Abstract
Coalescent-based inference of phylogenetic relationships among species takes into account gene tree incongruence due to incomplete lineage sorting, but for such methods to make sense species have to be correctly delimited. Because alternative assignments of individuals to species result in different parametric models, model selection methods can be applied to optimise model of species classification. In a Bayesian framework, Bayes factors (BF), based on marginal likelihood estimates, can be used to test a range of possible classifications for the group under study. Here, we explore BF and the Akaike Information Criterion (AIC) to discriminate between different species classifications in the flowering plant lineage Silene sect. Cryptoneurae (Caryophyllaceae). We estimated marginal likelihoods for different species classification models via the Path Sampling (PS), Stepping Stone sampling (SS), and Harmonic Mean Estimator (HME) methods implemented in BEAST. To select among alternative species classification models a posterior simulation-based analog of the AIC through Markov chain Monte Carlo analysis (AICM) was also performed. The results are compared to outcomes from the software BP&P. Our results agree with another recent study that marginal likelihood estimates from PS and SS methods are useful for comparing different species classifications, and strongly support the recognition of the newly described species S. ertekinii.
Highlights
Species is often regarded as a fundamental biological unit
The use of Bayes factors (BF) comparisons prevents users being dependent on a priori definitions of lineages, but the number of possible classifications [6] may restrict the number of models that can be tested in practice
Bayes Factors As in [20], we employed BF as they are used in formal model selection (e.g., [35]), to compare different classification models implemented in *BEAST
Summary
Species is often regarded as a fundamental biological unit. A major endeavor of the field of systematics is the discovery of biological diversity and its assignment to the species category [1,2,3]. Species recognition is especially challenging among closely related taxa with little differentiation due to recent divergence [5,6]. Species have been recognized primarily on morphological traits. As such traits may be under control of many different factors (e.g., genetic, epigenetic, environmental), the use of morphological data alone may underestimate the ‘‘real’’ number of species [7,8,9,10], and it is hard to devise an explicit, testable model based on such data alone
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