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

Abstract

Summary1. 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.

The Dynamic Discipline of Species Delimitation: Progress Toward Effectively Recognizing Species Boundaries in Natural Populations

“…Furthermore, research suggests that the so-called single-threshold version of the GMYC method likely outperforms the multiple-threshold approach (Fujisawa and Barraclough 2013;Monaghan et al 2009). However, other studies suggest that the GMYC method may often provide higher estimates for the total number of OTUs than other molecular species delimitation methods (Hamilton et al 2014;Miralles and Vences 2013;Talavera et al 2013), warranting a cautious interpretation of results from GMYC analyses.…”

“…Regardless of these improvements, it may be difficult to accurately infer an adequate ultrametric tree for large datasets. Although it appears that the GMYC is generally stable across a wide range of circumstances, including various methods of phylogenetic reconstruction, the presence of a high number singletons, high numbers of sampled species, and gaps in intraspecific sampling, the accuracy of the GMYC is most significantly affected by the mean population size relative to divergence times between them (Fujisawa and Barraclough 2013;Talavera et al 2013). Furthermore, research suggests that the so-called single-threshold version of the GMYC method likely outperforms the multiple-threshold approach (Fujisawa and Barraclough 2013;Monaghan et al 2009).…”

Barcode-based species delimitation in the marine realm: a test using Hexanauplia (Multicrustacea: Thecostraca and Copepoda)

“…Phylogenetic reconstruction for the novel data set used the best-fit model of nucleotide substitution as determined using the Bayesian information criterion implemented in the jModelTest2 program (Guindon and Gascuel 2003;Darriba et al 2012). Talavera et al (2013) recently investigated the results from GMYC using variously constructed input trees and largely found the same resulting clustering assignments. We also used three different tree-building methods (ultrametricized trees constructed using Bayesian, neighbour-joining, and maximum-likelihood methods) for use by the coalescence-based MOTU delineation programs.…”

Extensive cryptic diversity in the cosmopolitan sludge worm Limnodrilus hoffmeisteri (Clitellata, Naididae)

“…Unique haplotypes are often used to reduce computational time (Reid and Carstens 2012;Talavera et al 2013;Lemer et al 2014), although removing identical sequences may overestimate population size when inferring gene trees (Drummond and Bouckaert 2015). On the other hand, if identical sequences are not removed, zero length branches increase the risk of over-splitting (Reid and Carstens 2012).…”