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 Mordellistenaplatypoda , a new species of tumbling flower beetle from the island of Ischia in Italy (Coleoptera, Mordellidae).

Dávid SelnekovičKatarína GoffováJán ŠoltýsEva KováčováJán Kodada
Published in: ZooKeys (2023)
Mordellistena A. Costa, 1854, the most species-rich genus of tumbling flower beetles comprises more than 800 species worldwide and more than 150 reported from Europe. Here, a new species Mordellistena(s. str.)platypoda is described from the island of Ischia in Italy. The species hypothesis is based primarily on morphological characters which are visualised using scanning electron microscopy images, high-resolution photographs, and drawings. The species hypothesis is supported by analysis of a 658 bp fragment of cytochrome c oxidase subunit I (COI). Divergences in the COI gene are evaluated using maximum likelihood and Bayesian inference analyses. The species delimitation is assessed using Assemble Species by Automatic Partitioning (ASAP) and Poisson Tree Processes (PTP) methods. Genetic distances are visualised using multidimensional scaling. Mordellistenaplatypoda Selnekovič, Goffová & Kodada, sp. nov. is recovered as a well-separated species by both molecular and morphological analyses. Our results show that M.platypoda Selnekovič, Goffová & Kodada, sp. nov. is most closely related to M.tarsata Mulsant, 1856, although the two species differ significantly in vestiture colouration, presence of lateral ctenidia on the third metatarsomere, and presence of sexual dimorphism on the protibia. The results indicate that such morphological differences, which were traditionally used to distinguish between species groups, may in fact be present between closely related species. Interestingly, examination of the numerous museum material did not reveal additional specimens of the new species, and therefore M.platypoda Selnekovič, Goffová & Kodada, sp. nov. is currently known only from the Italian island of Ischia.
Keyphrases
  • high resolution
  • genetic diversity
  • electron microscopy
  • genome wide
  • deep learning
  • gene expression
  • transcription factor
  • ultrasound guided