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Factors affecting species delimitations with the GMYC model: insights from a butterfly survey

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TLDR
The results demonstrate that this method is remarkably stable under a wide array of circumstances, including most phylogenetic reconstruction methods, high singleton presence (up to 95%), taxon richness (above five species) and the presence of gaps in intraspecific sampling coverage (removal of intermediate haplotypes).
Abstract
Summary 1. The generalized mixed Yule-coalescent (GMYC) model has become one of the most popular approaches for species delimitation based on single-locus data, and it is widely used in biodiversity assessments and phylogenetic community ecology. We here examine an array of factors affecting GMYC resolution (tree reconstruction method, taxon sampling coverage/taxon richness and geographic sampling intensity/geographic scale). 2. We test GMYC performance based on empirical data (DNA barcoding of the Romanian butterflies) on a solid taxonomic framework (i.e. all species are thought to be described and can be determined with independent sources of evidence). The data set is comprehensive (176 species), and intensely and homogeneously sampled (1303 samples representing the main populations of butterflies in this country). Taxonomy was assessed based on morphology, including linear and geometric morphometry when needed. 3. The number of GMYC entities obtained constantly exceeds the total number of morphospecies in the data set. We show that c. 80% of the species studied are recognized as entities by GMYC. Interestingly, we show that this percentage is practically the maximum that a single-threshold method can provide for this data set. Thus, the c. 20% of failures are attributable to intrinsic properties of the COI polymorphism: overlap in inter- and intraspecific divergences and non-monophyly of the species likely because of introgression or lack of independent lineage sorting. 4. Our results demonstrate that this method is remarkably stable under a wide array of circumstances, including most phylogenetic reconstruction methods, high singleton presence (up to 95%), taxon richness (above five species) and the presence of gaps in intraspecific sampling coverage (removal of intermediate haplotypes). Hence, the method is useful to designate an optimal divergence threshold in an objective manner and to pinpoint potential cryptic species that are worth being studied in detail. However, the existence of a substantial percentage of species wrongly delimited indicates that GMYC cannot be used as sufficient evidence for evaluating the specific status of particular cases without additional data. 5. Finally, we provide a set of guidelines to maximize efficiency in GMYC analyses and discuss the range of studies that can take advantage of the method.

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Citations
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Journal ArticleDOI

Comparison of Methods for Molecular Species Delimitation Across a Range of Speciation Scenarios

TL;DR: In the absence of gene flow, the main factor influencing the performance of these methods is the ratio of population size to divergence time, while number of loci and sample size per species have smaller effects, highlighting the importance of using an informed starting point for molecular species delimitation.
Journal ArticleDOI

DNA barcode-based delineation of putative species: efficient start for taxonomic workflows.

TL;DR: This study tested the congruence of OTUs resulting from the application of three analytical methods to sequence data for Australian hypertrophine moths, revealing 124 OTUs of Hypertrophinae, a more than twofold increase from the currently recognized 51 species.
Journal ArticleDOI

Guidelines for DNA taxonomy, with a focus on the meiofauna

TL;DR: The recent advances in the acquisition of DNA sequence data and the analytical tools for DNA-based species delimitation are reviewed, with a focus on applications to the meiofauna.
Journal ArticleDOI

Effects of phylogenetic reconstruction method on the robustness of species delimitation using single-locus data

TL;DR: This work assesses how robust the generalised mixed Yule coalescent and Poisson tree process methods are to different phylogenetic reconstruction and branch smoothing methods and recommends simultaneous use of the PTP model with any model‐based gene tree and GMYC approaches with BEAST trees for obtaining species hypotheses.
References
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Journal ArticleDOI

MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

TL;DR: The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models, inferring ancestral states and sequences, and estimating evolutionary rates site-by-site.
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RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

TL;DR: UNLABELLED RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML) that has been used to compute ML trees on two of the largest alignments to date.
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BEAST: Bayesian evolutionary analysis by sampling trees

TL;DR: BEAST is a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree that provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions.
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APE: Analyses of Phylogenetics and Evolution in R language

TL;DR: UNLABELLED Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics that provides both utility functions for reading and writing data and manipulating phylogenetic trees.
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Biological identifications through DNA barcodes

TL;DR: It is established that the mitochondrial gene cytochrome c oxidase I (COI) can serve as the core of a global bioidentification system for animals and will provide a reliable, cost–effective and accessible solution to the current problem of species identification.
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