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2 result(s) for "University of Oulu [Finland] = Oulun yliopisto [Suomi] = Université d"
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Species-Level Para- and Polyphyly in DNA Barcode Gene Trees: Strong Operational Bias in European Lepidoptera
The proliferation of DNA data is revolutionizing all fields of systematic research. DNA barcode sequences, now available for millions of specimens and several hundred thousand species, are increasingly used in algorithmic species delimitations. This is complicated by occasional incongruences between species and gene genealogies, as indicated by situations where conspecific individuals do not form a monophyletic cluster in a gene tree. In two previous reviews, non-monophyly has been reported as being common in mitochondrial DNA gene trees. We developed a novel web service “Monophylizer” to detect non-monophyly in phylogenetic trees and used it to ascertain the incidence of species non-monophyly in COI (a.k.a. cox1) barcode sequence data from 4977 species and 41,583 specimens of European Lepidoptera, the largest data set of DNA barcodes analyzed from this regard. Particular attention was paid to accurate species identification to ensure data integrity. We investigated the effects of tree-building method, sampling effort, and other methodological issues, all of which can influence estimates of non-monophyly. We found a 12% incidence of non-monophyly, a value significantly lower than that observed in previous studies. Neighbor joining (NJ) and maximum likelihood (ML) methods yielded almost equal numbers of non-monophyletic species, but 24.1% of these cases of non-monophyly were only found by one of these methods. Non-monophyletic species tend to show either low genetic distances to their nearest neighbors or exceptionally high levels of intraspecific variability. Cases of polyphyly in COI trees arising as a result of deep intraspecific divergence are negligible, as the detected cases reflected misidentifications or methodological errors. Taking into consideration variation in sampling effort, we estimate that the true incidence of non-monophyly is ∼23%, but with operational factors still being included. Within the operational factors, we separately assessed the frequency of taxonomic limitations (presence of overlooked cryptic and oversplit species) and identification uncertainties. We observed that operational factors are potentially present in more than half (58.6%) of the detected cases of non-monophyly. Furthermore, we observed that in about 20% of non-monophyletic species and entangled species, the lineages involved are either allopatric or parapatric—conditions where species delimitation is inherently subjective and particularly dependent on the species concept that has been adopted. These observations suggest that species-level non-monophyly in COI gene trees is less common than previously supposed, with many cases reflecting misidentifications, the subjectivity of species delimitation or other operational factors.
Towards decoding the conifer giga-genome
Several new initiatives have been launched recently to sequence conifer genomes including pines, spruces and Douglas-fir. Owing to the very large genome sizes ranging from 18 to 35 gigabases, sequencing even a single conifer genome had been considered unattainable until the recent throughput increases and cost reductions afforded by next generation sequencers. The purpose of this review is to describe the context for these new initiatives. A knowledge foundation has been acquired in several conifers of commercial and ecological interest through large-scale cDNA analyses, construction of genetic maps and gene mapping studies aiming to link phenotype and genotype. Exploratory sequencing in pines and spruces have pointed out some of the unique properties of these giga-genomes and suggested strategies that may be needed to extract value from their sequencing. The hope is that recent and pending developments in sequencing technology will contribute to rapidly filling the knowledge vacuum surrounding their structure, contents and evolution. Researchers are also making plans to use comparative analyses that will help to turn the data into a valuable resource for enhancing and protecting the world’s conifer forests.