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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/186241
An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods
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Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtained from double digestion restriction site-associated DNA sequencing (ddRADseq) for species in the family Antarctophilinidae. We also reevaluated the fossil record associated with this taxon to provide further insights into the origin of the group. Novel approaches to identify distinctive genetic lineages, including unsupervised machine learning variational autoencoder plots, were used to establish species hypothesis frameworks. In this sense, three undescribed species and a complex of cryptic species were identifed, suggesting allopatric speciation connected to geographic or bathymetric isolation. We further observed that the shallow waters around the Scotia Arc and on the continental shelf in the Weddell Sea present high endemism and diversity. In contrast, likely due to the glacial pressure during the Cenozoic, a deep-sea group with fewer species emerged expanding over great areas in the South-Atlantic Antarctic Ridge. Our study agrees on how diachronic paleoclimatic and current environmental factors shaped Antarctic communities both at the shallow and deep-sea levels, promoting Antarctica as the center of origin for numerous taxa such as gastropod mollusks.
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MOLES, Juan, et al. An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods. Scientific Reports. 2021. Vol. 11, num. 8473. ISSN 2045-2322. [consulted: 10 of June of 2026]. Available at: https://hdl.handle.net/2445/186241