Affiliation:
1. Institute of Oceanography Federal University of Rio Grande – FURG Rio Grande Brazil
2. Department of Molecular Biosciences, UTEX Culture Collection of Algae University of Texas at Austin Austin Texas USA
Abstract
AbstractSeveral automated molecular methods have emerged for distinguishing eukaryote species based on DNA sequence data. However, there are knowledge gaps around which of these single‐locus methods is more accurate for the identification of microalgal species, such as the highly diverse and ecologically relevant diatoms. We applied genetic divergence, Automatic Barcode Gap Discovery for primary species delimitation (ABGD), Assemble Species by Automatic Partitioning (ASAP), Statistical Parsimony Network Analysis (SPNA), Generalized Mixed Yule Coalescent (GMYC) and Poisson Tree Processes (PTP) using partialcox1,rbcL,5.8S + ITS2,ITS1 + 5.8S + ITS2 markers to delineate species and compare to published polyphasic identification data (morphological features, phylogeny and sexual reproductive isolation) to test the resolution of these methods. ASAP, ABGD, SPNA and PTP models resolved species ofEunotia,Seminavis, Nitzschia, SellaphoraandPseudo‐nitzschiacorresponding to previous polyphasic identification, including reproductive isolation studies. In most cases, these models identified diatom species in similar ways, regardless of sequence fragment length. GMYC model presented smallest number of results that agreed with previous published identification. Following the recommendations for proper use of each model presented in the present study, these models can be useful tools to identify cryptic or closely related species of diatoms, even when the datasets have relatively few sequences.
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior