Login / Signup

Phylogenomic analysis of pseudocryptic diversity reveals the new genus Deltalsia (Rhodomelaceae, Rhodophyta).

Pilar Díaz-TapiaIván Rodríguez-BujánChristine A MaggsHeroen Verbruggen
Published in: Journal of phycology (2022)
Molecular analyses, in combination with morphological studies, provide invaluable tools for delineating red algal taxa. However, molecular datasets are incomplete and taxonomic revisions are often required once additional species or populations are sequenced. The small red alga Conferva parasitica was described from the British Isles in 1762 and then reported from other parts of Europe. Conferva parasitica was traditionally included in the genus Pterosiphonia (type species P. cloiophylla in Schmitz and Falkenberg 1897), based on its morphological characters, and later transferred to Symphyocladia and finally to Symphyocladiella using molecular data from an Iberian specimen. However, although morphological differences have been observed between specimens of Symphyocladiella parasitica from northern and southern Europe they have yet to be investigated in a phylogenetic context. In this study, we collected specimens from both regions, studied their morphology and analyzed rbcL and cox1 DNA sequences. We determined the phylogenetic position of a British specimen using a phylogenomic approach based on mitochondrial and plastid genomes. Northern and southern European populations attributed to S. parasitica represent different species. Symphyocladiella arecina sp. nov. is proposed for specimens from southern Europe, but British specimens were resolved as a distant sister lineage to the morphologically distinctive Amplisiphonia, so we propose the new genus Deltalsia for this species. Our study highlights the relevance of using materials collected close to the type localities for taxonomic reassessments, and showcases the utility of genome-based phylogenies for resolving classification issues in the red algae.
Keyphrases
  • genetic diversity
  • machine learning
  • gene expression
  • deep learning
  • electronic health record
  • artificial intelligence
  • data analysis
  • dna methylation
  • rna seq