACACCAGGACGTACACTCTTTCCCTACACGACGCTCTTCCGATCT
2nd_PCR_F_D508	AATGATACGGCGACCACCGAGATCTACACGTACTGACACACTCTTTCCCTACACGACGCTCTTCCGATCT
reverse primers for the second PCR (A series)
2nd_PCR_R_A701	CAAGCAGAAGACGGCATACGAGATGTCGTGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A702	CAAGCAGAAGACGGCATACGAGATACCACTGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A703	CAAGCAGAAGACGGCATACGAGATTGGATCTGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A704	CAAGCAGAAGACGGCATACGAGATCCGTTTGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A705	CAAGCAGAAGACGGCATACGAGATTGCTGGGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A706	CAAGCAGAAGACGGCATACGAGATGAGGGGTTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A707	CAAGCAGAAGACGGCATACGAGATAGGTTGGGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A708	CAAGCAGAAGACGGCATACGAGATGTGTGGTGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A709	CAAGCAGAAGACGGCATACGAGATTGGGTTTCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A710	CAAGCAGAAGACGGCATACGAGATTGGTCACAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A711	CAAGCAGAAGACGGCATACGAGATTTGACCCTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_A712	CAAGCAGAAGACGGCATACGAGATCCACTCCTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
reverse primers for the second PCR (D series)
2nd_PCR_R_D701	CAAGCAGAAGACGGCATACGAGATCGAGTAATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D702	CAAGCAGAAGACGGCATACGAGATTCTCCGGAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D703	CAAGCAGAAGACGGCATACGAGATAATGAGCGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D704	CAAGCAGAAGACGGCATACGAGATGGAATCTCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D705	CAAGCAGAAGACGGCATACGAGATTTCTGAATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D706	CAAGCAGAAGACGGCATACGAGATACGAATTCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D707	CAAGCAGAAGACGGCATACGAGATAGCTTCAGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D708	CAAGCAGAAGACGGCATACGAGATGCGCATTAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D709	CAAGCAGAAGACGGCATACGAGATCATAGCCGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D710	CAAGCAGAAGACGGCATACGAGATTTCGCGGAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D711	CAAGCAGAAGACGGCATACGAGATGCGCGAGAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
2nd_PCR_R_D712	CAAGCAGAAGACGGCATACGAGATCTATCGCTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT

Based on the above empirical observations, we constructed 14 dual-indexed, paired-end libraries through two-step tailed PCR (figure 2) for two to five water samples from each of the four aquarium tanks.

3.2.2. MiSeq sequencing and data analysis

The MiSeq paired-end sequencing (2× 150 bp) of the 14 libraries, together with another 129 libraries (total number of libraries =143), yielded a total of 14.86 million reads, with an average of 95.0% base calls being Phred quality scores of more than or equal to 30.0 (Q30; error rate =0.1% or base call accuracy =99.9%). This run was highly successful considering that the quality scores specified by Illumina is more than 80% bases higher than Q30 at 2×150 bp (Illumina Publication no. 770-2011-001 as of 27 May 2014).

After demultiplexing and subsequent pre-processing of the raw data from MiSeq, the outputs were subjected to the BLAST searches for taxonomic assignment. In total, 4 322 882 reads were assigned to fish species with more than or equal to 97% identity to reference sequences in the custom database. Of these, 4 053 184 (93.4%) are identified as those fishes contained in one of the four tanks (hereafter called ‘tank species’) and the remaining 286 446 (6.6%) are derived from ‘non-tank species’ (table 6), discussed below.

Table 6.

A summary of the BLAST searches for the four aquarium tanks.

number of readsa	total	Kuroshio	tropical fish	deep-sea	mangrove
more than or equal to 97% identity with reference sequences (number of libraries)	4 322 882 (14)	2 568 008 (5)	1 299 788 (4)	259 191 (3)	212 643 (2)
tank fish	4 053 184 (93.4%)	2 375 892 (92.5%)	1 237 546 (95.2%)	245 201 (94.6%)	194 545 (91.5%)
non-tank fish	286 446 (6.6%)	192 116 (7.5%)	62 242 (4.8%)	13 990 (5.4%)	18 098 (8.5%)
number of tank species	249	75	159	15	8
number of tank species with reference sequences	180	63	105	13	8
number of tank species detected in MiSeq analysis	168 (93.3%)	61 (96.8%)	95 (90.5%)	13 (100%)	8 (100%)
water volumes of tank (m3)	8465	7500	700	230	35.6

^aThose reads with less than 97% sequence identity are excluded from the above table for simplicity. They are 285 172 reads in total; 57 572 reads from the Kuroshio, 222 897 reads from the tropical fish, 1093 reads from the deep-sea and 3610 reads from the mangrove tanks, respectively.

According to the unpublished monthly report from the aquarium, the four tanks harboured a diverse range of 249 fish species distributed across 64 families and 146 genera at the time of sampling. Of these 249 species, we confirmed that 180 species have reference sequences in the custom database (tables 7 and 8) and detected eDNA from 168 species (93.3%; table 6). In the following, we describe and discuss results from the metabarcoding analyses of each tank separately.

Table 7.

Taxonomic composition and read numbers of the 168 species detected in MiSeq analyses of eDNA samples from the four aquarium tanks. (Only those species contained in the respective tanks with reference sequences in the custom database are shown.)

higher classificationa	species	total	Kuroshio	tropical	deep	mangrove
Class Chondrichthyes (cartilaginous fishes)
Subclass Elasmobranchii
Subdivision Selachii (sharks)
Order Orectolobiformes
Family Orectolobidae	Stegostoma fasciatum	788	788	0	0	0
Family Hemiscyllidae	Chiloscyllium punctatum	21	0	21	0	0
Family Gygliomostomatidae	Nebrius ferrugineus	997	997	0	0	0
Family Rhincodontidae	Rhincodon typus	6864	6864	0	0	0
Order Carcharhiniformes
Family Triakidae	Mustelus manazo	38	0	0	38	0
Family Carcharhinidae	Carcharhinus leucas	16	16	0	0	0
	Carcharhinus plumbeus	816	816	0	0	0
	Galeocerdo cuvier	2236	2236	0	0	0
	Negaprion acutidens	383	383	0	0	0
	Triaenodon obesus	24	24	0	0	0
Order Squaliformes
Family Squalidae	Cirrhigaleus barbifer	177	0	0	177	0
	Squalus brevirostrisb	129	0	0	129	0
Order Pristiophoriformes
Family Pristiophoridae	Pristiophorus japonicus	9484	0	0	9484	0
Subdivision Batoidea (rays)
Order Rajiformes
Family Rhinidae	Rhina ancylostoma	614	614	0	0	0
	Rhynchobatus djiddensis	10 405	10 405	0	0	0
Order Myliobatifrormes
Family Dasyatidae	Dasyatis ushiyei	265	265	0	0	0
	Himantura fai	2799	2799	0	0	0
	Himantura uarnak	3584	3584	0	0	0
	Urogymnus asperrimus	577	577	0	0	0
Family Myliobatidae	Aetobatus narinari	1167	1167	0	0	0
	Manta alfredi	7701	7701	0	0	0
	Rhinoptera javanicac	5464	5464	0	0	0
Class Actinopterygii (ray-finned fishes)
Subclass Neopterygii
Division Teleostei
Order Elopiformes
Family Elopidae	Elops hawaiensis	3040	3040	0	0	0
Order Anguilliformes
Family Muraenidae	Gymnothorax isingteena	739	0	739	0	0
Order Beryciformes
Family Trachichthyidae	Gephyroberyx japonicus	3240	0	0	3240	0
Family Holocentridae	Myripristis berndti	148	0	148	0	0
	Neoniphon sammara	149	0	149	0	0
	Ostichthys japonicus	2506	0	0	2506	0
	Sargocentron rubrum	766	0	766	0	0
Order Mugiliformes
Family Mugilidae	Ellochelon vaigiensis	491	0	0	0	491
Order Gasterosteiformes
Suborder Syngnathoidei
Family Fistulariidae	Fistularia commersonii	2458	0	2458	0	0
Family Centriscidae	Aeoliscus strigatus	404	0	404	0	0
Order Scorpaeniformes
Suborder Scorpaenoidei
Family Scorpaenidae	Pterois volitans	795	0	795	0	0
Order Perciformes
Suborder Percoidei
Family Serranidae	Cephalopholis argus	317	0	317	0	0
	Cephalopholis sonnerati	2403	0	2403	0	0
	Cephalopholis urodeta	2365	0	2365	0	0
	Epinephelus bruneus	983	983	0	0	0
	Epinephelus coioides	8639	0	8639	0	0
	Epinephelus fasciatus	5626	0	5626	0	0
	Epinephelus lanceolatus	67 311	21 026	46 285	0	0
	Epinephelus maculatus	5124	0	5124	0	0
	Epinephelus tukula	17 116	3579	13 537	0	0
	Plectropomus leopardus	3758	0	3758	0	0
	Variola louti	286	0	286	0	0
Family Priacanthidae	Priacanthus hamrur	16 641	0	16 641	0	0
Family Apogonidae	Sphaeramia orbicularis	22 946	0	0	0	22 946
Family Scombropidae	Scombrops gilbertid	649	0	0	649	0
Family Coryphaenidae	Coryphaena hippurus	7143	7143	0	0	0
Family Echeneidae	Echeneis naucrates	9187	9187	0	0	0
Family Carangidae	Alectis ciliaris	420	420	0	0	0
	Alectis indica	6071	6071	0	0	0
	Alepes vari	19 433	19 433	0	0	0
	Carangichthys dinema	532	532	0	0	0
	Caranx ignobilis	51 693	51 693	0	0	0
	Caranx melampygus	55 111	55 111	0	0	0
	Caranx papuensis	6029	6029	0	0	0
	Caranx sexfasciatus	48 578	48 578	0	0	0
	Decapterus muroadsi	1735	1735	0	0	0
	Elagatis bipinnulata	58 279	58 279	0	0	0
	Gnathanodon speciosus	22 634	22 634	0	0	0
	Selar crumenophthalmus	3985	3985	0	0	0
	Seriola dumerili	19 935	19 935	0	0	0
	Seriola rivoliana	16 863	16 863	0	0	0
	Trachinotus blochii	19 129	19 129	0	0	0
	Uraspis uraspis	200	200	0	0	0
Family Emmelichthyidae	Erythrocles schlegelii	24 447	0	0	24 447	0
Family Lutjanidae	Aprion virescens	2217	2217	0	0	0
	Etelis carbunculus	9747	0	0	9747	0
	Etelis coruscanse	19 271	0	0	19 271	0
	Lutjanus bohar	13 220	3667	9553	0	0
	Lutjanus decussatus	179	0	179	0	0
	Lutjanus fulvus	4207	0	4207	0	0
	Lutjanus kasmira	75 436	2476	72 960	0	0
	Lutjanus monostigma	7134	0	7134	0	0
	Lutjanus sebae	2477	0	2477	0	0
Family Caesionidae	Caesio caerulaurea	10 175	10 175	0	0	0
	Caesio cuning	8557	7886	671	0	0
	Caesio teres	57 962	25 958	32 004	0	0
	Pterocaesio marri	289 474	245 181	44 293	0	0
	Pterocaesio tile	97 437	97 437	0	0	0
Family Lobotidae	Lobotes surinamensis	29	0	29	0	0
Family Haemulidae	Diagramma picta	16 101	0	16 101	0	0
	Plectorhinchus lineatus	35 231	0	35 231	0	0
Family Lethrinidae	Gnathodentex aureolineatus	25 714	0	25 714	0	0
	Gymnocranius euanus	293	293	0	0	0
	Lethrinus microdon	3102	3102	0	0	0
	Lethrinus nebulosus	44 356	33 466	10 890	0	0
	Lethrinus olivaceus	3135	3135	0	0	0
	Lethrinus ornatus	779	779	0	0	0
Family Mullidae	Parupeneus pleurostigma	647	0	647	0	0
Family Pempheridae	Pempheris schwenkii	7113	0	7113	0	0
Family Monodactylidae	Monodactylus argenteus	133 612	0	0	0	133 612
Family Toxotidae	Toxotes chatareus	16 822	0	0	0	16 822
Family Kyphsidae	Girella mezina	5240	0	5 240	0	0
Family Chaetodontidae	Chaetodon auriga	2644	0	2644	0	0
	Chaetodon auripes	41 991	0	41 991	0	0
	Chaetodon lunula	2959	0	2959	0	0
	Chaetodon vagabundus	2495	0	2495	0	0
	Hemitaurichthys polylepis	1848	0	1848	0	0
	Heniochus diphreutes	706	0	706	0	0
Family Pomacanthidae	Pomacanthus semicirculatus	1100	0	1100	0	0
Family Pentacerotidae	Pentaceros japonicus	13 087	0	0	13 087	0
Family Kuhliidae	Kuhlia mugil	1275	0	1275	0	0
Family Cirrhitidae	Paracirrhites forsteri	707	0	707	0	0
Family Cheilodactylidae	Cheilodactylus zonatus	1983	0	1983	0	0
Suborder Labroidei
Family Pomacentridae	Abudefduf sexfasciatus	98 622	0	98 622	0	0
	Abudefduf sordidus	903	0	903	0	0
	Abudefduf vaigiensis	4216	0	4216	0	0
	Amblyglyphidodon curacaof	74 516	0	74 516	0	0
	Amphiprion frenatus	674	0	674	0	0
	Chromis atripectoralis	387	0	387	0	0
	Chromis viridis	853	0	853	0	0
	Chrysiptera cyanea	2236	0	2236	0	0
	Neopomacentrus taeniurus	1113	0	0	0	1113
	Pomacentrus amboinensisg	293	0	293	0	0
Family Labridae	Bodianus bilunulatus	10 489	0	10 489	0	0
	Cheilinus undulatus	31 336	0	31 336	0	0
	Choerodon schoenleinii	45 558	0	45 558	0	0
	Coris aygula	1292	0	1292	0	0
	Coris gaimard	1433	0	1433	0	0
	Halichoeres marginatus	337	0	337	0	0
	Hologymnosus doliatus	170	0	170	0	0
	Iniistius pavo	532	0	532	0	0
	Labrichthys unilineatus	289	0	289	0	0
	Labroides dimidiatus	1333	0	1333	0	0
	Oxycheilinus unifasciatus	337	0	337	0	0
	Thalassoma hardwicke	1718	0	1718	0	0
	Thalassoma lutescens	6028	0	6028	0	0
Family Scaridae	Bolbometopon muricatum	66	0	66	0	0
	Cetoscarus bicolor	145	0	145	0	0
	Chlorurus microrhinos	4297	0	4297	0	0
	Chlorurus sordidus	3701	0	3701	0	0
	Scarus frenatus	3855	0	3855	0	0
	Scarus ghobban	134 283	0	134 283	0	0
	Scarus rivulatus	564	0	564	0	0
	Scarus schlegeli	39 908	0	39 908	0	0
Suborder Trachinoidei
Family Pinguipedidae	Parapercis pacifica	516	0	516	0	0
Suborder Gobioidei
Family Gobiidae	Periophthalmus argentilineatus	928	0	0	0	928
Suborder Acanthuroidei
Family Ephippidae	Platax orbicularis	60 493	0	60 493	0	0
Family Scatophagidae	Scatophagus argus	9422	0	0	0	9422
Family Siganidae	Siganus doliatus	5628	0	5628	0	0
	Siganus guttatus	9211	0	0	0	9211
	Siganus unimaculatus	10 521	0	10 521	0	0
Family Zanclidae	Zanclus cornutus	8991	0	8991	0	0
Family Acanthuridae	Acanthurus blochii	35 342	0	35 342	0	0
	Acanthurus dussumieri	19 158	0	19 158	0	0
	Acanthurus nigricauda	500	0	500	0	0
	Acanthurus nigrofuscus	16 988	0	16 988	0	0
	Acanthurus olivaceus	7957	0	7957	0	0
	Acanthurus xanthopterus	23 671	0	23 671	0	0
	Ctenochaetus striatus	7742	0	7742	0	0
	Naso hexacanthus	66 487	572	65 915	0	0
	Zebrasoma flavescens	24 888	0	24 888	0	0
Suborder Scombroidei
Family Gempylidae	Thyrsitoides marleyi	150 624	0	0	150 624	0
Family Scombridae	Auxis thazard thazard	929	929	0	0	0
	Euthynnus affinis	50 100	50 100	0	0	0
	Grammatorcynus bilineatus	5605	5605	0	0	0
	Gymnosarda unicolor	27 267	27 267	0	0	0
	Katsuwonus pelamis	123 814	123 814	0	0	0
	Rastrelliger kanagurta	966 420	966 420	0	0	0
	Thunnus albacaresh	241 171	241 171	0	0	0
	Thunnus orientalisi	103 957	103 957	0	0	0
Suborder Stromateoidei
Family Centrolophidae	Hyperoglyphe japonica	11 802	0	0	11 802	0
Order Tetraodontiformes
Suborder Balistoidei
Family Balistidae	Melichthys vidua	1008	0	1008	0	0
	Odonus niger	3607	0	3607	0	0
Family Monacanthidae	Rhinecanthus verrucosusj	886	0	886	0	0
Suborder Tetraodontoidei
Family Tetraodontidae	Arothron hispidus	30 458	0	30 458	0	0
Family Diodontidae	Diodon hystrix	294	0	294	0	0

^aClassification follows ‘Fishes of the World’ [32].

^b96.7% identity with a congener Squalus mitsukurii.

^c95.0% identity with the reference sequence.

^d100% identity with a congener Scombrops gilberti.

^eNo reference sequence, but 95.3% identity with a congener Etelis coruscans.

^f100% identity with a congener Amblyglyphidodon aureus.

^g98.8% identity with a congener Pomacentrus albicaudatus.

^hTotal read number of those tuna species identified as T. albacares, T. maccoyii, T. thynnus and T. tonggol (see table 9).

ⁱTotal read number of those tuna species identified as T. alalungai and T. orientalis (see table 9).

^j100% identity with a congener Rhinecanthus aculeatus.

Table 8.

A list of species with reference sequences in the custom database, but undetected in the MiSeq analyses.

tank	family	species
Kuroshio	Carangidae	Carangoides orthogrammus
		Pseudocaranx dentex
tropical fish	Dactylopteridae	Dactyloptena orientalis
	Serranidae	Epinephelus merra
	Lutjanidae	Lutjanus stellatus
	Mullidae	Parupeneus multifasciatus
	Chaetodontidae	Forcipiger flavissimus
	Pomacentridae	Amphiprion ocellaris
	Labridae	Oxycheilinus digramma
	Scaridae	Scarus psittacus
	Acanthuridae	Zebrasoma scopas
	Balistidae	Balistapus undulatus

3.2.3. Kuroshio tank

The Kuroshio tank (figure 1a) is designed for exhibiting marine megafauna, with dimensions (L×W×D) of 35 m×27 m×10 m, large enough (7500 m³) to accommodate a number of mature whale sharks (more than 10 m in total length). It predominantly keeps large-sized fishes characteristic to areas around the Kuroshio, one of the western boundary currents flowing northeastwards along the entire length of Japan, including the Okinawa Islands. Preliminary experiments showed that the exclusive use of an MiFish-U primer pair was unable to detect most species of the elasmobranchs (including whale sharks); subsequent development of MiFish-E primers and application of multiplex PCR (MiFish-U/E), however, enabled us to detect all species of the elasmobranchs contained in the tank (table 7).

Out of the 63 fish species with reference sequences in the custom database, we detected 61 species (96.8%) including 17 and 44 species of elasmobranchs and teleosts, respectively, which are collectively distributed across 17 families and 44 genera (table 7). The two undetected species (3.2%) are carangids (Carangoides orthogrammus and Pseudocaranx dentex; table 8) and we visually confirmed their presence in the tank. There were no extra carangid sequences referable to those two species in the MiSeq outputs, suggesting that they may represent an example of false negative in our metabarcoding analyses.

Although yellowfin and Pacific bluefin are the only tuna species contained in the Kuroshio tank, our custom bioinformatic pipeline erroneously assigned assembled reads into supposedly six tuna species (table 9). This is apparently owing to small interspecific nucleotide differences among the seven species of tunas, with a mean pairwise p-distance of only 2.22 (range 0–5; figure 3) in the MiFish sequences. To resolve this erroneous taxonomic assignment, we developed new genus-specific primers (MiFish-tuna) that amplify a segment of the mitochondrial ND5 gene (180 bp). The amplified region has sufficient interspecific nucleotide variation, with a mean pairwise p-distance of 11.1 (range 2–16), and library preparations using multiplex PCR (simultaneous use of MiFish-U/E and MiFish-tuna) lead to correct assignment of the MiSeq outputs into both tuna species present (table 9). Based on this correct taxonomic assignment, we add those erroneous assignments for southern bluefin + Atlantic bluefin + longtail (1808 + 37 + 152 reads) and albacore (103 957 reads) to those of yellowfin (241 171 reads) and Pacific bluefin (306 reads), respectively (table 7).

Table 9.

Six species of tunas (genus Thunnus) and read numbers (pooled from five samples) detected in MiSeq analyses using the 12S primers only (MiFish-U/E) and 12S + ND5 primers (MiFish-U/E/tuna) in multiplex PCR. (Thunnus albacares (yellowfin) and T. orientalis (Pacific bluefin) in bold, are contained in the Kuroshio tank and the latter analysis with the ND5 sequences only correctly assigned the two species.)

	12S primers only (MiFish-U/E)	12S + ND5 primers (MiFish-U/E/tuna)
species (common name)	12S	12S	ND5
T. alalunga(albacore)	103 957	15 049	0
T. albacares(yellowfin)	241 171	40 578	13 259
T. maccoyii (southern bluefin)	1808	392	0
T. orientalis(Pacific bluefin)	306	0	17 174
T. thynnus (Atlantic bluefin)	37	0	0
T. tonggol (longtail)	152	14	0

Figure 3.

Neighbour-joining trees of the seven species of tunas based on the amplified regions with multiplex PCR using MiFish-U (12S rRNA gene) and MiFish-tuna (ND5 gene) primers. Two species contained in the Kuroshio tank (yellowfin and Pacific bluefin) are highlighted in bold. Distances are calculated by using the Kimura's two-parameter model of base substitution with gaps being completely deleted. Numerals beside the internal branches are bootstrap probabilities based on 300 pseudo-replicates, and branch lengths are proportional to substitutions per site. Photos of the two tuna species are courtesy of H. Senou (Kanagawa Prefectural Museum of Natural History).

It should be noted that MiFish-U/E primers also amplified eDNA from a non-fish marine vertebrate (spotted dolphin, Stenella attenuata) also present in the Kuroshio tank (excluded from table 7). We actually found many reads from the dolphin across the five samples totalling 37 056. A comparison between the primer sequences of MiFish-U-F/R and priming sites of the dolphin (EU557096) indicates that there is only one mismatch in the middle of the forward primers (excluding two T/G bonds), suggesting that the primers are also useful for detecting non-fish vertebrates by accommodating their unique nucleotide variations at the priming sites.

3.2.4. Tropical fish tank

The tropical fish tank (figure 1b) exhibits typical coastal environments around Okinawa Island (figure 1e,f), displaying soft corals and 155 species of reef-associated fishes. Of the 155 fish species, we confirmed reference sequences for 105 species in the custom database (tables 7 and 8) and detected eDNA from the 95 species distributed across 32 families and 65 genera (tables 6 and 7). The detection rate (90.5%) is somewhat lower than those of the other tanks (96.8–100%; table 6) and the 10 undetected species are taxonomically diverse, distributed across 10 families within 10 genera (table 8). We visually recognized the presence of these 10 species in the tank and reconfirmed detection of eDNA from the same families or genera of those 10 species. This suggests that strong PCR bias derived from primer–template mismatches seems unlikely and the lack of eDNA from these 10 fish species may represent false negatives. Note that co-occurrences of multiple species from some of the speciose genera, such as Epinephelus (five spp.), Lutjanus (six spp.) and Scarus (four spp.) (table 7), do not confuse the taxonomic assignments, because all undetected species from these genera show significant nucleotide differences from those congeners (p-distance =2.9−16.6%). The detection rate might also be affected by uncertainty in the species identification based on morphology for the tank species and/or for voucher specimens of the reference sequences.

The large species diversity in this tank (155 spp.) also highlights the importance for taxonomic coverage of the reference sequences in the custom database [45], which only attain approximately two-thirds of the tank species (105 spp.). For the tropical fish tank, we subjected 1 524 620 reads to BLAST searches and were unable to assign 222 897 reads (14.6%) into any species with more than or equal to 97% sequence identity (not shown in table 6). Such taxonomically unassignable reads are minor in other tanks, with 57 572 reads (2.2%) in the Kuroshio, 1093 reads (0.5%) in the deep-sea and 3610 reads (1.7%) in the mangrove tanks, respectively. In the latter three tanks, some species showing 95 to less than 97% sequence identity are referable to the tank species when they have congeners in the reference sequences and represent single members of those genera in the respective tanks (see footnotes in table 7). By contrast, such cases are quite rare in the tropical fish tank and presence of multiple confamilial or congeneric species with less than 97% sequence identity hinders further taxonomic assignments.

3.2.5. Deep-sea tank

The deep-sea tank (figure 1c) keeps 15 species of benthic and benthopelagic fishes from elasmobranchs to higher teleosts commonly found in slope waters off Okinawa. Of these 15 deep-sea fish species, we confirmed reference sequences for 13 species in the custom database (table 7) and detected all of these 13 species with eDNA (100%; tables 6 and 7).

3.2.6. Mangrove tank

The mangrove tank exhibits the brackish-water mangrove swamps in Okinawa (figure 1e), keeping eight species of teleosts common to those environments. We confirmed reference sequences for all of these eight teleosts in the custom database (table 7) and detected eDNA from all of them (100%; tables 6 and 7).

3.2.7. Detection of non-tank species

The most serious pitfall of eDNA is the risk of contamination, which remains among the greatest experimental challenges to this field [45,46]. To avoid such risk, we performed decontamination procedures for laboratory spaces and equipment and physically separated pre- and post-PCR work spaces (see Material and methods), which are known to significantly limit the contamination [47]. Despite these efforts, a total of 286 446 reads (6.6%) were considered as those from non-tank species and most of them may represent false positives from various sources. In a similar metabarcoding study using universal primers, Kelly et al. [12] reported that approximately 25.5% of the tank sequences were assigned to taxa not living in the mesocosm tank (non-tank species) at the Monterey Bay Aquarium.

Although this study is not designed to rigorously determine the extent of detection rates of such false positives, it would be useful for future eDNA research using the metabarcoding approach to list possible sources of the non-tank species as exogenous DNA with some comments. They can tentatively be classified into: (i) other tank species (62 218 reads; 23.8%); (ii) species from other libraries on the same run (8925 reads; 3.1%); (iii) fish feed (86 204 reads; 30.1%); (iv) non-fish vertebrates (68 735 reads; 2.4%) excluding a spotted dolphin contained in the Kuroshio tank; and (v) unknown (116 264 reads; 42.3%) (figure 4).

Figure 4.

Compositions of the non-tank species (with more than or equal to 97% sequence identity to reference sequences in the custom database) for eDNA from the four tanks in the Okinawa Churaumi Aquarium. Percentages in parentheses are based on the total number of reads with sequence identity of more than or equal to 97% (table 6). For classification of the non-tank species, see text.

One of the most noteworthy examples is detection of non-tank species showing abundant reads in their respective tanks. Those tank species with pooled reads of more than 100 000 were consistently found across other tanks and even from some negative controls, including four species of tunas and mackerels (Rastrelliger kanagurta, Thunnus albacares, T. orientalis, Katsuwonus pelamis) plus a fussiler (Pterocaesio marri) from the Kuroshio tank, a parrotfish (Scarus ghobban) from the tropical fish tank, a snake mackerel (Thyrsitoides marleyii) from the deep-sea tank and a moonyfish (Monodactylus argenteus) from the mangrove tank. The occasional detection of those reads in the negative controls strongly suggests cross contamination in the laboratory, which seems unavoidable in eDNA studies using PCR amplifications [45]. Although we are unable to pinpoint the experimental step of such contamination, PCR-amplified eDNA during the library preparation, which generate billions of DNA copies in a single reaction, would be the most critical source for large amounts of exogenous DNA [45].

Detection of such non-tank species can be partly explained by re-intake of discharged seawater from the aquarium as it continuously pumps fresh seawater into the facility from the outer reef slope at a depth of 20 m (350 m offshore). Subsequently, the water is directed to various tanks after filtration and is finally led through a drain discharging on the same outer reef slope. Because of the close proximity of the influx and outflow of water (300 m separation), eDNA from non-tank species are likely to occasionally circulate in other tanks as exogenous DNA.

We also encountered putatively exogenous DNA from other libraries (figure 4), which notably consists of subarctic pelagic and benthic fishes from the Bering Sea and adjacent waters (e.g. salmon, northern smoothtongue, sculpins; 8925 reads; 3.1%). All of these dual-indexed paired-end libraries were constructed in other laboratories and cross contamination is highly unlikely. Kircher et al. [48] demonstrated such misassignment on the Illumina sequencing platform and the Illumina document (pub. no. 770-2013-046 as of 20 November 2013) recently acknowledged that it can occur during the demultiplexing, a process by which reads are assigned to the sample of origin.

Another source of exogenous DNA includes fish feed (e.g. mackerel, herring, flying fish). They are predominant in the Kuroshio tank (figure 4) where large amounts of those fishes are regularly fed to large-sized elasmobranchs, teleosts and dolphins. We also detected exogenous DNA from non-fish vertebrates (figure 4), mostly from that of humans and domesticated animals such as chickens and pigs, similar to that observed in the mesocosm tank at the Monterey Bay Aquarium [12]. Human eDNA is obviously present from staff diving and maintenance, whereas domesticated animal DNA have frequently been found in chemical reagents [49].

Finally, significant amounts of eDNA from non-tank species are derived from unknown sources other than fish or non-fish vertebrates listed above (116 264 reads; 40.6% among non-tank species and 2.5% among tank + non-tank species). Most of those reads comprise eDNA from non-subtropical marine and freshwater fishes from various localities. It should be noted that such dubious reads are few in eDNA from natural seawater (see below), only comprising 0.58% (5502 reads) of the total reads with more than or equal to 97% sequence identity (954 326 reads). This suggests that seawater from the aquarium tanks contain more exogenous DNA with unknown sources than those from natural environments. Further investigations are needed to more rigorously specify the identity of those dubious sequences from unknown sources.

3.3. Primer testing with eDNA from natural seawaters

In addition to the aquarium tanks, we also sampled natural seawater from a rocky coast around the coral reef nearby the aquarium (figure 1e,f) on two separate days (4 June and 7 November 2014). Using eDNA from four 2 l samples, we prepared four dual-indexed libraries and they were subjected to the MiSeq paired-end sequencing. After demultiplexing and subsequent pre-processing of the raw data from MiSeq, the outputs were subjected to the BLAST searches for taxonomic assignments. In total, 954 326 reads were assigned to fish species with more than or equal to 97% sequence identity to reference sequences in the custom database, of which 948 824 (99.4%) were putatively considered as endogenous eDNA.

From the four water samples, we detected 93 fish species distributed across 36 families and 62 genera (table 10). We confirmed that all of these species occur in the subtropical western North Pacific, although most of them are not particularly obvious and colourful, usually small-sized and/or fossorial reef-associated fishes unsuitable for the aquarium display. Of these 93 fish species, 64 are unique in these samples not detected in the four aquarium tanks and 11 families are new to the taxonomic list (table 10). Unfortunately, there is no background faunal information on fishes in this area, and we are unable to compare the present results with those from previous studies.

Table 10.

Taxonomic composition and read numbers for the 93 species of teleost fishes detected in the MiSeq analyses of eDNA samples from a rocky coast near the aquarium. (Only those species with identity more than or equal to 97% are shown with numbers of pooled reads from two samples. Asterisks indicate those species also occur in the four aquarium tanks (table 6).)

higher classificationa	species	total	no. 1 (3 June)	no. 2 (7 November)
Order Anguilliformes
Family Muraenidae	Echidna nebulosa	5085	5085	0
	Echidna polyzona	111	0	111
	Gymnothorax pictus	1141	1141	0
	Gymnothorax richardsonii	5850	5850	0
Order Clupeiformes
Family Clupeidae	Amblygaster sirm	94	0	94
Order Gonorynchiformes
Family Chanidae	Chanos chanos	32	0	32
Order Siluriformes
Family Plotosidae	Plotosus japonicus	43	43	0
Order Mugilliformes
Family Mugilidae	Chelon affinis	61	61	0
	Crenimugil crenilabis	440	440	0
	Mugil cephalus	20 700	20 700	0
Order Atheriniformes
Family Atherinidae	Atherinomorus lacunosus	980	0	980
	Hypoatherina lunata	830	0	830
Order Beloniformes
Family Exocoetidae	Oxporhamphus convexus	2489	0	2489
Family Belonidae	Tylosurus acus melanotus	6592	0	6592
	Tylosurus crocodilus	261 390	261 390	0
Order Beryciformes
Family Holocentridae	Neoniphon sammara*	4139	4139	0
	Sargocentron punctatissimum*	1579	0	1579
Order Gasterosteiformes
Suborder Syngnathoidei
Family Fistulariidae	Fistularia commersonii*	3258	2234	1024
Order Perciformes
Suborder Percoidei
Family Serranidae	Epinephelus polyphekadion	1408	1408	0
Family Carangidae	Caranx papuensis*	1152	1152	0
	Trachinotus blochii*	1882	1882	0
Family Lutjanidae	Lutjanus fulviflamma	11 748	11 748	0
Family Caesionidae	Pterocaesio chrysozona	673	0	673
Family Gerreidae	Gerres equulus	14	14	0
Family Lethrinidae	Lethrinus nebulosus*	60 040	59 414	626
Family Sparidae	Acanthopagrus sivicolus	19 625	16 511	3114
Family Mullidae	Parupeneus ciliatus	2865	2865	0
Family Pempheridae	Pempheris schwenkii*	8319	8319	0
Family Kyphosidae	Kyphosus bigibbus	1076	28	1048
	Kyphosus cinerascens	7861	7861	0
	Girella mezina*	16 978	16 978	0
Family Chaetodontidae	Chaetodon auriga*	27 016	27 016	0
	Chaetodon auripes*	2534	0	2534
	Chaetodon lunula*	6530	6530	0
	Chaetodon rafflesii	5780	5780	0
	Chaetodon vagabundus*	1151	1151	0
Suborder Labroidei
Family Pomacentridae	Abudefduf septemfasciatus	139	139	0
	Abudefduf sordidus*	3138	2089	1049
	Abudefduf vaigiensis*	1251	0	1251
	Cheiloprion labiatus	27 314	27 314	0
	Chrysiptera biocellata	1389	1389	0
	Chrysiptera cyanea*	53 598	52 632	966
	Chrysiptera glauca	1085	1085	0
	Chrysiptera rex	2493	0	2493
	Chrysiptera unimaculata	23 428	23 428	0
	Plectroglyphidodon lacrymatus	1 669	0	1669
	Pomacentrus albicaudatus	2025	2025	0
	Stegastes albifasciatus	27 359	27 359	0
	Stegastes fasciolatus	838	0	838
	Stegastes nigricans	37 494	37 494	0
Family Labridae	Halichoeres marginatus*	1973	1973	0
	Halichoeres trimaculatus	15 601	15 601	0
	Hemigymnus fasciatus	26	0	26
	Labroides dimidiatus*	745	745	0
	Stethojulis bandanensis	222	222	0
	Thalassoma bifasciatum	4453	4453	0
	Thalassoma hardwicke*	1091	1091	0
	Thalassoma lutescens*	2200	294	1906
	Thalassoma quinquevittatum	536	0
Family Scaridae	Chlorurus sordidus*	1777	1329	448
	Leptoscarus vaigiensis	280	280	0
	Scarus forsteni	1825	1825	0
	Scarus psittacus	1189	0	1189
	Scarus rivulatus*	1572	1572	0
	Scarus schlegeli*	2165	0	2165
Suborder Trachinoidei
Family Pinguipedidae	Parapercis cylindrica	751	751	0
Suborder Blennioidei
Family Blenniidae	Cirripectes castaneus	1442	0	1442
	Cirripectes imitator	3098	0	3098
	Istiblennius edentulus	120 080	118 090	1990
	Rhadoblennius ellipes	5585	0	5585
	Salarias fasciatus	3919	3248	671
Suborder Gobioidei
Family Gobiidae	Bathygobius cocosensis	1149	0	1149
	Bathygobius fuscus	70	70	0
	Trimma annosum	148	148	0
	Trimma caesiura	279	279	0
Suborder Acanthuroidei
Family Siganidae	Siganus fuscescens	42 912	35 205	7707
Family Acanthuridae	Acanthurus dussumieri*	2453	2453	0
	Acanthurus leucosternon	12 954	6492	6462
	Acanthurus lineatus	515	0	515
	Acanthurus nigrofuscus*	1516	1516	0
	Ctenochaetus binotatus	543	0	543
	Ctenochaetus striatus*	72	0	72
	Naso lopezi	0	3611	0
Suborder Scombroidei
Family Scombridae	Euthynnus affinis*	5147	0	5147
	Rastrelliger kanagurta*	20 734	12 870	7864
	Thunnus albacares*	1190	1190	0
Order Pleuronectiformes
Suborder Pleuronectoidei
Family Bothidae	Bothus pantherinus	244	244	0
Order Tetraodontiformes
Suborder Balistoidei
Family Balistidae	Balistapus undulatus	1124	0	1124
Family Monacanthidae	Cantherhines dumerilii	875	0	875
	Melichthys vidua*	583	0	583
	Rhinecanthus aculeatus	6785	5138	1647
Suborder Tetraodontoidei
Family Tetraodontidae	Arothron nigropunctatus	552	552	0
Family Diodontidae	Diodon holocanthus	152	152	0

^aClassification follows ‘Fishes of the World’ [32].

4. Concluding remarks

With the use of newly developed universal primers (MiFish-U/E) and a high-throughput NGS platform (Illumina MiSeq) in a metabarcoding approach to fish eDNA, we confirmed the detection of 232 fish species distributed across 70 families and 152 genera from four aquarium tanks and coral reefs in the subtropical western North Pacific. Those 232 species are not only taxonomically diverse, ranging from sharks and rays to higher teleosts, but are also greatly varied in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. The eDNA metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods. It could serve as an alternative (or complementary) tool for biodiversity monitoring that will greatly aid natural resource management and ecological studies of fish communities on larger spatial and temporal scales. In addition to eDNA, this metabarcoding approach is applicable to bulk samples (total DNA), such as those from net collections containing multiple life stages and damaged specimens with no diagnostic characters for species identification. Furthermore, the detection of various mammals suggests the broad applicability of this approach to non-fish vertebrates with slight modifications of primer sequences to accommodate unique nucleotide variations among those organisms.

Nevertheless, there are several methodological challenges that must be addressed before this metabarcoding approach is likely to become a mainstream technology in fish biodiversity research. The first one would be to explore a method that generates a greater diversity of MiFish sequences at a lower cost to avoid PCR dropouts (=false negatives). Those taxa that are prone to the dropouts can potentially skew the relative abundance in eDNA sequences, making it difficult to assess biologically relevant differences across taxonomic groups [34]. Considering stochasticity of individual PCR reactions and PCR bias derived from primer–template mismatches, optimal number of PCR replicates and use of multiple annealing temperatures should be explored to comprehensively detect fish eDNA without the dropouts. In a fungal metabarcoding study, pooling multiple repeated PCRs and using multiple annealing temperatures were recommended to facilitate the recovery of more correct species richness [50].

The second one is false positives that are consistently observed in our metabarcoding analyses of the four aquarium tanks (figure 4). Although sources of the majority of those reads (57.7%) can be identified (e.g. exogenous DNA from other tank species, other libraries, fish feed, non-fish vertebrates), there are a significant number of reads from unknown sources other than the former (42.3%; 2.5% of the total number of reads with more than or equal to 97% sequence identity). Such dubious reads are relatively few in eDNA from the coral reefs near the aquarium (0.58%) and subsequent analyses of eDNA from oceanic waters that are remote from human activities support this observation (results not shown). This also illustrates the limits of the eDNA metabarcoding approach that cannot discriminate sources of eDNA from either exogenous or endogenous origins.

The third one is completeness of the reference sequence database, which is indispensable for correct taxonomic assignments. Reference sequences in the custom database used in the present analyses were derived from two data sources. The first one is MitoFish, from which all whole mitogenome sequences (1324 sequences) and partial mitogenome sequences containing MiFish sequences (2953 sequences) were obtained. The second one is supplementary MiFish sequences assembled in M.M.'s laboratory (648 sequences; electronic supplementary material, table S3). In total, it covers approximately 4230 fish species distributed across 457 families and 1827 genera as of 4 October 2014. Obviously, this taxonomic coverage is far from satisfactory, considering the enormous diversity of fishes with at least 27 977 species placed in 515 families and 1827 genera [32]. Nevertheless, total number of fish whole mitogenome sequences in MitoFish [17] has steadily increased since its 2006 onset and the number of original MiFish sequences has increased considerably as a result of recent massive sequencing of the two large tissue collections (figure 5), currently reaching 2364 sequences from a wide variety of fish taxa. Obviously, our custom-made database for newly designed eDNA markers is not compatible to that of other online resources. For example, the Fish Barcode of Life project (http://www.fishbol.org/index.php) currently deposits 107 033 barcoded sequences, which include approximately 10 800 species. Although the increase in mitogenomic sequences will continuously improve this situation, we agree with Thomsen & Willerslev [45] who suggested that, given the massive increase in DNA sequencing cost-efficiency, future DNA reference databases should focus on whole mitochondrial or even nuclear genomes for much wider applications than traditional DNA barcoding.

Figure 5.

Temporal accumulation of the number of whole mitogenome sequences (ca 16 500 bp) curated in MitoFish and the MiFish sequences (ca 170 bp) in the custom database. The former data were taken from a change log recorded in MitoFish (http://mitofish.aori.u-tokyo.ac.jp/about/log.html).

Ethics

This study was approved by the Okinawa Churaumi Aquarium and water sampling permissions in or around the aquarium were not needed.

Data accessibility

Custom Ruby scripts used in in silico evaluation of interspecific variation are available from http://dx.doi.org/10.5061/dryad.54v2q. Raw reads from the MiSeq sequencing are available from the DDBJ Sequence Read Archive (DRR030411–030428). The bioinformatic pipeline from data pre-processing through taxonomic assignment (including Perl scripts) is available from http://dx.doi.org/10.5061/dryad.n245j.

Author' contributions

M.M. conceived and designed the study, designed the primers, carried out water sampling and the molecular laboratory work for metabarcoding and data analysis, and drafted the manuscript; Y.S. constructed the bioinformatic pipeline, carried out data analysis and drafted the manuscript; T.F. carried out in silico evaluation of the primer performance; T.S. and J.Y.P. carried out the molecular laboratory work for building the custom database; K.S. designed and carried out the water sampling at the aquarium and helped the data analyses; T.M. designed the study, carried out water sampling and helped draft the manuscript; S.Y. helped the data analysis and draft the manuscript; H.Y. designed the study, carried out water sampling and helped draft the manuscript; H.A. conceived and designed the study and helped the data analyses and draft the manuscript; M.K. coordinated the study and helped draft the manuscript; W.I. helped design of the primers, carried out in silico evaluation of the primer performance, helped construct the bioinformatic pipeline and drafted the manuscript. All authors gave final approval for publication.

Competing interests

We have no competing interests.

Funding

This study was supported as basic research by CREST from the Japan Science and Technology Agency (JST), by a grant from the Canon Foundation, and by MEXT/JSPS KAKENHI no. 26291083 to M.M. and nos. 23710231/268036 to W.I. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.