Species-Level Para- and Polyphyly in DNA Barcode Gene Trees: Strong Operational Bias in European Lepidoptera

Abstract

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.

Ecological determinants of butterfly vulnerability across the European continent

“…To obtain an estimate of phylogeny, we re-used the results of a recent survey of gene genealogies of the cytochrome oxidase I (COI) barcode marker in European Lepidoptera (Mutanen et al 2016). Because the authors relied on computationally intensive methods of phylogenetic inference and incorporated numerous haplotypes per species, scalability constraints required their analyses to be partitioned into a number of monophyletic higher taxa.…”

“…However, in our comparative analysis, we require a single, composite estimate. We synthesized this following a two-step (2) occasional colonization events over <10 km, (3) urban areas and gardens, (4) occasional colonization events over >10 km, (5) rapid range expansions over >100 km in 10 years, (6) short-distance overseas dispersal-at sea records-island populations, (7) incidental long-distance (mass) movements, (8) regular reversed long distance migrations Voltinism Ordinal (1) 0.5, (2) 1, (3) 1-2, (4) 2, (5) 2-3, (6) 3-4 (generations per year) Overwintering Ordinal (1) Egg, (2) first instar larva, (3) half-grown, (4) last instar, (5) pupa, (6) adult, (7) Antonelli et al (2016), by first inferring a backbone tree and then grafting the trees for the monophyletic higher taxon partitions from Mutanen et al (2016) onto it. To obtain input data for backbone tree inference, we subsampled exemplar taxa from each of the higher taxon partitions and aligned their COI sequences using MUSCLE (Edgar 2004), under default settings.…”

“…This starting tree we then provided as input for RAxML (Stamatakis 2014), which further improved the topology (under the same GTR + γ substitution model). On the thus constructed backbone topology we then grafted the gene tree estimates from Mutanen et al (2016). Since these gene trees include multiple haplotypes per species we first collapsed these to species level, under a conservative approach where, in every case where haplotypes from different species were entangled (i.e.…”

Faunistic survey of the zooplankton community in an oligotrophic sinkhole, Cenote Azul (Quintana Roo, Mexico), using different sampling methods, and documented with DNA barcodes

“…We selected this method because it allows the rapid analysis of large datasets and since it has shown to perform well for species delimitation (Mutanen et al, 2016). The simplified tree was prepared using Mega 7.0 (Kumar et al, 2016).…”

From writing to reading the encyclopedia of life

“…In the many taxa where geographical variation in barcode sequences is small [64], a few records per species are sufficient to create an effective identification system. However, the analysis of more specimens is advantageous because it often reveals discordances that indicate misidentifications or cryptic taxa [65], and it also provides insights into the extent of geographical variation in barcode sequences [66,67]. There are two animal phyla in which COI often fails to deliver species-level resolution, sponges [68,69] and some benthic cnidarians [70], apparently because of their slowed rates of mitochondrial evolution.…”