Simulium (Simulium) thimphuense sp. nov. is described from a pupa and a mature larva in Bhutan. This new species is placed in the S. multistriatum species-group, and is characterized by the pupal gill with eight thread-like filaments divergent basally at an acute angle when viewed laterally and the cocoon slipper-shaped with several small openings anterolaterally. Four species of Simulium (Simulium) are newly recorded from Bhutan: S. barraudi Puri in the S. multistriatum species-group, S. nodosum Puri in the S. nobile species-group, S. chiangmaiense Takaoka & Suzuki in the S. striatum species-group and S. himalayense Puri in the S. variegatum species-group. Our study increases the number of black fly species known from Bhutan from 18 to 23.

Some species of the Anopheles dirus species complex are considered to be highly competent malaria vectors in Southeast Asia. Anopheles dirus is the primary vector of Plasmodium falciparum and P. vivax while An. cracens is the main vector of P. knowlesi. However, these two species are difficult to distinguish and identify based on morphological characters. Hence, the aim of this study was to investigate the potential use of antennal sensilla to distinguish them. Large sensilla coeloconica borne on the antennae of adult females were counted under a compound light microscope and the different types of antennal sensilla were examined in a scanning electron microscope. The antennae of both species bear five types of sensilla: ampullacea, basiconica, chaetica, coeloconica and trichodea. Observations revealed that the mean numbers of large sensilla coeloconica on antennal flagellomeres 2, 3, 7, 10 and 12 on both antennae of both species were significantly different. This study is the first to describe the types of antennal sensilla and to discover the usefulness of the large coeloconic sensilla for distinguishing the two species. The discovery provides a simple, reliable and inexpensive method for distinguishing them.

Blow flies, flesh flies, and house flies can provide excellent evidence for forensic entomologists and are also essential to the fields of public health, medicine, and animal health. In all questions, the correct identification of fly species is an important initial step. The usual methods based on morphology or even molecular approaches can reach their limits here, especially when dealing with larger numbers of specimens. Since machine learning already plays a major role in many areas of daily life, such as education, business, industry, science, and medicine, applications for the classification of insects have been reported. Here, we applied the decision tree method with wing morphometric data to construct a model for discriminating flies of three families [Calliphoridae, Sarcophagidae, Muscidae] and seven species [Chrysomya megacephala (Fabricius), Chrysomya rufifacies (Macquart), Chrysomya (Ceylonomyia) nigripes Aubertin, Lucilia cuprina (Wiedemann), Hemipyrellia ligurriens (Wiedemann), Musca domestica Linneaus, and Parasarcophaga (Liosarcophaga) dux Thomson]. One hundred percent overall accuracy was obtained at a family level, followed by 83.33% at a species level. The results of this study suggest that non-experts might utilize this identification tool. However, more species and also samples per specimens should be studied to create a model that can be applied to the different fly species in Thailand.