Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam

doi:10.21203/rs.3.rs-6219031/v1

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Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam

https://doi.org/10.21203/rs.3.rs-6219031/v1

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Accurate assessment of overall biodiversity, and specifically ichthyoplankton diversity, is often hindered by the morphological complexity and small size of eggs and larvae. To address this challenge, we developed and optimized two novel COI primer sets for improved species identification. First, universal primers were designed in silico based on five target phyla, and subsequently in silico assessment on 67 non-target phyla, demonstrating broad taxonomic applicability for biodiversity assessments. High successful amplification was achieved in four out of five target phyla, and an additional five non-target phyla, this confirmed the primers utility across a wide range of taxa. Second, Chordata-specific primers were designed to precisely target fish species. The Chordata-specific primers were then applied to an ichthyoplankton case study in Vietnam, enabling a comparison between molecular and traditional morphological identification methods. Molecular identification revealed higher species richness and provided more detailed genetic diversity insights, particularly for eggs and larvae with underdeveloped features. However, the lack of reference sequences in databases occasionally limited molecular identification. This study underscores the importance of integrating molecular and morphological approaches for accurate ichthyoplankton species identification. The optimized COI primers provide a valuable tool for biodiversity research, especially in complex ecosystems like Vietnam. These findings contribute to a deeper understanding of ichthyoplankton diversity and have significant implications for conservation and fisheries management.

COI

primer

Ichthyoplankton

morphology

DNA barcode

In recent decades, the mitochondrial gene encoding cytochrome c oxidase subunit I (COI) has emerged as a widely used DNA barcode for numerous animal species, as evidenced by the growing number of sequences deposited in databases such as NCBI and BOLD (Frantine-Silva et al. 2015, Becker et al. 2015). A 650 bp COI mtDNA fragment has been widely adopted as the "standard barcode" for a broad range of taxa (Hebert et al. 2003a, Hebert et al. 2003b). This success has demonstrated the effectiveness of DNA barcoding as a valuable tool for biodiversity inventory, monitoring, and assessment studies (Imtiaz et al. 2017, Petit-Marty et al. 2021, Souza et al. 2016). Following the establishment of COI mtDNA as a universal barcode, universal primer sets were widely employed, successfully identifying a vast number of taxa and addressing population and conservation issues in biodiversity research (Raju and Haldar 2018, Imtiaz et al. 2017).

However, several challenges have hindered the success of PCR amplification reactions. These include the unsuitability of universal primers for certain phyla (Ivanova et al. 2007, Melo et al. 2021), and the overlap between intraspecific and interspecific genetic variation (Imtiaz et al. 2017, Hou et al. 2020).

These challenges have necessitated improvements in DNA barcoding techniques. Ongoing research efforts are directed towards developing specific gene regions (Souza et al. 2016), designing primers tailored to different taxonomic levels and species (Hoareau and Boissin 2010, Bhavan et al. 2015, Melo et al. 2021, Martínez-Arce and Elías-Gutiérrez 2013), utilizing primer cocktails and stepwise protocols (Ivanova et al. 2007, Weigt et al. 2012), and exploring novel methodologies for primer design (Liu et al. 2020).

In silico primer design and validation, supported by robust bioinformatic tools (Kumar and Chordia 2015) have significantly advanced the field. These approaches not only increase the efficiency of primer design but also reduce the time and cost associated with empirical testing (Wu et al. 2021, Melo et al. 2021).

In addition to developing highly effective primer pairs for barcoding projects (Martínez-Arce and Elías-Gutiérrez 2013, Wu et al. 2021, Melo et al. 2021), there is a growing emphasis on obtaining longer COI gene sequences (Liu et al. 2020, Hoareau and Boissin 2010, Salonna et al. 2021). The rapid growth of complete genome databases and the availability of high-throughput sequencing technologies, such as genome skimming (Trevisan et al. 2019), facilitate the acquisition of longer sequences, even from single genes. As sequence length increases, the percentage of sequence matches also rises, enhancing the reliability of species identifications.

To gain a comprehensive understanding of an organism, information beyond genetic data is crucial. This includes diagnostic characters, life history, population dynamics, and environmental interactions. DNA barcoding can effectively identify unknown samples (Keele et al. 2014), link developmental stages within a species' life cycle (Lira et al. 2023, Jiang et al. 2023, Wibowo et al. 2015), and reveal cryptic diversity (Batubara et al. 2021, Wibowo et al. 2017). However, in most cases, genetic tools like DNA barcoding are most effectively used in conjunction with morphometric data, geographic distribution, and environmental parameters to provide a holistic understanding of the study organism (Rathnasuriya et al. 2021, Lira et al. 2023, Becker et al. 2015, Jiang et al. 2023).

A continuing challenge for monitoring and resource management programs is the accurate identification of early developmental stages of species (eggs and larvae) in collected plankton samples (Labare 2022, Becker et al. 2015). This not only aids in identifying spawning sites and seasons but also facilitates predictions of dispersal patterns, enabling the implementation of appropriate management strategies and conservation measures. The major obstacle hindering the wider use of planktonic egg and larval surveys has been effectively addressed through the integration of morphometric identification and DNA barcoding (Lewis et al. 2016, Lira et al. 2023, Frantine-Silva et al. 2015, Becker et al. 2015). More recently, non-invasive metabarcoding technology utilizing environmental DNA (eDNA) has emerged as a promising solution (Rubbmark et al. 2018, Breitbart et al. 2023, Hajibabaei et al. 2019).

Here, we employed both in silico and empirical approaches to: i) design universal and phylum-specific primers to amplify longer COI mtDNA sequences, ii) comparatively evaluate those with large datasets of available sequences from a wide range of phyla to select the most suitable primer sets for barcoding projects, considering specific project conditions, and iii) investigate the integration of morphological identification and DNA barcoding through a case study of ichthyoplankton monitoring in Vietnam.

In silico primer design and validation

In order to design universal and specific primers for amplifying the mitochondrial cytochrome c oxidase subunit I (COI) gene, a sequence database was constructed. This database included partial and complete COI mtDNA gene sequences retrieved from both the National Center for Biotechnology Information (NCBI) and the Barcode of Life Data Systems (BOLD) and was collectively named “pcmDB”. Subsequently, COI gene sequences exceeding 950 base pairs (bp) were isolated from “pcmDB” using the tidyverse package (Wickham et al. 2019) in R software to generate a refined database named "filter-pcmDB". Satisfactory sequences primarily belonging to five phyla (Chordata, Arthropoda, Echinodermata, Mollusca, and Platyhelminthes) were then extracted from "filter-pcmDB" (1,500 bp maximum length) and designated as "filter-pcmDB-5p". These sequences were subsequently aligned using the Graph Clustering Merger (GCM) algorithm implemented in the Magus software (Smirnov and Warnow 2021). Gap-removal was performed using Biopython in Python v3.10 (https://www.biopython.org/). Finally, the resulting aligned sequences were saved in FASTA format ("asfasta") to facilitate primer design.

To identify the specific region for primer design, a script named 'ConsensusAlign.R' was created. This script generates a single consensus sequence adhering to the International Union of Pure and Applied Chemistry (IUPAC) nomenclature. Degeneration-reducing modifications were incorporated into all primers, allowing for the use of IUPAC ambiguity codes at three positions for both universal primer design (UP) and Chordata-specific primer design (CSP). The target region was selected based on flanking highly conserved sequences and a gap tolerance of less than 20%. Subsequently, the 'UP_generations.R' script was employed to design multiple primers. The analysis scripts are available on GitHub (https://github.com/quangsang52sh/UPD_analysis). The primer design parameters were generally set as follows: melting temperature (Tm) minimum of 30°C and maximum of 60°C, primer length between 20 and 26 nucleotides, GC content (GC%) ranging from 30–60%, minimum identity of 80%, minimum coverage of 90%, AT penalties ≤ 2, degeneracy ≤ 96, maximum of 3 loop and stem structures; GC clamps (minimum 50% GC content in the last 6 bases at the 3' end). Primers fulfilling all these criteria were considered valid and are provided in Supplement S1.

The efficiency of candidate primers was validated by analyzing their properties using the IDT OligoAnalyzer tool (https://www.idtdna.com/calc/analyzer). Key properties, including DNA duplex formation (ΔG), melting temperature (Tm), self-dimer, and hetero-dimer formations, were calculated for all forward and reverse primers. Primers were considered efficient only if they lacked secondary structures like self-dimers and hairpins.

To facilitate the selection of optimal primers, each pair of forward and reverse primers was evaluated based on the stability of their secondary structures. Criteria for selection included: similar melting temperatures (Tm difference ≤ 6°C); limited hetero-dimer formation (maximum base pair match ≤ 6, number of structures ≤ 35 with ΔG ≥ -45 kcal/mol), and absence of stable 3'-terminal dimers formed by either primer hybridizing with itself or its partner (ΔG ≥ -2.0 kcal/mol) (Chavali et al. 2005) (Supplement S1).

The specificity of the optimized primers was assessed by performing local alignments against the pcmDB databases using the SeqMap tool (version 1.0.8, Jiang and Wong 2008) implemented in C++. The universal primer (UP) was aligned with the ‘filter-pcmDB-5p’ database, while the Chordata specific primer - CSP was aligned with sequences from the phylum Chordata in ‘filter-pcmDB-5p’. Validated primer sequences were assessed for their ability to match database entries and their efficiency in amplifying PCR products of the expected size. Primers were aligned to the database, allowing for 0–5 substitutions. The first match was considered on-target, while the second match was designated off-target. Primer pairs were selected to ensure that the predicted amplicon size matched the COI mtDNA gene. The 'tidyverse' package in R was used to determine the total number of on- and off-target matches. To minimize non-specific amplification, a total matching was calculated by subtracting the total number of off-target sites from the total number of on-target sites, excluding any overlapping regions. Primers with the lowest off-target rates and highest on-target rates, and matching rate (≥ 50% for UP and ≥ 70% for CSP) were prioritized. The number of target phyla and species was also considered during primer selection (Supplement S2).

Next, the performance of newly design primers were compared to others universal primers and Chordata specific primers sets (Table 1). Matching positions of all primers were manually visualized along the ≥ 1000 bp COI gene. Two criteria were used for comparison: % sequence similarity and primer sequence index. Sequence similarity (%) was calculated as the percentage of species within the phylum that the primer could match. The primer sequence index was calculated as the inverse of the degeneracy value (i.e., index = 1/D).

Table 1

**Information of universal and specific primers applied to test the primer performance.** The bold texts are our primer
No	Primer name	Primer sequences	Degeneracy value	Melting temp. *	GC %	Amplicon length	Target	References
Universal primers (5’-3’)
1F	LCO2198	F: TCAACAAATCATAAAGATATTGG	1	56	26.1	650–700	Metazoan invertebrates	(Black et al. 1994)
1R	HCO1490	R: TAAACTTCAGGGTGACCAAAAAATCA	1	64	34.6	650–700		(Black et al. 1994)
2F	dgLCO	F: GGTCAACAAATCATAAAGAYATYGG	4	61	36	650–700		(Geller et al. 2013; Meyer, Geller, and Paulay 2005)
2R	dgHCO	R: TAAACTTCAGGGTGACCAAARAAYCA	4	65	38.5	650–700		(Geller et al. 2013; Meyer, Geller, and Paulay 2005)
3F	FISHCOILBC	F: CTCAACYAATCAYAAAGATATYGGCAC	8	63	38.9	650–700	Fishes, mammal, and bird	(Giusti et al. 2017a)
3R	FISHCOIHBC	R: ACTTCYGGGTGRCCRAARAATCA	16	66	47.8	650–700	Fishes, mammal, and bird	(Giusti et al. 2017a)
4F	UPF.22	F: TCWACMAAYCAYAAAGAYATYGG	64	51	38.6	830–850	Metazoan invertebrates (5 phyla)	This study
4R	UPR.880	R: TCGKGTRTCWACRTCYATTCCWAC	64	55	45.1	830–850	Metazoan invertebrates (5 phyla)	This study
Fish specific primers (5’-3’)
5F	Fish-F1	F: TCTCAACCAACCATAAAGACATTGG	1	63	40	650–700	Freshwater fish	(Ward et al. 2005)
5R	Fish-R1	R: TATACTTCTGGGTGCCCAAAGAATCA	1	66	42	650–700	Freshwater fish	(Ward et al. 2005)
6F	FISH-F6	F: ACYAAYCACAAAGAYATTGGCA	8	62	38.6	650–700	Freshwater fish	(Handy et al. 2011; Giusti et al. 2017b)
6R	FISH-R7	R: TARACTTCTGGRTGDCCRAAGAAYCA	24	66	43.6	650–700		(Handy et al. 2011; Giusti et al. 2017b)
7F	Fish-F2	F: CATCCTACCTGTGGCAATCAC	1	63	52.4	650–700		(Liu et al. 2020)
7R	Fish-R2	R: GGGCTCAGACAATAAATCCT	1	60	45	650–700		(Liu et al. 2020)
8F	FF2d	F: TTCTCCACCAACCACAARGAYATYGG	12	67	48.1	655–700	Marine fish	(Ivanova et al. 2007)
8R	FR1d	R: CACCTCAGGGTGTCCGAARAAYCARAA	8	69	50	655–700	Marine fish	(Ivanova et al. 2007)
9F	FISH_CO1LBC	F: TCAACYAATCAYAAAGATATYGGCAC	8	63	36.5	655–700	Fish	(Handy et al., 2011; Weigt et al. 2012)
9R	FISH_CO1HBC	R: ACTTCYGGGTGRCCRAARAATCA	16	66	47.8	655–700	Fish	(Handy et al., 2011; Weigt et al. 2012)
10F	CSPF.25	F: ACMAAYCAYAAAGAYATYGG	32	54	45	860–880	Chordata (mainly fish)	This study
10R	CSPR.950	R: ARTCARCTRAAKACTTTSACSCC	8	53	37	900–950	Chordata (mainly fish)	This study
Calculated from https://sg.idtdna.com/calc/analyzer (except our primers that already did in* supplement S1)

The final selected primers were further evaluated for their specificity against other non-target phyla (filter-pcmDB). To assess this, primers were considered suitable if they exhibited a matching capacity of ≥ 5% of the total sequences within the phyla, and maintained the matching rate exceeding > 10%.

New sets of primer: Integrated morphological and DNA barcoding

Tissue collection and morphological analysis

Planktonic samples, comprising eggs and larvae, were collected along the Vietnamese coast in 2023 for morphological and molecular analyses. Specimens, preserved in 95% ethanol, were sorted into major phyla: Arthropoda, Mollusca, Chaetognatha, and Cnidaria. Ichthyoplankton (fish eggs and larvae), representing the phylum Chordata, were selected, encompassing distinct species and examples of intraspecific morphological variation. Morphometric measurements were recorded, and taxonomic keys (Leis 2010, Leis 2014, Richards 2005, Ahlstrom and Moser 1980, Brownwell 1979, Rass, 1972) were used to identify specimens to the lowest possible taxonomic level using a Euromex stereomicroscope.

To evaluate the efficacy of universal primers (UPs) across diverse taxa, tissue samples (one to two specimens each) were collected from pre-identified organisms representing various phyla, including Arthropoda (lobster, parasitic rhizocephalan, amphipod, barnacle, ant, midge), Chordata (swiftlets, adults of bony and cartilaginous fish, humans), Echinodermata (sea cucumbers, sea stars), Mollusca (squids, bivalves), and Platyhelminths (digenean and monogenean flatworms). Cross-amplification tests were also conducted using previously identified Annelida (Polychaeta), Anthozoa (sea anemone), Nematoda (Anisakis), and Acanthocephala (thorny-headed worm). The planktonic samples collected were included in both the UP evaluation and cross-amplification tests.

For the ichthyoplankton case study using CSP primers, eggs (1–2 mm diameter) or larvae (2–5 mm length) were obtained from three to thirty-eight specimens of 63 species within each of seventeen common fish orders and series: Acanthuriformes, Acropomatiformes, Anguilliformes, Aulopiformes, Blenniiformes, Callionymiformes, Carangiformes, Clupeiformes, Gobiiformes, Mugiliformes, Ophidiiformes, Perciformes, Pleuronectiformes, Scombriformes, Tetraodontiformes, Eupercaria incertae sedis, and Ovalentaria incertae sedis.

Primer evaluation and cross-amplification tests

Total genomic DNA was extracted from tissue samples using the Genomic DNA Isolation Kit (Promega, USA). PCR amplification of partial COI gene sequences was performed using the new primer sets on a Biorad C1000 Touch™ Thermal Cycler. Each 25 µL PCR reaction mixture contained 12.5 µL GoTaq® G2 Green Master Mix (Promega, USA), 1.5 µL of each primer (10 nM, IDT, USA), 5 µL of DNA template (25 ng/µL), and 6 µL of nuclease-free water (Promega, USA). Thermal cycling conditions were standardized as follows: an initial denaturation at 95°C for 3 minutes, followed by 34 cycles of denaturation at 95°C for 30 seconds, annealing at an optimized temperature gradient (42°C − 54°C) for 45 seconds, and extension at 72°C for 50 seconds, with a final extension step at 72°C for 5 minutes.

To assess PCR performance, 1 µL of each PCR product was subjected to electrophoresis on a 1.5% agarose gel. The presence of expected amplicons (approximately 850 bp for UP and 930 bp for CSP) was confirmed by comparison with a 100 bp DNA Ladder (Promega, USA). PCR efficiency was evaluated based on the amplification rate (percentage of DNA samples that yielded the expected amplicon).

Sequencing reactions were performed bidirectionally using the appropriate amplification primers and a BigDye Terminator Cycle Sequencing Kit v.2.0 (Applied Biosystems, USA). Sequencing products were analyzed on an ABI 3730 automated sequencer (Applied Biosystems, USA). Base-calling of sense and antisense chromatograms was conducted using Tracy software (Rausch et al. 2020) with a Q-score cutoff of ≥ 10. Forward and reverse reads were then merged using PEAR (Zhang et al. 2014) with a p-value threshold of ≤ 0.01 for overlap consensus. Finally, all generated contigs were compiled into a single FASTA file. Species identification was performed using BLASTn v2.12 (Camacho et al. 2009) against the 'pcmDB' database. BLASTn searches employed megaBLAST parameters, using a nucleotide match score of 1 and a mismatch penalty of -2, effectively corresponding to a 95% sequence identity threshold.

Ichthyoplankton - Morphological identification versus DNA barcoding

Ichthyoplankton morphological identifications (30 species) were compared with those obtained using the COI mtDNA barcoding method. The barcoding identification was used as the reference to calculate sensitivity, specificity, positive predictive value, and negative predictive value, following the methodology described by (Kara and Yüksek 2023). Statistical analyses were performed according to the formulas devised by (Trevethan 2017). Figure S2 presents the comparison criteria and calculation formulas.

Reference database and in silico primer design

A total of 16,514,108 available sequences were retrieved from both the NCBI and BOLD databases to construct the 'pcmDB'. This database encompassed 1,365,355 species across 91 phyla, 333 classes, 1,376 orders, 7,573 families, and 69,059 genera. The 'filter-pcmDB' dataset, comprising 658,711 sequences extracted from 'pcmDB', included 108,892 species belonging to 79 phyla, 272 classes, 1,044 orders, 4,510 families, and 21,886 genera. The 'filter-pcmDB-5p' dataset, specifically focusing on 5 target phyla, included 20,060 species from Chordata, 52,452 species from Arthropoda, 1,245 species from Echinodermata, 870 species from Platyhelminthes, and 2,664 species from Mollusca. Database information is presented in Table S1 After rigorous removal of sequences and positions containing gaps, the 'asfasta' file contained 30,158 sequences with an average length of 1,520 base pairs, which were used for the subsequent primer design steps.

Based on key properties and criteria in the primer design pipeline, 12 and 14 candidate primers were initially selected from 88 and 135 total primers for UP and CSP, respectively. Of these, only 8 primers (2 forward and 6 reverse) passed the GC clamp test for both primer design strategies. Most primers met the criteria for self-dimer formation, with the exception of reverse primer UPR.878 (which exhibited a maximum base pair overlap > 6). Subsequent hetero-dimer analysis identified two effective primer pairs for UP (UPF.22-UPR.880 and UPF.22-UPR.883) and three effective primer pairs for CSP (CSPF25-CSPR883, CSPF25-CSPR947, and CSPF25-CSPR950). Information about the primers through the in-silico design steps is presented in Table 2.

Table 2

**In Silico** **designed primers for Universal and Chordata targets** (PL: Primer length, Ident: Identity, Cov: Coverage, D: Degeneration).
Primer ID	Primer sequence	PL	Iden.	Cov.	D	Tm	%GC	GC clamp	Self-dimer		Hetero-dimer			Amplicon length
Primer ID	Primer sequence	PL	Iden.	Cov.	D	Tm	%GC	GC clamp	ΔG (kcal/mol)	Max bp /No. structure	F-R primer	ΔG (kcal/ mol)	Max bp /No. structure	Amplicon length
Universal primer (UP) 5’ – 3’
UPF.22	TCWACMAAYCAYAAAGAYATYGG	23	0,86	1	64	51	30,43	TRUE	-40.11	6(21)
UPR.883	TCGKGTRTCWACRTCYATTCC	21	0,85	1	32	52	42,86	TRUE	-37.88	4 (17)	UPF22-883	-40,1	5 (31)	861
UPR.878	GTRTCSACRTCYATTCCSACSG	22	0,85	1	64	50	40,91	TRUE	-42.98	8 (25)	UPF22-878	-42,98	4 (29)	851
UPR.880	TCGKGTRTCWACRTCYATTCCWAC	24	0,84	1	64	55	41,67	TRUE	-42.45	4 (23)	UPF22-882	-42.45	6 (31)	858
Chordata-specific primer (CSP) 5’- 3’
CSPF.25	ACMAAYCAYAAAGAYATYGG	20	0,89	1	32	54	45	TRUE	-35.31	6(16)
CSPR.883	TCGKGTATCWACATCYATTCC	21	0,86	1	8	55	47,62	TRUE	-36.68	4 (16)	CSPF25-883	-36.68	5 (23)	858
CSPR.947	ARTCAACTAAATACTTTSACSC	22	0,88	1	8	50	31,82	TRUE	-37.02	5 (15)	CSPF25-947	-37.02	5 (28)	922
CSPR.950	ARTCAACTAAATACTTTSACSCC	23	0,89	1	8	53	34,78	TRUE	-40.9	5 (15)	CSPF25-950	-40.09	5 (29)	925

The total number of on- and off-target matches for optimized UP primer pairs across available target phyla (Fig. 1) showed that UPR.880 had fewer off-target matches than UPR.883 (52,737 ± 24,185 (9,410 − 165,427) vs. 96,311 ± 28,725 (32,311–217,193), and a similar number of on-target matches (250,692 ± 51,060 (25,379 − 352,293) vs. 283,682 ± 36,420 (136,305–363,139). UPR.883 targeted the highest number of phyla and species (66 and 60,953, respectively), followed by UPF.25 (64 and 30,543), while UPR.880 targeted the fewest (61 and 58,724). All primers had a matching ratio greater than 50%. For CSP, all primers exceeded the 70% matching rate threshold, exhibiting minimal variation in the number of targeted species (ranging from 17,725 to 17,794) (Supplement S2). While all primers satisfied the selection criteria, the UPF.22/UPR.880 and CSPF.25/CSPR.950 primer pairs were chosen for subsequent analysis due to their larger predicted amplicon sizes.

Figure 2 compares the performance of the newly designed primers with eight published primers (three universal and five specific). Amplicon size predictions (based on primer placement within the COI mtDNA gene segment) demonstrate that the novel primers encompass the amplification range of all existing primers (Fig. 2A). While the newly designed UP primers exhibit a low index (indicating high degeneracy), their sequence similarity is comparable to, or greater than, existing primers (numbered 3,4), particularly within the Arthropoda and Chordata phyla (Fig. 2B). A lower efficiency observed in Platyhelminthes is likely attributable to the limited number of available sequences for this phylum. Among five primer pairs compared, the CSP pair exhibited sequence similarity greater than or equal to that of three other primer pairs (numbered 6–8, Fig. 2C). For the two degenerate primers (8–9), the CSP pair had a higher primer index and sequence similarity equivalent to that of primer 9 (which was primarily designed for fish) (Fig. 2C).

The specificity of the designed-UP primer pair and the compared primer pairs for target and non-target phyla is shown in Fig. 3. Most primers exhibited near-expected (50% matching) values for non-target phyla, while target phyla showed higher observed values. Phyla with minimal sequence matching (< 5%, indicated by red points) were observed for all primer pairs, but the proportion of these phyla was lower for the degenerate primers. While all compared forward primers exhibited < 10% non-target phyla matches (reverse primers ≥ 39%), both new designed universal primer pairs met the required matching rate (10% and 23%, respectively).

Morphological identification

As previously noted, morphological analysis focused exclusively on planktonic samples. Because these specimens were primarily larvae (except for Chordata, which consisted mainly of Actinopterygii ichthyoplankton), identification for both target and non-target phyla was generally limited to the class or family level. Species identified are marked with an asterisk (*) in Table 3, and include Copepods, Gastropod snails, Sagittoidea (arrow worm), and Hydrozoa (small jellyfish). Their external morphologies are shown in Fig. 4, and morphological descriptions are presented in Table S2. Ichthyoplankton specimens, representing a broad range of taxonomic levels (detail morphological description not showed), were applied to compare the efficacy of DNA barcoding and traditional morphology-based identification (Figure S1). Representative images of early developmental stages (eggs and larvae) with further details provided in Figure S2.

Table 3

Species composition and PCR amplification efficiency using Universal and Phylum-specific primers
No	Phylum/Class	Orders	PCR reaction	Successful rate (%)	Genbank/BOLD identity (%)
A	Universal primer (UPF.22 – UPR.880) – Target phyla		43	90.69%
I	Chordata		13	100
1.1	Aves	Apodiformes (Aerodramus fuciphagus)	3	3	99.08
1.2	Mammalia	Primates (Homo sapiens)	2	2	100
1.3	Actinopterygii	Blenniiformes (Omobranchus fasciolatoceps Parablennius thysanius Plagiotremus tapeinosoma )	3	3	99.88 98.89 99.85
1.3	Actinopterygii	Acanthuriformes (Leiognathus berbis Photolateralis stercorarius Photopectoralis bindus )	3	3	100 100 99.76
1.4	Elasmobranchii	Myliobatiformes (Pteroplatytrygon violacea)	1	1	99.99
1.4	Elasmobranchii	Rhinopristiformes (Rhinobatos jimbaranensis)	1	1	99.98
1.5	Reptilia	Testudines (Indotestudo elongata)	1	1	99.87
II	Arthropoda		10	100
II.1	Crustacean	Decapoda (Panulirus homarus)	2	2	99.4
II.2	Thecostraca	Rhizocephala (Sacculina angulata)	1	1	98
II.2	Thecostraca	Scalpellomorpha (Octolasmis angulata)	1	1	98.27
II.3	Malacostraca	Amphipoda (Amphipoda sp.)	1	1	97
II.4	Copepoda	Calanoida (Canthocalanus pauper)*	1	1	98
II.4	Copepoda	Harpacticoida (Normanellidae sp.)	1	1	80
II.5	Insecta	Hymenoptera (Oecophylla smaragdina)	1	1	99.8
		Diptera (Kiefferulus longilobus)	1	1	98.69
		Coleoptera (Mesosa sp.)	1	1	89
III	Mollusk		7	85.71
III.1	Cephalopoda	Sepiida (Sepiola sp.)*	1	1	91
III.2	Gastropoda	Littorinimorpha (Linatella caudata)*	1	1	100
III.2	Gastropoda	Neogastropoda (Babylonia areolata)	1	1	98.5
III.3	Bivalvia	Pterioida (Pinna atropurpurea)	3	2	99.83
III.3	Bivalvia	Arcida (Barbatia sp.)	1	1	91.79
IV	Echinodermata		6	83.33
IV.1	Asteroidea	Valvatida (Nardoa variolata)	2	2	98.2
IV.2	Holothuroidea	Holothuriida (Holothuria fuscogilva Holothuria atra Holothuria hilla)	4	3	99.9 99.8 97.9
V	Platyhelminthes		7	57.14
V.1	Trematoda	Plagiorchiida (Haplorchis taichui Centrocestus_formosanus Allocreadium sp.)	5	3	99.2 97.3 91
V.2	Monogenea	Mazocraeidea (Thaparocleidus sp.)	2	1	92
B	Universal primer (UPF.22 – UPR.880) - Cross amplification		8	75
I	Acanthocephala / Polyacanthocephala	Polyacanthorhynchida (Polyacanthorhynchus sp.)	1	1	92
II	Nematoda/ Chromadorea	Rhabditida (Raphidascaris trichiuri)	1	1	98.9
III	Cnidaria/ Anthozoa	Actiniaria (Heteractis aurora)	1	1	98.2
	Cnidaria/ Hydrozoa	Tiaropsidae (Tiaropsis sp.)*	2	1	95.3
IV	Chaetognatha/ Sagittoidea	Aphragmophora (Zonosagitta bedoti)*	1	1	99.9
V	Annelida/ Polychaeta	Dinophilidae (Dinophilus sp.)	2	1	90
	Order/Family	Species	PCR reaction	Successful rate (%)	Genbank/BOLD identity (%)
C	Chordata specific primer (CSPF.22 – CSPR.950) – Ichthyoplankton case study		179	94.97
1	Acanthuriformes Leiognathidae Leiognathidae Leiognathidae	Leiognathus berbis Photolateralis stercorarius Photopectoralis bindus	9	9	100 100 100
2	Acropomatiformes Pempheridae Trichonotidae	Pempheris schwenkii Ophichthidae sp.	3	3	99.8 93.6
3	Anguilliformes Muraenidae Ophichthidae	Gymnothorax buroensis Callechelys sp.	3	3	99 91.08
4	Aulopiformes Synodontidae	Synodus dermatogenys Saurida microlepis Trachinocephalus myops	13	12	99 98.9 99.2
5	Blenniiformes Blenniidae	Gerres sp. Omobranchus fasciolatoceps Parablennius thysanius Plagiotremus tapeinosoma	10	10	82 99.88 98.89 99.85
6	Callionymiformes Callionymidae	Callionymus sp. Callionymus meridionalis Callionymus schaapii	9	9	97 99.76 99.81
7	Carangiformes Carangidae	Alepes kleinii Selaroides leptolepis Megalaspis cordyla	8	8	99.52 100 98.5
8	Clupeiformes Dorosomatidae Engraulidae Engraulidae Engraulidae Engraulidae	Sardinella fijiensis Encrasicholina heteroloba Encrasicholina punctifer Stolephorus insularis Thryssa hamiltonii	38	36	99.76 100 99.08 99.85 99.76
9	Eupercaria incertae sedis Caesionidae Labridae Nemipteridae Nemipteridae Nemipteridae Nemipteridae Scaridae Sciaenidae Sillaginidae Sparidae	Pterocaesio digramma Halichoeres nigrescens Nemipterus furcosus Nemipterus japonicus Pentapodus setosus Scolopsis taenioptera Scaridae sp. Johnius carouna Sillago sihama Sparus aurata	28	26	100 99.65 100 99.85 100 99.85 81 98.97 99.53 100
10	Gobiiformes Gobiidae Gobiidae Gobiidae Oxudercidae	Acentrogobius sp. Boleophthalmus pectinirostris Gobiidae sp. Oxuderces dentatus	18	17	92 99.76 84 98.89
11	Mugiliformes Mugilidae	Osteomugil sp. Planiliza macrolepis Crenimugil buchanani	4	4	90 99.27 99
12	Ophidiiformes Bythitidae Aphyonidae	Bythitidae sp. Mugilidae sp.	3	2	84.92 94.5
13	Ovalentaria incertae sedis Pomacentridae Pomacentridae Ambassidae	Abudefduf bengalensis Neopomacentrus bankieri Ambassis gymnocephalus	12	11	99.65 100 99.9
14	Perciformes Scorpaenidae Serranidae Platycephalidae Pinguipedidae Platycephalidae	Parascorpaena aurita Epinephelus bleekeri Thysanophrys celebica Parapercis filamentosa Sorsogona tuberculata	5	5	99 99.2 99.7 98.4 99.2
15	Pleuronectiformes Cynoglossidae Cynoglossidae Soleidae Soleidae	Cynoglossus nanhaiensis Cynoglossus sp. Heteromycteris japonicus Zebrias quagga	7	8	99.53 99.54 99.9 99.4
16	Scombriformes Trichiuridae Trichiuridae Trichiuridae Scombridae	Trichiurus brevis Rastrelliger brachysoma Lepturacanthus savala Mastacembelidae sp.	5	4	99.76 99.8 98.9 93.1
17	Tetraodontiformes Monacanthidae	Acreichthys tomentosus Monacanthus chinensis Paramonacanthus choirocephalus	4	3	97 98.2 98.7

Primer application testing

Overall, both newly designed primer pairs achieved amplification success rates > 90% (Table 3). Universal primers demonstrated high efficiency across four of the five target phyla. Chordata (five classes) exhibited a 100% success rate, with > 99% identity of species correctly identified. Arthropoda (five classes) also achieved a 100% success rate, though three species remained unidentified (< 97% sequence similarity). A planktonic copepod was successfully identified (Canthocalanus pauper, 98% sequence similarity). Mollusca and Echinodermata (three and two classes, respectively) achieved success rates of 85.71% and 88.83%. While all Echinodermata specimens were successfully identified (98% sequence identity), two Mollusc species, including planktonic squid larvae (Sepiola sp.), could not be identified. However, the snail was successfully identified (Linatella caudata, 100% identity). Consistent with in-silico analysis, Platyhelminthes exhibited the lowest success rate (57.4%), with two of four species unidentifiable to the species level. Cross-application tests involving nine species from five phyla achieved a success rate greater than 75%. While six species were successfully identified, three remained unidentified at the species level. These included planktonic larvae of a small jellyfish (Tiaropsis sp., 95.3% sequence identity). Arrowworm, however, were successfully identified as Zonosagitta bedoti (> 99% sequence identity).

The Chordata-specific primer pair proved effective in the ichthyoplankton case study, yielding a 94.97% amplification success rate. While most species were successfully identified, 10 of the 63 species showed sequence identities between 81% and 97% and could not be definitively identified. This difficulty was largely attributable to two issues: a scarcity of reference sequences in public databases and the presence of sequences matching an unidentified (ex. Callionymius sp. 97%, Cynoglossus sp. 99.54% sequence identity).

Ichthyoplankton -Morphological identification versus DNA barcoding

Table 4 compares species identifications using morphology and COI mtDNA barcoding. COI accurately identified 25 of 30 taxa. Sensitivity (concordance between morphology and COI) reached 100% (4/4) when COI identified specimens only to the class Actinopterygii, but dropped to 25% for Gobiidae sp. and Drombus sp., where both methods disagreed. Specificity ranged from 50–100%, highest with accurate molecular identification and lowest when morphology was more reliable. Morphological identification was often limited to higher taxonomic levels (unidentified, order, or family). Species-level identification was possible when COI data were unavailable and taxonomic keys existed for common species (e.g., Gerres decacanthus, Callechelys marmorata).

Table 4

Comparison of parameters between morphological identification methods and DNA barcodes using COI mtDNA markers with designed CSP primer pairs (* taxa identification false by mtDNA barcode)
Sample ID	Species identification	Devel. Stages	No. Samples	Morph. Ident. True	Morph. Ident. False	mtDNA Ident. False	Sen. (%)	Spe. (%)	PPP (%)	NPP (%)	False Ident. Egg	False Iden. Larvae
1	Stolephorus insularis	E2-6	26	21	5	0	80.8	100	100	50	Dorosomatidae (2) E. heteroloba (3)
1	Stolephorus insularis	PrFL	6	4	2	0	66.7	100	100	50		E. heteroloba (2)
2	Photopectoralis bindus	E2,E4	22	6	16	0	27.3	100	100	50	Perciformes (3) Eupercaria incertae sedis (2) Acanthuridae (4), Ephippidae (2), Leiognathidae (3) Unidentified (2)
3	Ambassis gymnocephalus	E3-6	9	3	6	0	33.3	100	100	50	Mugiliformes (1) Mugilidae (2) Ambassidae (1) Unidentified (2)
3	Ambassis gymnocephalus	FL- PoFL	11	4	7	0	36.4	100	100	50		Perciformes (2) Sciaenidae (3) Ambassis sp. (2)
4	Sillago sihama	E5-6	11	3	8	0	27.3	100	100	50	Eupercaria incertae sedis (3) Mugiliformes (1) Nemipteridae (2) Unidentified (2)
4	Sillago sihama	FL-PoFL	7	2	5	0	28.6	100	100	50		Scombridae (2) Gobiidae (1) Sillago sp. (2)
5	Sardinella fijiensis	E5,E6	16	6	10	0	37.5	100	100	50	Clupeiformes (1) Dorosomatidae (2) S. gibosa (7)
6	Encrasicholina punctifer	E3-6	15	10	5	0	66.7	100	100	50		Stolephorus insularis (2) E. heteroloba (3)
7	Scolopsis taenioptera	E3,E4	14	2	12	0	14.3	100	100	50	Perciformes (5) Nemipteridae (1) Dorosomatidae (1) Nemipterus sp. (3) E. heteroloba (4) Unidentified (3)
8	Omobranchus fasciolatoceps	FL	12	3	9	0	25	100	100	50		Blenniidae (6) Gobiidae (2) Omobranchus sp. (1)
9	Alepes kleinii	E4,E5	4	1	3	0	25	100	100	50	Carangiformes (2) Unidentified (1)
9	Alepes kleinii	FL	8	2	6	0	25	100	100	50		Coryphaenidae (2) Carangidae (3) Ambassidae (1)
10	Encrasicholina heteroloba	E4-6	12	4	8	0	33.3	100	100	50	E. puncifer (4) E. pseudoheterobola (1) S. insularis (3)
11	Boleophthalmus pectinirostris	PrFL-FL	12	3	9	0	25	100	100	50		Blenniiformes (3) Blenniidae (3) Gobiidae (3)
12	Pterocaesio digramma	E3	11	1	10	0	9.1	100	100	50	Acanthuriformes (4) Eupercaria incertae sedis (2) Nemipteridae (1) Unidentified (3)
13	Synodus dermatogenys	E4,E5	9	4	5	0	44.4	100	100	50	Synodontidae (2) Trachinocephalus (2) Synodus sp. (1)
14	Leiognathus berbis	E3,E5	8	2	6	0	25	100	100	50	Eupercaria incertae sedis (2) Acanthuridae (2) Leiognathidae (2)
15	Trachinocephalus myops	E4	8	1	7	0	12.5	100	100	50	Synodontidae (3) Synodus sp. (4)
15	Trachinocephalus myops	FL	3	0	3	0	0	100	0	50		Perciformes (2) Scombridae (1)
16	Photolateralis stercorarius	E5	7	2	5	0	28.6	100	100	50	Acanthuridae (3) Anguilliformes (2)
17	Gerres sp. G. decacanthus	E6	6	2	4	1	33.3	83.3	66.7	55.56	Acanthuriformes (2) Labridae (2) G. decacanthus (1)*
18	Monacanthus chinensis	PoFL	4	0	4	0	0	100	0	50		Perciformes (1) Monocanthidae (1) Acreichthys sp. (2)
19	Johnius carouna	FL	6	2	4	0	33.3	100	100	50		Ambassidae (3) Sparidae (1)
20	Oxuderces dentatus	PrFL-FL	6	0	6	0	0	100	0	50		Scombridae (2) Sciaenidae (1) Gobiidae (3)
21	Mugilidae sp. Mugil sp.	E3,E4	6	2	4	1	33.3	83.3	66.7	55.56	Acanthuriformes (1) Mugiliformes (2) Unidentified (1) Mugil sp. (1)*
22	Callechelys sp. C. marmorata	E3,E4	5	2	3	1	40	80	66.7	57.14	Ophichthidae (2) Yirrkala sp. (1) C. marmorata (1)*
23	Gobiidae sp. Drombus sp.	FL-PoFL	4	1	3	1	25	80	50	57.14		Scombriformes (2) Scombridae (1) Drombus sp. (1)*
24	Neopomacentrus bankieri	PrFL	5	1	4	0	20	100	100	50		Sparidae (2) Pomacentridae (2)
25	Gymnothorax buroensis	E3	4	0	4	0	0	100	0	50	Anguillidae (2) Unidentified (2)
26	Parablennius thysanius	PrFL	5	1	4	0	20	100	100	50		Blenniidae (2) Gobiidae (2)
27	Actinopterygii Triglidae Cepolidae Callionymidae	FL-PrFL	4	4	0	4	100	50	50	100		Triglidae (1)* Cepolidae(1)* Callionymidae (2)*
28	Pentapodus setosus	E2	5	1	4	0	20	100	100	50	Eupercaria incertae sedis (2) Pleuronectiformes (1) Unidentified (1)
29	Creediidae sp.	FL	4	0	4	0	0	100	0	50		Mastacembelidae (2) Gobiidae (2)
30	Sparus aurata	E6	4	0	4	0	0	100	0	50	Clupeiformes (2) Sparidae (1) Escualosa sp. (1)

Morphological accuracy decreased at the egg stage for some species. For instance, Sillago sihama identification fell from 28.6% (larvae) to 27.3% (eggs). Challenges also arose at the larval stage, highlighting difficulties in morphological classification, particularly at early life stages. COI accuracy remained relatively consistent across developmental stages.

These findings demonstrate the value of both methods for ichthyoplankton identification, but underscore the risk of misidentification, especially with single markers or morphological traits. The reduced egg-stage accuracy of morphology suggests greater reliance on molecular methods for early life stage studies.

This study successfully designed and evaluated Universal and Chordata-specific COI primer sets for enhanced DNA barcoding of planktonic and ichthyoplankton samples. While universal primers offer broad taxonomic applicability (Raju and Haldar 2018, Imtiaz et al. 2017), a Phylum-specific set was developed to address limitations in amplifying certain phyla with universal primers (Ivanova et al. 2007, Melo et al. 2021) and to mitigate potential issues with intra- and interspecific variation (Imtiaz et al. 2017, Hou et al. 2020).

Leveraging extensive sequence databases (NCBI and BOLD), our in silico analysis strategically targeted longer COI fragments, crucial for improved species identification (Liu et al. 2020, Hoareau and Boissin 2010, Salonna et al. 2021). This in silico approach streamlined primer design, reducing time and resources needed for empirical testing (Wu et al. 2021, Melo et al. 2021). Longer COI amplicons are increasingly favored in DNA barcoding for maximizing phylogenetic information and enhancing species-level identification confidence (Liu et al. 2020, Hoareau and Boissin 2010, Salonna et al. 2021, Ward et al. 2005). Although shorter amplicons can be advantageous for degraded DNA (Folmer et al. 1994), our focus on longer fragments prioritized richer taxonomic information for wide range taxa identification. To further broaden taxonomic coverage, we incorporated degenerate bases into our primer designs. While these primers can be valuable for diverse groups with high sequence variability (Linhart and Shamir 2005), they also pose a risk of non-specific amplification (Ward et al. 2005), necessitating careful consideration of research objectives and target organisms.

Our empirical testing confirmed the efficacy of both primer sets in amplifying target COI fragments from collected samples. Crucially, both the Universal and Chordata-specific primers yielded amplicons suitable for accurate species-level identification, demonstrating the value of our combined in silico and empirical approach (Prosser et al. 2013, Wu et al. 2021, Melo et al. 2021). The successful application of CSP primer pair in ichthyoplankton identification underscores their potential for addressing a key challenge in marine monitoring and resource management: the accurate identification of early life stages (Labare 2022, Becker et al. 2015). As noted by Lewis et al. (2016), Lira et al. (2023), Frantine-Silva et al. (2015), and Becker et al. (2015), integrating morphological identification with DNA barcoding is crucial for effective ichthyoplankton studies. Our findings support this integrated approach, demonstrating the power of combining traditional morphological expertise with the discriminatory power of DNA barcoding for comprehensive ichthyoplankton monitoring.

DNA barcoding offers a powerful tool for species identification (Keele et al. 2014), but its effectiveness is maximized when integrated with other data sources, particularly morphology, distribution, and environmental parameters (Rathnasuriya et al. 2021, Lira et al. 2023, Becker et al. 2015, Jiang et al. 2023). This is especially true for ichthyoplankton, where morphological identification can be exceptionally challenging (Leis 2014). While our study demonstrates the utility of DNA barcoding with optimized COI primers for ichthyoplankton identification, it also highlights its limitations, particularly for early developmental stages. As others have noted (Vernooy et al. 2010; Sheth and Thaker 2017), the availability and quality of reference sequences are crucial for accurate DNA barcoding, and gaps in these databases, especially for early life stages, can limit taxonomic resolution. In these instances, detailed morphological analysis, guided by established taxonomic expertise and identification keys (Ahern et al. 2018), becomes essential for achieving finer-scale identifications (Kato et al. 2012, Krehenwinkel and Pomerantz 2017).

Our findings underscore the complementary nature of morphology and DNA barcoding, reinforcing the importance of integrated taxonomic approaches (Von Cräutlein et al. 2011). While DNA barcoding can overcome some challenges associated with traditional morphology, it is not a panacea, especially in the case of plankton samples. Our study, along with others (Sheth and Thaker 2020), demonstrates that when reference data is limited, morphological analysis is crucial. Detailed morphological examination, using established keys and descriptions, can provide valuable information, enabling identification to lower taxonomic levels (e.g., genus or even species) when DNA barcode data is inconclusive (Kato et al. 2009, Krehenwinkel and Pomerantz 2017). This emphasizes the need for continued efforts to expand and curate comprehensive DNA barcode libraries, particularly for early life stages, and to maintain and develop taxonomic expertise in morphology.

Moving forward, standardized protocols for integrating morphological and molecular data are crucial for maximizing the effectiveness of ichthyoplankton monitoring and contributing to robust biodiversity assessments (Mariacher et al. 2019). Combining optimized molecular tools, such as the COI primers developed in this study, with advanced morphological techniques, including advancements in imaging and automated image analysis (e.g., machine learning-based approaches), will be essential for addressing the challenges of biodiversity assessment and conservation in marine ecosystems. Integrating environmental DNA (eDNA) metabarcoding (Rubbmark et al. 2018, Breitbart et al. 2023, Hajibabaei et al. 2019) with traditional plankton tows and morphological identification offers a powerful approach for understanding ichthyoplankton dynamics and distribution. Further research exploring the use of genome skimming (Trevisan et al. 2019) to obtain longer COI sequences or other barcoding genes (e.g., 16S rRNA) could further enhance the accuracy and resolution of ichthyoplankton identification, especially for challenging taxa.

In summary, optimizing primer design through strategic sequence length adjustments significantly enhances the accuracy of species identification. This approach not only increases the likelihood of precise sequence matches but also elevates the overall confidence in taxonomic classifications. Further research should explore the synergistic potential of combining optimized primer design with both morphological and DNA barcode data to provide a more comprehensive understanding of Ichthyoplankton biodiversity and population dynamics

Conflict of interest

The Authors declare that there is no conflict of interest

Author Contribution

Sang Quang Tran conducted specimen processing, performed analyses, drafted and reviewed the manuscript. Kien Phuong Tran, My Thao Nguyen Le and Quyen Vu Dang Ha contributed to data analysis and manuscript revision. Huy Quoc Pham and Ha Vu Viet were responsible for specimen collection and manuscript editing. Binh Thuy Dang, as the principal investigator (PI), contributed to data analysis, and played a key role in manuscript writing and revision.

Acknowledgement

We extend our sincerest gratitude to our invaluable partners for their crucial support in the Ichthyoplankton sample collection. A heartfelt thank you to our dedicated student volunteers for their tireless efforts in the time-consuming task of sorting Ichthyoplankton samples. Their expertise and dedication were instrumental in the success of this research. This research was funded by Vingroup Innovation Foundation (VINIF) under project code VINIF.2022.DA00021.

Data Availability

The references data were obtained directly from NCBI using the search query '(COI[All Fields] AND mtDNA[All Fields]) AND (is_nuccore[filter] AND mitochondrion[filter])' and from BOLD via its website (https://v3.boldsystems.org/index.php/databases).

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No competing interests reported.

SupplementS1.xlsx
Supplement S1: All primers meet the required parameters and IDT calculations of primer design.
SupplementS2.xlsx
Supplement S2: Calculation of on-target and off-target rates in primer design.
SupplementS3.docx
Supplement S3: GenBank accession of COI mtDNA using our primer design.
TableS1.docx
Table S1. Characteristics of complete and COI mitochondrial DNA databases used for primer design and validation
TableS2.docx
Table S2. Morphological description of invertebrate larvae in plankton samples (Scale: Egg = 0.25 mm; Larvae = 3.5 mm)
FigureS1.tif
Figure S1. Application of diagnostic accuracy metrics in comparing identification methods
FigureS2.tif
Figure S2. Early Life Stages of Ichthyoplankton: Morphological Identification

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You are reading this latest preprint version

Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam

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Version 1

Abstract

Figures

Introduction

Materials and Methods

New sets of primer: Integrated morphological and DNA barcoding

Tissue collection and morphological analysis

Primer evaluation and cross-amplification tests

Ichthyoplankton - Morphological identification versus DNA barcoding

Results

Morphological identification

Primer application testing

Ichthyoplankton -Morphological identification versus DNA barcoding

Discussion

Conclusion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1