Khedkar Gulab Dattarao, Jamdade Rahul, Naik Suresh, David Lior, Haymer David
Paul Hebert Centre for DNA Barcoding and Biodiversity Studies, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India.
Biodiversity Institute of Ontario, University of Guelph, Guelph, Ontario, Canada.
PLoS One. 2014 Jul 3;9(7):e101460. doi: 10.1371/journal.pone.0101460. eCollection 2014.
This study describes the species diversity of fishes of the Narmada River in India. A total of 820 fish specimens were collected from 17 sampling locations across the whole river basin. Fish were taxonomically classified into one of 90 possible species based on morphological characters, and then DNA barcoding was employed using COI gene sequences as a supplemental identification method. A total of 314 different COI sequences were generated, and specimens were confirmed to belong to 85 species representing 63 genera, 34 families and 10 orders. Findings of this study include the identification of five putative cryptic or sibling species and 43 species not previously known from the Narmada River basin. Five species are endemic to India and three are introduced species that had not been previously reported to occur in the Narmada River. Conversely, 43 species previously reported to occur in the Narmada were not found. Genetic diversity and distance values were generated for all of the species within genera, families and orders using Kimura's 2 parameter distance model followed by the construction of a Neighbor Joining tree. High resolution clusters generated in NJ trees aided the groupings of species corresponding to their genera and families which are in confirmation to the values generated by Automatic Barcode Gap Discovery bioinformatics platform. This aided to decide a threshold value for the discrimination of species boundary from the Narmada River. This study provides an important validation of the use of DNA barcode sequences for monitoring species diversity and changes within complex ecosystems such as the Narmada River.
本研究描述了印度讷尔默达河鱼类的物种多样性。在整个流域的17个采样点共采集了820份鱼类标本。根据形态特征，鱼类被分类为90种可能的物种之一，然后使用细胞色素氧化酶亚基I（COI）基因序列进行DNA条形码分析，作为一种补充鉴定方法。共生成了314个不同的COI序列，标本被确认为属于85个物种，代表63个属、34个科和10个目。本研究的结果包括鉴定出5个假定的隐存种或姐妹种，以及43个以前在讷尔默达河流域未知的物种。5个物种为印度特有种，3个为引入种，此前未报道在讷尔默达河出现。相反，以前报道在讷尔默达河出现的43个物种未被发现。使用木村二参数距离模型为属、科和目内的所有物种生成遗传多样性和距离值，随后构建邻接树。邻接树中生成的高分辨率聚类有助于将物种按其属和科进行分组，这与自动条形码间隙发现生物信息学平台生成的值一致。这有助于确定区分讷尔默达河物种边界的阈值。本研究为使用DNA条形码序列监测复杂生态系统（如讷尔默达河）中的物种多样性和变化提供了重要验证。
