Ying Zhang, Liming Wei, Bingjian Liu, Liqin Liu, Zhenming Lü, Li Gong. Two complete mitogenomes of Ocypodoidea (Decapoda: Brachyura), Cleistostoma dilatatum (Camptandriidae) and Euplax sp. (Macrophthalmidae) and its phylogenetic implications[J]. Acta Oceanologica Sinica, 2023, 42(4): 81-92. doi: 10.1007/s13131-022-2054-9
Citation: Ping Du, Dingyong Zeng, Feilong Lin, Sanda Naing, Zhibing Jiang, Jingjing Zhang, Di Tian, Qinghe Liu, Yuanli Zhu, Soe Moe Lwin, Wenqi Ye, Chenggang Liu, Lu Shou, Feng Zhou. Epipelagic mesozooplankton communities in the northeastern Indian Ocean off Myanmar during the winter monsoon[J]. Acta Oceanologica Sinica, 2023, 42(6): 57-69. doi: 10.1007/s13131-022-2090-5

Epipelagic mesozooplankton communities in the northeastern Indian Ocean off Myanmar during the winter monsoon

doi: 10.1007/s13131-022-2090-5
Funds:  The Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources under contract No. JG2210; the Global Change and Air-Sea Interaction II Program under contract No. GASI-01-EIND-STwin; the National Natural Science Foundation of China under contract Nos 42176148 and 42176039.
More Information
  • Corresponding author: E-mail: shoulu981@sio.org.cn
  • Received Date: 2022-05-09
  • Accepted Date: 2022-07-28
  • Available Online: 2023-02-02
  • Publish Date: 2023-06-25
  • The northern Andaman Sea off Myanmar is one of the relatively high productive regions in the Indian Ocean. The abundance, biomass and species composition of mesozooplankton and their relationships with environmental variables in the epipelagic zone (~200 m) were studied for the first time during the Sino-Myanmar joint cruise (February 2020). The mean abundance and biomass of mesozooplankton were (1916.7±1192.9) ind./m3 and (17.8±7.9) mg/m3, respectively. A total of 213 species (taxa) were identified from all samples. The omnivorous Cyclopoida Oncaea venusta and Oithona spp. were the top two dominant taxa. Three mesozooplankton communities were determined via cluster analysis: the open ocean in the Andaman Sea and the Bay of Bengal (Group A), the transition zone across the Preparis Channel (Group B), and nearshore water off the Ayeyarwady Delta and along the Tanintharyi Coast (Group C). Variation partitioning analysis revealed that the interaction of physical and biological factors explained 98.8% of mesozooplankton community spatial variation, and redundancy analysis revealed that column mean chlorophyll a concentration (CMCHLA) was the most important explanatory variable (43.1%). The abundance and biomass were significantly higher in Group C, the same as CMCHLA and column mean temperature (CMT) and in contrast to salinity, and CMT was the dominant factor. Significant taxon spatial variations were controlled by CMCHLA, salinity and temperature. This study suggested that mesozooplankton spatial variation was mainly regulated by physical processes through their effects on CMCHLA. The physical processes were simultaneously affected by heat loss differences, freshwater influx, eddies and depth.
  • With the rapid development of next-generation sequencing (NGS) technologies that can effectively analyze huge pools of molecular data, an increasing number of mitogenomes provide important insights into species evolution and phylogenetic relationships (Tan et al., 2018; Ruan et al., 2020, Yang et al., 2021). Generally, gene order in most vertebrate mitogenomes is considered conserved, e.g., less than 4% rearrangement ratio in fish mitogenomes (Li et al., 2013). However, extensive gene rearrangements have been observed in invertebrate mitogenomes (Wu et al., 2012; Liu et al., 2017; Jiang et al., 2018). Recent studies have shown that some of these rearrangements contain useful information for phylogeny, and many scholars have applied gene rearrangement as a new molecular marker in phylogenetic studies. For example, Akasaki et al. (2006) compared the gene rearrangement of subclass Coleoidea and proposed that the arrangements of mitochondrial genes in Oegopsida and Sepiida were derived from those of Octopoda. This conclusion is consistent with the results of their phylogenetic analysis based on mitochondrial genes. Through a comparative study of gene rearrangement and phylogenetic relationships of five species from the superfamily Tellinoidea, Yuan et al. (2012) suggested that the genus Sinonovacula should be placed in the superfamily Solenoidea instead of the superfamily Tellinoidea. Besides, Tan et al. (2018) compared the published mitogenome sequences of two infraorders (Anomura and Brachyura) and affirmed the potential value of using rearrangement information to investigate the phylogeny of Anomura.

    In contrast, there are also some scholars consider that gene order is not suitable for phylogenetic reconstruction. For example, Xie et al. (2019) demonstrated that approach based on gene order alone is clearly inferior to sequence-based approaches to resolve major phylogenetic relationships. In their research, none of the relationships among major stylommatophoran groups were resolved in the gene order tree. Recently, Zhang et al. (2021b) reconstructed the phylogeny of Paguroidea based on both sequence data and gene order. The results indicated that gene order data did not seem to work well for phylogenetic analysis within families. From their gene order tree, the relationships within families are suspicious because two close relatives belonging to the same genus (Dardanus arrosor and D. aspersus) owned two different gene rearrangements. Of course, increasing the availability of mitogenomic data from different taxa will help to validate the applicability of gene order data in inferring phylogenetic relationships.

    The infraorder Brachyura contains approximately 7 250 known species inhabiting marine, freshwater, and terrestrial habitats (Chen et al., 2018; Ma et al., 2019). Brachyura, as the oldest crab, originated in the Jurassic period (Schweitzer and Feldmann, 2010; Davie et al., 2015a), and a group of its members with extremely diverse morphology and ecology was finally formed after massive radiative evolution. However, the diversity has also caused remarkably challenges for species identification, and their real phylogenetic relationships remain controversial (Camargo et al., 2020; Tan et al., 2018). Grapsoidea and Ocypodoidea, two of the most abundant and economically important groups in Brachyura, are of commercial value to fisheries and aquaculture. However, the classification of Grapsoidea and Ocypodoidea has been controversial for a long time. Previous studies based on morphological features considered them to be monophyletic clades (Ng et al., 2008; Davie et al., 2015b). Recently, an increasing number of molecular studies have challenged the monophyly of these taxa (Chen et al., 2018, 2019; Lu et al., 2020). For example, molecular study of Wang et al. (2020) revealed that Ocypodoidea and Grapsoidea are divided into three clades, and similar findings were presented in Tan et al. (2018). Larger taxon samples are required to fully understand the phylogenetic relationships between Ocypodoidea and Grapsoidea in future studies.

    Members of the family Camptandriidae Stimpson, 1858 are commonly found in the estuarine, mangrove mudflat, and open mudflat habitats in the Indo-West Pacific regions (Jones and Clayton, 1983). Species of this family share a distinct condition in the male first gonopod, in which the distal part is bent or twisted over the proximal base by about 180°, producing a strongly recurved structure (Naruse et al., 2015). Initially, this family was regarded as a subfamily of Ocypodidae. Subsequently, Camptandriinae was raised to the family level and a complete diagnosis of this family was carried out (Cheryl, 1997). According to WoRMS (http://www.marinespecies.org/), Camptandriidae consists of 42 species belonging to 24 genera. Most studies of this family focused on morphological features (Naderloo, 2017a; Trivedi et al., 2017). Although there are few researches on molecular level, most of them were based on partial mitochondrial gene sequences (Kitaura et al., 1998; Miura et al., 2007). To date, no complete mitogenome from Camptandriidae has ever been reported. The phylogenetic relationships among Camptandriidae and even the evolutionary status of this family have not been well resolved due to limited mitogenomic data.

    Members of the family Macrophthalmidae Dana, 1851 occur throughout the Indo-West Pacific, with most of the known species living in intertidal habitat (Mendoza and Ng, 2007). The macrophthalmids are distinguished primarily by having antennules that fold transversely or obliquely, a narrow inter-antennulary septum, external maxillipeds that do not completely close the buccal cavern, and eyestalks that are usually elongate (Davie, 2002). Although this family had long been regarded as a subfamily of Ocypodidae, Kitaura et al. (2002) provided clear molecular evidence that it should be regarded as a distinct family. At present, it includes 86 species belonging to 13 genera. Similarly, the genus Euplax H. Milne Edwards, 1852, it was initially regarded as a subgenus of Macrophthalmus Desmarest, 1823. Afterward, McLay et al. (2010) updated it to a valid genus. According to WoRMS, the genus Euplax only contains two species, Euplax dagohoyi (Mendoza and Ng, 2007) and Euplax leptophthalmus (H. Milne Edwards, 1852). To date, only three complete mitogenomes of this family are available from the National Center for Biotechnology Information (NCBI) dataset, and all of them belong to the genus Macrophthalmus. The phylogenetic relationships among Macrophthalmidae have been poorly resolved.

    Accordingly, in the present study, two newly sequenced mitogenomes of Ocypodoidea (C. dilatatum and Euplax sp.) were reported for the first time, one of which (C. dilatatum) is the first species in the family Camptandriidae whose complete mitogenome was sequenced. The characteristics of these two mitogenomes and the other 17 mitogenomes clustering in one branch of the phylogenetic tree were compared. Genome collinearity analysis of 19 mitogenomes showed that 18 of them shared the same gene rearrangement, while that of C. dilatatum mitogenome was consistent with the ancestral gene arrangement of Brachyura. Possible models were proposed to explain the current mitogenomic rearrangements. The phylogeny of Brachyura was reconstructed and the evolutionary status of Camptandriidae was revealed for the first time from the mitogenomic level. These results will not only enrich the mitogenomes of Ocypodoidea and mitogenomic rearrangements, but also lay a foundation for further evolutionary studies of Brachyura.

    Specimens of C. dilatatum and Euplax sp. were collected from Jiangsu Province, China (34°47′48.80″N, 119°13′42.38″E) and Hainan Province, China (18°24′39.48″N, 109°58′20.60″E), respectively. Specimens were immediately preserved in 95% ethanol until DNA extraction. According to the key morphological features of crabs, these two specimens were identified with a stereo dissecting microscope (Naderloo, 2017a, 2017b). The SQ Tissue DNA Kit (OMEGA) was used to extract the total genomic DNA from muscle tissue following the manufacturer’s instructions. The genomic DNA was sent to Shanghai Origingene Biopharm Technology Co., Ltd. for library preparation and high-throughput sequencing. The libraries were constructed by using the VAHTS Universal Plus DNA Library Prep Kit, with an insert size of 150 bp. Paired-end sequencing with a read length of 150 bp was performed on an Illumina Hiseq 6000 platform. Adapters and low-quality bases were removed using cutadapt v1.16 (Martin, 2011) with the following parameters: q, 20; m, 20. Trimmed reads shorter than 50 bp were discarded. Quality control of raw and trimmed reads was performed using FastQC v0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The filtered clean data were assembled and mapped to complete mitogenome sequence using NOVOPlasty v2.7.2 (Dierckxsens et al., 2017).

    The newly assembled mitogenomes of C. dilatatum and Euplax sp. were annotated using the software of Sequin (version 15.10, http://www.ncbi.nlm.nih.gov/Sequin/). The boundaries of protein-coding and ribosomal RNA genes were performed using NCBI-BLAST (http://blast.ncbi.nlm.nih.gov). Transfer RNA genes were manually plotted, according to the secondary structure predicted by the MITOS Web Server (Bernt et al., 2013) and tRNAscan-SE 1.21 (Lowe and Chan, 2016). The control region was determined by the locations of adjacent genes. Finally, circular mitogenome maps of C. dilatatum and Euplax sp. were drawn with the BLAST Ring Image Generator v0.95 (Alikhan et al., 2011).

    The base composition and relative synonymous codon usage (RSCU) were obtained using MEGA X (Kumar et al., 2018). The strand asymmetry was calculated using the following formulas: AT-skew = (A − T)/(A + T); GC-skew = (G − C)/(G + C) (Perna and Kocher, 1995). Synteny analysis between the genomes was performed using Mauve v2.4.0 (Darling et al., 2004). To estimate the evolutionary-selection constraints on 13 PCGs, the nonsynonymous (dN) and synonymous (dS) substitution rates were calculated using Mega X. The genetic distances of 13 PCGs were also estimated using Mega X based on the Kimura 2-parameter (K2P) substitution model.

    The phylogeny of Brachyura was inferred based on 107 available complete mitogenomes and two newly determined ones (Table S1). The species Pagurus nigrofascia and P. gracilipes from Anomura were used as outgroups. PhyloSuite (Zhang et al., 2020a) was used to extract the nucleotide sequences of 13 PCGs for each of the above species from the GenBank files. The MAFFT program (Katoh et al., 2002) integrated into PhyloSuite was executed to align multiple sequences in normal-alignment mode, and ambiguously aligned regions were identified and moved by Gblocks (Talavera and Castresana, 2007). The alignments of individual genes were then concatenated and used to generate input files (Phylip and Nexus formats) for phylogenetic analysis. The best-fit models were selected by ModelFinder (Kalyaanamoorthy et al., 2017) based on the Bayesian Information Criterion (BIC). Phylogenetic trees were built under maximum likelihood (ML) and Bayesian inference (BI) methods. The ML analysis was carried out in IQ-TREE (Nguyen et al., 2015) using an ML+rapid bootstrap (BS) algorithm with 1000 replicates. The BI analysis was performed in MrBayes 3.2.6 (Ronquist et al., 2012) with default parameters and 3×106 Markov Chain Monte Carlo generations. The trees were sampled every 1000 generations with a burn-in of 25%. The average standard deviation of split frequencies below 0.01 was considered to reach convergence.

    The complete mitogenomes of C. dilatatum and Euplax sp. are 15 444 bp and 16 129 bp in length, respectively (GenBank accessions MW191756 and MT176431; the order of the following data is the same as these) (Fig. 1; Tables 1 and 2). These two mitogenomes both contain a typical set of 37 genes (13 PCGs, 22 tRNAs, and two rRNAs) and a putative control region (CR). Nine PCGs and 14 tRNAs are encoded by the heavy (H-) strand, while the remaining genes are encoded by the light (L-) strand. There are 140 intergenic nucleotides dispersed in 13 locations in C. dilatatum mitogenome, and 537 intergenic nucleotides in 17 locations in Euplax sp. mitogenome; respectively. The longest one is 53 bp (between ND5 and ND4) and 169 bp (between ND4L and ND6) in these two mitogenomes (Tables 1 and 2). The base composition of C. dilatatum mitogenome is 34.4% A, 34.7% T, 11.4% C, 19.5% G, and that of Euplax sp. is 34.9% A, 34.0% T, 10.4% C, 20.7% G; the AT contents are 69.1% and 68.9%, suggesting a strong AT bias (Tables S2 and S3).

    Figure  1.  Gene maps of Cleistostoma dilatatum (a) and Euplax sp. (b) mitogenomes. Genes encoded on the heavy or light strands are shown outside or inside the circular gene map, respectively.
    Table  1.  Features of the mitochondrial genome of Cleistostoma dilatatum
    GenePositionLength/bpAmino acidStart/Stop codonAnticodonIntergenic regionStrand
    FromTo
    COI1 bp1534 bp1534511ATG/T 0H
    Leu (L2)1535 bp1599 bp65 TAA6H
    COII1606 bp2293 bp688229ATG/T0H
    Lys (K)2294 bp2363 bp70 TTT0H
    Asp (D)2364 bp2424 bp61 GTC1H
    ATP82426 bp2584 bp15952ATG/TAA –4H
    ATP62581 bp3252 bp672223ATA/TAA –1H
    COIII3252 bp4041 bp790263ATG/T 0H
    Gly (G)4042 bp4105 bp64 TCC–3H
    ND34103 bp4456 bp354117ATT/TAA 4H
    Ala (A)4461 bp4525 bp65 TGC4H
    Arg (R)4530 bp4593 bp64 TCG0H
    Asn (N)4594 bp4662 bp69 GTT0H
    Ser (S1)4663 bp4729 bp67 TCT0H
    Glu (E)4730 bp4795 bp66 TTC2H
    His (H)4798 bp4862 bp65 GTG1L
    Phe (F)4864 bp4928 bp65GAA–1L
    ND54928 bp6643 bp1716571ATT/TAA53L
    ND46697 bp8034 bp1338445ATG/TAA–7L
    ND4L8028 bp8330 bp303100ATG/TAA9L
    Thr (T)8340 bp8405 bp66TGT0H
    Pro (P)8406 bp8470 bp65TGG2L
    ND68473 bp8976 bp504167ATT/TAA–1H
    Cyt b8976 bp10 110 bp1135378ATG/T0H
    Ser (S2)10 111 bp10 177 bp67TGA15H
    ND110 193 bp11 131 bp939312ATA/TAA34L
    Leu (L1)11 166 bp11 232 bp67TAG0L
    16S11 233 bp12 546 bp13140L
    Val (V)12 547 bp12 619 bp73TAC0L
    12S12 620 bp13 435 bp8160L
    CR13 436 bp14 024 bp5890H
    Ile (I)14 025 bp14 090 bp66GAT–3H
    Gln (Q)14 088 bp14 156 bp69TTG8L
    Met (M)14 165 bp14 234 bp70CAT0H
    ND214 235 bp15 245 bp1011336ATT/TAG–2H
    Trp (W)15 244 bp15 315 bp72TCA1H
    Cys (C)15 317 bp15 380 bp64GCA0L
    Tyr (Y)15 381 bp15 444 bp64GTA–1L
    Note: – represents no data.
     | Show Table
    DownLoad: CSV
    Table  2.  Features of the mitochondrial genome of Euplax sp.
    GenePositionLength/bpAmino acidStart/Stop codonAnticodonIntergenic regionStrand
    FromTo
    COI1 bp1539 bp1539512ATG/TAA–5H
    Leu (L2)1535 bp1600 bp66TAA8H
    COII1609 bp2296 bp688229ATG/T28H
    ATP82325 bp2486 bp16253ATT/TAA–4H
    ATP62 483 bp3154 bp672223ATA/TAA–1H
    COIII3154 bp3943 bp790263ATG/T0H
    Gly (G)3 944 bp4006 bp63TCC–3H
    ND34004 bp4357 bp354117ATA/TAA1H
    Ala (A)4359 bp4422 bp64TGC1H
    Arg (R)4424 bp4487 bp64TCG0H
    Asn (N)4488 bp4554 bp67GTT0H
    Ser (S1)4555 bp4621 bp67TCT6H
    Thr (T)4 628 bp4689 bp62TGT16H
    Pro (P)4 706 bp4770 bp65TGG10L
    ND14781 bp5707 bp927308ATA/TAG33L
    Leu (L1)5741 bp5807 bp67TAG0L
    16S5808 bp7170 bp13630L
    12S7171 bp8048 bp8780L
    His (H)8 049 bp8113 bp65GTG–1L
    ND58113 bp9813 bp1701566ATG/TAA125L
    Val (V)9939 bp10 011 bp73TAG0L
    CR10 012 bp10 806 bp7950H
    Gln (Q)10 807 bp10 875 bp69TTG7L
    Cys (C)10 883 bp10 944 bp62GCA0L
    Tyr (Y)10 945 bp11 010 bp66GTA37L
    Lys (K)11 048 bp11 116 bp69TTT0H
    Asp (D)11 117 bp11 182 bp66GTC4H
    Glu (E)11 187 bp11 249 bp63TTC–1H
    Phe (F)11 249 bp11 314 bp66GAA7L
    ND411 322 bp12 659 bp1338445ATG/TAA–7L
    ND4L12 653 bp12 955 bp303100ATG/TAA169L
    ND613 125 bp13 649 bp525174ATT/TAA–20H
    Cyt b13 630 bp14 764 bp1135378ATG/T0H
    Ser (S2)14 765 bp14 830 bp66TGA76H
    Ile (I)14 907 bp14 971 bp65GAT2H
    Met (M)14 974 bp15 042 bp69CAT0H
    ND215 043 bp16 053 bp1011336ATG/TAG–2H
    Trp (W)16 052 bp16 121 bp70TCA7H
    Note: – represents no data.
     | Show Table
    DownLoad: CSV

    All the 13 PCGs initiate with typical ATN codons in the two mitogenomes. The majority of PCGs terminate with TAA or TAG, while four PCGs in C. dilatatum mitogenome (COI, COII, COIII, and Cyt b) and three PCGs in Euplax sp. mitogenome (COII, COIII, and Cyt b) use a single T as a stop codon (Tables 1 and 2). Incomplete stop codons are common in metazoan mitogenomes and may be recovered via post-transcriptional polyadenylation (Ojala et al., 1981). The GC-skew values of nine PCGs (COI, COII, ATP8, ATP6, COIII, ND3, ND6, Cyt b, and ND2) are negative, indicating they are encoded by the H-strand, whereas the remaining four exhibit positive values, indicating they are encoded by the L-strand (Tables S2 and S3). The most frequently used amino acids are Leu and Ser. In comparison, the least common amino acids are Cys and Arg (Figs 2a, b). The RSCU values of each codon in these two mitogenomes are roughly identical (Figs 2c, d; Table S4). It is worth noting that the RSCU values for the codons NNU and NNA are usually greater than one, suggesting a strong AT bias in the third codon position.

    Figure  2.  Amino acid composition in the mitogenome of Cleistostoma dilatatum (a) and Euplax sp. (b); relative synonymous codon usage in the mitogenome of C. dilatatum (c) and Euplax sp. (d). RSCU: relative synonymous codon usage.

    Twenty-two tRNAs are scattered throughout the entire mitogenome (Tables 1 and 2). All of them can be folded into typical cloverleaf secondary structures except for S1 in both two mitogenomes (Figs S1 and S2). The lack of DHU arm in S1 is thought to be a common phenomenon in metazoan mitogenomes (Gong et al., 2020; Lu et al., 2020; Ruan et al., 2020). The 16S rRNA and 12S rRNA genes of C. dilatatum mitogenome are located between L1 and V, V and CR, respectively. While Euplax sp. mitogenome shares different rRNA arrangements (L1- 16S- 12S- H).

    To estimate the evolutionary-selection constraints on 13 PCGs in 19 mitogenomes, we perform dN/dS analysis for each PCG. It is commonly accepted that dN/dS>1, dN/dS=1, and dN/dS<1 generally indicate positive selection, neutral mutation, and purifying selection, respectively (Yang, 2006). All of the dN/dS ratios are lower than one (<1), indicating that all 13 PCGs are evolving under purifying selection. ATP8 gene exhibits a highly relaxed purifying selection with the highest dN/dS value (0.619), whereas COI gene exhibits the strongest purifying selection with the lowest dN/dS value (0.077) (Fig. 3). The lowest dN/dS value of COI gene indicates that this gene is bound by the protein-coding function and bears strong natural selection pressure, thus ensuring the normal function of its encoded protein, which means that COI gene has an important role in the survival and evolution of the above species. Besides, we conduct genetic distance analysis for 13 PCGs. COI gene possesses the least genetic distance (average 0.214), and ATP gene captures the largest value (average 0.409), representing the most conserved and variable genes, respectively (Fig. 3).

    Figure  3.  Genetic distance (on average) and dN/dS substitution rates of 13 PCGs among 19 mitogenomes.

    Genomic synteny analysis reveals that four large genomic homologous regions are prevalent in all 19 mitogenomes (marked A–D in Fig. 4). It is evident that the homologous regions B and C are rearranged in C. dilatatum mitogenome when choosing Eriocheir sinensis (Brachyura: Varunidae) mitogenome as the reference sequence (Fig. 4). The two homologous regions show a C-B order in C. dilatatum mitogenome, while that the remaining crabs display a B-C order (Fig. 4). Further analysis indicated that C. dilatatum mitogenome was consistent with the ancestral gene arrangement of Brachyura, while that of the remaining crabs shared exactly the same gene rearrangements.

    Figure  4.  Multiple genome alignments of 19 mitogenomes. The mitogenome of Eriocheir sinensis at the top as the reference genome. All genomes are started from the COI gene. The number at the top of each genome shows nucleotide positions. Within each of the alignments, local collinear blocks are represented by blocks of the same color connected by lines.

    Gene arrangements in C. dilatatum and Euplax sp. mitogenomes are shown in Fig. 5. For C. dilatatum mitogenome, only a single H moves from the downstream of ND5 to downstream of E (Fig. 5A①) when compared with the gene order in ancestral crustaceans (the pancrutacean ground pattern) mitogenomes (Boore, 1999). In contrast, gene order in Euplax sp. mitogenome underwent large-scale gene rearrangements. At least nine gene clusters (or genes) significantly differ from the typical order, involving 12 tRNA genes (K, D, E, F, H, T, P, L1, V, Q, C, and Y), two rRNAs (16S rRNA and 12S rRNA), one PCG (ND1), and a putative CR (Fig. 5B). Of these gene rearrangements, three tRNA gene pairs (K-D, E-F, and C-Y) and two single tRNA genes (V and Q) are moved into the ND5 and ND4 junction (Fig. 5B①②⑥⑧⑨), forming an eight-tRNA cluster (V-Q-C-Y-K-D-E-F) if CR is not considered. The CR is shifted from the typical area between 12S rRNA and I to the V and Q junction (Fig. 5B⑦). A single H gene, one tRNA gene pair (T-P), and the ND1- L1-16S-12S gene cluster are moved to the position between S1 and ND5 (Fig. 5B③④⑤).

    Figure  5.  Gene arrangements in Cleistostoma dilatatum (A) and Euplax sp. (B) mitogenome.

    Currently, four widely-accepted mechanisms have been used to account for mitogenomic rearrangements, including tandem duplication and random loss (TDRL) model (Moritz and Brown, 1987), intramitochondrial recombination model (Poulton et al., 1993), tandem duplication and non-random loss model (Lavrov et al., 2002), and double replications and random loss model (Shi et al., 2014). How did the gene orders in these two newly sequenced mitogenomes emerge? Here, we proposed that the TDRL mechanism resulted in the generation of these two mitogenomes. The hypothesized intermediate steps are as follows. Firstly, the F-ND5-H genes underwent a complete copy, forming a dimeric block, (F-ND5-H)-(F-ND5-H). Consecutive copies were then followed by a random loss of the duplicated genes, forming a novel H-F-ND5 gene order (Fig. 6B). The H-F-ND5 gene cluster is a common phenomenon in the mitogenome of ancestral and most living species of Brachyura (Lu et al., 2020; Zhang et al., 2020b), including Portunidae, Grapsidae, Ocypodidae, Leucosiidae, Eriphiidae, and the C. dilatatum mitogenome in this study. In the second rearrangement event, the gene block from K to Y underwent a complete copy, forming a dimeric block (K-D-ATP8-ATP6-COIII-G-ND3-A-R-N-S1-E-H-F-ND5-ND4-ND4L-T-P-ND6-Cyt b-S2-ND1-L1-16S-V-12S-CR-I-Q-M-ND2-W-C-Y)-(K-D-ATP8-ATP6-COIII-G-ND3-A-R-N-S1-E-H-F-ND5-ND4-ND4L-T-P-ND6-Cyt b-S2-ND1-L1-16S-V-12S-CR-I-Q-M-ND2-W-C-Y). Consecutive copies were then followed by a random loss of supernumerary genes, forming a new gene block, (K-D-ATP8-ATP6-COIII-G-ND3-A-R-N-S1-E-F-ND4-ND4L-T-P-ND6-Cyt b-S2-ND1-L1-16S-12S-I-M-ND2-W-H-ND5-V-CR-Q-C-Y). In the following step, the newly formed gene block from K to Y underwent a second copy and likewise experienced a random loss of redundant genes. Finally, the ultimate gene arrangement in Euplax sp. mitogenome was generated (Fig. 6C), which is consistent with the ancestral gene arrangement of Varunidae and Macrophthalmidae (Wang et al., 2020). Summarily, all the rearrangement events mentioned above can be explained by TDRL model, which supposes that the rearranged gene order occurs via tandem duplications followed by random deletion of certain duplications (Moritz et al., 1987).

    Figure  6.  Inferred intermediate steps between the ancestral gene arrangement of crustaceans and two newly sequenced mitogenomes. The ancestral gene arrangement of crustaceans (A); the results of one tandem duplication and random loss (TDRL) event, the ancestral gene arrangement in Brachyuran mitogenome, and the final gene arrangement in Cleistostoma dilatatum mitogenome (B); the results of two TDRL events, the ancestral gene arrangement in Varunidae and Macrophthalmidae mitogenomes, and the final gene arrangement in Euplax sp. mitogenome (C). The duplicated gene block is underlined and the lost genes are labeled with gray.

    The phylogenetic trees obtained using BI and ML methods resulted in identical topological structures except for supporting values. Here, only one topology (BI) with both support values was presented (Fig. 7). The results show that all Macrophalmidae species cluster together as a group, wherein Euplax sp. shows the closest relationship with Macrophthalmus darwinensi. Our phylogenetic trees firstly show the evolutionary status of Camptandriidae that it has the most closely related relationship with Macrophalmidae. These two families (Camptandriidae and Macrophalmidae) as a group then form a sister clade with Varunidae. Macrophalmidae and Varunidae sharing exactly the same mitogenomic rearrangements gather together in the phylogenetic tree, which is in consistence with most molecular results (Chen et al., 2018; Wang et al., 2020; Zhang et al., 2021a). Camptandriidae mitogenome, however, capturing the conserved gene arrangement (ancestral gene arrangement of Brachyura) forms a clade with the taxa that share the identically large-scale gene rearrangements. Similar phenomena have been reported in increasing number of crab mitogenomes (Tan et al., 2018; Li et al., 2020; Zhang et al., 2020c, 2021b). For instance, our recent work found that two closely related species belonging to the same genus (D. arrosor and D. aspersus) possessed two different gene rearrangements (Zhang et al., 2021b). More complex situations exist in Potamidae mitogenomes (Zhang et al., 2020c). Thus it echoes the viewpoint that the mitogenomic gene rearrangement is likely a continuous and dynamic process and may occur very recently even after speciation events (Zhang et al., 2021b). Of course, since here C. dilatatum is the only species of the family Camptandriidae, the phylogenetic status of Camptandriidae and the aforesaid thought-provoking hypothesis should be confirmed with more species.

    Figure  7.  Phylogenetic tree of brachyuran species inferred from the nucleotide sequences of 13 PCGs based on maximum likelihood (ML) and Bayesian inference (BI) analyses. The node marked with a solid circle indicates 100 ML bootstrap support and 100% BI posterior probability. The numbers after the species name are the GenBank accession number.

    Of the 30 families in our phylogenetic tree, except for Xanthidae, Gecarcinidae, and Homolidae, each family forms a monophyletic clade (Fig. 7). Regarding the non-monophyly of Xanthidae, four Xanthidae species are divided into two clades. Three of them cluster together as a clade, and the remaining one (Leptodius sanguineus) forms a sister clade with the single representative of the family Oziidae (Epixanthus frontalis), which calls attention to authoritative identification of these two species (L. sanguineus and E. frontalis). Of course, the increasing samples of Oziidae will also help to clarify the suspicious classification and relationships. For two Gecarcinidae species, one of them (Gecarcoidea natalis) forms a sister clade with Sesarmidae species, and then clusters with the remaining one (Cardisoma carnifex). As far as the non-monophyly of Homolidae, the single representative of Latreilliidae (Latreillia valida) forms a sister clade with a member of the family Homolidae (Moloha majora), which calls attention to authoritative identification of L. valida. Furthermore, it is worth noting that almost one-third of the families (11/30) include only one representative, so the non-monophyly of relevant families should be treated with caution.

    Viewed from a higher taxonomic level, most superfamilies of Brachyura are found to be monophyletic, with the exception of Eriphioidea, Ocypodoidea, and Grapsoidea (Fig. 7). Although the polyphyly of the above three superfamilies is well supported in our phylogenetic tree, the interrelationships of these groups remain largely disputable. Regarding the interrelationships among Ocypodoidea and Grapsoidea, no consensus has been reached in current studies. For example, Sesarmidae (Grapsoidea) have a close relationship with Gecarcinidae (Grapsoidea), and Dotillidae (Ocypodoidea) form a sister clade with Grapsidae (Grapsoidea) in our phylogenetic tree. However, in Tan et al. (2018) , Sesarmidae (Grapsoidea) first clustered with Dotillidae (Ocypodoidea), and then formed a sister clade with Gecarcinidae (Grapsoidea). While in Wang et al. (2020) , Dotillidae (Ocypodoidea) and Xenograpsidae (Grapsoidea) formed a sister clade, and then clustered with Sesarmidae (Grapsoidea). These three families as a group then formed a clade with Gecarcinidae (Grapsoidea). Therefore, more sampling across a breadth of taxonomic groups and integration of additional molecular data need to be mined in order to substantially resolve the interrelationships of these groups.

    In this study, two newly sequenced mitogenomes of Ocypodoidea, C. dilatatum and Euplax sp., were reported for the first time. TDRL model is proposed to be involved in the evolution of these two mitochondrial gene rearrangements. Comparative mitogenomic analyses of the species clustering in one branch in the tree display two types of gene arrangements. The dN/dS ratio analysis of all PCGs indicates that purifying selection plays a leading role in the evolution of mitochondrial PCGs. Phylogenetic analyses show that Camptandriidae and Macrophalmidae are the most closely related species, and the polyphyly of three superfamilies (Ocypodoidea, Eriphioidea, and Grapsoidea) is well supported. Nevertheless, large-scale taxonomic samplings are still needed to confirm the phylogenetic status of Camptandriidae and the non-monophyly of relative families due to limited representatives. Also, the authentic relationships within Brachyura will be better understood with the help of increasing samplings and data.

  • Ashjian C J, Campbell R G, Gelfman C, et al. 2017. Mesozooplankton abundance and distribution in association with hydrography on Hanna Shoal, NE Chukchi Sea, during August 2012 and 2013. Deep-Sea Research Part II: Topical Studies in Oceanography, 144: 21–36. doi: 10.1016/j.dsr2.2017.08.012
    Cepeda G D, Viñas M D, Molinari G N, et al. 2020. The impact of Río de la Plata plume favors the small-sized copepods during summer. Estuarine, Coastal and Shelf Science, 245: 107000
    Chatterjee A, Shankar D, McCreary J P, et al. 2017. Dynamics of Andaman Sea circulation and its role in connecting the equatorial Indian Ocean to the Bay of Bengal. Journal of Geophysical Research: Oceans, 122(4): 3200–3218. doi: 10.1002/2016JC012300
    Cornils A, Schnack-Schiel S B, Böer M, et al. 2006. Feeding of Clausocalanids (Calanoida, Copepoda) on naturally occurring particles in the northern Gulf of Aqaba (Red Sea). Marine Biology, 151(4): 1261–1274
    Dai Luping, Li Chaolun, Yang Guang, et al. 2016. Zooplankton abundance, biovolume and size spectra at western boundary currents in the subtropical North Pacific during winter 2012. Journal of Marine Systems, 155: 73–83. doi: 10.1016/j.jmarsys.2015.11.004
    Damotharan P, Perumal N V, Arumugam M, et al. 2010. Studies on zooplankton ecology from Kodiakkarai (point calimere) coastal waters (south east coast of India). Research Journal of Biological Sciences, 5(2): 187–198. doi: 10.3923/rjbsci.2010.187.198
    Domínguez R, Garrido S, Santos A M P, et al. 2017. Spatial patterns of mesozooplankton communities in the northwestern Iberian shelf during autumn shaped by key environmental factors. Estuarine, Coastal and Shelf Science, 198: 257–268
    Dur G, Hwang J S, Souissi S, et al. 2007. An overview of the influence of hydrodynamics on the spatial and temporal patterns of calanoid copepod communities around Taiwan. Journal of Plankton Research, 29(S1): i97–i116
    Fernandes V. 2008. The effect of semi-permanent eddies on the distribution of mesozooplankton in the central Bay of Bengal. Journal of Marine Research, 66(4): 465–488. doi: 10.1357/002224008787157430
    Fernandes V, Ramaiah N. 2009. Mesozooplankton community in the Bay of Bengal (India): spatial variability during the summer monsoon. Aquatic Ecology, 43(4): 951–963. doi: 10.1007/s10452-008-9209-4
    Fernandes V, Ramaiah N. 2013. Mesozooplankton community structure in the upper 1, 000 m along the western Bay of Bengal during the 2002 fall intermonsoon. Zoological Studies, 52(1): 31. doi: 10.1186/1810-522X-52-31
    Fernandes V, Ramaiah N. 2014. Distributional characteristics of surface-layer mesozooplankton in the Bay of Bengal during the 2005 winter monsoon. Indian Journal of Geo-Marine Sciences, 43(1): 176–188
    Fernandes V, Ramaiah N. 2019. Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon. Turkish Journal of Zoology, 43(1): 68–93. doi: 10.3906/zoo-1805-6
    Fernández-Álamo M A, Färber-Lorda J. 2006. Zooplankton and the oceanography of the eastern tropical Pacific: A review. Progress in Oceanography, 69(2–4): 318–359
    Fernández De Puelles M L, Molinero J C. 2008. Decadal changes in hydrographic and ecological time-series in the Balearic Sea (western Mediterranean), identifying links between climate and zooplankton. ICES Journal of Marine Science, 65(3): 311–317. doi: 10.1093/icesjms/fsn017
    Gomes H R, Goes J I, Saino T. 2000. Influence of physical processes and freshwater discharge on the seasonality of phytoplankton regime in the Bay of Bengal. Continental Shelf Research, 20(3): 313–330. doi: 10.1016/S0278-4343(99)00072-2
    Hossain M S, Sarker S, Sharifuzzaman S M, et al. 2020. Primary productivity connects hilsa fishery in the Bay of Bengal. Scientific Reports, 10(1): 5659. doi: 10.1038/s41598-020-62616-5
    Irigoien X, Harris R P. 2006. Comparative population structure, abundance and vertical distribution of six copepod species in the North Atlantic: Evidence for intraguild predation?. Marine Biology Research, 2(4): 276–290
    Ittekkot V, Nair R R, Honjo S, et al. 1991. Enhanced particle fluxes in Bay of Bengal induced by injection of fresh water. Nature, 351(6325): 385–387. doi: 10.1038/351385a0
    Ivanenko V N, Defaye D. 2006. Planktonic deep-water copepods of the family Mormonillidae Giesbrecht, 1893 from the East Pacific Rise (13°N), the Northeastern Atlantic, and near the North Pole (Copepoda, Mormonilloida). Crustaceana, 79(6): 707–726. doi: 10.1163/156854006778026861
    Jagadeesan L, Jyothibabu R, Anjusha A, et al. 2013. Ocean currents structuring the mesozooplankton in the Gulf of Mannar and the Palk Bay, southeast coast of India. Progress in Oceanography, 110: 27–48. doi: 10.1016/j.pocean.2012.12.002
    Jagadeesan L, Jyothibabu R, Arunpandi N, et al. 2017. Dominance of coastal upwelling over Mud Bank in shaping the mesozooplankton along the southwest coast of India during the Southwest Monsoon. Progress in Oceanography, 156: 252–275. doi: 10.1016/j.pocean.2017.07.004
    Jayalakshmi K J, Sabu P, Devi C R A, et al. 2015. Response of micro- and mesozooplankton in the southwestern Bay of Bengal to a cyclonic eddy during the winter monsoon, 2005. Environmental Monitoring and Assessment, 187(7): 473. doi: 10.1007/s10661-015-4609-0
    Jeong M K, Suh H L, Soh H Y. 2011. Taxonomy and zoogeography of euchaetid copepods (Calanoida, Clausocalanoidea) from Korean waters, with notes on their female genital structure. Ocean Science Journal, 46(2): 117–132. doi: 10.1007/s12601-011-0011-1
    Jyothibabu R, Madhu N V, Maheswaran P A, et al. 2008. Seasonal variation of microzooplankton (20–200 μm) and its possible implications on the vertical carbon flux in the western Bay of Bengal. Continental Shelf Research, 28(6): 737–755. doi: 10.1016/j.csr.2007.12.011
    Jyothibabu R, Win N N, Shenoy D M, et al. 2014. Interplay of diverse environmental settings and their influence on the plankton community off Myanmar during the Spring Intermonsoon. Journal of Marine Systems, 139: 446–459. doi: 10.1016/j.jmarsys.2014.08.003
    Koppelmann R, Fabian H, Weikert H. 2003. Temporal variability of deep-sea zooplankton in the Arabian Sea. Marine Biology, 142(5): 959–970. doi: 10.1007/s00227-002-0999-y
    Köster M, Paffenhöfer G A. 2016. How efficiently can doliolids (Tunicata, Thaliacea) utilize phytoplankton and their own fecal pellets?. Journal of Plankton Research, 39(2): 305–315
    Li Kaizhi, Yin Jianqiang, Huang Liangmin, et al. 2010. Advances on classification and ecology of pelagic tunicates. Acta Ecologica Sinica (in Chinese), 30(1): 174–185
    Li Kaizhi, Yin Jianqiang, Huang Liangmin, et al. 2017. A comparison of the zooplankton community in the Bay of Bengal and South China Sea during April–May, 2010. Journal of Ocean University of China, 16(6): 1206–1212. doi: 10.1007/s11802-017-3229-4
    Liao Jiawen, Peng Shiqiu, Wen Xixi. 2020. On the heat budget and water mass exchange in the Andaman Sea. Acta Oceanologica Sinica, 39(7): 32–41. doi: 10.1007/s13131-019-1627-8
    Liu Yanliang, Li Kuiping, Ning Chunlin, et al. 2018. Observed seasonal variations of the upper ocean structure and air-sea interactions in the Andaman Sea. Journal of Geophysical Research: Oceans, 123(2): 922–938. doi: 10.1002/2017JC013367
    Madhu N V, Jyothibabu R, Maheswaran P A, et al. 2006. Lack of seasonality in phytoplankton standing stock (chlorophyll a) and production in the western Bay of Bengal. Continental Shelf Research, 26(16): 1868–1883. doi: 10.1016/j.csr.2006.06.004
    Madhupratap M, Gauns M, Ramaiah N, et al. 2003. Biogeochemistry of the Bay of Bengal: physical, chemical and primary productivity characteristics of the central and western Bay of Bengal during summer monsoon 2001. Deep-Sea Research Part II: Topical Studies in Oceanography, 50(5): 881–896. doi: 10.1016/S0967-0645(02)00611-2
    McCreary J P, Kundu P K, Molinari R L. 1993. A numerical investigation of dynamics, thermodynamics and mixed-layer processes in the Indian Ocean. Progress in Oceanography, 31(3): 181–244. doi: 10.1016/0079-6611(93)90002-U
    Mills C E. 1995. Medusae, siphonophores, and ctenophores as planktivorous predators in changing global ecosystems. ICES Journal of Marine Science, 52(3–4): 575–581
    Mohanty A K, Sahu G, Singhsamanta B, et al. 2010. Zooplankton diversity in the nearshore waters of Bay of Bengal, off Rushikulya Estuary. The IUP Journal of Environmental Sciences, 4(2): 61–85
    Nuncio M, Kumar S P. 2012. Life cycle of eddies along the western boundary of the Bay of Bengal and their implications. Journal of Marine Systems, 94: 9–17. doi: 10.1016/j.jmarsys.2011.10.002
    Paffenhoefer G A, Knowles S C. 1979. Ecological implications of fecal pellet size, production and consumption by copepods. Journal of Marine Research, 37: 35–49
    Paffenhöfer G A. 1993. On the ecology of marine cyclopoid copepods (Crustacea, Copepoda). Journal of Plankton Research, 15(1): 37–55. doi: 10.1093/plankt/15.1.37
    Prasanna Kumar S, Muraleedharan P M, Prasad T G, et al. 2002. Why is the Bay of Bengal less productive during summer monsoon compared to the Arabian Sea?. Geophysical Research Letters, 29(24): 2235
    Prasanna Kumar S, Narvekar J, Nuncio M, et al. 2010. Is the biological productivity in the Bay of Bengal light limited?. Current Science, 98(10): 1331–1339
    Prasanna Kumar S, Nuncio M, Narvekar J, et al. 2004. Are eddies nature’s trigger to enhance biological productivity in the Bay of Bengal?. Geophysical Research Letters, 31(7): L07309
    Rakhesh M, Raman A V, Sudarsan D. 2006. Discriminating zooplankton assemblages in neritic and oceanic waters: a case for the northeast coast of India, Bay of Bengal. Marine Environmental Research, 61(1): 93–109. doi: 10.1016/j.marenvres.2005.06.002
    Ramaswamy V, Gaye B, Shirodkar P V, et al. 2008. Distribution and sources of organic carbon, nitrogen and their isotopic signatures in sediments from the Ayeyarwady (Irrawaddy) continental shelf, northern Andaman Sea. Marine Chemistry, 111(3–4): 137–150
    Ramaswamy V, Rao P S, Rao K H, et al. 2004. Tidal influence on suspended sediment distribution and dispersal in the northern Andaman Sea and Gulf of Martaban. Marine Geology, 208(1): 33–42. doi: 10.1016/j.margeo.2004.04.019
    Rao P S, Ramaswamy V, Thwin S. 2005. Sediment texture, distribution and transport on the Ayeyarwady continental shelf, Andaman Sea. Marine Geology, 216(4): 239–247. doi: 10.1016/j.margeo.2005.02.016
    Rodolfo K S. 1969. Sediments of the Andaman Basin, northeastern Indian Ocean. Marine Geology, 7(5): 371–402. doi: 10.1016/0025-3227(69)90014-0
    Sabu P, Devi C R A, Lathika C T, et al. 2015. Characteristics of a cyclonic eddy and its influence on mesozooplankton community in the northern Bay of Bengal during early winter monsoon. Environmental Monitoring and Assessment, 187(6): 330. doi: 10.1007/s10661-015-4571-x
    Schott F A, McCreary J P. 2001. The monsoon circulation of the Indian Ocean. Progress in Oceanography, 51(1): 1–123. doi: 10.1016/S0079-6611(01)00083-0
    Shankar D, Vinayachandran P N, Unnikrishnan A S. 2002. The monsoon currents in the north Indian Ocean. Progress in Oceanography, 52(1): 63–120. doi: 10.1016/S0079-6611(02)00024-1
    Skjoldal H R, Wiebe P H, Postel L, et al. 2013. Intercomparison of zooplankton (net) sampling systems: results from the ICES/GLOBEC sea-going workshop. Progress in Oceanography, 108: 1–42. doi: 10.1016/j.pocean.2012.10.006
    Šmilauer P, Lepš J. 2014. Multivariate analysis of ecological data using Canoco 5, second edition. Cambridge, UK: Cambridge University Press, 362
    Srichandan S, Baliarsingh S K, Prakash S, et al. 2018. Zooplankton research in Indian seas: a review. Journal of Ocean University of China, 17(5): 1149–1158. doi: 10.1007/s11802-018-3463-4
    Steinberg D K, Lomas M W, Cope J S. 2012. Long-term increase in mesozooplankton biomass in the Sargasso Sea: linkage to climate and implications for food web dynamics and biogeochemical cycling. Global Biogeochemical Cycles, 26(1): GB1004
    Subramanian V. 1993. Sediment load of Indian Rivers. Current Science, 64(11–12): 928–930
    Yang Guang, Li Chaolun, Wang Yanqing, et al. 2017. Spatial variation of the zooplankton community in the western tropical Pacific Ocean during the summer of 2014. Continental Shelf Research, 135: 14–22. doi: 10.1016/j.csr.2017.01.009
    Yuan L L, Pollard A I. 2018. Changes in the relationship between zooplankton and phytoplankton biomasses across a eutrophication gradient. Limnology and Oceanography, 63(6): 2493–2507. doi: 10.1002/lno.10955
  • Relative Articles

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-042024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-03010203040
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 25.4 %FULLTEXT: 25.4 %META: 70.9 %META: 70.9 %PDF: 3.7 %PDF: 3.7 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 1.8 %其他: 1.8 %China: 22.0 %China: 22.0 %France: 0.4 %France: 0.4 %Georgia: 0.4 %Georgia: 0.4 %Germany: 0.1 %Germany: 0.1 %Hong Kong, China: 0.1 %Hong Kong, China: 0.1 %India: 1.0 %India: 1.0 %Indonesia: 1.0 %Indonesia: 1.0 %Japan: 0.1 %Japan: 0.1 %Korea Republic of: 0.3 %Korea Republic of: 0.3 %Malaysia: 0.1 %Malaysia: 0.1 %Myanmar: 1.3 %Myanmar: 1.3 %Pakistan: 1.4 %Pakistan: 1.4 %Russian Federation: 6.7 %Russian Federation: 6.7 %Saudi Arabia: 0.4 %Saudi Arabia: 0.4 %Singapore: 1.1 %Singapore: 1.1 %Switzerland: 0.1 %Switzerland: 0.1 %Thailand: 0.4 %Thailand: 0.4 %Turkey: 0.4 %Turkey: 0.4 %United Kingdom: 0.4 %United Kingdom: 0.4 %United States: 60.1 %United States: 60.1 %其他ChinaFranceGeorgiaGermanyHong Kong, ChinaIndiaIndonesiaJapanKorea Republic ofMalaysiaMyanmarPakistanRussian FederationSaudi ArabiaSingaporeSwitzerlandThailandTurkeyUnited KingdomUnited States

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(6)

    Article Metrics

    Article views (498) PDF downloads(29) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return