Review of Microleon Butler, 1885 (Lepidoptera: Limacodidae) from Taiwan with description of a new species based on morphology and DNA barcoding.

The Taiwanese species of Microleon are reviewed with morphological and DNA data. Our review recognized two congeners, including a new species, M. taiwanensis n. sp. and one known species, M. longipalpis Butler, 1885. The findings from this study request a reconsideration of the previous records of Microleon from Taiwan. The minor differences in external appearance among the species of Microleon pose a challenge for reliable identification. Our study shows that examination of the male genitalia and COI barcoding helps distinguish the species. A checklist of the world species of Microleon with their distributional range is provided.

Genetic differentiation and species diversification of the Adoxophyes orana complex (Lepidoptera: Tortricidae) in East Asia.

The Adoxophyes tea tortrix (Lepidoptera: Tortricidae) is a group of leaf rollers that cause enormous economic losses on tea and apple crops. In East Asia, taxonomic ambiguity of the Adoxophyes orana complex (AOC), which consists of A. orana, A. dubia, A. honmai, and A. paraorana, has persisted for decades because of vague diagnostic characters. In this study, differences in the AOC were examined to improve species identification, determine genetic variations, and develop control strategies. Analyses revealed that A. orana comprised 2 lineages, a widely distributed Palearctic lineage and an East Asian lineage that was nested with other Asian species. Genetic divergence of >3% is proposed to confirm the AOC species that would benefit subsequent taxonomic revision. The monophyletic Taiwanese A. sp. with 2.8-4% from other AOC species appeared to suggest it as an independent taxon, and low interspecific divergence between A. honmai and A. dubia of 0.3% indicated possibility of recent divergence or intraspecific variations. Our result further suggested that the Z9-14:Ac ratio of semiochemicals could be a reference for the reblending of pheromone attractants in Taiwanese tea plantations. Moreover, the AOC species appeared to have a tendency of specific geographic distributions, with A. dubia and A. honmai in Japan and China, A. paraorana in Korea, and A. sp. in Taiwan. Maintaining the unique genetic composition of Adoxophyes species in each geographic region and preventing the possible invasions into those AOC-free countries through the transportation of host plants are essential in managing the AOC in East Asia.

Three new Chinese species of Palpifer Hampson from the collection of Franz Daniel (Lepidoptera: Hepialidae).

New Chinese Palpifer species are described from Yunnan and Fujian provinces. The male of Palpifer nielseni sp. n. is described from specimens housed at the Witt Museum Weiden and the Zoologisches Forschungsmuseum Alexander Koenig, while a male of P. chui sp. n. and a male and female of Palpifer climoi sp. n., are described from specimens in the latter collection only. Specimens were originally part of the Franz Daniel collection, collected in 1934-1935 from elevations of 2,300 and 3,000 m. The new species are diagnosed primarily by differences in the male genitalia. The female genitalia of P. climoi sp. n. represent the second published description for Palpifer. Four unique features of the forewing supporting monophyly of Palpifer are discussed.

Artificial intelligence reveals environmental constraints on colour diversity in insects.

Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.