Northern Arabian Sea: Rare Fish Diversity and Biogeographic Affinities
Northern Arabian Sea: Rare Fish Diversity and Biogeographic Affinities
Bushra Sial1, Arif Muhammad Khan1, Mohsan Raza1,6*, Azhar Abbas Khan2, Sayyad Ali Raza Bukhari1, Muhammad Kashif Hanif3, Sher Khan Panhwar4, Muhammad Ashfaq5
1Department of Biotechnology, University of Sargodha
2Department of Entomology, University of Layyah
3Institute of Molecular Biology and Biotechnology, The University of Lahore, Sargodha Campus
4Centre of Excellence in Marine Biology, University of Karachi
5University of Guelph, Canada
6College of Fisheries and Life Sciences, Shanghai Ocean University, Shanghai 201306, China
Abstract | Recent molecular approaches have revolutionized the world of species classification and identification. In this study, we delved into the fascinating domain of DNA barcoding precisely for rare marine species and delineated species population genetic variability, genetic differences, and phylogenetic relationships between families/genera. 542 COI sequences from experimental species and an online database were considered for phylogenetic and Fst analysis. Moreover, an online QR code generator was used to develop the first-ever QR codes for nucleotide information of these species. It is the first study from Pakistan to reveal the barcode gap, phylogenetic relationship, and genetic diversity of the fish species in the northern Arabian Sea. A notable genetic variation level was revealed, with the highest value of 0.75 indicating a significant differentiation between populations of Taiwan and Pakistan. In contrast, the lowest Fst value of 0.04 manifested minimal genetic differentiation between populations in the USA and Bangladesh. An average genetic distance using the Kimura 2 parameter (K2P) model using BOLD systems revealed 20.17 and 19.87 percent within genus and family respectively. Nevertheless, this study documented the COI sequence of Caesio varilineata and Uranoscopus dollfusi, for the first time. The combined use of taxonomy, DNA barcoding, and QR codes appeared to be robust approaches, and have paved the way for a better understanding of fishes rarely found in Pakistan, northern Arabian Sea.
Novelty Statement | This study demonstrates the utility of integrating DNA barcoding and QR coding for elucidating the genetic diversity and phylogenetic relationships of rare marine fish species in the northern Arabian Sea, thereby enhancing our understanding of this previously undercharacterized region.
Article History
Received: July 05, 2024
Revised: July 25, 2024
Accepted: August 07, 2024
Published: October 19, 2024
Authors’ Contributions
BS and MR performed research and wrote manuscript. AMK designed research, contributed reagents and analyzed data. AAK designed and performed research. SAB performed analysis. MKH did statistical/data analysis and proofreading. SKP performed research and MA analyzed sequence data.
Keywords
Northern Arabian sea, Genetic diversity, Phylogenetic analysis, Barcode gap, Genetic variations, Rare species
Copyright 2024 by the authors. Licensee ResearchersLinks Ltd, England, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Corresponding author: Mohsan Raza
To cite this article: Sial, B., Khan, A.M., Raza, M., Khan, A.A., Bukhari, S.A.R. Hanif, M.K., Panhwar, S.K., Ashfaq, M., 2024. Northern Arabian Sea: Rare fish diversity and biogeographic affinities. Punjab Univ. J. Zool., 39(2): 191-212. https://dx.doi.org/10.17582/journal.pujz/2024/39.2.191.212
Introduction
Due to contemporary advancements and the involvement of molecular tools, fish taxonomy has grabbed the interest of fisheries biologists globally. Pakistan is rich in fish diversity due to its subtropical position on the globe. Previously, conventional taxonomy was used as a major tool to describe species (Farooq and Panhwar, 2023; Qamar et al., 2016; Rauf et al., 2019). Conventional taxonomy based on just morphology has many limitations, as we are unable to identify processed meat, broken samples, or individuals at their early developmental stages (Keskin and Atar, 2013; Zhang and Hanner, 2012). Cryptic species, species having incomplete morphological characters, and novel species may not be identified accurately, leading to misidentification. Moreover, there is a knowledge gap for taxonomists to properly identify some fish groups at the species level. The classification description of fish in different literature is different which can also lead to misidentification by beginners. Currently, DNA barcoding is being successively used and has been proven as an efficient tool for the rapid and accurate identification of fish (DeSalle and Goldstein, 2019). Furthermore, the identification of cryptic species, species at earlier stages of their life, and processed samples having incomplete morphology can be more reliably executed using molecular techniques as compared to identification based on morphology (Galal-Khallaf et al., 2014; Raharinaivo et al., 2020; Wang et al., 2020). Molecular approaches together with morphological identification can serve as a potential tool in fish biology because they provide rapid, precise, and reasonable systems for identification. Moreover, the delineation of genetic distances within different species and even in the population of the same species can be measured using single gene-based studies (de Sousa et al., 2022; Habib et al., 2022; Khan et al., 2023; Tang et al., 2023). The 650 bp region of COI a mitochondrial gene is being extensively used in molecular identification (Lohman et al., 2009). This gene possesses great importance as it has a slow amino acid change rate (Hebert et al., 2003; Lynch and Jarrell, 1993). Practically, DNA barcoding using the COI gene is being worldwide used for the identification of freshwater and marine fish species with a success rate of up to 93% (Ward, 2012). However, there are some limitations of the COI gene too, such as partial lineage sorting phenomena and gene introgression, which may lead sometime towards misidentification (Eberle et al., 2020; Galimberti et al., 2021). So, by employing the combination of morphological and molecular techniques, many ambiguities can be removed. Moreover, many analysis tools in addition to the conventional morphological-based identification and DNA barcoding studies are applied to improve the accuracy of fish identification, which will further empower the discovery of cryptic species and will enrich the genetic diversity of fish species (Breman et al., 2016; Hou et al., 2018). Northern Arabian Sea possesses a complex and high ratio of biodiversity including abundantly found species as well as some unique and rarely occurring species. Normally the focus of researchers is on abundantly present fishes as they contribute a major part in daily fish landing. However, the study of rare fish holds significant importance for conservation, ecological, and scientific reasons. In this study, some rarely occurring fishes were tried to cover taxonomy at the molecular level. In total, 15 species belonging to 13 families and eight different classes were collected from daily fish landing facilities along the Karachi coast. By considering a 650 bp region of COI, a comprehensive DNA barcode database of 45 samples representing 15 marine species, 13 families, and 7 orders (Lactarius lactarius, Rachycentron canadum, Caesio varilineata, pempheris russellii, Pomacanthus annularis, Myripristis botche, Sargocentron rubrum, Plotosus lineatus, Chanos chanos, Uranoscopus dollfusi, Terapon jarbua, Terapon puta, Drepane longimana, Scatophagus argus, Pampus argenteus) was developed as these are rarely present in daily fish landing at Karachi coast.
This study aimed to provide robust taxonomic and molecular descriptions of rare fishes and to delineate the genetic variability, species population differentiation, and biogeographic affinities, to facilitate researchers for further findings on population structure, understanding of evolutionary processes, and conservation strategies for species inhabiting circumglobally.
Materials and Methods
Sampling and morphological identification
In total 542 sequences were used for analysis of genetic diversity in rarely occurring marine species. Detailed information on sampling and Genbank accession numbers of extracted sequences from each species is mentioned in Supplementary Table 1. Type species (Lactarius lactarius, Rachycentron canadum, Caesio varilineata, Pempheris russellii, Pomacanthus annularis, Myripristis botche, Sargocentron rubrum, Plotosus lineatus, Chanos chanos, Uranoscopus dollfusi, Terapon jarbua, Terapon puta, Drepane longimana, Scatophagus argus, Pampus argenteus) belonging to 13 families and 7 orders were collected from the commercial catches at the daily fish landing facility (Karachi Fisheries Harbor). The exact location of the sample collection is shown in Figure 1. Samples were transported to the fisheries laboratory, Centre of Excellence in Marine Biology (CEMB). Each species was identified based on its morphology by using taxonomic keys and an FAO field guide (Psomadakis, 2015). Photographs for each of the specimens were taken. Samples were then transported to the Department of Biotechnology, University of Sargodha for molecular analysis. Tissue excision was done under sterile conditions and properly labeled. All of the samples and excised tissues were preserved in 95% ethanol for future use. All procedures performed involving animals were approved by the ethical committee of the University of Karachi, approval no IBC KU-260/2022.
Amplification and sequencing of DNA barcode region
The DNA extraction was accomplished using a GeneJET DNA purification kit (Catalogue no. K0721). The recommended protocol for animal tissue DNA extraction was followed for DNA extraction. The product of DNA extraction was stored at -20°C for further experimental work.
The target region of 652 bp was amplified using published primers (Fish F1 and Fish R1) for DNA barcoding of fishes (Karim et al., 2016). PCR amplified product was sequenced commercially. Further, the excised tissues were also sent to the Canadian Centre for DNA barcoding (CCDB), the University of Guelph for sequencing and generation of DNA barcodes. Obtained sequences are uploaded to BOLDsystems for further analysis and also submitted to NCBI Genbank, the details are mentioned in Table 1.
Barcode index number allocation
Uploaded data on the BOLD workbench is automatically referred to a unique relevant BIN (Barcode Index Number). All the samples in one bin share the similarity at the molecular level. Available sequences of type species from across the globe were also downloaded and included in the analysis to explore genetic diversity. Species identification using molecular tools was also reconfirmed by BLAST search analysis. The significant E-values that are generated pairwise also play a paramount role in the delineation of fish species. If there was any mismatch between morphological identification and molecular identification then both were revisited to reach the concrete decision after the consideration of both parameters. The genetic distances based on the COI gene were determined with the help of MEGA X by using the Kimura 2 Parameter (K2P). Additionally, other analyses including a distance summary, nucleotide diversity, and a barcode gap analysis were done using the BOLD systems workbench, while the Fst analysis used R studio.
Phylogenetic relationship of experimental species from Pakistan with specimens across the globe
A phylogenetic analysis was conducted by using sequences obtained from experimental species from Pakistan and also retrieved data of the same species from across the globe. All of the sequences were aligned using the MSA/MAFT method. The trees to the explicit phylogenetic relationship were developed using the Fast tree method. Upon formation, the GGTree method was used for the visualization of relationships (Yu, 2020). The Genbank/Accession number for each species was manifested within the center of the tree. All of the work was done via UNIX, R, and R Studio. The species belonging to the same order and families were
Table 1: Geographical coordinates, sampling location, and GenBank accession number of rare species.
|
Family |
Specie name |
Order |
Location |
Voucher no. |
Accession no |
BOLD ID |
|
Lactariidae |
Lactarius lactarius |
Perciformes |
Latitude; 24°50'53.2"N Longitude; 66°58'40.6"E Fish Harbour Rd., West Wharf Karachi, Karachi City, Sindh, Pakistan |
MAK-142 |
OQ801201 |
BOLD:AAD4634 |
|
Rachycentridae |
Rachycentron canadum |
Carangiformes |
MAK-73 |
OQ807163 |
BOLD:AAB2939 |
|
|
Caesionidae |
Caesio varilineata |
Perciformes |
MAK-103 |
OQ825963 |
BOLD:ADK7119 |
|
|
Pempheridae |
Pempheris russellii |
Perciformes |
MAK-108 |
OQ826120 |
BOLD:AAD1777 |
|
|
Pomacanthidae |
Pomacanthus annularis |
Perciformes |
MAK-59 |
OQ807215 |
BOLD:AAF1425 |
|
|
Holocentridae |
Myripristis botche |
Holocentri-formes |
MAK-119 |
OQ807657 |
BOLD:AAX2837 |
|
|
Holocentridae |
Sargocentron rubrum |
Holocentri-formes |
MAK-120 |
OQ808566 |
BOLD:AAB9306 |
|
|
Plotosidae |
Plotosus lineatus |
Siluriformes |
MAK-37 |
OQ808970 |
BOLD:ABY8174 |
|
|
Chanidae |
Chanos chanos |
Gonoryn-chiformes |
MAK-145 |
OQ809068 |
BOLD:AAC1320 |
|
|
Uranoscopidae |
Uranoscopus dollfusi |
Trachiniformes |
MAK-114 |
OQ809069 |
BOLD:ACX9882 |
|
|
Terapontidae |
Terapon jarbua |
Perciformes |
MAK-05 |
OQ810001 |
BOLD:AAA9351 |
|
|
Terapontidae |
Terapon puta |
Perciformes |
MAK-13 |
OQ810033 |
BOLD:AAB0170 |
|
|
Drepaneidae |
Drepane longimana |
Perciformes |
MAK-44 |
OQ814428 |
BOLD:AAB0170 |
|
|
scatophagidae |
Scatophagus argus |
Perciformes |
MAK-92 |
OQ814532 |
BOLD:AAB3530 |
|
|
Stromateidae |
Pampus argenteus |
Scombriformes |
MAK-27 |
OQ815713 |
BOLD:AAB6557 |
Table 2: Values obtained from barcode gap analysis.
|
Order |
Family |
Species |
Max intra-Sp |
Nearest species |
Nearest neighbour |
Distance to NN |
Barcode gap |
|
Carangiformes |
Rachycentridae |
Rachycentron canadum |
2.74 |
Drepane longimana |
SUFIS237-21 |
21.85 |
19.11 |
|
Holocentriformes |
Holocentridae |
Myripristis botche |
0.74 |
Terapon jarbua |
SUFIS385-21 |
17.17 |
16.43 |
|
Holocentriformes |
Holocentridae |
Sargocentron rubrum |
4.23 |
Myripristis botche |
SUFIS330-21 |
19.26 |
15.03 |
|
Moroniformes |
Drepaneidae |
Drepane longimana |
0.31 |
Terapon jarbua |
SUFIS005-20 |
19.26 |
18.95 |
|
Perciformes |
Caesionidae |
Caesio varilineata |
0.96 |
Myripristis botche |
SUFIS330-21 |
18.83 |
17.87 |
|
Perciformes |
Pempheridae |
Pempheris russellii |
2.16 |
Sargocentron rubrum |
SUFIS331-21 |
21.44 |
19.28 |
|
Perciformes |
Pomacanthidae |
Pomacanthus annularis |
1.72 |
Myripristis botche |
SUFIS329-21 |
22.02 |
20.3 |
|
Perciformes |
Scatophagidae |
Scatophagus argus |
2.64 |
Pampus argenteus |
SUFIS188-21 |
20.43 |
17.79 |
|
Perciformes |
Terapontidae |
Terapon jarbua |
3.62 |
Myripristis botche |
SUFIS329-21 |
17.17 |
13.55 |
|
Perciformes |
Terapontidae |
Terapon puta |
1.59 |
Terapon jarbua |
SUFIS386-21 |
19.63 |
18.04 |
|
Scombriformes |
Stromateidae |
Pampus argenteus |
3.96 |
Caesio varilineata |
SUFIS149-21 |
19.42 |
15.46 |
|
Siluriformes |
Plotosidae |
Plotosus lineatus |
1.44 |
Scatophagus argus |
SUFIS138-21 |
21.2 |
19.76 |
|
Trachiniformes |
Uranoscopidae |
Uranoscopus dollfusi |
0.68 |
Sargocentron rubrum |
SUFIS331-21 |
24.59 |
23.91 |
clustered under the same clade and the species belonging to different families and orders were clustered under different clades. So, a unique pattern of phylogeny was depicted based on COI nucleotide information.
Generation of DNA barcodes and QR codes
In the present era technology is very developed and Smartphones can widely be used to access 2D QR codes. At a single time, almost 4000 characters can be accessible, hence such technology can be used in fish identification coupled with genetic information. The sequence data were used to generate DNA barcodes and QR codes using online code generator tools (Ayesha et al., 2019).
Results and Discussion
A series of analyses were performed on sequence data obtained from the experimental species and data retrieved from online databases (NCBI, BOLDsystems). A total of 542 sequences including sequences from experimental species were considered for Phylogenetic and Fst analysis using R studio. The metadata of all the species is given in Supplementary Table 1.
Barcode gap analysis
The barcode gap can be measured by finding the distribution of intra-species distances and the distance between their nearest neighbors. The barcode gap can be an impressive tool to delineate the species boundaries via nucleotide sequence information. All of the data from experimental species were uploaded to the online database Bold systems and were tested to reveal the barcode gap between them using the bold systems workbench. Three scatterplots are provided to confirm the existence and magnitude of the Barcode Gap. The results are listed in Table 2. The first two scatterplots show the overlap of the max and mean intra-specific distances vs the inter-specific (nearest neighbor) distances. The third scatterplot plots the number of individuals in each species against their max intra-specific distances, as a test for sampling bias (Figure 2).
Genetic distances and nucleotide diversity
The genetic distances based on COI nucleotide data were measured by using the K2P model and Muscel alignment on the BOLD systems workbench. Distribution of distances on different levels was manifested between all specimens and showed 20.17, and 19.87 percentages within genus and family, respectively. Table 3 shows that thymine (T) is the most common nucleotide, with a mean percentage of 29.06%, followed by adenine (A) at 23.71%. The overall GC content is 47.23%, with the first codon position being the most GC-rich at 57.60%. Interestingly, the third codon position has a lot of variability, which could affect how genes are expressed and proteins are made.
Table 3: Summary statistics for nucleotide frequency distribution are provided in the table.
|
Nucleotide |
Min |
Mean |
Max |
SE |
|
G % |
17.04 |
18.42 |
20.22 |
0.0984 |
|
C % |
24.88 |
28.81 |
33.57 |
0.2441 |
|
A % |
20.22 |
23.71 |
25.35 |
0.1617 |
|
T % |
25.08 |
29.06 |
32.57 |
0.2340 |
|
GC % |
42.09 |
47.23 |
51.54 |
0.2923 |
|
GC % Codon Pos 1 |
55.30 |
57.60 |
59.72 |
0.1217 |
|
GC % Codon Pos 2 |
42.11 |
43.03 |
45.50 |
0.0821 |
|
GC % Codon Pos 3 |
27.19 |
41.05 |
53.93 |
0.8530 |
Fst analysis
Fraction of genetic variance manifests differentiation within a population. The sequence data from experimental species and already available sequence data from NCBI were taken for each species. Overall, 542 sequences representing 12 species were used in this analysis. The metadata of each sequence is provided in Supplementary Table 1. The results were illustrated by heatmap charts and principle component analysis (Figures 3, 4, 5, 6). Fst analysis and development of heatmaps and PCA charts was done by R script. Sequence data taken from NCBI represents different geographical regions of the world.
Chanos chanos
Experimental sequences of Chanos chanos and sequences downloaded from NCBI were considered for Fst to determine genetic differentiation between species populations. Fst analysis revealed a maximum genetic variance of 0.5 between populations of Australia/ Iran, China/ Iran, and Bangladesh/ Iran. Moreover, a minimum of 0.04 genetic variation was revealed within the species population of the USA and Bangladesh. The values for Fst were interpreted by using heatmap charts (Figure 3A) and PCA (Figure 4A). The countries having similar
divergence were interpreted under the same cluster. According to PCA interpretation of Fst, the maximum divergence was shown in the population of Bangladesh followed by France, the Philippines, and Pakistan.
Drepane longimana
The maximum genetic variance (0.48) was manifested by the study population of India and Bangladesh, while the minimum genetic variance of (0.09) was revealed between the species populations of Indonesia and South Africa (Figure 3B). The countries having related genetic divergence were clustered under the same cluster as compared to ones that have distant genetic variance. PCA from Fst values representing Drepane longimana manifested maximum genetic variation between the species population of Indonesia followed by South Africa and Pakistan (Figure 4B).
Lactarius lactarius
The COI-based Fst analysis of sequences of Lactarius lactarius shows a maximum value of 0.5 genetic variations from the study population of Taiwan and Pakistan. Moreover, the species population representing India and Pakistan showed the lowest genetic variance value which was 0.08. The results obtained from Fst were interpreted with heatmap charts (Figure 3C) and PCA (Figure 4C). The countries having less distant genetic variance within their population were seen to gather under a single cluster. Principle component analysis of the values obtained from Fst shows that the L. lactarius population of the Philippines shows significantly higher divergence followed by the population of China, Pakistan, and India.
Myripristis botche
From all sequences under study populations from multiple countries show the maximum genetic variance of 0.33. The minimum genetic variance was 0.1 as shown between populations of the Philippines and USA. The values of Fst were interpreted by a heatmap chart (Figure 3D) darker colors show greater values and lighter colors show lower values. Multiple countries illustrate the maximum genetic variance hence falling in darker regions. Moreover, species populations from Taiwan, Reunion, Pakistan, and South Africa made one cluster, and species populations from the USA and Philippines formed another cluster. PCA interpretation of Fst values showed a similar pattern (Figure 4D). The species population from South Africa showed maximum genetic variance followed by the population of Taiwan and Pakistan.
Pampus argenteus
Fst analysis showed maximum genetic variance (0.33) between study populations of Australia and the USA. Meanwhile, the study population from India and China showed a minimum Fst value of 0.05. PCA (Figure 4E) and heatmap charts (Figure 3E) were plotted by using values obtained from Fst. The darker colors on the heatmap represent high genetic variation and lighter colors on the heatmap chart display lower values. In heatmap charts countries that form a single cluster show relatively similar genetic variations among their populations like India, China, and Pakistan. PCA values indicate that Pakistan showed significantly higher divergence followed by Australia, China, and India.
Plotosus lineatus
The study populations of Plotosus lineatus representing different regions of the world were considered for Fst. Maximum genetic variance (0.75) was found between countries viz Taiwan/Pakistan, Taiwan/Australia, Reunion/Pakistan, Reunion/Australia, Japan/Taiwan, Indonesia/Taiwan, Lebanon/Reunion, and Taiwan/Australia. Similarly, minimum genetic divergence (0.05) was revealed between the species population of Lebanon and China based on COI as a potential marker. Heatmap (Figure 3F) and PCA (Figure 4D) were used for the illustration of values obtained from Fst data. Moreover, species populations from countries like Taiwan and Reunion form one cluster, meanwhile all other countries’ species population was clustered under separate clusters according to Fst values. PCA based on values obtained from Fst was also done and depicts that the Plotosus lineatus population of Pakistan is significantly more divergent followed by the population of the USA and Philippines.
Pomacanthus annularis
The species population of Pomacanthus annularis depicts a range of Fst values between 0.33-0.25 taken from different geographical regions of the world. Heatmap (Figure 5A) illustrated that the species population from Pakistan and Bangladesh formed one cluster meanwhile species populations representing different regions of the world were clustered under separate clusters concerning their Fst values. Principle component analysis (Figure 6A) depicts that the most significant divergence was produced by the population of Pakistan followed by Vietnam and India.
Rachycentron canadum
A maximum of 0.61 genetic divergence was revealed between Australia and the UK, meanwhile, a minimum of 0.06 genetic divergence was illustrated by species populations belonging to Mozambique and South Africa. So, the population species of Mozambique and South Africa are more related as compared to others. The heatmap chart depicts that species populations of countries having somehow similar genetic divergence were clustered under a single cluster, while species populations having different genetic variances were clustered under different clusters (Figure 5B). PCA analysis of data obtained from Fst showed that Bangladesh produced more significant divergence followed by Bangladesh, Pakistan, and the UK (Figure 6B).
Sargocentron rubrum
Study populations of this species belonging to different regions of the world were tested for genetic variance between them using Fst. It produces a maximum value of divergence of about 0.57 between the study population of Australia and Saudia Arabia, while minimum genetic variation (0.06) was revealed in the species populations of Australia and Indonesia. Countries that share somehow similar values were combined to form one cluster and countries that depict different colors were considered to have distant genetic divergence (Figure 5C). PCA analysis depicts that species population from Pakistan and the Philippines shows significantly higher differentiation followed by other countries (Figure 6C).
Scatophagus argus
Fst analysis revealed a maximum genetic variance (0.75) between the study population of Australia/Vietnam, and Pakistan/Vietnam, while a minimum genetic variance of 0.04 was found to be present in populations of India and Sri Lanka. The presence of low genetic variance between two populations indicates that both of them are related to each other. The study populations of countries that show similar genetic differentiation (Indonesia, China, Sri Lanka, India, and Malaysia) were clustered under the same cluster, on the other hand, species populations of (Vietnam) were clustered under separate clusters (Figure 5D). PCA analysis of values obtained from Fst illustrated that the study population of Australia showed a significantly high level of differentiation followed by that of Pakistan and China (Figure 6D).
Terapon jarbua
Terapon jarbua manifested a maximum of 0.56 genetic variance between the species population of Pakistan and Israel. While a minimum of 0.11 genetic variance was displayed by study populations of France and the Philippines. Figure 5E depicted heatmap chart of values obtained from the Fst Species population of (France/Philippines, Taiwan/ China/ India) having similar genetic differentiation formed a single cluster, and the population of (Australia, Mexico, and Israel) having different genetic variance formed different clusters. Principle component analysis (Figure 6E) showed significantly higher differentiation for the species populations from Pakistan followed by other countries.
Terapon puta
A maximum of 0.35 genetic variance was displayed between the species populations of Philippines/Pakistan, and Lebanon/Pakistan, while a minimum of 0.05 genetic variance was observed between populations of Pakistan and India hence they are more closely related to each other. A heatmap chart (Figure 5F) was used to manifest data obtained from Fst Species populations from India and Pakistan were clustered in a single cluster as they show similar genetic divergence and all other countries’ populations were gathered to form a separate cluster as they show different divergence levels. Fst values-based PCA (Figure 6F) was also conducted and showed that China shows a significantly higher level of differentiation followed by Lebanon and other countries.
Phylogenetic analysis among marine species occurring worldwide
Figure 7A illustrates a phylogenetic tree for species of Chanos chanos occurring worldwide to delineate evolutionary relationships and genetic similarities between them. Every country was highlighted with a unique pattern of color. The orange color represents sequences grabbed from NCBI and the blue color shows sequenced species from Pakistan. All species were clustered under clades according to evolutionary relationships. Sequences from Pakistan formed a clade surrounded by India and the Philippines. Figure 7B depicts the phylogenetic relationship of Drepane longimana each of the sequences representing different countries was clustered according to their evolutionary relationship. Type specimens from Pakistan formed a clade with species from Bangladesh and showed more similarity to sequences from Bangladesh and India. Figure 7C shows the evolutionary relationship of Lactarius lactarius occurring across the globe, the studied species from Pakistan were clustered under a clade with China, India, and USA. Figure 7D illustrates the phylogenetic tree of Myripristis botche. The experimental species were clustered under the clade representing species from Pakistan along with species from the island of Reunion. Figure 7E shows the phylogenetic relationship of Pampus argenteus samples occurring across the globe. The experimental samples from Pakistan were clustered together with sequences from India. Both regions of Pakistan share a common Arabian Sea so they also share common ancestors. Figure 7F illustrates the evolutionary relationship of Plotosus lineatus occurring across the globe. The experimental species formed a clade with the species from Saudi Arabia, India, and the Philippines. So, the individuals from these regions are more related to each other and are considered to have evolved from the same ancestors.
Figure 8A illustrates a phylogenetic relationship of Pomacanthus annularis members occurring in different regions of the world. The experimental species from Pakistan were clustered under a clade shared by species members from Myanmar and Sri Lanka. Figure 8B depicts the evolutionary relationship of worldwide occurring members belonging to the Rachycentron canadum species. Our experimental species were clustered under the clade in which the members from Bangladesh and India were clustered. So, the experimental species was evolutionary more similar to members from Bangladesh and India. Figure 8C illustrates the phylogenetic relationship between members of Sargocentron rubrum representing different regions of the world. The studied samples were clustered in a clade with members from India and Australia. Figure 8D represents the phylogenetic relationship of Scatophagus argus members from different regions of the world. All of the members were clustered under different clades according to their evolutionary relationship. Experimental species were clustered under a clade with Australia, Sri Lanka, and India. Figure 8E manifested phylogenetic relationship members of Terapon jarbua from available COI sequences on NCBI and sequences obtained from experimental samples. The blue color on the tips represents the type specimen and the orange color on the tips shows sequences obtained from NCBI. The studied species were clustered under the clade with the members of species from France and India. Figure 8F manifested the phylogenetic relationship of Terapon puta occurring across the globe. Each of the countries was labeled with a unique pattern of colors. The studied samples from Pakistan were clustered under a clade with members from Lebanon, Israel, and Saudi Arabia.
Phylogenetic relationship at the family level
Based on results obtained from phylogenetic analysis at the family level it was depicted that species belonging to the same family were clustered under the same clades whereas species belonging to different families were clustered under different clades (Figure 9). Moreover, the species members of Chanos chanos from Pakistan were more similar to South Africa, Qatar, and India. Species members of Myripristis botche formed a distinct clade and type member from Pakistan was surrounded by USA and Reunion depicting a close genetic resemblance. Plotosus lineatus from Pakistan showed close genetic resemblance with Saudia Arabia and Malaysia. Pampus argenteus members from Pakistan showed close resemblance with China and India. Species members of Scatophagus argus representing Pakistan were more closely related to India and Sri Lanka. Likewise, each of type species showed their resemblance with the members from countries that showed more closed evolutionary relationships or the members that were inferred from common ancestors.
The results based on mitochondrial gene COI show significant differentiation between the species population of (Lactarius lactarius, Rachycentron canadum, Caesio varilineata, Pempheris russellii, Pomacanthus annularis, Myripristis botche, Sargocentron rubrum, Plotosus lineatus, Chanos chanos, Uranoscopus dollfusi, Terapon jarbua, Terapon puta, Drepane longimana, Scatophagus argus, Pampus argenteus) belonging to 13 families and 7 orders, representing different regions of the world. To our knowledge, our study provides the first-ever COI nucleotide sequence addition of Caesio Varilineata, and Uranscopus dollfusi to Genbank.
This study revealed genetic differentiation between various fish populations via Fst analysis. The results showed a substantial genetic differentiation (Fst = 0.75) in populations of Plotosus lineatus and Scatophagus argus, illustrating a significant divergence between them. Other species displayed varying levels of genetic differentiation (Fst = 0.51, 0.57, 0.48, and 0.3), implying some degree of population divergence. Possible factors contributing to these differences can be due to geographical barriers, limited gene flow, and local adaptation. The findings have important implications for conservation efforts, as high levels of genetic differentiation may require tailored conservation strategies. Additionally, the study contributes to our understanding of the genetic structure of the studied fish species and underscores the importance of maintaining genetically diverse populations for species resilience and adaptability.
Within the suborder Stromateoidei the family Pempheridae is the most speciose family, this family illustrated the poorly resolved systematics owing to its highly conserved anatomical features and wide distribution (Haedrich, 1967; Liu et al., 2013). Previously work done on Pampus argenteus lacked comprehensive information regarding the phylogenetics and population divergence. They showed within species genetic distance of about 0.000 to 0.005, However, Fst analysis revealed 0.05-0.33 genetic differentiation within the population of the same species this variation can be due to the large sample size (Cui et al., 2010). Moreover, our study has illustrated the detailed phylogenetics and population differentiation.
Nasihin-Seth et al. (2019) provide the DNA barcodes of Plotosus lineatus based on COI and the limited phylogenetic information from Malaysia. However, the members of this species particularly from Pakistan were under-explored on a molecular basis. Our study manifested the detailed phylogenetic relation as well as the species’ genetic diversity and population divergence within different regions of the world. The divergence between the members of species belonging to different regions of the world was measured using Fst analysis and showed 0.05-0.75 genetic divergence. The species population of Plotosus lineatus from the Arabian Sea was more similar to the population of India as they share less genetic differentiation between them. This similarity is obvious, as Pakistan and India share a common border. Over time, species and their relatives may travel within these places and share common ancestors as well.
Barton et al. (2018) documented the detailed information of Rachycentron canadum on the taxonomic characters as well as based on COI sequencing of the members of species from the Australian Coast. However, the species data from Pakistan still need to be explored. Our study provides detailed information on basic morphology, DNA barcodes, population genetic divergence, and phylogenetic relationship between the complex diversity of the Arabian Sea and all of the COI sequence data available on the online databases. The population divergence was found using Fst analysis and shows (0.06-0.61) values. The type species population showed more resemblance to Chinese and Indian populations. The low population differentiation can be due to different factors, including geographical proximity, historical gene flow, shared environmental conditions, and high dispersal ability.
Washim et al. (2022) illustrated the information based on morphometric measurements of Scatophagus argus, particularly from Bangladesh. However, in addition to morphometric measurements molecular-based exploration is required for more authentic species delimitation, particularly from Pakistan. Our study offers comprehensive insights into DNA barcodes, population divergence, genetic distances, and phylogenetic relationships between the experimental species and other species found globally. The online QR code for the nucleotide information of sequences was also generated. The population divergence was revealed using Fst analysis and illustrated (0.04-0.75) values. The phylogenetic tree shows a grouping of the taxas based on nucleotide sequence similarity. The population differentiation of type species showed maximum resemblance with populations of India and China. The possible reason can be the geographical proximity as they share common borders and shorter distances for potential migration. There can be other reasons such as high dispersal ability, historical gene flow, and shared environmental conditions.
The Fst analysis based on a single gene, COI, unravels significant genetic differentiation among species populations of type species. The maximum value of 0.75 manifested considerable genetic divergence between different populations indicating limited gene flow and potential population isolation. In contrast minimal value of 0.04 implies a lower level of genetic divergence between other populations, depicting higher gene flow and potential connectivity. These findings depict that genetic structure within type species varies across regions and populations. Higher differentiation values proposed the presence of subpopulations or local adaptations, that are typically influenced by factors such as environmental conditions, geographical barriers, and historical events. The higher degree of gene flow is illustrated by the lower values, potentially facilitated by factors like geographical proximity, shared environmental conditions, or the active dispersal mechanism. The differentiation values obtained from a single gene, such as COI give an insight about genetic divergence at that particular locus. To get a detailed insight into genetic structure it is recommended to consider additional genetic markers and broad sampling across different genomic regions. The above findings highlighted the importance of maintaining connectivity between populations having low differentiation to preserve overall genetic diversity and to prevent the loss of locally adapted potential traits. In addition, populations that manifested higher differentiation may warrant specific conservation measures to protect their unique genetic resources and ensure their long-term viability. The present study could serve as an integrative genetic analysis that unravels differentiation and its implications for the conservation and management of type species populations.
Phylogenetic trees were constructed using R script delineating the evolutionary relationship between experimental species and species occurring across the globe. The species were clustered under the clades according to their orders, families, genera, and species. The pattern of clustering obtained from phylogenetic relationships unraveled an interesting genetic relationship among species occurring across the globe. The species members of Myripristis botche showed close resemblance with species members of Reunion. The island of Reunion is very far from the northern Arabian Sea but due to many natural changes and adaptations some species might have traveled and may share common ancestors at the time of course. Likewise, the other species revealed their evolutionary history and genetic similarity. These findings may manifest historical migration events or they may share common ancestors across the covered regions. Further studies may be required to reveal the true phylogeny of life for these groups of fishes and could serve as a valuable insight into evolutionary relationships, and practical implications for conservation, genetics, and ecology.
As a whole, the COI barcode data obtained from members of these 13 families have delineated species explicitly. However, only two species (Pempheris russellii and Caesio varilineata) were not able to be discriminated against and shared barcode index numbers with Pempheris nesogallica, Pempheris mangula, and Pterocaesio chrysozona respectively, keeping in mind their morphological characters were considered again and were matched with all members representing that bin, so a strong decision was made after considering morphological characters as we cannot rely solely on one method for identification. Moreover, there is a need for more molecular markers for the exact identification of these species.
Just like supermarkets that contain a specific QR code for each product, QR codes for species nucleotide sequences can easily be developed and accessed via mobile applications QR code scanners, etc. Before this study QR codes for fish species were developed (Ghouri et al., 2020) but codes for our experimental species were developed for the first time. Bio-Rad DNA barcodes are also being generated using online tools (Yang et al., 2019) for species identification on a molecular basis. Our study developed both DNA barcodes on BOLD systems and the QR codes containing the sequence information are given as a Supplementary Figure 1. As a whole DNA barcoding together with taxonomy can be an effective approach to developing strategies for the management, conservation, and monitoring of the fisheries sector. The present study targets some rare 15 species belonging to 13 families and 7 orders to explore them on morphology and molecular basis. DNA barcoding is not commonly practiced in Pakistan so identification on a molecular basis and generation of DNA-based QR codes can be validated as a basic approach for this purpose.
Conclusions and Recommendations
The present data could serve as a baseline for the identification of new species, environmental studies, and biogeographic patterns. It documented the COI-based DNA database of 15 rarely occurring marine species and two species (Caesio varilineata, Uranoscopus dollfusi) for the first time in Genbank from a coastal area inhabiting Pakistan. Moreover, the COI sequence of five species (Chanos chanos, Sargocentron rubrum, Plotosus lineatus, Myripristis botche, and Pomacanthus annularis) was first time documented from Pakistan to BOLD systems and NCBI repository. The QR codes and DNA barcodes for robust nucleotide information availability were introduced. Moreover, this study serves as a crucial tool for developing management and conservation strategies for marine fish diversity.
Declarations
Funding
We are delighted to accomplish the current research with the help of the Higher Education Commission of Pakistan (NRPU-10403). Sequencing work was performed at the Canadian Centre for DNA Barcoding (CCDB), University of Guelph, Canada.
IRB approval
This study was approved by the Institutional Review Board of University of Karachi approval no IBC KU-260/2022.
Ethical statement
This study utilized fish samples collected from local markets in Karachi, Pakistan, ensuring minimal harm and no direct impact on fish populations. No harmful or destructive sampling methods were employed.
The supplementary material associated with this article is given after the references.
Conflict of interest
The authors have declared no conflict of interest.
References
Ayesha, U.R., Shafi, N., Akhtar, T., Zareen, A. and Ayub, H., 2019. DNA barcoding of cyprinids (Labeo rohita, Catla catla and Cirrhinus mrigala), mitochondrial CO1-based study. Mitochondrial DNA B, 4: 405-407. https://doi.org/10.1080/23802359.2018.1547132
Barton, D.P., Smales, L. and Morgan, J.A., 2018. A redescription of Serrasentis sagittifer (Rhadinorhynchidae: Serrasentinae) from Rachycentron canadum (Rachycentridae) with comments on its biology and its relationship to other species of Serrasentis. J. Parasitol., 104: 117-132. https://doi.org/10.1645/17-94
Breman, F.C., Loix, S., Jordaens, K., Snoeks, J. and Van Steenberge, M., 2016. Testing the potential of DNA barcoding in vertebrate radiations: The case of the littoral cichlids (Pisces, Perciformes, Cichlidae) from Lake Tanganyika. Mol. Ecol. Resour., 16: 1455-1464. https://doi.org/10.1111/1755-0998.12523
Cui, Z., Liu, Y., Liu, J. and Luan, W., 2010. Molecular identification of Pampus fishes (Perciformes, Stromateidae). Ichthyol. Res., 57: 32-39. https://doi.org/10.1007/s10228-009-0119-9
de Sousa, R.P.C., Bessa-Brito, C.D., Guimarães-Costa, A., Evangelista-Gomes, G., Sampaio, I., de Oliveira, E. H.C. and Vallinoto, M., 2022. Exploring the diversity of elopidae (Teleostei; Elopiformes) using DNA barcoding analysis. Diversity, 14: 1008. https://doi.org/10.3390/d14111008
DeSalle, R. and Goldstein, P., 2019. Review and interpretation of trends in DNA barcoding. Front. Ecol. Evol., 7: 302. https://doi.org/10.3389/fevo.2019.00302
Eberle, J., Ahrens, D., Mayer, C., Niehuis, O. and Misof, B., 2020. A plea for standardized nuclear markers in metazoan DNA taxonomy. Trends Ecol. Evol., 35: 336-345. https://doi.org/10.1016/j.tree.2019.12.003
Farooq, N. and Panhwar, S.K., 2023. Taxonomic and otolith shape parameters of nine sympatric catfishes commercially harvested in Pakistan. Croatian J. Fish., 81: 23-32. https://doi.org/10.2478/cjf-2023-0003
Galal-Khallaf, A., Ardura, A., Mohammed-Geba, K., Borrell, Y.J. and Garcia-Vazquez, E., 2014. DNA barcoding reveals a high level of mislabeling in Egyptian fish fillets. Fd. Contr., 46: 441-445. https://doi.org/10.1016/j.foodcont.2014.06.016
Galimberti, A., Assandri, G., Maggioni, D., Ramazzotti, F., Baroni, D., Bazzi, G. and Galuppi, M., 2021. Italian odonates in the Pandora’s box: A comprehensive DNA barcoding inventory shows taxonomic warnings at the Holarctic scale. Mol. Ecol. Resour., 21: 183-200. https://doi.org/10.1111/1755-0998.13235
Ghouri, M.Z., Ismail, M., Javed, M.A., Khan, S.H., Munawar, N., Umar, A.B. and Ahmad, A., 2020. Identification of edible fish species of Pakistan through DNA barcoding. Front. Mar. Sci., 7: 554183. https://doi.org/10.3389/fmars.2020.554183
Habib, K.A., Nam, K., Xiao, Y., Sathi, J., Islam, M.N., Panhwar, S.K. and Habib, A.S., 2022. Population structure, phylogeography and demographic history of Tenualosa ilisha populations in the Indian Ocean region inferred from mitochondrial DNA sequence variation. Regional Stud. Mar. Sci., 54: 102478. https://doi.org/10.1016/j.rsma.2022.102478
Haedrich, R.L., 1967. The stromateoid fishes: Systematics and a classification. Bull. Mus. Comp. Zool. Harv. Univ., 135: 31-139.
Hebert, P.D., Cywinska, A., Ball, S.L. and DeWaard, J.R., 2003. Biological identifications through DNA barcodes. Proc. R. Soc. Lon. Ser. B Biol. Sci., 270: 313-321. https://doi.org/10.1098/rspb.2002.2218
Hou, G., Chen, W.T., Lu, H.S., Cheng, F. and Xie, S.G., 2018. Developing a DNA barcode library for perciform fishes in the South China Sea: Species identification, accuracy and cryptic diversity. Mol. Ecol. Resour., 18: 137-146. https://doi.org/10.1111/1755-0998.12718
Karim, A., Iqbal, A., Akhtar, R., Rizwan, M., Amar, A., Qamar, U. and Jahan, S., 2016. Barcoding of fresh water fishes from Pakistan. Mitochond. DNA Part A, 27: 2685-2688. https://doi.org/10.3109/19401736.2015.1043544
Keskin, E. and Atar, H.H., 2013. DNA barcoding commercially important fish species of T urkey. Mol. Ecol. Resour., 13: 788-797. https://doi.org/10.1111/1755-0998.12120
Khan, P., Ali, Q., Ahmed, Q. and Bat, L., 2023. Molecular characterization of demersal marine fish species Pseudorhombus arsius, Psettods erumei, and Cynoglossus cynoglossus from Sindh coasts of Pakistan through DNA barcodes. J. Mater. Environ. Sci., 14: 210-223.
Liu, J., Li, C. and Ning, P., 2013. A redescription of grey pomfret Pampus cinereus (Bloch, 1795) with the designation of a neotype (Teleostei: Stromateidae). Chinese J. Oceanol. Limnol., 31: 140-145. https://doi.org/10.1007/s00343-013-2039-9
Lohman, D.J., Prawiradilaga, D.M. and Meier, R., 2009. Improved COI barcoding primers for Southeast Asian perching birds (Aves: Passeriformes). Mol. Ecol. Resour., 9: 37-40. https://doi.org/10.1111/j.1755-0998.2008.02221.x
Lynch, M. and Jarrell, P., 1993. A method for calibrating molecular clocks and its application to animal mitochondrial DNA. Genetics, 135: 1197-1208. https://doi.org/10.1093/genetics/135.4.1197
Nasihin-Seth, S., Kawamura, G., Nazia, A.K. and Manjaji-Matsumoto, B.M., 2019. DNA barcoding of common catfish in Malaysia. Aquacult. Aquar. Conserv. Legislat., 12: 2212-2220.
Psomadakis, P.N., 2015. Field identification guide to the living marine resources of Pakistan.
Qamar, N., Panhwar, S.K. and Siddiqui, G., 2016. Fishery status and taxonomy of the Carangids (Pisces) in the Northern Arabian Sea Coast of Pakistan. Fish. Aquacult. Modern World, 169. https://doi.org/10.5772/62627
Raharinaivo, L.R., Jaonalison, H., Mahafina, J. and Ponton, D., 2020. How to efficiently determine the size at maturity of small-sized tropical fishes: A case study based on 144 species identified via DNA barcoding from southwestern Madagascar. J. Appl. Ichthyol., 36: 402-413. https://doi.org/10.1111/jai.14046
Rauf, H.T., Lali, M.I.U., Zahoor, S., Shah, S.Z.H., Rehman, A.U. and Bukhari, S.A.C., 2019. Visual features based automated identification of fish species using deep convolutional neural networks. Comp. Electron. Agric., 167: 105075. https://doi.org/10.1016/j.compag.2019.105075
Tang, Q., Deng, L., Luo, Q., Duan, Q., Wang, X. and Zhang, R., 2023. DNA barcoding of fish species diversity in Guizhou, China. Diversity, 15: 203. https://doi.org/10.3390/d15020203
Wang, Y.H., Duan, J.N., Shi, H.L., Guo, J.X., Wang, X.Y., Gao, T.X. and Li, Z.L., 2020. Species identification of small fish in Xixuan Island coastal waters of Zhoushan using DNA barcoding. J. Appl. Ichthyol., 36: 75-84. https://doi.org/10.1111/jai.13995
Ward, R.D., 2012. Fish-Bol, a case study for DNA barcodes. DNA Barcodes Methods Protocols, pp. 423-439. https://doi.org/10.1007/978-1-61779-591-6_21
Washim, M.R., Rubel, A.S.A. and Islam, M.L., 2022. Morphometric and meristic traits of spotted scat Scatophagus argus (Linnaeus, 1766) a mangrove fish from south-west coast of Bangladesh. J. Aquat. Sci., 6: 1-7.
Yang, C.H., Wu, K.C., Chuang, L.Y. and Chang, H.W., 2019. Decision theory-based COI-SNP tagging approach for 126 scombriformes species tagging. Front. Genet., 10: 259. https://doi.org/10.3389/fgene.2019.00259
Yu, G., 2020. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinf., 69: e96. https://doi.org/10.1002/cpbi.96
Zhang, J. and Hanner, R., 2012. Molecular approach to the identification of fish in the South China Sea. PLoS One, 7: e30621. https://doi.org/10.1371/journal.pone.0030621
Supplementary Material
Supplementary Table 1: Metadata for each species included in the study.
|
Specie name |
Accession no |
Country |
Type |
|
Rachycentron canadum |
OQ807163 |
Pakistan |
Studied |
|
Rachycentron canadum |
OQ387350 |
Philippines |
NCBI |
|
Rachycentron canadum |
OQ386365 |
Philippines |
NCBI |
|
Rachycentron canadum |
OQ386340 |
Philippines |
NCBI |
|
Rachycentron canadum |
OQ385628 |
Philippines |
NCBI |
|
Rachycentron canadum |
OQ385483 |
Philippines |
NCBI |
|
Rachycentron canadum |
KF809415 |
Philippines |
NCBI |
|
Rachycentron canadum |
KJ202194 |
Philippines |
NCBI |
|
Table continued on next page.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Rachycentron canadum |
KF715002 |
Philippines |
NCBI |
|
Rachycentron canadum |
KC970500 |
Philippines |
NCBI |
|
Rachycentron canadum |
MT455681 |
USA |
NCBI |
|
Rachycentron canadum |
KF461225 |
USA |
NCBI |
|
Rachycentron canadum |
MN839528 |
USA |
NCBI |
|
Rachycentron canadum |
MH378655 |
USA |
NCBI |
|
Rachycentron canadum |
MH378586 |
USA |
NCBI |
|
Rachycentron canadum |
HQ956531 |
Australia |
NCBI |
|
Rachycentron canadum |
HQ956530 |
Australia |
NCBI |
|
Rachycentron canadum |
EF609446 |
Australia |
NCBI |
|
Rachycentron canadum |
KY372071 |
China |
NCBI |
|
Rachycentron canadum |
KY372070 |
China |
NCBI |
|
Rachycentron canadum |
KY372069 |
China |
NCBI |
|
Rachycentron canadum |
KY372068 |
China |
NCBI |
|
Rachycentron canadum |
KY372067 |
China |
NCBI |
|
Rachycentron canadum |
EU600160 |
China |
NCBI |
|
Rachycentron canadum |
EU600159 |
China |
NCBI |
|
Rachycentron canadum |
EF607498 |
China |
NCBI |
|
Rachycentron canadum |
KP266829 |
China |
NCBI |
|
Rachycentron canadum |
JF494343 |
South Africa |
NCBI |
|
Rachycentron canadum |
HQ945936 |
South Africa |
NCBI |
|
Rachycentron canadum |
HQ945904 |
South Africa |
NCBI |
|
Rachycentron canadum |
KF489739 |
South Africa |
NCBI |
|
Rachycentron canadum |
KF489738 |
South Africa |
NCBI |
|
Rachycentron canadum |
JF494342 |
Mozambique |
NCBI |
|
Rachycentron canadum |
JF494341 |
Mozambique |
NCBI |
|
Rachycentron canadum |
JQ841350 |
Belize |
NCBI |
|
Rachycentron canadum |
JQ841349 |
Belize |
NCBI |
|
Rachycentron canadum |
KU236035 |
Saudi Arabia |
NCBI |
|
Rachycentron canadum |
MH235696 |
Myanmar |
NCBI |
|
Rachycentron canadum |
MK777204 |
Taiwan |
NCBI |
|
Rachycentron canadum |
MK777203 |
Taiwan |
NCBI |
|
Rachycentron canadum |
KU943713 |
Taiwan |
NCBI |
|
Rachycentron canadum |
KU943712 |
Taiwan |
NCBI |
|
Rachycentron canadum |
KU943711 |
Taiwan |
NCBI |
|
Rachycentron canadum |
MN458362 |
Bangladesh |
NCBI |
|
Rachycentron canadum |
MN083099 |
Bangladesh |
NCBI |
|
Rachycentron canadum |
MF588559 |
Bangladesh |
NCBI |
|
Rachycentron canadum |
MF588558 |
Bangladesh |
NCBI |
|
Rachycentron canadum |
MF588657 |
Bangladesh |
NCBI |
|
Rachycentron canadum |
KU168730 |
UK |
NCBI |
|
Rachycentron canadum |
KP410326 |
Pakistan |
NCBI |
|
Rachycentron canadum |
KJ590063 |
India |
NCBI |
|
Rachycentron canadum |
EF609586 |
India |
NCBI |
|
Rachycentron canadum |
EF609585 |
India |
NCBI |
|
Rachycentron canadum |
EF609584 |
India |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Rachycentron canadum |
EF609583 |
India |
NCBI |
|
Rachycentron canadum |
EF609582 |
India |
NCBI |
|
Rachycentron canadum |
HQ589294 |
India |
NCBI |
|
Rachycentron canadum |
HQ589293 |
India |
NCBI |
|
Rachycentron canadum |
OL851817 |
India |
NCBI |
|
Lactarius lactarius |
OQ801201 |
Pakistan |
Studied |
|
Lactarius lactarius |
EF607416 |
China |
NCBI |
|
Lactarius lactarius |
EF607417 |
China |
NCBI |
|
Lactarius lactarius |
EF607418 |
China |
NCBI |
|
Lactarius lactarius |
JQ681391 |
China |
NCBI |
|
Lactarius lactarius |
MZ329509 |
China |
NCBI |
|
Lactarius lactarius |
MZ329510 |
China |
NCBI |
|
Lactarius lactarius |
MZ329511 |
China |
NCBI |
|
Lactarius lactarius |
OL494216 |
China |
NCBI |
|
Lactarius lactarius |
KY371638 |
China |
NCBI |
|
Lactarius lactarius |
KY371639 |
China |
NCBI |
|
Lactarius lactarius |
KY371640 |
China |
NCBI |
|
Lactarius lactarius |
KY371641 |
China |
NCBI |
|
Lactarius lactarius |
KY371642 |
China |
NCBI |
|
Lactarius lactarius |
EF609529 |
India |
NCBI |
|
Lactarius lactarius |
EF609530 |
India |
NCBI |
|
Lactarius lactarius |
EF609531 |
India |
NCBI |
|
Lactarius lactarius |
FJ347949 |
India |
NCBI |
|
Lactarius lactarius |
MK775650 |
India |
NCBI |
|
Lactarius lactarius |
MK775651 |
India |
NCBI |
|
Lactarius lactarius |
MK775652 |
India |
NCBI |
|
Lactarius lactarius |
MN747963 |
India |
NCBI |
|
Lactarius lactarius |
MW167118 |
India |
NCBI |
|
Lactarius lactarius |
MW167119 |
India |
NCBI |
|
Lactarius lactarius |
HQ564485 |
Indonesia |
NCBI |
|
Lactarius lactarius |
HQ564486 |
Indonesia |
NCBI |
|
Lactarius lactarius |
JN312892 |
Indonesia |
NCBI |
|
Lactarius lactarius |
MH085843 |
Indonesia |
NCBI |
|
Lactarius lactarius |
KT883634 |
USA |
NCBI |
|
Lactarius lactarius |
KT883657 |
USA |
NCBI |
|
Lactarius lactarius |
KU535572 |
Philippines |
NCBI |
|
Lactarius lactarius |
KU943707 |
Taiwan |
NCBI |
|
Lactarius lactarius |
MK777483 |
Taiwan |
NCBI |
|
Lactarius lactarius |
MH429314 |
Bangladesh |
NCBI |
|
Lactarius lactarius |
MK340629 |
Bangladesh |
NCBI |
|
Lactarius lactarius |
MN511922 |
Pakistan |
NCBI |
|
Pomacanthus annularis |
OQ807215 |
Pakistan |
Studied |
|
Pomacanthus annularis |
MH235689 |
Myanmar |
NCBI |
|
Pomacanthus annularis |
MK340680 |
Bangladesh |
NCBI |
|
Pomacanthus annularis |
MK340679 |
Bangladesh |
NCBI |
|
Pomacanthus annularis |
MH085785 |
Indonesia |
NCBI |
|
Table continued on next page.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Pomacanthus annularis |
KU944248 |
Taiwan |
NCBI |
|
Pomacanthus annularis |
KF268138 |
India |
NCBI |
|
Pomacanthus annularis |
KF268137 |
India |
NCBI |
|
Pomacanthus annularis |
KF268136 |
India |
NCBI |
|
Pomacanthus annularis |
FJ583876 |
Sri Lanka |
NCBI |
|
Pomacanthus annularis |
FJ583875 |
Vietnam |
NCBI |
|
Myripristis botche |
OQ807657 |
Pakistan |
Studied |
|
Myripristis botche |
KU943300 |
Taiwan |
NCBI |
|
Myripristis botche |
JX390744 |
USA |
NCBI |
|
Myripristis botche |
OQ385782 |
USA |
NCBI |
|
Myripristis botche |
OQ387325 |
Philippines |
NCBI |
|
Myripristis botche |
MF409583 |
Reunion |
NCBI |
|
Myripristis botche |
KF489654 |
South Africa |
NCBI |
|
Myripristis botche |
MK777368 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
OQ808566 |
Pakistan |
Studied |
|
Sargocentron rubrum |
OQ387824 |
Philippines |
NCBI |
|
Sargocentron rubrum |
OQ387696 |
Philippines |
NCBI |
|
Sargocentron rubrum |
OQ386629 |
Philippines |
NCBI |
|
Sargocentron rubrum |
KJ202198 |
Philippines |
NCBI |
|
Sargocentron rubrum |
MZ421439 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
MK777374 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
MK777373 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
MK777372 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
MK777371 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
MK777370 |
Vietnam |
NCBI |
|
Sargocentron rubrum |
GU673963 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
GU673717 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MN870501 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MN870474 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MN870057 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MN869979 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MH049175 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
MH190810 |
Indonesia |
NCBI |
|
Sargocentron rubrum |
GU673204 |
Australia |
NCBI |
|
Sargocentron rubrum |
HQ956403 |
Australia |
NCBI |
|
Sargocentron rubrum |
HQ956402 |
Australia |
NCBI |
|
Sargocentron rubrum |
KR861551 |
Lebanon |
NCBI |
|
Sargocentron rubrum |
KC501252 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501251 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501250 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501249 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501248 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501247 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501246 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501245 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501244 |
Turkey |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Sargocentron rubrum |
KC501243 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501242 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501241 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501240 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501239 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501238 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501237 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501236 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501235 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501234 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KC501233 |
Turkey |
NCBI |
|
Sargocentron rubrum |
KY176608 |
Turkey |
NCBI |
|
Sargocentron rubrum |
JQ623980 |
Turkey |
NCBI |
|
Sargocentron rubrum |
EU600150 |
China |
NCBI |
|
Sargocentron rubrum |
EU600149 |
China |
NCBI |
|
Sargocentron rubrum |
FJ237913 |
China |
NCBI |
|
Sargocentron rubrum |
FJ237912 |
China |
NCBI |
|
Sargocentron rubrum |
FJ265867 |
India |
NCBI |
|
Sargocentron rubrum |
MK962512 |
India |
NCBI |
|
Sargocentron rubrum |
KU987435 |
India |
NCBI |
|
Sargocentron rubrum |
KJ632830 |
India |
NCBI |
|
Sargocentron rubrum |
KJ632826 |
India |
NCBI |
|
Sargocentron rubrum |
KJ466132 |
India |
NCBI |
|
Sargocentron rubrum |
KJ466131 |
India |
NCBI |
|
Sargocentron rubrum |
KF442242 |
India |
NCBI |
|
Sargocentron rubrum |
MK340699 |
Bangladesh |
NCBI |
|
Sargocentron rubrum |
MK340698 |
Bangladesh |
NCBI |
|
Sargocentron rubrum |
MK340697 |
Bangladesh |
NCBI |
|
Sargocentron rubrum |
MH331859 |
Saudi Arabia |
NCBI |
|
Sargocentron rubrum |
KU943292 |
Taiwan |
NCBI |
|
Plotosus lineatus |
OQ808970 |
Pakistan |
Studied |
|
Plotosus lineatus |
EF607323 |
China |
NCBI |
|
Plotosus lineatus |
EF607324 |
China |
NCBI |
|
Plotosus lineatus |
EU595234 |
China |
NCBI |
|
Plotosus lineatus |
EU595235 |
China |
NCBI |
|
Plotosus lineatus |
EU595236 |
China |
NCBI |
|
Plotosus lineatus |
EU595237 |
China |
NCBI |
|
Plotosus lineatus |
EU595238 |
China |
NCBI |
|
Plotosus lineatus |
EU595239 |
China |
NCBI |
|
Plotosus lineatus |
FJ238012 |
China |
NCBI |
|
Plotosus lineatus |
MG220589 |
China |
NCBI |
|
Plotosus lineatus |
MK264717 |
China |
NCBI |
|
Plotosus lineatus |
EU148553 |
India |
NCBI |
|
Plotosus lineatus |
FJ237535 |
India |
NCBI |
|
Plotosus lineatus |
FJ384696 |
India |
NCBI |
|
Plotosus lineatus |
KF268171 |
India |
NCBI |
|
Table continued on next page.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Plotosus lineatus |
KF268172 |
India |
NCBI |
|
Plotosus lineatus |
KF268173 |
India |
NCBI |
|
Plotosus lineatus |
KF268174 |
India |
NCBI |
|
Plotosus lineatus |
KF824841 |
India |
NCBI |
|
Plotosus lineatus |
KF824842 |
India |
NCBI |
|
Plotosus lineatus |
KF824843 |
India |
NCBI |
|
Plotosus lineatus |
KP296155 |
India |
NCBI |
|
Plotosus lineatus |
EU490870 |
USA |
NCBI |
|
Plotosus lineatus |
FJ918911 |
USA |
NCBI |
|
Plotosus lineatus |
FJ583870 |
Philippines |
NCBI |
|
Plotosus lineatus |
FJ583871 |
Philippines |
NCBI |
|
Plotosus lineatus |
FJ583872 |
Philippines |
NCBI |
|
Plotosus lineatus |
FJ583873 |
Philippines |
NCBI |
|
Plotosus lineatus |
FJ583874 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604684 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604685 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604686 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604687 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604688 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604689 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF604690 |
Philippines |
NCBI |
|
Plotosus lineatus |
KF809412 |
Philippines |
NCBI |
|
Plotosus lineatus |
HQ956387 |
Australia |
NCBI |
|
Plotosus lineatus |
JF494186 |
South Africa |
NCBI |
|
Plotosus lineatus |
JF494187 |
South Africa |
NCBI |
|
Plotosus lineatus |
JF494188 |
South Africa |
NCBI |
|
Plotosus lineatus |
JF494189 |
South Africa |
NCBI |
|
Plotosus lineatus |
JF952819 |
Japan |
NCBI |
|
Plotosus lineatus |
JN313096 |
Indonesia |
NCBI |
|
Plotosus lineatus |
JQ350233 |
Reunion |
NCBI |
|
Plotosus lineatus |
KM538482 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538483 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538484 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538485 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538486 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538487 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538488 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538489 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538490 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538491 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538492 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538493 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538494 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538495 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538496 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538497 |
Israel |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Plotosus lineatus |
KM538498 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538499 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538500 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538501 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538502 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538503 |
Israel |
NCBI |
|
Plotosus lineatus |
KM538504 |
Israel |
NCBI |
|
Plotosus lineatus |
KP221606 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KP221607 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KP221608 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KP258657 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KP258658 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KP258659 |
Malaysia |
NCBI |
|
Plotosus lineatus |
KR861548 |
Lebanon |
NCBI |
|
Plotosus lineatus |
KU179077 |
Saudi Arabia |
NCBI |
|
Plotosus lineatus |
KU499687 |
Saudi Arabia |
NCBI |
|
Plotosus lineatus |
MH331830 |
Saudi Arabia |
NCBI |
|
Plotosus lineatus |
KU943007 |
Taiwan |
NCBI |
|
Chanos chanos |
OQ809068 |
Pakistan |
Studied |
|
Chanos chanos |
DQ884995 |
South Africa |
NCBI |
|
Chanos chanos |
DQ884996 |
South Africa |
NCBI |
|
Chanos chanos |
DQ885083 |
Australia |
NCBI |
|
Chanos chanos |
DQ885085 |
Australia |
NCBI |
|
Chanos chanos |
KJ669401 |
Australia |
NCBI |
|
Chanos chanos |
EU752071 |
USA |
NCBI |
|
Chanos chanos |
EU752072 |
USA |
NCBI |
|
Chanos chanos |
KF929723 |
USA |
NCBI |
|
Chanos chanos |
GU674244 |
Indonesia |
NCBI |
|
Chanos chanos |
KP856764 |
Indonesia |
NCBI |
|
Chanos chanos |
KP856765 |
Indonesia |
NCBI |
|
Chanos chanos |
KP856766 |
Indonesia |
NCBI |
|
Chanos chanos |
KU692424 |
Indonesia |
NCBI |
|
Chanos chanos |
KU692425 |
Indonesia |
NCBI |
|
Chanos chanos |
KU692426 |
Indonesia |
NCBI |
|
Chanos chanos |
HQ149824 |
Iran |
NCBI |
|
Chanos chanos |
HQ654696 |
Philippines |
NCBI |
|
Chanos chanos |
HQ654697 |
Philippines |
NCBI |
|
Chanos chanos |
HQ654698 |
Philippines |
NCBI |
|
Chanos chanos |
HQ654699 |
Philippines |
NCBI |
|
Chanos chanos |
HQ654700 |
Philippines |
NCBI |
|
Chanos chanos |
OQ385882 |
Philippines |
NCBI |
|
Chanos chanos |
OQ386950 |
Philippines |
NCBI |
|
Chanos chanos |
OQ387320 |
Philippines |
NCBI |
|
Chanos chanos |
JN242677 |
China |
NCBI |
|
Chanos chanos |
JN242678 |
China |
NCBI |
|
Chanos chanos |
JN242679 |
China |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Chanos chanos |
JQ431600 |
France |
NCBI |
|
Chanos chanos |
JQ431601 |
France |
NCBI |
|
Chanos chanos |
KP308089 |
India |
NCBI |
|
Chanos chanos |
LT669927 |
India |
NCBI |
|
Chanos chanos |
MK301234 |
India |
NCBI |
|
Chanos chanos |
MK301235 |
India |
NCBI |
|
Chanos chanos |
MK902719 |
India |
NCBI |
|
Chanos chanos |
DQ885084 |
Taiwan |
NCBI |
|
Chanos chanos |
KU893046 |
Taiwan |
NCBI |
|
Chanos chanos |
KU942899 |
Taiwan |
NCBI |
|
Chanos chanos |
KU942900 |
Taiwan |
NCBI |
|
Chanos chanos |
KU942901 |
Taiwan |
NCBI |
|
Chanos chanos |
KU942902 |
Taiwan |
NCBI |
|
Chanos chanos |
KY802063 |
UK |
NCBI |
|
Chanos chanos |
KY802076 |
UK |
NCBI |
|
Chanos chanos |
MK216571 |
Qatar |
NCBI |
|
Chanos chanos |
MK216572 |
Qatar |
NCBI |
|
Chanos chanos |
MK216573 |
Qatar |
NCBI |
|
Chanos chanos |
MK216574 |
Qatar |
NCBI |
|
Chanos chanos |
MK216575 |
Qatar |
NCBI |
|
Chanos chanos |
MK216576 |
Qatar |
NCBI |
|
Chanos chanos |
MK216577 |
Qatar |
NCBI |
|
Chanos chanos |
MK216578 |
Qatar |
NCBI |
|
Chanos chanos |
MK241873 |
Qatar |
NCBI |
|
Chanos chanos |
MK241874 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241875 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241876 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241877 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241878 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241879 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241880 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241881 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241882 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241883 |
Vietnam |
NCBI |
|
Chanos chanos |
MK241884 |
Vietnam |
NCBI |
|
Chanos chanos |
MK777366 |
Vietnam |
NCBI |
|
Chanos chanos |
MN083123 |
Bangladesh |
NCBI |
|
Terapon jarbua |
OQ810001 |
Pakistan |
Studied |
|
Terapon jarbua |
EF607573 |
China |
NCBI |
|
Terapon jarbua |
EF607574 |
China |
NCBI |
|
Terapon jarbua |
EF607575 |
China |
NCBI |
|
Terapon jarbua |
EF607576 |
China |
NCBI |
|
Terapon jarbua |
EF607577 |
China |
NCBI |
|
Terapon jarbua |
EF607578 |
China |
NCBI |
|
Terapon jarbua |
EF607579 |
China |
NCBI |
|
Terapon jarbua |
EF607580 |
China |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Terapon jarbua |
EU871691 |
China |
NCBI |
|
Terapon jarbua |
EU871692 |
China |
NCBI |
|
Terapon jarbua |
FJ237549 |
India |
NCBI |
|
Terapon jarbua |
FJ265859 |
India |
NCBI |
|
Terapon jarbua |
FJ347885 |
India |
NCBI |
|
Terapon jarbua |
FJ347886 |
India |
NCBI |
|
Terapon jarbua |
FJ347887 |
India |
NCBI |
|
Terapon jarbua |
FJ384681 |
India |
NCBI |
|
Terapon jarbua |
JX260979 |
India |
NCBI |
|
Terapon jarbua |
JX983498 |
India |
NCBI |
|
Terapon jarbua |
KC241987 |
India |
NCBI |
|
Terapon jarbua |
KC417308 |
India |
NCBI |
|
Terapon jarbua |
KC774674 |
India |
NCBI |
|
Terapon jarbua |
KF268188 |
India |
NCBI |
|
Terapon jarbua |
KF268189 |
India |
NCBI |
|
Terapon jarbua |
KJ920134 |
India |
NCBI |
|
Terapon jarbua |
KM079294 |
India |
NCBI |
|
Terapon jarbua |
KM079295 |
India |
NCBI |
|
Terapon jarbua |
HQ149959 |
Iran |
NCBI |
|
Terapon jarbua |
HQ149960 |
Iran |
NCBI |
|
Terapon jarbua |
HQ149961 |
Iran |
NCBI |
|
Terapon jarbua |
JF494663 |
South Africa |
NCBI |
|
Terapon jarbua |
JF494664 |
South Africa |
NCBI |
|
Terapon jarbua |
JF494665 |
South Africa |
NCBI |
|
Terapon jarbua |
JF494666 |
South Africa |
NCBI |
|
Terapon jarbua |
JN021254 |
Philippines |
NCBI |
|
Terapon jarbua |
JN021255 |
Philippines |
NCBI |
|
Terapon jarbua |
KC970423 |
Philippines |
NCBI |
|
Terapon jarbua |
KF009671 |
Philippines |
NCBI |
|
Terapon jarbua |
KF715030 |
Philippines |
NCBI |
|
Terapon jarbua |
KF715031 |
Philippines |
NCBI |
|
Terapon jarbua |
KJ202209 |
Philippines |
NCBI |
|
Terapon jarbua |
KJ202210 |
Philippines |
NCBI |
|
Terapon jarbua |
JQ342095 |
France |
NCBI |
|
Terapon jarbua |
JQ342096 |
France |
NCBI |
|
Terapon jarbua |
JQ342097 |
France |
NCBI |
|
Terapon jarbua |
JQ342098 |
France |
NCBI |
|
Terapon jarbua |
JQ342099 |
France |
NCBI |
|
Terapon jarbua |
JQ342100 |
France |
NCBI |
|
Terapon jarbua |
JQ342101 |
France |
NCBI |
|
Terapon jarbua |
JQ342102 |
France |
NCBI |
|
Terapon jarbua |
JQ342103 |
France |
NCBI |
|
Terapon jarbua |
JQ342104 |
France |
NCBI |
|
Terapon jarbua |
JQ342105 |
France |
NCBI |
|
Terapon jarbua |
JQ342106 |
France |
NCBI |
|
Terapon jarbua |
JQ342107 |
France |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Terapon jarbua |
JQ342108 |
France |
NCBI |
|
Terapon jarbua |
JQ342109 |
France |
NCBI |
|
Terapon jarbua |
JQ342110 |
France |
NCBI |
|
Terapon jarbua |
JQ342111 |
France |
NCBI |
|
Terapon jarbua |
JQ342112 |
France |
NCBI |
|
Terapon jarbua |
JQ741340 |
Mexico |
NCBI |
|
Terapon jarbua |
KJ466137 |
Israel |
NCBI |
|
Terapon jarbua |
KJ466138 |
Israel |
NCBI |
|
Terapon jarbua |
KP194256 |
Australia |
NCBI |
|
Terapon jarbua |
KP194261 |
Australia |
NCBI |
|
Terapon jarbua |
KP194552 |
Australia |
NCBI |
|
Terapon jarbua |
KP194928 |
Australia |
NCBI |
|
Terapon jarbua |
KP204162 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204163 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204164 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204165 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204166 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204167 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204168 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204169 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204170 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204171 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204172 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204173 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204174 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204175 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204176 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204177 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204178 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204179 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204180 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204181 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204182 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204183 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204184 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204185 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204186 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204187 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204188 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204189 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204190 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204191 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204192 |
Taiwan |
NCBI |
|
Terapon jarbua |
KP204193 |
Taiwan |
NCBI |
|
Terapon puta |
OQ810033 |
Pakistan |
Studied |
|
Terapon puta |
KC774675 |
India |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Terapon puta |
KC774676 |
India |
NCBI |
|
Terapon puta |
KJ920126 |
India |
NCBI |
|
Terapon puta |
KP308091 |
India |
NCBI |
|
Terapon puta |
KP308092 |
India |
NCBI |
|
Terapon puta |
KX064469 |
India |
NCBI |
|
Terapon puta |
KX064470 |
India |
NCBI |
|
Terapon puta |
KX064471 |
India |
NCBI |
|
Terapon puta |
MK348200 |
India |
NCBI |
|
Terapon puta |
MK348201 |
India |
NCBI |
|
Terapon puta |
MK902721 |
India |
NCBI |
|
Terapon puta |
KF564317 |
Israel |
NCBI |
|
Terapon puta |
KF809425 |
Philippines |
NCBI |
|
Terapon puta |
KR861565 |
Lebanon |
NCBI |
|
Terapon puta |
KU499747 |
Saudi Arabia |
NCBI |
|
Terapon puta |
KU499748 |
Saudi Arabia |
NCBI |
|
Terapon puta |
KU499749 |
Saudi Arabia |
NCBI |
|
Terapon puta |
KU499750 |
Saudi Arabia |
NCBI |
|
Terapon puta |
KU499751 |
Saudi Arabia |
NCBI |
|
Terapon puta |
KY372206 |
China |
NCBI |
|
Terapon puta |
KY372207 |
China |
NCBI |
|
Terapon puta |
KY372208 |
China |
NCBI |
|
Terapon puta |
KY372209 |
China |
NCBI |
|
Terapon puta |
MN512110 |
Pakistan |
NCBI |
|
Drepane longimana |
OQ814428 |
Pakistan |
Studied |
|
Drepane longimana |
FJ459579 |
India |
NCBI |
|
Drepane longimana |
GU674215 |
Indonesia |
NCBI |
|
Drepane longimana |
GU674218 |
Indonesia |
NCBI |
|
Drepane longimana |
GU674220 |
Indonesia |
NCBI |
|
Drepane longimana |
JF493392 |
South Africa |
NCBI |
|
Drepane longimana |
JF493393 |
South Africa |
NCBI |
|
Drepane longimana |
JF493394 |
South Africa |
NCBI |
|
Drepane longimana |
JF493395 |
South Africa |
NCBI |
|
Drepane longimana |
MH429335 |
Bangladesh |
NCBI |
|
Drepane longimana |
MK340610 |
Bangladesh |
NCBI |
|
Drepane longimana |
MK340611 |
Bangladesh |
NCBI |
|
Drepane longimana |
MT012667 |
Bangladesh |
NCBI |
|
Drepane longimana |
MN511883 |
Pakistan |
NCBI |
|
Drepane longimana |
MN511884 |
Pakistan |
NCBI |
|
Scatophagus argus |
OQ814532 |
Pakistan |
Studied |
|
Scatophagus argus |
DQ107757 |
Australia |
NCBI |
|
Scatophagus argus |
EF607516 |
China |
NCBI |
|
Scatophagus argus |
EF607517 |
China |
NCBI |
|
Scatophagus argus |
EF607518 |
China |
NCBI |
|
Scatophagus argus |
EF607519 |
China |
NCBI |
|
Scatophagus argus |
KP260476 |
China |
NCBI |
|
Scatophagus argus |
KT951732 |
China |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Scatophagus argus |
KX254482 |
China |
NCBI |
|
Scatophagus argus |
KX254483 |
China |
NCBI |
|
Scatophagus argus |
KY372108 |
China |
NCBI |
|
Scatophagus argus |
KY372109 |
China |
NCBI |
|
Scatophagus argus |
KY372110 |
China |
NCBI |
|
Scatophagus argus |
EF609604 |
India |
NCBI |
|
Scatophagus argus |
EF609605 |
India |
NCBI |
|
Scatophagus argus |
EF609606 |
India |
NCBI |
|
Scatophagus argus |
EF609607 |
India |
NCBI |
|
Scatophagus argus |
FJ347948 |
India |
NCBI |
|
Scatophagus argus |
FJ584086 |
India |
NCBI |
|
Scatophagus argus |
JX983493 |
India |
NCBI |
|
Scatophagus argus |
KC774668 |
India |
NCBI |
|
Scatophagus argus |
KJ920130 |
India |
NCBI |
|
Scatophagus argus |
KM079335 |
India |
NCBI |
|
Scatophagus argus |
KM079336 |
India |
NCBI |
|
Scatophagus argus |
KP212376 |
India |
NCBI |
|
Scatophagus argus |
KP212377 |
India |
NCBI |
|
Scatophagus argus |
KP296156 |
India |
NCBI |
|
Scatophagus argus |
KY009860 |
India |
NCBI |
|
Scatophagus argus |
KY634864 |
India |
NCBI |
|
Scatophagus argus |
KY634866 |
India |
NCBI |
|
Scatophagus argus |
MG923401 |
India |
NCBI |
|
Scatophagus argus |
MG923402 |
India |
NCBI |
|
Scatophagus argus |
MG923403 |
India |
NCBI |
|
Scatophagus argus |
MG923404 |
India |
NCBI |
|
Scatophagus argus |
MG923405 |
India |
NCBI |
|
Scatophagus argus |
FJ584087 |
Sri Lanka |
NCBI |
|
Scatophagus argus |
FJ584088 |
Sri Lanka |
NCBI |
|
Scatophagus argus |
FJ584089 |
Sri Lanka |
NCBI |
|
Scatophagus argus |
FJ584090 |
Sri Lanka |
NCBI |
|
Scatophagus argus |
GU674034 |
Indonesia |
NCBI |
|
Scatophagus argus |
GU674036 |
Indonesia |
NCBI |
|
Scatophagus argus |
KU692847 |
Indonesia |
NCBI |
|
Scatophagus argus |
KU692848 |
Indonesia |
NCBI |
|
Scatophagus argus |
KU692849 |
Indonesia |
NCBI |
|
Scatophagus argus |
KU692850 |
Indonesia |
NCBI |
|
Scatophagus argus |
MG921203 |
Indonesia |
NCBI |
|
Scatophagus argus |
MG921207 |
Indonesia |
NCBI |
|
Scatophagus argus |
MH085813 |
Indonesia |
NCBI |
|
Scatophagus argus |
MH085814 |
Indonesia |
NCBI |
|
Scatophagus argus |
JN021246 |
Philippines |
NCBI |
|
Scatophagus argus |
JN021247 |
Philippines |
NCBI |
|
Scatophagus argus |
JN021250 |
Philippines |
NCBI |
|
Scatophagus argus |
KF715006 |
Philippines |
NCBI |
|
Scatophagus argus |
KF715007 |
Philippines |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Scatophagus argus |
KF715008 |
Philippines |
NCBI |
|
Scatophagus argus |
KF809418 |
Philippines |
NCBI |
|
Scatophagus argus |
KF930378 |
Thailand |
NCBI |
|
Scatophagus argus |
KF930379 |
Thailand |
NCBI |
|
Scatophagus argus |
KU944950 |
Taiwan |
NCBI |
|
Scatophagus argus |
KU944951 |
Taiwan |
NCBI |
|
Scatophagus argus |
KU944952 |
Taiwan |
NCBI |
|
Scatophagus argus |
KX223946 |
Malaysia |
NCBI |
|
Scatophagus argus |
KX223947 |
Malaysia |
NCBI |
|
Scatophagus argus |
KX223948 |
Malaysia |
NCBI |
|
Scatophagus argus |
KX281938 |
Malaysia |
NCBI |
|
Scatophagus argus |
MH674067 |
Malaysia |
NCBI |
|
Scatophagus argus |
MH721188 |
Vietnam |
NCBI |
|
Pampus argenteus |
OQ815713 |
Pakistan |
Studied |
|
Pampus argenteus |
DQ107596 |
Australia |
NCBI |
|
Pampus argenteus |
DQ107597 |
Australia |
NCBI |
|
Pampus argenteus |
DQ107598 |
Australia |
NCBI |
|
Table continued on next column.......... |
|||
|
Specie name |
Accession no |
Country |
Type |
|
Pampus argenteus |
DQ107599 |
Australia |
NCBI |
|
Pampus argenteus |
DQ107600 |
Australia |
NCBI |
|
Pampus argenteus |
EF607457 |
China |
NCBI |
|
Pampus argenteus |
EF607458 |
China |
NCBI |
|
Pampus argenteus |
EF607459 |
China |
NCBI |
|
Pampus argenteus |
EF607460 |
China |
NCBI |
|
Pampus argenteus |
EU119289 |
China |
NCBI |
|
Pampus argenteus |
EU119292 |
China |
NCBI |
|
Pampus argenteus |
EU119293 |
China |
NCBI |
|
Pampus argenteus |
EU595224 |
China |
NCBI |
|
Pampus argenteus |
HM068249 |
China |
NCBI |
|
Pampus argenteus |
EU752147 |
USA |
NCBI |
|
Pampus argenteus |
EU752148 |
USA |
NCBI |
|
Pampus argenteus |
FJ226531 |
India |
NCBI |
|
Pampus argenteus |
FJ226532 |
India |
NCBI |
|
Pampus argenteus |
FJ226533 |
India |
NCBI |
|
Pampus argenteus |
FJ384702 |
India |
NCBI |
To share on other social networks, click on any share button. What are these?
