Aupalee Kittipat, Srisuka Wichai, Limsopatham Kwankamol, Sanit Sangob, Takaoka Hiroyuki, Saeung Atiporn
Parasitology and Entomology Research Cluster (PERC), Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Entomology Section, Queen Sirikit Botanic Garden, Mae Rim, Chiang Mai, 50180, Thailand.
Parasit Vectors. 2024 Dec 18;17(1):508. doi: 10.1186/s13071-024-06597-8.
Fast and reliable species identification of black flies is essential for research proposes and effective vector control. Besides traditional identification based on morphology, which is usually supplemented with molecular methods, geometric morphometrics (GM) has emerged as a promising tool for identification. Despite its potential, no specific GM techniques have been established for the identification of black fly species.
Adult female black flies collected using human bait, as well as those reared from pupae, were used in this study. Here, landmark-based GM analysis of wings was assessed for the first time to identify human-biting black fly species in Thailand, comparing this approach with the standard morphological identification method and DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene. To explore genetic relationships between species, maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic trees were built. Additionally, three different methods of species delimitation, i.e., assemble species by automatic partitioning (ASAP), generalized mixed yule coalescent (GMYC), and single Poisson tree processes (PTP), were utilized to identify the morphologically defined species. The effectiveness of a COI barcode in identifying black fly species was further examined through the best match (BM) and best close match (BCM) methods.
Seven black fly species, namely Simulium tenebrosum Takaoka, Srisuka & Saeung, 2018 (complex), S. doipuiense Takaoka & Choochote, 2005 (complex), S. nigrogilvum Summers, 1911, S. nodosum Puri, 1933, S. asakoae Takaoka & Davies, 1995, S. chamlongi Takaoka & Suzuki, 1984, and S. umphangense Takaoka, Srisuka & Saeung, 2017 were morphologically identified. Compared with the standard method, the GM analysis based on wing shape showed high success in separating species, achieving an overall accuracy rate of 88.54%. On the other hand, DNA barcoding surpassed wing GM for species identification with a correct identification rate of 98.57%. Species delimitation analyses confirmed the validity of most nominal species, with an exception for S. tenebrosum complex and S. doipuiense complex, being delimited as a single species. Moreover, the analyses unveiled hidden diversity within S. asakoae, indicating the possible existence of up to four putative species.
This study highlights the potential of wing GM as a promising and reliable complementary tool for species identification of human-biting black flies in Thailand.
快速且可靠地鉴定蚋类物种对于研究目的和有效的病媒控制至关重要。除了基于形态学的传统鉴定方法（通常辅以分子方法）外，几何形态测量学（GM）已成为一种有前景的鉴定工具。尽管其具有潜力，但尚未建立用于鉴定蚋类物种的特定GM技术。
本研究使用了用人诱饵收集的成年雌性蚋以及从蛹羽化出的蚋。在此，首次评估了基于地标点的翅膀GM分析，以鉴定泰国的嗜人蚋类物种，并将该方法与标准形态学鉴定方法以及基于线粒体细胞色素c氧化酶亚基I（COI）基因的DNA条形码技术进行比较。为了探索物种之间的遗传关系，构建了最大似然（ML）和邻接法（NJ）系统发育树。此外，还利用了三种不同的物种界定方法，即自动划分组装物种（ASAP）、广义混合尤尔合并法（GMYC）和单泊松树过程（PTP）来鉴定形态学定义的物种。通过最佳匹配（BM）和最佳近匹配（BCM）方法进一步检验了COI条形码在鉴定蚋类物种中的有效性。
在形态学上鉴定出了七种蚋类物种，即2018年的阴暗蚋（Simulium tenebrosum Takaoka, Srisuka & Saeung）（复合种）、2005年的多普蚋（S. doipuiense Takaoka & Choochote）（复合种）、1911年的黑腹蚋（S. nigrogilvum Summers）、1933年的结节蚋（S. nodosum Puri）、1995年的朝子蚋（S. asakoae Takaoka & Davies）、1984年的占隆蚋（S. chamlongi Takaoka & Suzuki）以及2017年的乌邦蚋（S. umphangense Takaoka, Srisuka & Saeung）。与标准方法相比，基于翅膀形状的GM分析在区分物种方面成功率较高，总体准确率为88.54%。另一方面，DNA条形码在物种鉴定方面超过了翅膀GM分析，正确鉴定率为98.57%。物种界定分析证实了大多数命名物种的有效性，但阴暗蚋复合种和多普蚋复合种被界定为单一物种是个例外。此外，分析揭示了朝子蚋内部隐藏的多样性，表明可能存在多达四个假定物种。
本研究强调了翅膀GM分析作为泰国嗜人蚋类物种鉴定的一种有前景且可靠的补充工具的潜力。
