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

Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam Sang Q. Tran, Kien P. Tran, My T. L. Nguyen, Quyen V. D. Ha, Huy Q. Pham, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6219031/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2026 Read the published version in Aquatic Sciences → Version 1 posted 11 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 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. Materials and Methods 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 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 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 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 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 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 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 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 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 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 *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. Results 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 ΔG (kcal/mol) Max bp /No. structure F-R primer ΔG (kcal/ mol) Max bp /No. structure 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. 2 A). 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. 2 B). 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. 2 C). 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. 2 C). 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 Acanthuriformes ( Leiognathus berbis Photolateralis stercorarius Photopectoralis bindus ) 3 3 100 100 99.76 1.4 Elasmobranchii Myliobatiformes ( Pteroplatytrygon violacea ) 1 1 99.99 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 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 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 Neogastropoda ( Babylonia areolata ) 1 1 98.5 III.3 Bivalvia Pterioida ( Pinna atropurpurea ) 3 2 99.83 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) 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) 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) 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) 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) 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. Discussion 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. Conclusion 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 Declarations 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). References Ahern LAM, Gómez-Gutiérrez J, Aburto-Oropeza O, Saldierna-Martínez RJ, Johnson AF, Harada AE, Erisman B, Castro-Arvizú DI, Sánchez-Uvera AR, Burton RS (2018) Using molecular identification of ichthyoplankton to monitor spawning activity in a subtropical no-take marine reserve. Mar Ecol Prog Ser 592:159–179 Ahlstrom EH, Moser HG (1980) Characters useful in identification of pelagic marine fish eggs. CalCOFI Rep 21:121–131 Batubara AS, Muchlisin ZA, Efizon D, Elvyra R, Fadli N, Rizal S, Siti-Azizah MN, Wilkes M (2021) DNA barcoding (COI genetic marker) revealed hidden diversity of cyprinid fish ( Barbonymus spp.) from Aceh Waters, Indonesia. Biharean Biol 15(1):39–47 Becker RA, Sales NG, Santos GM, Santos GB, Carvalho DC (2015) DNA barcoding and morphological identification of Neotropical ichthyoplankton from the Upper Paraná and São Francisco. J Fish Biol 87(1):159–168. https://doi.org/10.1111/jfb.12707 Bhavan SP, Rajkumar G, Udayasuriyan R, Vadivalagan C, Rajkumar G, Bhavan PS (2015) Efficiency of different COI markers in DNA barcoding of freshwater prawn species. J Entomol Zool Stud 3(3):98–110 Breitbart M, Kerr M, Schram MJ, Williams I, Koziol G, Peebles E, Stallings CD (2023) Evaluation of DNA metabarcoding for identifying fish eggs: A case study on the West Florida Shelf. PeerJ 11. https://doi.org/10.7717/peerj.15016 Brownell CL (1979) Stages in the early development of 40 marine fish species with pelagic eggs from the Cape of Good Hope. ISSN 0868100048, pp 1–84 Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421 Chavali S, Mahajan A, Tabassum R, Maiti S, Bharadwaj D (2005) Oligonucleotide properties determination and primer designing: a critical examination of predictions. Bioinformatics 21(20):3918–3925. https://doi.org/10.1093/bioinformatics/bti633 Frantine-Silva W, Sofia SH, Orsi ML, Almeida FS (2015) DNA barcoding of freshwater ichthyoplankton in the Neotropics as a tool for ecological monitoring. Mol Ecol Resour 15(5):1226–1237. https://doi.org/10.1111/1755-0998.12385 Hajibabaei M, Porter TM, Wright M, Rudar J (2019) COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS One 14(9). https://doi.org/10.1371/journal.pone.0220953 Hebert PDN, Cywinska A, Ball SL, DeWaard JR (2003a) Biological identifications through DNA barcodes. Proc R Soc B Biol Sci 270(1512):313–321. https://doi.org/10.1098/rspb.2002.2218 Hebert PDN, Ratnasingham S, DeWaard JR (2003b) Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc R Soc B Biol Sci 270(Suppl 1). https://doi.org/10.1098/rsbl.2003.0025 Hoareau TB, Boissin E (2010) Design of phylum-specific hybrid primers for DNA barcoding: addressing the need for efficient COI amplification in the Echinodermata. Mol Ecol Resour 10(6):960–967. https://doi.org/10.1111/j.1755-0998.2010.02848.x Hou G, Wang J, Chen Z, Zhou J, Huang W, Zhang H (2020) Molecular and morphological identification and seasonal distribution of eggs of four Decapterus fish species in the northern South China Sea: A key to conservation of spawning ground. Front Mar Sci 7(November). https://doi.org/10.3389/fmars.2020.590564 Imtiaz A, Mohd Nor SA, Md Naim D (2017) Progress and potential of DNA barcoding for species identification of fish species. Biodiversitas 18(4):1394–1405. https://doi.org/10.13057/biodiv/d180415 Ivanova NV, Zemlak TS, Hanner RH, Hebert PDN (2007) Universal primer cocktails for fish DNA barcoding. Mol Ecol Notes 7(4):544–548. https://doi.org/10.1111/j.1471-8286.2007.01748.x Jiang H, Wong WH (2008) SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 24(20):2395–2396. https://doi.org/10.1093/bioinformatics/btn429 Jiang R, Mchura M, Liang Z, Yin R, Zhou Y, Chen Y, Mchura MT (2023) Identification of ichthyoplankton in the East China Sea off the coast of Zhoushan Archipelago using an integrated strategy of morphology and DNA barcoding. Authorea. https://doi.org/10.22541/au.167627361.18256464/v1 Kato T, Jinbo U, Ito M (2012) DNA barcoding: a novel tool for observation of biodiversity. Ecol Res Monogr:259–266. https://doi.org/10.1007/978-4-431-54032-8_19 Kara A, Yüksek A (2023) Comparison of morphological and molecular identifications in ichthyoplankton of the Marmara Sea. Acta Biol Turc 36(3):1–11 Keele J, Carmon J, Pucherelli SF, Hosler D (2014) Identification of unknown organisms by DNA barcoding: a molecular method for species classification. Bureau of Reclamation. https://www.usbr.gov/research/ Krehenwinkel P, Pomerantz A (2019) Genetic biomonitoring and biodiversity assessment using portable sequencing technologies: current uses and future directions. Genes 10(11):858. https://doi.org/10.3390/genes10110858 Kumar A, Chordia N (2015) In silico PCR primer designing and validation. Methods Mol Biol 1275:143–151. https://doi.org/10.1007/978-1-4939-2365-6_10 Labare MF (2022) A preliminary comparison of ichthyoplankton surveys vs. environmental DNA to survey a nearshore marine fish community. University of California San Diego. https://escholarship.org/uc/item/7z2417f4 Leis JM (2010) Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol Res 57(4):325–342. https://doi.org/10.1007/s10228-010-0177-z Leis JM (2014) Taxonomy and systematics of larval Indo-Pacific fishes: a review of progress since 1981. Ichthyol Res 62(1):9–28. https://doi.org/10.1007/s10228-014-0426-7 Lewis LA, Richardson DE, Zakharov EV, Hanner R (2016) Integrating DNA barcoding of fish eggs into ichthyoplankton monitoring programs. Fish Bull 114(2):153–165. https://doi.org/10.7755/FB.114.2.3 Linhart C, Shamir R (2005) The degenerate primer design problem: theory and applications. J Comput Biol 12(4):431–456. https://doi.org/10.1089/cmb.2005.12.431 Lira NL, Tonello S, Lui RL, Traldi JB, Brandão H, Oliveira C, Blanco DR (2023) Identifying fish eggs and larvae: from classic methodologies to DNA metabarcoding. Mol Biol Rep. https://doi.org/10.1007/s11033-022-08091-9 Liu K, Zhao S, Yu Z, Zhou Y, Yang J, Zhao R, Yang C et al (2020) Application of DNA barcoding in fish identification of supermarkets in Henan Province, China: more and longer COI gene sequences were obtained by designing new primers. Food Res Int 136(Oct). https://doi.org/10.1016/j.foodres.2020.109516 Mariacher A, Garofalo L, Fanelli R, Lorenzini R, Fico R (2019) A combined morphological and molecular approach for hair identification to comply with the European ban on dog and cat fur trade. PeerJ 7:e7955. https://doi.org/10.7717/peerj.7955 Melo AA, Nunes R, Telles MPC (2021) Same information, new applications: revisiting primers for the avian COI gene and improving DNA barcoding identification. Org Divers Evol 21(3):599–614. https://doi.org/10.1007/s13127-021-00507-x Petit-Marty N, Vázquez-Luis M, Hendriks IE (2021) Use of the nucleotide diversity in COI mitochondrial gene as an early diagnostic of conservation status of animal species. Conserv Lett. https://doi.org/10.1111/conl.12756 Prosser S, Martínez-Arce A, Elías-Gutiérrez M (2013) A new set of primers for COI amplification from freshwater microcrustaceans. Mol Ecol Resour 13(6):1151–1155. https://doi.org/10.1111/1755-0998.12132 Raju R, Haldar C (2018) DNA barcoding: a tool for diverse conservation in mangrove ecosystem. Ann Aquac Res 5(1):1048. www.fishbase.org Rathnasuriya MIG, Mateos-Rivera A, Skern-Mauritzen R, Wimalasiri HBU, Jayasinghe RPPK, Krakstad JO, Dalpadado P (2021) Composition and diversity of larval fish in the Indian Ocean using morphological and molecular methods. Mar Biodivers 51:39. https://doi.org/10.1007/s12526-021-01169-w Rass TS (1972) Ichthyoplankton from Cuban waters: pelagic fish eggs. Tr Inst Okeanol 93:5–41 [in Russian] Rausch T, Fritz MHY, Untergasser A et al (2020) Tracy: basecalling, alignment, assembly and deconvolution of Sanger chromatogram trace files. BMC Genomics 21:230. https://doi.org/10.1186/s12864-020-6635-8 Richards WJ (2005) Early stages of Atlantic fishes: an identification guide for the western central North Atlantic. CRC Press, Boca Raton. https://doi.org/10.1201/9780203500217 Rubbmark RO, Sint D, Horngacher N, Traugott M (2018) A broadly applicable COI primer pair and an efficient single-tube amplicon library preparation protocol for metabarcoding. Ecol Evol 8(24):12335–12350. https://doi.org/10.1002/ece3.4520 Salonna M, Gasparini F, Huchon D, Montesanto F, Haddas-Sasson M, Ekins M, McNamara M, Mastrototaro F, Gissi C (2021) An elongated COI fragment to discriminate botryllid species and as an improved ascidian DNA barcode. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-83127-x Sheth BP, Thaker VS (2017) DNA barcoding and traditional taxonomy: an integrated approach for biodiversity conservation. Genome 60(7):618–628. https://doi.org/10.1139/gen-2015-0167 Smirnov V, Warnow T (2021) MAGUS: multiple sequence alignment using graph clustering. Bioinformatics 37(12):1666–1672. https://doi.org/10.1093/bioinformatics/btaa992 Souza HV, Marchesin SRC, Itoyama MM (2016) Analysis of the mitochondrial COI gene and its informative potential for evolutionary inferences in the families Coreidae and Pentatomidae (Heteroptera). Genet Mol Res 15(1). https://doi.org/10.4238/gmr.15017428 Trevethan R (2017) Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health 5(November). https://doi.org/10.3389/fpubh.2017.00307 Vernooy R, Haribabu E, Muller MR, Vogel JH, Hebert PDN, Schindel DE et al (2010) Barcoding life to conserve biological diversity: beyond the taxonomic imperative. PLoS Biol 8(7):e1000417. https://doi.org/10.1371/journal.pbio.1000417 Von Cräutlein M, Korpelainen H, Pietiläinen M, Rikkinen J (2011) DNA barcoding: a tool for improved taxon identification and detection of species diversity. Biodivers Conserv 20(2):373–389. https://doi.org/10.1007/s10531-010-9964-0 Ward RD, Zemlak TS, Innes BH, Last PR, Hebert PDN (2005) DNA barcoding Australia’s fish species. Philos Trans R Soc B Biol Sci 360(1462):1847–1857. https://doi.org/10.1098/rstb.2005.1716 Weigt LA, Driskell AC, Baldwin CC, Ormos A (2012) DNA barcoding fishes. In: Kress WJ, Erickson DL (eds) DNA barcodes: methods and protocols. Methods Mol Biol 858:109–126. https://doi.org/10.1007/978-1-61779-591-6_6 Wibowo A, Sloterdijk H, Ulrich SP (2015) Identifying Sumatran peat swamp fish larvae through DNA barcoding, evidence of complete life history pattern. Procedia Chem 14:76–84. https://doi.org/10.1016/j.proche.2015.03.012 Wibowo A, Wahlberg N, Vasemägi A (2017) DNA barcoding of fish larvae reveals uncharacterised biodiversity in tropical peat swamps of New Guinea, Indonesia. Mar Freshw Res 68(6):1079–1087. https://doi.org/10.1071/MF16078 Trevithick R, Alcantara DMC, Machado DJ, Marques FPL, Lahr DJG (2019) Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol-preserved specimens in biodiversity studies. PeerJ 7:e7543. https://doi.org/10.7717/peerj.7543 Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R et al (2019) Welcome to the tidyverse. J Open Source Softw 4(43):1686. https://doi.org/10.21105/joss.01686 Wu TH, Yang CH, Pai TW, Ho LP, Wu JL, Chou HY (2021) Identification of fish species through tRNA-based primer design. BMC Bioinformatics 22(May). https://doi.org/10.1186/s12859-022-04717-8 Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina paired-end read merger. Bioinformatics 30(5):614–620. https://doi.org/10.1093/bioinformatics/btt593 Additional Declarations No competing interests reported. Supplementary Files 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 Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2026 Read the published version in Aquatic Sciences → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 22 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 16 Mar, 2025 Submission checks completed at journal 15 Mar, 2025 First submitted to journal 13 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6219031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433177886,"identity":"887df8a1-3d03-47b4-84a9-f28048b4eb4c","order_by":0,"name":"Sang Q. Tran","email":"","orcid":"","institution":"Nha Trang University","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"Q.","lastName":"Tran","suffix":""},{"id":433177887,"identity":"f9380394-a812-40f9-870d-88cdae68425e","order_by":1,"name":"Kien P. Tran","email":"","orcid":"","institution":"Nha Trang University","correspondingAuthor":false,"prefix":"","firstName":"Kien","middleName":"P.","lastName":"Tran","suffix":""},{"id":433177888,"identity":"1d3c535f-748a-4993-885a-f08a46cfd3ee","order_by":2,"name":"My T. L. Nguyen","email":"","orcid":"","institution":"Institute of Oceanography","correspondingAuthor":false,"prefix":"","firstName":"My","middleName":"T. L.","lastName":"Nguyen","suffix":""},{"id":433177889,"identity":"ee7adab5-1bb9-4131-b603-bbe417da4409","order_by":3,"name":"Quyen V. D. Ha","email":"","orcid":"","institution":"Nha Trang University","correspondingAuthor":false,"prefix":"","firstName":"Quyen","middleName":"V. D.","lastName":"Ha","suffix":""},{"id":433177890,"identity":"3ddafbac-60df-4e3a-9aab-49243283095d","order_by":4,"name":"Huy Q. Pham","email":"","orcid":"","institution":"South Research Sub-Institute for Marine Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Huy","middleName":"Q.","lastName":"Pham","suffix":""},{"id":433177891,"identity":"075e8dc4-5cc1-4c1f-abe7-965ba78a2b30","order_by":5,"name":"Ha V. Vu","email":"","orcid":"","institution":"Research Institute for Marine Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Ha","middleName":"V.","lastName":"Vu","suffix":""},{"id":433177892,"identity":"3fc863db-aa92-4fc2-827b-b9f786f1e629","order_by":6,"name":"Binh T. Dang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACA2YGNgYeBhsIj4d4LQlpENXEaWEAazlMghZzdt5jD97+OJ9nL5HA+OBtG0PidkJaLJv50g3nJNwu5pFIYDacC9Sys4GQww7zmEnzJNxO7JFIYJPmbWMwNjhAnJZzIC3sv0nRcgBsCzNQixxBLZbNPGaSc9KSE3vOPGyWnHNOgrAWc/4zZhJvbOwS29uTD354U2bDQ1ALEmBsABISxKsfBaNgFIyCUYAbAABN8DdI1Z7APAAAAABJRU5ErkJggg==","orcid":"","institution":"Nha Trang University","correspondingAuthor":true,"prefix":"","firstName":"Binh","middleName":"T.","lastName":"Dang","suffix":""}],"badges":[],"createdAt":"2025-03-13 10:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6219031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6219031/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00027-026-01274-7","type":"published","date":"2026-02-15T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79587382,"identity":"f33991c7-706f-45b3-a2fd-a30b9d5d5cd4","added_by":"auto","created_at":"2025-03-31 12:39:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218265,"visible":true,"origin":"","legend":"\u003cp\u003eOn - and off-target matchingresults for UP primers against the 'filter-pcmDB-5p' database, generated using Seqmap with up to 5 substitutions. The lines represent the number of phyla and species\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/60fb268afbcaccb73f4dd0fa.jpg"},{"id":79586123,"identity":"82344755-d4a2-4f44-9300-1b4fc0026c60","added_by":"auto","created_at":"2025-03-31 12:31:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":853398,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance comparison of newly designed (UPF.22/UPR.880 and CSPF.25/CSPR.950) and published primer pairs. (A) Visualization of primer annealing sites on the COI mtDNA gene. (B, C) Sequence similarity and primer index.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/c96393e4deaf544f8902307f.jpg"},{"id":79586126,"identity":"66e595b8-0baf-47ae-b459-e26e6b8810e0","added_by":"auto","created_at":"2025-03-31 12:31:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":820900,"visible":true,"origin":"","legend":"\u003cp\u003eSpecificity of the designed UP and compared primer pairs across 72 phyla. Target phyla are indicated. Red points represent phyla with a sequence matching rate of less than 5%. Maching rate (%) of non - target phyla on the right conner.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/2b65a435079c6ae97dcfc9ad.jpg"},{"id":79586128,"identity":"a893ef2f-87ae-4b93-b1a7-4e90582d0c03","added_by":"auto","created_at":"2025-03-31 12:31:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":712753,"visible":true,"origin":"","legend":"\u003cp\u003eImages illustrating the external morphology of representative species from target and non-target phyla. Asterisks (*) denote planktonic larvae\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/74d9d86c1827db2bc378315f.jpg"},{"id":102785295,"identity":"f826098f-3aa3-4cd9-9a98-9a31ece13133","added_by":"auto","created_at":"2026-02-16 16:04:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5359777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/c59c3b8b-60ae-41a0-b82b-784e6333b30c.pdf"},{"id":79587381,"identity":"e326c325-bf5f-4817-8578-9ae6aaee285a","added_by":"auto","created_at":"2025-03-31 12:39:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplement S1: \u003c/strong\u003eAll primers meet the required parameters and IDT calculations of primer design.\u003c/p\u003e","description":"","filename":"SupplementS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/f14d2deec654280c969e2057.xlsx"},{"id":79586130,"identity":"ad54deb7-1a04-4500-8a42-e3f530e1dc1e","added_by":"auto","created_at":"2025-03-31 12:31:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplement S2: \u003c/strong\u003eCalculation of on-target and off-target rates in primer design.\u003c/p\u003e","description":"","filename":"SupplementS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/03ee5f0d6d941d8d00cdea0e.xlsx"},{"id":79586127,"identity":"4d2adba2-27f1-414c-900a-001a46b6b35d","added_by":"auto","created_at":"2025-03-31 12:31:06","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplement S3:\u003c/strong\u003e GenBank accession of COI mtDNA using our primer design.\u003c/p\u003e","description":"","filename":"SupplementS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/5c0c54414f07b749554e7d41.docx"},{"id":79587385,"identity":"d3c606bd-91a4-4c3a-b43c-e8ecada21bf4","added_by":"auto","created_at":"2025-03-31 12:39:07","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e Characteristics of complete and COI mitochondrial DNA databases used for primer design and validation\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/f82f32bbd10f99ca124b2e04.docx"},{"id":79587386,"identity":"d9069eee-9f4f-4a2c-be7d-528321df9afe","added_by":"auto","created_at":"2025-03-31 12:39:07","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eMorphological description of invertebrate larvae in plankton samples (Scale: Egg = 0.25 mm; Larvae = 3.5 mm)\u003c/p\u003e","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/41a95dfb5c82d9c40606961f.docx"},{"id":79586144,"identity":"7a043cdd-7d22-4293-877e-ed92ca0cc697","added_by":"auto","created_at":"2025-03-31 12:31:07","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2253500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eApplication of diagnostic accuracy metrics in comparing identification methods\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/e8b1bf1a9de35a71d83cc24b.tif"},{"id":79586157,"identity":"e5d7fd74-d3e8-44b8-81cd-80f4d0c4b2c6","added_by":"auto","created_at":"2025-03-31 12:31:08","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":7646324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003eEarly Life Stages of Ichthyoplankton: Morphological Identification\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6219031/v1/a9106edbd28adb1c3d6c4ecf.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 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. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A 650 bp COI mtDNA fragment has been widely adopted as the \"standard barcode\" for a broad range of taxa (Hebert et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e, Hebert et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003b\u003c/span\u003e). This success has demonstrated the effectiveness of DNA barcoding as a valuable tool for biodiversity inventory, monitoring, and assessment studies (Imtiaz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Petit-Marty et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Souza et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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 \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Imtiaz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, several challenges have hindered the success of PCR amplification reactions. These include the unsuitability of universal primers for certain phyla (Ivanova et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the overlap between intraspecific and interspecific genetic variation (Imtiaz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hou et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese challenges have necessitated improvements in DNA barcoding techniques. Ongoing research efforts are directed towards developing specific gene regions (Souza et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), designing primers tailored to different taxonomic levels and species (Hoareau and Boissin \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Bhavan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Mart\u0026iacute;nez-Arce and El\u0026iacute;as-Guti\u0026eacute;rrez 2013), utilizing primer cocktails and stepwise protocols (Ivanova et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Weigt et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and exploring novel methodologies for primer design (Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e primer design and validation, supported by robust bioinformatic tools (Kumar and Chordia \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) 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. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to developing highly effective primer pairs for barcoding projects (Mart\u0026iacute;nez-Arce and El\u0026iacute;as-Guti\u0026eacute;rrez 2013, Wu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is a growing emphasis on obtaining longer COI gene sequences (Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Hoareau and Boissin \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Salonna et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eTo 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. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), link developmental stages within a species' life cycle (Lira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Wibowo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and reveal cryptic diversity (Batubara et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Wibowo et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Lira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA 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 \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 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. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Lira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Frantine-Silva et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). More recently, non-invasive metabarcoding technology utilizing environmental DNA (eDNA) has emerged as a promising solution (Rubbmark et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Breitbart et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Hajibabaei et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we employed both \u003cem\u003ein silico\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eprimer design and validation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn 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 \u0026ldquo;pcmDB\u0026rdquo;. Subsequently, COI gene sequences exceeding 950 base pairs (bp) were isolated from \u0026ldquo;pcmDB\u0026rdquo; using the tidyverse package (Wickham et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) 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 \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gap-removal was performed using Biopython in Python v3.10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biopython.org/\u003c/span\u003e\u003cspan address=\"https://www.biopython.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, the resulting aligned sequences were saved in FASTA format (\"asfasta\") to facilitate primer design.\u003c/p\u003e \u003cp\u003eTo 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/quangsang52sh/UPD_analysis\u003c/span\u003e\u003cspan address=\"https://github.com/quangsang52sh/UPD_analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The primer design parameters were generally set as follows: melting temperature (Tm) minimum of 30\u0026deg;C and maximum of 60\u0026deg;C, primer length between 20 and 26 nucleotides, GC content (GC%) ranging from 30\u0026ndash;60%, minimum identity of 80%, minimum coverage of 90%, AT penalties\u0026thinsp;\u0026le;\u0026thinsp;2, degeneracy\u0026thinsp;\u0026le;\u0026thinsp;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 \u003cb\u003eSupplement S1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe efficiency of candidate primers was validated by analyzing their properties using the IDT OligoAnalyzer tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idtdna.com/calc/analyzer\u003c/span\u003e\u003cspan address=\"https://www.idtdna.com/calc/analyzer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eTo 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\u0026thinsp;\u0026le;\u0026thinsp;6\u0026deg;C); limited hetero-dimer formation (maximum base pair match\u0026thinsp;\u0026le;\u0026thinsp;6, number of structures\u0026thinsp;\u0026le;\u0026thinsp;35 with ΔG \u0026ge; -45 kcal/mol), and absence of stable 3'-terminal dimers formed by either primer hybridizing with itself or its partner (ΔG \u0026ge; -2.0 kcal/mol) (Chavali et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) (\u003cb\u003eSupplement S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe 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 \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) implemented in C++. The universal primer (UP) was aligned with the \u0026lsquo;filter-pcmDB-5p\u0026rsquo; database, while the Chordata specific primer - CSP was aligned with sequences from the phylum Chordata in \u0026lsquo;filter-pcmDB-5p\u0026rsquo;. 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\u0026ndash;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 (\u0026ge;\u0026thinsp;50% for UP and \u0026ge;\u0026thinsp;70% for CSP) were prioritized. The number of target phyla and species was also considered during primer selection (\u003cb\u003eSupplement S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eNext, the performance of newly design primers were compared to others universal primers and Chordata specific primers sets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Matching positions of all primers were manually visualized along the \u0026ge;\u0026thinsp;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\u0026thinsp;=\u0026thinsp;1/D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eInformation of universal and specific primers applied to test the primer performance.\u003c/b\u003e The bold texts are our primer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegeneracy value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMelting temp. *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGC %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmplicon length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eUniversal primers (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO2198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: TCAACAAATCATAAAGATATTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMetazoan\u003c/p\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Black et al. 1994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCO1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: TAAACTTCAGGGTGACCAAAAAATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edgLCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: GGTCAACAAATCATAAAGAYATYGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Geller et al. 2013; Meyer, Geller, and Paulay 2005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edgHCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: TAAACTTCAGGGTGACCAAARAAYCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISHCOILBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: CTCAACYAATCAYAAAGATATYGGCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFishes, mammal, and bird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Giusti et al. 2017a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISHCOIHBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: ACTTCYGGGTGRCCRAARAATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUPF.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eF: TCWACMAAYCAYAAAGAYATYGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e38.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e830\u0026ndash;850\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMetazoan\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003einvertebrates (5 phyla)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eThis study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUPR.880\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eR: TCGKGTRTCWACRTCYATTCCWAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e45.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e830\u0026ndash;850\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eFish specific primers (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish-F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: TCTCAACCAACCATAAAGACATTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFreshwater fish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Ward et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish-R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: TATACTTCTGGGTGCCCAAAGAATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISH-F6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: ACYAAYCACAAAGAYATTGGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFreshwater fish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Handy et al. 2011; Giusti et al. 2017b)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISH-R7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: TARACTTCTGGRTGDCCRAAGAAYCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish-F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: CATCCTACCTGTGGCAATCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish-R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: GGGCTCAGACAATAAATCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e650\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFF2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: TTCTCCACCAACCACAARGAYATYGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarine fish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Ivanova et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFR1d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: CACCTCAGGGTGTCCGAARAAYCARAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISH_CO1LBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: TCAACYAATCAYAAAGATATYGGCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Handy et al., 2011; Weigt et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISH_CO1HBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: ACTTCYGGGTGRCCRAARAATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655\u0026ndash;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCSPF.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eF: ACMAAYCAYAAAGAYATYGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e860\u0026ndash;880\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eChordata (mainly fish)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eThis study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCSPR.950\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eR: ARTCARCTRAAKACTTTSACSCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e900\u0026ndash;950\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003e*Calculated from\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sg.idtdna.com/calc/analyzer\u003c/span\u003e\u003cspan address=\"https://sg.idtdna.com/calc/analyzer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(except our primers that already did in\u003c/em\u003e \u003cb\u003esupplement S1\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 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 \u0026ge;\u0026thinsp;5% of the total sequences within the phyla, and maintained the matching rate exceeding\u0026thinsp;\u0026gt;\u0026thinsp;10%.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNew sets of primer: Integrated morphological and DNA barcoding\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eTissue collection and morphological analysis\u003c/h2\u003e \u003cp\u003ePlanktonic 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 \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Leis \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Richards \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Ahlstrom and Moser \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1980\u003c/span\u003e, Brownwell 1979, Rass, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) were used to identify specimens to the lowest possible taxonomic level using a Euromex stereomicroscope.\u003c/p\u003e \u003cp\u003eTo 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.\u003c/p\u003e \u003cp\u003eFor the ichthyoplankton case study using CSP primers, eggs (1\u0026ndash;2 mm diameter) or larvae (2\u0026ndash;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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePrimer evaluation and cross-amplification tests\u003c/h3\u003e\n\u003cp\u003eTotal 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\u0026trade; Thermal Cycler. Each 25 \u0026micro;L PCR reaction mixture contained 12.5 \u0026micro;L GoTaq\u0026reg; G2 Green Master Mix (Promega, USA), 1.5 \u0026micro;L of each primer (10 nM, IDT, USA), 5 \u0026micro;L of DNA template (25 ng/\u0026micro;L), and 6 \u0026micro;L of nuclease-free water (Promega, USA). Thermal cycling conditions were standardized as follows: an initial denaturation at 95\u0026deg;C for 3 minutes, followed by 34 cycles of denaturation at 95\u0026deg;C for 30 seconds, annealing at an optimized temperature gradient (42\u0026deg;C \u0026minus;\u0026thinsp;54\u0026deg;C) for 45 seconds, and extension at 72\u0026deg;C for 50 seconds, with a final extension step at 72\u0026deg;C for 5 minutes.\u003c/p\u003e \u003cp\u003eTo assess PCR performance, 1 \u0026micro;L 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).\u003c/p\u003e \u003cp\u003eSequencing 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. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with a Q-score cutoff of \u0026ge;\u0026thinsp;10. Forward and reverse reads were then merged using PEAR (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) with a p-value threshold of \u0026le;\u0026thinsp;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. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) 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.\u003c/p\u003e\n\u003ch3\u003eIchthyoplankton - Morphological identification versus DNA barcoding\u003c/h3\u003e\n\u003cp\u003eIchthyoplankton 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 Y\u0026uuml;ksek \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Statistical analyses were performed according to the formulas devised by (Trevethan \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e presents the comparison criteria and calculation formulas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eReference database and\u003c/b\u003e \u003cb\u003ein silico\u003c/b\u003e \u003cb\u003eprimer design\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of \u003cb\u003e16,514,108\u003c/b\u003e available sequences were retrieved from both the NCBI and BOLD databases to construct the 'pcmDB'. This database encompassed \u003cb\u003e1,365,355 species\u003c/b\u003e across \u003cb\u003e91 phyla\u003c/b\u003e, 333 classes, 1,376 orders, 7,573 families, and 69,059 genera. The 'filter-pcmDB' dataset, comprising \u003cb\u003e658,711\u003c/b\u003e sequences extracted from 'pcmDB', included \u003cb\u003e108,892\u003c/b\u003e species belonging to \u003cb\u003e79\u003c/b\u003e 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 \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e After rigorous removal of sequences and positions containing gaps, the 'asfasta' file contained \u003cb\u003e30,158\u003c/b\u003e sequences with an average length of 1,520 base pairs, which were used for the subsequent primer design steps.\u003c/p\u003e \u003cp\u003eBased 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\u0026thinsp;\u0026gt;\u0026thinsp;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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eIn Silico\u003c/b\u003e \u003cb\u003edesigned primers for Universal and Chordata targets\u003c/b\u003e (PL: Primer length, Ident: Identity, Cov: Coverage, D: Degeneration).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrimer ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIden.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCov.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e%GC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGC \u003c/p\u003e \u003cp\u003eclamp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eSelf-dimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eHetero-dimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAmplicon length\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΔG \u003c/p\u003e \u003cp\u003e\u003cem\u003e(kcal/mol)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eMax bp /No. structure\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eF-R primer\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eΔG \u003c/p\u003e \u003cp\u003e\u003cem\u003e(kcal/\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003emol)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eMax bp /No. structure\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUniversal primer (UP) 5\u0026rsquo; \u0026ndash; 3\u0026rsquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPF.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCWACMAAYCAYAAAGAYATYGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-40.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPR.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGKGTRTCWACRTCYATTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-37.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUPF22-883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-40,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPR.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTRTCSACRTCYATTCCSACSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-42.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUPF22-878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-42,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPR.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGKGTRTCWACRTCYATTCCWAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-42.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUPF22-882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-42.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChordata-specific primer (CSP) 5\u0026rsquo;- 3\u0026rsquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSPF.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACMAAYCAYAAAGAYATYGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-35.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSPR.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGKGTATCWACATCYATTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCSPF25-883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSPR.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARTCAACTAAATACTTTSACSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-37.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCSPF25-947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-37.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSPR.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARTCAACTAAATACTTTSACSCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCSPF25-950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-40.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe total number of on- and off-target matches for optimized UP primer pairs across available target phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that UPR.880 had fewer off-target matches than UPR.883 (52,737\u0026thinsp;\u0026plusmn;\u0026thinsp;24,185 (9,410\u0026thinsp;\u0026minus;\u0026thinsp;165,427) vs. 96,311\u0026thinsp;\u0026plusmn;\u0026thinsp;28,725 (32,311\u0026ndash;217,193), and a similar number of on-target matches (250,692\u0026thinsp;\u0026plusmn;\u0026thinsp;51,060 (25,379\u0026thinsp;\u0026minus;\u0026thinsp;352,293) vs. 283,682\u0026thinsp;\u0026plusmn;\u0026thinsp;36,420 (136,305\u0026ndash;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) (\u003cb\u003eSupplement S2\u003c/b\u003e). 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). 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\u0026ndash;8, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). For the two degenerate primers (8\u0026ndash;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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe specificity of the designed-UP primer pair and the compared primer pairs for target and non-target phyla is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Most primers exhibited near-expected (50% matching) values for non-target phyla, while target phyla showed higher observed values. Phyla with minimal sequence matching (\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;10% non-target phyla matches (reverse primers\u0026thinsp;\u0026ge;\u0026thinsp;39%), both new designed universal primer pairs met the required matching rate (10% and 23%, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMorphological identification\u003c/h2\u003e \u003cp\u003eAs 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 (\u003cb\u003e*\u003c/b\u003e) in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and include Copepods, Gastropod snails, Sagittoidea (arrow worm), and Hydrozoa (small jellyfish). Their external morphologies are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and morphological descriptions are presented in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. 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 (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Representative images of early developmental stages (eggs and larvae) with further details provided in \u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecies composition and PCR amplification efficiency using Universal and Phylum-specific primers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhylum/Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCR reaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSuccessful rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGenbank/BOLD identity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUniversal primer (UPF.22 \u0026ndash; UPR.880) \u0026ndash; Target phyla\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.69%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChordata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApodiformes\u003c/p\u003e \u003cp\u003e(\u003cem\u003eAerodramus fuciphagus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMammalia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimates\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Homo sapiens)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActinopterygii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlenniiformes\u003c/p\u003e \u003cp\u003e(\u003cem\u003eOmobranchus fasciolatoceps\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParablennius thysanius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePlagiotremus tapeinosoma\u003c/em\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.88\u003c/p\u003e \u003cp\u003e98.89\u003c/p\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcanthuriformes\u003c/p\u003e \u003cp\u003e(\u003cem\u003eLeiognathus berbis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePhotolateralis stercorarius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePhotopectoralis bindus\u003c/em\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElasmobranchii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyliobatiformes\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePteroplatytrygon violacea\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhinopristiformes\u003c/p\u003e \u003cp\u003e(\u003cem\u003eRhinobatos jimbaranensis\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReptilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTestudines\u003c/p\u003e \u003cp\u003e(\u003cem\u003eIndotestudo elongata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eArthropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrustacean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecapoda\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePanulirus homarus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eII.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThecostraca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhizocephala\u003c/p\u003e \u003cp\u003e(\u003cem\u003eSacculina angulata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScalpellomorpha\u003c/p\u003e \u003cp\u003e(\u003cem\u003eOctolasmis angulata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalacostraca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmphipoda\u003c/p\u003e \u003cp\u003e(\u003cem\u003eAmphipoda\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eII.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCopepoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalanoida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eCanthocalanus pauper\u003c/em\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHarpacticoida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eNormanellidae\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eII.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInsecta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHymenoptera\u003c/p\u003e \u003cp\u003e(\u003cem\u003eOecophylla smaragdina\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiptera\u003c/p\u003e \u003cp\u003e(\u003cem\u003eKiefferulus longilobus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColeoptera\u003c/p\u003e \u003cp\u003e(\u003cem\u003eMesosa\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMollusk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e85.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephalopoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSepiida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eSepiola\u003c/em\u003e sp.)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIII.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGastropoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLittorinimorpha\u003c/p\u003e \u003cp\u003e(\u003cem\u003eLinatella caudata\u003c/em\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeogastropoda\u003c/p\u003e \u003cp\u003e(\u003cem\u003eBabylonia areolata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIII.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBivalvia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePterioida\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePinna atropurpurea\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArcida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eBarbatia\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEchinodermata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e83.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsteroidea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValvatida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eNardoa variolata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHolothuroidea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHolothuriida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eHolothuria fuscogilva\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHolothuria atra\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHolothuria hilla\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003cp\u003e99.8\u003c/p\u003e \u003cp\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePlatyhelminthes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e57.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrematoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlagiorchiida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eHaplorchis taichui\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCentrocestus_formosanus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAllocreadium\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.2\u003c/p\u003e \u003cp\u003e97.3\u003c/p\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonogenea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMazocraeidea\u003c/p\u003e \u003cp\u003e(\u003cem\u003eThaparocleidus\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUniversal primer (UPF.22 \u0026ndash; UPR.880) - Cross amplification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcanthocephala / Polyacanthocephala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolyacanthorhynchida\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePolyacanthorhynchus\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNematoda/\u003c/p\u003e \u003cp\u003eChromadorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhabditida\u003c/p\u003e \u003cp\u003e(\u003cem\u003eRaphidascaris trichiuri\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCnidaria/\u003c/p\u003e \u003cp\u003eAnthozoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActiniaria\u003c/p\u003e \u003cp\u003e(\u003cem\u003eHeteractis aurora\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCnidaria/\u003c/p\u003e \u003cp\u003eHydrozoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTiaropsidae\u003c/p\u003e \u003cp\u003e(\u003cem\u003eTiaropsis\u003c/em\u003e sp.)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChaetognatha/\u003c/p\u003e \u003cp\u003eSagittoidea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAphragmophora\u003c/p\u003e \u003cp\u003e(\u003cem\u003eZonosagitta bedoti\u003c/em\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnelida/\u003c/p\u003e \u003cp\u003ePolychaeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDinophilidae\u003c/p\u003e \u003cp\u003e(\u003cem\u003eDinophilus\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOrder/Family\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpecies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePCR reaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSuccessful rate (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eGenbank/BOLD identity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChordata specific primer (CSPF.22 \u0026ndash; CSPR.950) \u0026ndash; Ichthyoplankton case study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e179\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e94.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcanthuriformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLeiognathidae\u003c/p\u003e \u003cp\u003eLeiognathidae\u003c/p\u003e \u003cp\u003eLeiognathidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLeiognathus berbis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePhotolateralis stercorarius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePhotopectoralis bindus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcropomatiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePempheridae\u003c/p\u003e \u003cp\u003eTrichonotidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePempheris schwenkii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOphichthidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.8\u003c/p\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnguilliformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMuraenidae\u003c/p\u003e \u003cp\u003eOphichthidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGymnothorax buroensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCallechelys\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003cp\u003e91.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAulopiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSynodontidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSynodus dermatogenys\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSaurida microlepis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTrachinocephalus myops\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003cp\u003e98.9\u003c/p\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBlenniiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBlenniidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGerres\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eOmobranchus fasciolatoceps\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParablennius thysanius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePlagiotremus tapeinosoma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82\u003c/p\u003e \u003cp\u003e99.88\u003c/p\u003e \u003cp\u003e98.89\u003c/p\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCallionymiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCallionymidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCallionymus\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eCallionymus meridionalis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCallionymus schaapii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003cp\u003e99.76\u003c/p\u003e \u003cp\u003e99.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCarangiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCarangidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAlepes kleinii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSelaroides leptolepis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMegalaspis cordyla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.52\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eClupeiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDorosomatidae\u003c/p\u003e \u003cp\u003eEngraulidae\u003c/p\u003e \u003cp\u003eEngraulidae\u003c/p\u003e \u003cp\u003eEngraulidae\u003c/p\u003e \u003cp\u003eEngraulidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSardinella fijiensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEncrasicholina heteroloba\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEncrasicholina punctifer\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStolephorus insularis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eThryssa hamiltonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.76\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.08\u003c/p\u003e \u003cp\u003e99.85\u003c/p\u003e \u003cp\u003e99.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEupercaria incertae sedis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCaesionidae\u003c/p\u003e \u003cp\u003eLabridae\u003c/p\u003e \u003cp\u003eNemipteridae\u003c/p\u003e \u003cp\u003eNemipteridae\u003c/p\u003e \u003cp\u003eNemipteridae\u003c/p\u003e \u003cp\u003eNemipteridae\u003c/p\u003e \u003cp\u003eScaridae\u003c/p\u003e \u003cp\u003eSciaenidae\u003c/p\u003e \u003cp\u003eSillaginidae\u003c/p\u003e \u003cp\u003eSparidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePterocaesio digramma\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHalichoeres nigrescens\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNemipterus furcosus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNemipterus japonicus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePentapodus setosus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eScolopsis taenioptera\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eScaridae\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eJohnius carouna\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSillago sihama\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSparus aurata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.65\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.85\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.85\u003c/p\u003e \u003cp\u003e81\u003c/p\u003e \u003cp\u003e98.97\u003c/p\u003e \u003cp\u003e99.53\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGobiiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003cp\u003eOxudercidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcentrogobius\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eBoleophthalmus pectinirostris\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eGobiidae\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eOxuderces dentatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003cp\u003e99.76\u003c/p\u003e \u003cp\u003e84\u003c/p\u003e \u003cp\u003e98.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMugiliformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMugilidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOsteomugil\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003ePlaniliza macrolepis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCrenimugil buchanani\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e99.27\u003c/p\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOphidiiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBythitidae\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAphyonidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBythitidae\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eMugilidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.92\u003c/p\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOvalentaria incertae sedis\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePomacentridae\u003c/p\u003e \u003cp\u003ePomacentridae\u003c/p\u003e \u003cp\u003eAmbassidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAbudefduf bengalensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNeopomacentrus bankieri\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAmbassis gymnocephalus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.65\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePerciformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eScorpaenidae\u003c/p\u003e \u003cp\u003eSerranidae\u003c/p\u003e \u003cp\u003ePlatycephalidae\u003c/p\u003e \u003cp\u003ePinguipedidae\u003c/p\u003e \u003cp\u003ePlatycephalidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParascorpaena aurita\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEpinephelus bleekeri\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eThysanophrys celebica\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParapercis filamentosa\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSorsogona tuberculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003cp\u003e99.2\u003c/p\u003e \u003cp\u003e99.7\u003c/p\u003e \u003cp\u003e98.4\u003c/p\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePleuronectiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCynoglossidae\u003c/p\u003e \u003cp\u003eCynoglossidae\u003c/p\u003e \u003cp\u003eSoleidae\u003c/p\u003e \u003cp\u003eSoleidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCynoglossus nanhaiensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCynoglossus\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e\u003cem\u003eHeteromycteris japonicus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eZebrias quagga\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.53\u003c/p\u003e \u003cp\u003e99.54\u003c/p\u003e \u003cp\u003e99.9\u003c/p\u003e \u003cp\u003e99.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eScombriformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTrichiuridae\u003c/p\u003e \u003cp\u003eTrichiuridae\u003c/p\u003e \u003cp\u003eTrichiuridae\u003c/p\u003e \u003cp\u003eScombridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTrichiurus brevis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eRastrelliger brachysoma\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLepturacanthus savala\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMastacembelidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.76\u003c/p\u003e \u003cp\u003e99.8\u003c/p\u003e \u003cp\u003e98.9\u003c/p\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTetraodontiformes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMonacanthidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcreichthys tomentosus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMonacanthus chinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParamonacanthus choirocephalus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003cp\u003e98.2\u003c/p\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimer application testing\u003c/h3\u003e\n\u003cp\u003eOverall, both newly designed primer pairs achieved amplification success rates\u0026thinsp;\u0026gt;\u0026thinsp;90% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Universal primers demonstrated high efficiency across four of the five target phyla. Chordata (five classes) exhibited a 100% success rate, with \u0026gt;\u0026thinsp;99% identity of species correctly identified. Arthropoda (five classes) also achieved a 100% success rate, though three species remained unidentified (\u0026lt;\u0026thinsp;97% sequence similarity). A planktonic copepod was successfully identified (\u003cem\u003eCanthocalanus pauper\u003c/em\u003e, 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 (\u003cem\u003eSepiola\u003c/em\u003e sp.), could not be identified. However, the snail was successfully identified (\u003cem\u003eLinatella caudata\u003c/em\u003e, 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 (\u003cem\u003eTiaropsis\u003c/em\u003e sp., 95.3% sequence identity). Arrowworm, however, were successfully identified as \u003cem\u003eZonosagitta bedoti\u003c/em\u003e (\u0026gt;\u0026thinsp;99% sequence identity).\u003c/p\u003e \u003cp\u003eThe Chordata-specific primer pair proved effective in the ichthyoplankton case study, yielding a \u003cb\u003e94.97%\u003c/b\u003e 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. \u003cem\u003eCallionymius\u003c/em\u003e sp. 97%, \u003cem\u003eCynoglossus\u003c/em\u003e sp. 99.54% sequence identity).\u003c/p\u003e\n\u003ch3\u003eIchthyoplankton -Morphological identification versus DNA barcoding\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e 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 \u003cem\u003eGobiidae\u003c/em\u003e sp. and \u003cem\u003eDrombus\u003c/em\u003e sp., where both methods disagreed. Specificity ranged from 50\u0026ndash;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., \u003cem\u003eGerres decacanthus\u003c/em\u003e, \u003cem\u003eCallechelys marmorata\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of parameters between morphological identification methods and DNA barcodes using COI mtDNA markers with designed CSP primer pairs (* taxa identification false by mtDNA barcode)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies identification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevel. Stages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMorph. Ident. True\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMorph. Ident. False\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003emtDNA Ident. False\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSen.\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSpe.\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePPP\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNPP\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFalse Ident. Egg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFalse Iden. Larvae\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eStolephorus insularis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE2-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDorosomatidae (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eE. heteroloba\u003c/em\u003e (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eE. heteroloba\u003c/em\u003e (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhotopectoralis bindus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE2,E4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePerciformes (3)\u003c/p\u003e \u003cp\u003eEupercaria incertae sedis (2)\u003c/p\u003e \u003cp\u003eAcanthuridae (4),\u003c/p\u003e \u003cp\u003eEphippidae (2),\u003c/p\u003e \u003cp\u003eLeiognathidae (3)\u003c/p\u003e \u003cp\u003eUnidentified (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAmbassis gymnocephalus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMugiliformes (1)\u003c/p\u003e \u003cp\u003eMugilidae (2)\u003c/p\u003e \u003cp\u003eAmbassidae (1)\u003c/p\u003e \u003cp\u003eUnidentified (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL- PoFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePerciformes (2)\u003c/p\u003e \u003cp\u003eSciaenidae (3)\u003c/p\u003e \u003cp\u003e\u003cem\u003eAmbassis\u003c/em\u003e sp. (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSillago sihama\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE5-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEupercaria incertae sedis (3)\u003c/p\u003e \u003cp\u003eMugiliformes (1)\u003c/p\u003e \u003cp\u003eNemipteridae (2)\u003c/p\u003e \u003cp\u003eUnidentified (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL-PoFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eScombridae (2)\u003c/p\u003e \u003cp\u003eGobiidae (1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eSillago\u003c/em\u003e sp. (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSardinella fijiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE5,E6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClupeiformes (1)\u003c/p\u003e \u003cp\u003eDorosomatidae (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eS. gibosa\u003c/em\u003e (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEncrasicholina punctifer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eStolephorus insularis\u003c/em\u003e (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eE. heteroloba\u003c/em\u003e (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eScolopsis taenioptera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3,E4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePerciformes (5)\u003c/p\u003e \u003cp\u003eNemipteridae (1)\u003c/p\u003e \u003cp\u003eDorosomatidae (1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eNemipterus\u003c/em\u003e sp. (3)\u003c/p\u003e \u003cp\u003e\u003cem\u003eE. heteroloba\u003c/em\u003e (4)\u003c/p\u003e \u003cp\u003eUnidentified (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOmobranchus fasciolatoceps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBlenniidae (6)\u003c/p\u003e \u003cp\u003eGobiidae (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eOmobranchus\u003c/em\u003e sp. (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAlepes kleinii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE4,E5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCarangiformes (2)\u003c/p\u003e \u003cp\u003eUnidentified (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCoryphaenidae (2)\u003c/p\u003e \u003cp\u003eCarangidae (3)\u003c/p\u003e \u003cp\u003eAmbassidae (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEncrasicholina heteroloba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE4-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eE. puncifer\u003c/em\u003e (4)\u003c/p\u003e \u003cp\u003e\u003cem\u003eE. pseudoheterobola\u003c/em\u003e (1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eS. insularis\u003c/em\u003e (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBoleophthalmus pectinirostris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrFL-FL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBlenniiformes (3)\u003c/p\u003e \u003cp\u003eBlenniidae (3)\u003c/p\u003e \u003cp\u003eGobiidae (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePterocaesio digramma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAcanthuriformes (4)\u003c/p\u003e \u003cp\u003eEupercaria incertae sedis (2)\u003c/p\u003e \u003cp\u003eNemipteridae (1)\u003c/p\u003e \u003cp\u003eUnidentified (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSynodus dermatogenys\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE4,E5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSynodontidae (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eTrachinocephalus\u003c/em\u003e (2)\u003c/p\u003e \u003cp\u003e\u003cem\u003eSynodus\u003c/em\u003e sp. (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLeiognathus berbis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3,E5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEupercaria incertae sedis (2)\u003c/p\u003e \u003cp\u003eAcanthuridae (2)\u003c/p\u003e \u003cp\u003eLeiognathidae (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTrachinocephalus myops\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSynodontidae (3)\u003c/p\u003e \u003cp\u003e\u003cem\u003eSynodus\u003c/em\u003e sp. (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePerciformes (2)\u003c/p\u003e \u003cp\u003eScombridae (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhotolateralis stercorarius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAcanthuridae (3)\u003c/p\u003e \u003cp\u003eAnguilliformes (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGerres\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG. decacanthus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eE6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e33.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e83.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e66.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e55.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eAcanthuriformes (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLabridae (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eG. decacanthus\u003c/b\u003e \u003cb\u003e(1)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMonacanthus chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePerciformes (1)\u003c/p\u003e \u003cp\u003eMonocanthidae (1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eAcreichthys\u003c/em\u003e sp. (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJohnius carouna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAmbassidae (3)\u003c/p\u003e \u003cp\u003eSparidae (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOxuderces dentatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrFL-FL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eScombridae (2)\u003c/p\u003e \u003cp\u003eSciaenidae (1)\u003c/p\u003e \u003cp\u003eGobiidae (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMugilidae sp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMugil\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eE3,E4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e33.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e83.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e66.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e55.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eAcanthuriformes (1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMugiliformes (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eUnidentified (1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMugil\u003c/b\u003e \u003cb\u003esp. (1)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCallechelys\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eC. marmorata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eE3,E4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e66.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e57.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eOphichthidae (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eYirrkala\u003c/b\u003e \u003cb\u003esp. (1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eC. marmorata\u003c/b\u003e \u003cb\u003e(1)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGobiidae sp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eDrombus\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFL-PoFL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e57.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eScombriformes (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eScombridae (1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eDrombus\u003c/b\u003e \u003cb\u003esp. (1)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNeopomacentrus bankieri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSparidae (2)\u003c/p\u003e \u003cp\u003ePomacentridae (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGymnothorax buroensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAnguillidae (2)\u003c/p\u003e \u003cp\u003eUnidentified (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParablennius thysanius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBlenniidae (2)\u003c/p\u003e \u003cp\u003eGobiidae (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eActinopterygii\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eTriglidae\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCepolidae\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCallionymidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFL-PrFL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eTriglidae (1)*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCepolidae(1)*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCallionymidae (2)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePentapodus setosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEupercaria incertae sedis (2)\u003c/p\u003e \u003cp\u003ePleuronectiformes (1)\u003c/p\u003e \u003cp\u003eUnidentified (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCreediidae\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMastacembelidae (2)\u003c/p\u003e \u003cp\u003eGobiidae (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSparus aurata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClupeiformes (2)\u003c/p\u003e \u003cp\u003eSparidae (1)\u003c/p\u003e \u003cp\u003e\u003cem\u003eEscualosa\u003c/em\u003e sp. (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMorphological accuracy decreased at the egg stage for some species. For instance, \u003cem\u003eSillago sihama\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eThese 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.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis 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 \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Imtiaz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a Phylum-specific set was developed to address limitations in amplifying certain phyla with universal primers (Ivanova et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and to mitigate potential issues with intra- and interspecific variation (Imtiaz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hou et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeveraging extensive sequence databases (NCBI and BOLD), our \u003cem\u003ein silico\u003c/em\u003e analysis strategically targeted longer COI fragments, crucial for improved species identification (Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Hoareau and Boissin \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Salonna et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This \u003cem\u003ein silico\u003c/em\u003e approach streamlined primer design, reducing time and resources needed for empirical testing (Wu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Longer COI amplicons are increasingly favored in DNA barcoding for maximizing phylogenetic information and enhancing species-level identification confidence (Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Hoareau and Boissin \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Salonna et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ward et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). 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 \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), they also pose a risk of non-specific amplification (Ward et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), necessitating careful consideration of research objectives and target organisms.\u003c/p\u003e \u003cp\u003eOur 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 \u003cem\u003ein silico\u003c/em\u003e and empirical approach (Prosser et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Melo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As noted by Lewis et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Lira et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Frantine-Silva et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Becker et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), 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.\u003c/p\u003e \u003cp\u003eDNA barcoding offers a powerful tool for species identification (Keele et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but its effectiveness is maximized when integrated with other data sources, particularly morphology, distribution, and environmental parameters (Rathnasuriya et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Lira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Becker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is especially true for ichthyoplankton, where morphological identification can be exceptionally challenging (Leis \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sheth and Thaker \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), 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. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), becomes essential for achieving finer-scale identifications (Kato et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Krehenwinkel and Pomerantz 2017).\u003c/p\u003e \u003cp\u003eOur findings underscore the complementary nature of morphology and DNA barcoding, reinforcing the importance of integrated taxonomic approaches (Von Cr\u0026auml;utlein et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eMoving 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. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Breitbart et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Hajibabaei et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn 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\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe Authors declare that there is no conflict of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSang 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.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe 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.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 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).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhern LAM, G\u0026oacute;mez-Guti\u0026eacute;rrez J, Aburto-Oropeza O, Saldierna-Mart\u0026iacute;nez RJ, Johnson AF, Harada AE, Erisman B, Castro-Arviz\u0026uacute; DI, S\u0026aacute;nchez-Uvera AR, Burton RS (2018) Using molecular identification of ichthyoplankton to monitor spawning activity in a subtropical no-take marine reserve. Mar Ecol Prog Ser 592:159\u0026ndash;179\u003c/li\u003e\n\u003cli\u003eAhlstrom EH, Moser HG (1980) Characters useful in identification of pelagic marine fish eggs. CalCOFI Rep 21:121\u0026ndash;131\u003c/li\u003e\n\u003cli\u003eBatubara AS, Muchlisin ZA, Efizon D, Elvyra R, Fadli N, Rizal S, Siti-Azizah MN, Wilkes M (2021) DNA barcoding (COI genetic marker) revealed hidden diversity of cyprinid fish (\u003cem\u003eBarbonymus\u003c/em\u003e spp.) from Aceh Waters, Indonesia. Biharean Biol 15(1):39\u0026ndash;47\u003c/li\u003e\n\u003cli\u003eBecker RA, Sales NG, Santos GM, Santos GB, Carvalho DC (2015) DNA barcoding and morphological identification of Neotropical ichthyoplankton from the Upper Paran\u0026aacute; and S\u0026atilde;o Francisco. J Fish Biol 87(1):159\u0026ndash;168. https://doi.org/10.1111/jfb.12707\u003c/li\u003e\n\u003cli\u003eBhavan SP, Rajkumar G, Udayasuriyan R, Vadivalagan C, Rajkumar G, Bhavan PS (2015) Efficiency of different COI markers in DNA barcoding of freshwater prawn species. J Entomol Zool Stud 3(3):98\u0026ndash;110\u003c/li\u003e\n\u003cli\u003eBreitbart M, Kerr M, Schram MJ, Williams I, Koziol G, Peebles E, Stallings CD (2023) Evaluation of DNA metabarcoding for identifying fish eggs: A case study on the West Florida Shelf. PeerJ 11. https://doi.org/10.7717/peerj.15016\u003c/li\u003e\n\u003cli\u003eBrownell CL (1979) Stages in the early development of 40 marine fish species with pelagic eggs from the Cape of Good Hope. ISSN 0868100048, pp 1\u0026ndash;84\u003c/li\u003e\n\u003cli\u003eCamacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421\u003c/li\u003e\n\u003cli\u003eChavali S, Mahajan A, Tabassum R, Maiti S, Bharadwaj D (2005) Oligonucleotide properties determination and primer designing: a critical examination of predictions. Bioinformatics 21(20):3918\u0026ndash;3925. https://doi.org/10.1093/bioinformatics/bti633\u003c/li\u003e\n\u003cli\u003eFrantine-Silva W, Sofia SH, Orsi ML, Almeida FS (2015) DNA barcoding of freshwater ichthyoplankton in the Neotropics as a tool for ecological monitoring. Mol Ecol Resour 15(5):1226\u0026ndash;1237. https://doi.org/10.1111/1755-0998.12385\u003c/li\u003e\n\u003cli\u003eHajibabaei M, Porter TM, Wright M, Rudar J (2019) COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS One 14(9). https://doi.org/10.1371/journal.pone.0220953\u003c/li\u003e\n\u003cli\u003eHebert PDN, Cywinska A, Ball SL, DeWaard JR (2003a) Biological identifications through DNA barcodes. Proc R Soc B Biol Sci 270(1512):313\u0026ndash;321. https://doi.org/10.1098/rspb.2002.2218\u003c/li\u003e\n\u003cli\u003eHebert PDN, Ratnasingham S, DeWaard JR (2003b) Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc R Soc B Biol Sci 270(Suppl 1). https://doi.org/10.1098/rsbl.2003.0025\u003c/li\u003e\n\u003cli\u003eHoareau TB, Boissin E (2010) Design of phylum-specific hybrid primers for DNA barcoding: addressing the need for efficient COI amplification in the Echinodermata. Mol Ecol Resour 10(6):960\u0026ndash;967. https://doi.org/10.1111/j.1755-0998.2010.02848.x\u003c/li\u003e\n\u003cli\u003eHou G, Wang J, Chen Z, Zhou J, Huang W, Zhang H (2020) Molecular and morphological identification and seasonal distribution of eggs of four Decapterus fish species in the northern South China Sea: A key to conservation of spawning ground. Front Mar Sci 7(November). https://doi.org/10.3389/fmars.2020.590564\u003c/li\u003e\n\u003cli\u003eImtiaz A, Mohd Nor SA, Md Naim D (2017) Progress and potential of DNA barcoding for species identification of fish species. Biodiversitas 18(4):1394\u0026ndash;1405. https://doi.org/10.13057/biodiv/d180415\u003c/li\u003e\n\u003cli\u003eIvanova NV, Zemlak TS, Hanner RH, Hebert PDN (2007) Universal primer cocktails for fish DNA barcoding. Mol Ecol Notes 7(4):544\u0026ndash;548. https://doi.org/10.1111/j.1471-8286.2007.01748.x\u003c/li\u003e\n\u003cli\u003eJiang H, Wong WH (2008) SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 24(20):2395\u0026ndash;2396. https://doi.org/10.1093/bioinformatics/btn429\u003c/li\u003e\n\u003cli\u003eJiang R, Mchura M, Liang Z, Yin R, Zhou Y, Chen Y, Mchura MT (2023) Identification of ichthyoplankton in the East China Sea off the coast of Zhoushan Archipelago using an integrated strategy of morphology and DNA barcoding. Authorea. https://doi.org/10.22541/au.167627361.18256464/v1\u003c/li\u003e\n\u003cli\u003eKato T, Jinbo U, Ito M (2012) DNA barcoding: a novel tool for observation of biodiversity. Ecol Res Monogr:259\u0026ndash;266. https://doi.org/10.1007/978-4-431-54032-8_19\u003c/li\u003e\n\u003cli\u003eKara A, Y\u0026uuml;ksek A (2023) Comparison of morphological and molecular identifications in ichthyoplankton of the Marmara Sea. Acta Biol Turc 36(3):1\u0026ndash;11\u003c/li\u003e\n\u003cli\u003eKeele J, Carmon J, Pucherelli SF, Hosler D (2014) Identification of unknown organisms by DNA barcoding: a molecular method for species classification. Bureau of Reclamation. https://www.usbr.gov/research/\u003c/li\u003e\n\u003cli\u003eKrehenwinkel P, Pomerantz A (2019) Genetic biomonitoring and biodiversity assessment using portable sequencing technologies: current uses and future directions. Genes 10(11):858. https://doi.org/10.3390/genes10110858\u003c/li\u003e\n\u003cli\u003eKumar A, Chordia N (2015) In silico PCR primer designing and validation. Methods Mol Biol 1275:143\u0026ndash;151. https://doi.org/10.1007/978-1-4939-2365-6_10\u003c/li\u003e\n\u003cli\u003eLabare MF (2022) A preliminary comparison of ichthyoplankton surveys vs. environmental DNA to survey a nearshore marine fish community. University of California San Diego. https://escholarship.org/uc/item/7z2417f4\u003c/li\u003e\n\u003cli\u003eLeis JM (2010) Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol Res 57(4):325\u0026ndash;342. https://doi.org/10.1007/s10228-010-0177-z\u003c/li\u003e\n\u003cli\u003eLeis JM (2014) Taxonomy and systematics of larval Indo-Pacific fishes: a review of progress since 1981. Ichthyol Res 62(1):9\u0026ndash;28. https://doi.org/10.1007/s10228-014-0426-7\u003c/li\u003e\n\u003cli\u003eLewis LA, Richardson DE, Zakharov EV, Hanner R (2016) Integrating DNA barcoding of fish eggs into ichthyoplankton monitoring programs. Fish Bull 114(2):153\u0026ndash;165. https://doi.org/10.7755/FB.114.2.3\u003c/li\u003e\n\u003cli\u003eLinhart C, Shamir R (2005) The degenerate primer design problem: theory and applications. J Comput Biol 12(4):431\u0026ndash;456. https://doi.org/10.1089/cmb.2005.12.431\u003c/li\u003e\n\u003cli\u003eLira NL, Tonello S, Lui RL, Traldi JB, Brand\u0026atilde;o H, Oliveira C, Blanco DR (2023) Identifying fish eggs and larvae: from classic methodologies to DNA metabarcoding. Mol Biol Rep. https://doi.org/10.1007/s11033-022-08091-9\u003c/li\u003e\n\u003cli\u003eLiu K, Zhao S, Yu Z, Zhou Y, Yang J, Zhao R, Yang C et al (2020) Application of DNA barcoding in fish identification of supermarkets in Henan Province, China: more and longer COI gene sequences were obtained by designing new primers. Food Res Int 136(Oct). https://doi.org/10.1016/j.foodres.2020.109516\u003c/li\u003e\n\u003cli\u003eMariacher A, Garofalo L, Fanelli R, Lorenzini R, Fico R (2019) A combined morphological and molecular approach for hair identification to comply with the European ban on dog and cat fur trade. PeerJ 7:e7955. https://doi.org/10.7717/peerj.7955\u003c/li\u003e\n\u003cli\u003eMelo AA, Nunes R, Telles MPC (2021) Same information, new applications: revisiting primers for the avian COI gene and improving DNA barcoding identification. Org Divers Evol 21(3):599\u0026ndash;614. https://doi.org/10.1007/s13127-021-00507-x\u003c/li\u003e\n\u003cli\u003ePetit-Marty N, V\u0026aacute;zquez-Luis M, Hendriks IE (2021) Use of the nucleotide diversity in COI mitochondrial gene as an early diagnostic of conservation status of animal species. Conserv Lett. https://doi.org/10.1111/conl.12756\u003c/li\u003e\n\u003cli\u003eProsser S, Mart\u0026iacute;nez-Arce A, El\u0026iacute;as-Guti\u0026eacute;rrez M (2013) A new set of primers for COI amplification from freshwater microcrustaceans. Mol Ecol Resour 13(6):1151\u0026ndash;1155. https://doi.org/10.1111/1755-0998.12132\u003c/li\u003e\n\u003cli\u003eRaju R, Haldar C (2018) DNA barcoding: a tool for diverse conservation in mangrove ecosystem. Ann Aquac Res 5(1):1048. www.fishbase.org\u003c/li\u003e\n\u003cli\u003eRathnasuriya MIG, Mateos-Rivera A, Skern-Mauritzen R, Wimalasiri HBU, Jayasinghe RPPK, Krakstad JO, Dalpadado P (2021) Composition and diversity of larval fish in the Indian Ocean using morphological and molecular methods. Mar Biodivers 51:39. https://doi.org/10.1007/s12526-021-01169-w\u003c/li\u003e\n\u003cli\u003eRass TS (1972) Ichthyoplankton from Cuban waters: pelagic fish eggs. Tr Inst Okeanol 93:5\u0026ndash;41 [in Russian]\u003c/li\u003e\n\u003cli\u003eRausch T, Fritz MHY, Untergasser A et al (2020) Tracy: basecalling, alignment, assembly and deconvolution of Sanger chromatogram trace files. BMC Genomics 21:230. https://doi.org/10.1186/s12864-020-6635-8\u003c/li\u003e\n\u003cli\u003eRichards WJ (2005) Early stages of Atlantic fishes: an identification guide for the western central North Atlantic. CRC Press, Boca Raton. https://doi.org/10.1201/9780203500217\u003c/li\u003e\n\u003cli\u003eRubbmark RO, Sint D, Horngacher N, Traugott M (2018) A broadly applicable COI primer pair and an efficient single-tube amplicon library preparation protocol for metabarcoding. Ecol Evol 8(24):12335\u0026ndash;12350. https://doi.org/10.1002/ece3.4520\u003c/li\u003e\n\u003cli\u003eSalonna M, Gasparini F, Huchon D, Montesanto F, Haddas-Sasson M, Ekins M, McNamara M, Mastrototaro F, Gissi C (2021) An elongated COI fragment to discriminate botryllid species and as an improved ascidian DNA barcode. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-83127-x\u003c/li\u003e\n\u003cli\u003eSheth BP, Thaker VS (2017) DNA barcoding and traditional taxonomy: an integrated approach for biodiversity conservation. Genome 60(7):618\u0026ndash;628. https://doi.org/10.1139/gen-2015-0167 \u003c/li\u003e\n\u003cli\u003eSmirnov V, Warnow T (2021) MAGUS: multiple sequence alignment using graph clustering. Bioinformatics 37(12):1666\u0026ndash;1672. https://doi.org/10.1093/bioinformatics/btaa992\u003c/li\u003e\n\u003cli\u003eSouza HV, Marchesin SRC, Itoyama MM (2016) Analysis of the mitochondrial COI gene and its informative potential for evolutionary inferences in the families Coreidae and Pentatomidae (Heteroptera). Genet Mol Res 15(1). https://doi.org/10.4238/gmr.15017428\u003c/li\u003e\n\u003cli\u003eTrevethan R (2017) Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health 5(November). https://doi.org/10.3389/fpubh.2017.00307\u003c/li\u003e\n\u003cli\u003eVernooy R, Haribabu E, Muller MR, Vogel JH, Hebert PDN, Schindel DE et al (2010) Barcoding life to conserve biological diversity: beyond the taxonomic imperative. PLoS Biol 8(7):e1000417. https://doi.org/10.1371/journal.pbio.1000417\u003c/li\u003e\n\u003cli\u003eVon Cr\u0026auml;utlein M, Korpelainen H, Pietil\u0026auml;inen M, Rikkinen J (2011) DNA barcoding: a tool for improved taxon identification and detection of species diversity. Biodivers Conserv 20(2):373\u0026ndash;389. https://doi.org/10.1007/s10531-010-9964-0\u003c/li\u003e\n\u003cli\u003eWard RD, Zemlak TS, Innes BH, Last PR, Hebert PDN (2005) DNA barcoding Australia\u0026rsquo;s fish species. Philos Trans R Soc B Biol Sci 360(1462):1847\u0026ndash;1857. https://doi.org/10.1098/rstb.2005.1716 \u003c/li\u003e\n\u003cli\u003eWeigt LA, Driskell AC, Baldwin CC, Ormos A (2012) DNA barcoding fishes. In: Kress WJ, Erickson DL (eds) DNA barcodes: methods and protocols. Methods Mol Biol 858:109\u0026ndash;126. https://doi.org/10.1007/978-1-61779-591-6_6\u003c/li\u003e\n\u003cli\u003eWibowo A, Sloterdijk H, Ulrich SP (2015) Identifying Sumatran peat swamp fish larvae through DNA barcoding, evidence of complete life history pattern. Procedia Chem 14:76\u0026ndash;84. https://doi.org/10.1016/j.proche.2015.03.012\u003c/li\u003e\n\u003cli\u003eWibowo A, Wahlberg N, Vasem\u0026auml;gi A (2017) DNA barcoding of fish larvae reveals uncharacterised biodiversity in tropical peat swamps of New Guinea, Indonesia. Mar Freshw Res 68(6):1079\u0026ndash;1087. https://doi.org/10.1071/MF16078\u003c/li\u003e\n\u003cli\u003eTrevithick R, Alcantara DMC, Machado DJ, Marques FPL, Lahr DJG (2019) Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol-preserved specimens in biodiversity studies. PeerJ 7:e7543. https://doi.org/10.7717/peerj.7543\u003c/li\u003e\n\u003cli\u003eWickham H, Averick M, Bryan J, Chang W, McGowan L, Fran\u0026ccedil;ois R et al (2019) Welcome to the tidyverse. J Open Source Softw 4(43):1686. https://doi.org/10.21105/joss.01686 \u003c/li\u003e\n\u003cli\u003eWu TH, Yang CH, Pai TW, Ho LP, Wu JL, Chou HY (2021) Identification of fish species through tRNA-based primer design. BMC Bioinformatics 22(May). https://doi.org/10.1186/s12859-022-04717-8\u003c/li\u003e\n\u003cli\u003eZhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina paired-end read merger. Bioinformatics 30(5):614\u0026ndash;620. https://doi.org/10.1093/bioinformatics/btt593 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aquatic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aqsc","sideBox":"Learn more about [Aquatic Sciences](http://link.springer.com/journal/27)","snPcode":"27","submissionUrl":"https://submission.nature.com/new-submission/27/3","title":"Aquatic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COI, primer, Ichthyoplankton, morphology, DNA barcode","lastPublishedDoi":"10.21203/rs.3.rs-6219031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6219031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate 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 \u003cem\u003ein silico\u003c/em\u003e based on five target phyla, and subsequently \u003cem\u003ein silico\u003c/em\u003e 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.\u003c/p\u003e","manuscriptTitle":"Development of optimized COI primers for biodiversity assessment: A case study of Ichthyoplankton in Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 12:31:01","doi":"10.21203/rs.3.rs-6219031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T08:46:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T17:11:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T14:11:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117674614545530146375243312257269858959","date":"2025-09-12T17:26:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275656555282164977431945807202115723671","date":"2025-09-09T12:56:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202028645379520790602685388518235316442","date":"2025-09-09T06:35:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339822336307269304138405473279401360571","date":"2025-03-22T04:36:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-22T01:39:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-16T10:27:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-15T17:26:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aquatic Sciences","date":"2025-03-13T10:13:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aquatic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aqsc","sideBox":"Learn more about [Aquatic Sciences](http://link.springer.com/journal/27)","snPcode":"27","submissionUrl":"https://submission.nature.com/new-submission/27/3","title":"Aquatic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0f4fc7df-85b1-4fd7-898b-91d1448f70b7","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:01:41+00:00","versionOfRecord":{"articleIdentity":"rs-6219031","link":"https://doi.org/10.1007/s00027-026-01274-7","journal":{"identity":"aquatic-sciences","isVorOnly":false,"title":"Aquatic Sciences"},"publishedOn":"2026-02-15 15:57:16","publishedOnDateReadable":"February 15th, 2026"},"versionCreatedAt":"2025-03-31 12:31:01","video":"","vorDoi":"10.1007/s00027-026-01274-7","vorDoiUrl":"https://doi.org/10.1007/s00027-026-01274-7","workflowStages":[]},"version":"v1","identity":"rs-6219031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6219031","identity":"rs-6219031","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}