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The fish diversity in the upper reaches of the Salween River, Nujiang River, revealed by DNA barcoding.

Chen W, Ma X, Shen Y, Mao Y, He S - Sci Rep (2015)

Bottom Line: At the other end of the spectrum, ten species (from three genera) that are characterized by an overlap between their intra- and interspecific genetic distances form a single genetic cluster and share haplotypes.The neighbor-joining phenogram, Barcode Index Numbers (BINs) and Automatic Barcode Gap Discovery (ABGD) identified 43 putative species, while the General Mixed Yule-coalescence (GMYC) identified five more OTUs.Thus, our study established a reliable DNA barcode reference library for the fish in the NR and sheds new light on the local fish diversity.

View Article: PubMed Central - PubMed

Affiliation: The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, 430072, China.

ABSTRACT
Nujiang River (NR), an essential component of the biodiversity hotspot of the Mountains of Southwest China, possesses a characteristic fish fauna and contains endemic species. Although previous studies on fish diversity in the NR have primarily consisted of listings of the fish species observed during field collections, in our study, we DNA-barcoded 1139 specimens belonging to 46 morphologically distinct fish species distributed throughout the NR basin by employing multiple analytical approaches. According to our analyses, DNA barcoding is an efficient method for the identification of fish by the presence of barcode gaps. However, three invasive species are characterized by deep conspecific divergences, generating multiple lineages and Operational Taxonomic Units (OTUs), implying the possibility of cryptic species. At the other end of the spectrum, ten species (from three genera) that are characterized by an overlap between their intra- and interspecific genetic distances form a single genetic cluster and share haplotypes. The neighbor-joining phenogram, Barcode Index Numbers (BINs) and Automatic Barcode Gap Discovery (ABGD) identified 43 putative species, while the General Mixed Yule-coalescence (GMYC) identified five more OTUs. Thus, our study established a reliable DNA barcode reference library for the fish in the NR and sheds new light on the local fish diversity.

No MeSH data available.


Related in: MedlinePlus

Bayesian inference gene tree with delineated OTUs.Grey rectangles represent species that share a COI lineage.
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f4: Bayesian inference gene tree with delineated OTUs.Grey rectangles represent species that share a COI lineage.

Mentions: The BIN analysis led to the recognition of 43 OTUs (Fig. 4,Table S4). Twenty-six BIN clusterswere found to be taxonomically concordant with the other barcode data that wereBOLD-assigned to the same species name, while 16 BIN clusters were discordant withmorphological species (Table S4).Moreover, one record (Pseudexostoma brachysoma) was indicated as a singleton,which means that this BIN only refers to one specimen that was not reported by BOLD.The count of OTUs produced by ABGD varied from 34 to 59 (Table S5). The ABGD analyses conducted with theJC69 and K2P models both produced two initial partitions with OTU counts of 34(P = 0.0215–0.0599) and 43(P = 0.0017–0.0129), respectively, whereas theuse of the p distance returned 34(P = 0.0215–0.0599) and 49(P = 0.0017–0.0129) OTUs. The results obtainedwith the p distance were excluded because of the conflict with the results fromother results with ABGD and those obtained with other analytical methods. Therefore,we chose the result of 43 OTUs because it was concordant with the outcome of boththe BIN and NJ analyses (Fig. 4). Both the single- andmultiple-threshold GMYC models outperformed the model, indicating the presenceof more than one species in the dataset (TableS6). The single-threshold model (48 OTUs) and the multiple-threshold model(50 OTUs) did not differ significantly from each other(χ2 = 5.11, d.f. = 8,0.1 < P < 0.9). Thus,the outcome of the single-threshold model was adopted for further study. Acomparison between the Bayesian inference (Fig. 4) andmaximum-likelihood gene trees (Fig. S1)did not reveal obvious differences in the positioning of OTUs. The three methodsyielded congruent results, but with five exceptions characterized by the assignmentof OTUs to the PARTIAL MATCH category (highlighted in grey in Fig.4). Both the BIN and ABGD analyses merged each of these five OTUs into acorresponding single OTU, whereas the GMYC approach partitioned each OTU into twoOTUs.


The fish diversity in the upper reaches of the Salween River, Nujiang River, revealed by DNA barcoding.

Chen W, Ma X, Shen Y, Mao Y, He S - Sci Rep (2015)

Bayesian inference gene tree with delineated OTUs.Grey rectangles represent species that share a COI lineage.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663501&req=5

f4: Bayesian inference gene tree with delineated OTUs.Grey rectangles represent species that share a COI lineage.
Mentions: The BIN analysis led to the recognition of 43 OTUs (Fig. 4,Table S4). Twenty-six BIN clusterswere found to be taxonomically concordant with the other barcode data that wereBOLD-assigned to the same species name, while 16 BIN clusters were discordant withmorphological species (Table S4).Moreover, one record (Pseudexostoma brachysoma) was indicated as a singleton,which means that this BIN only refers to one specimen that was not reported by BOLD.The count of OTUs produced by ABGD varied from 34 to 59 (Table S5). The ABGD analyses conducted with theJC69 and K2P models both produced two initial partitions with OTU counts of 34(P = 0.0215–0.0599) and 43(P = 0.0017–0.0129), respectively, whereas theuse of the p distance returned 34(P = 0.0215–0.0599) and 49(P = 0.0017–0.0129) OTUs. The results obtainedwith the p distance were excluded because of the conflict with the results fromother results with ABGD and those obtained with other analytical methods. Therefore,we chose the result of 43 OTUs because it was concordant with the outcome of boththe BIN and NJ analyses (Fig. 4). Both the single- andmultiple-threshold GMYC models outperformed the model, indicating the presenceof more than one species in the dataset (TableS6). The single-threshold model (48 OTUs) and the multiple-threshold model(50 OTUs) did not differ significantly from each other(χ2 = 5.11, d.f. = 8,0.1 < P < 0.9). Thus,the outcome of the single-threshold model was adopted for further study. Acomparison between the Bayesian inference (Fig. 4) andmaximum-likelihood gene trees (Fig. S1)did not reveal obvious differences in the positioning of OTUs. The three methodsyielded congruent results, but with five exceptions characterized by the assignmentof OTUs to the PARTIAL MATCH category (highlighted in grey in Fig.4). Both the BIN and ABGD analyses merged each of these five OTUs into acorresponding single OTU, whereas the GMYC approach partitioned each OTU into twoOTUs.

Bottom Line: At the other end of the spectrum, ten species (from three genera) that are characterized by an overlap between their intra- and interspecific genetic distances form a single genetic cluster and share haplotypes.The neighbor-joining phenogram, Barcode Index Numbers (BINs) and Automatic Barcode Gap Discovery (ABGD) identified 43 putative species, while the General Mixed Yule-coalescence (GMYC) identified five more OTUs.Thus, our study established a reliable DNA barcode reference library for the fish in the NR and sheds new light on the local fish diversity.

View Article: PubMed Central - PubMed

Affiliation: The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, 430072, China.

ABSTRACT
Nujiang River (NR), an essential component of the biodiversity hotspot of the Mountains of Southwest China, possesses a characteristic fish fauna and contains endemic species. Although previous studies on fish diversity in the NR have primarily consisted of listings of the fish species observed during field collections, in our study, we DNA-barcoded 1139 specimens belonging to 46 morphologically distinct fish species distributed throughout the NR basin by employing multiple analytical approaches. According to our analyses, DNA barcoding is an efficient method for the identification of fish by the presence of barcode gaps. However, three invasive species are characterized by deep conspecific divergences, generating multiple lineages and Operational Taxonomic Units (OTUs), implying the possibility of cryptic species. At the other end of the spectrum, ten species (from three genera) that are characterized by an overlap between their intra- and interspecific genetic distances form a single genetic cluster and share haplotypes. The neighbor-joining phenogram, Barcode Index Numbers (BINs) and Automatic Barcode Gap Discovery (ABGD) identified 43 putative species, while the General Mixed Yule-coalescence (GMYC) identified five more OTUs. Thus, our study established a reliable DNA barcode reference library for the fish in the NR and sheds new light on the local fish diversity.

No MeSH data available.


Related in: MedlinePlus