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    Marginal Likelihood Estimate Comparisons to Obtain Optimal Species Delimitations in Silene sect. Cryptoneurae (Caryophyllaceae)
    (PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA, 2014) Aydın, Zeynep; Marcussen, Thomas; Ertekin, Alaattin Selçuk; Oxelman, Bengt
    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.
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    Species delimitation without prior knowledge: DISSECT reveals extensive cryptic speciation in the Silene aegyptiaca complex (Caryophyllaceae)
    (Academic Press Inc Elsevier Science, 2016) Toprak, Zeynep; Pfeil, Bernard E.; Jones, Graham; Marcussen, Thomas; Ertekin, Alaattin Selcuk; Oxelman, Bengt
    Species delimitation is a major focus of biosystematics. In recent years, considerable progress has been achieved with the development of the multispecies coalescent (MSC) model, where species constitute the branches of the species tree or network. However, researchers are faced with the limitation that the MSC method of choice often requires a priori assignment of individuals to species. This not only introduces subjectivitiy into the analyses, but may also lead to meaningless species tree hypotheses, if the allele-to-species assignments are inaccurate. DISSECT is a recently introduced method that does not require a priori allele-to-species assignments, but instead examines the posterior probabilities of groupings (clusterings) of individuals under study. Using the DISSECT approach, we analysed genetic data from 75 individual plants belonging to the Silene aegyptiaca species complex that has previously been divided into 3-5 species. Marginal likelihood estimates from *BEAST analyses, run with predefined species classifications, strongly favour those compatible with the DISSECT result over those from morphology- and geography-based taxonomy. We found at least nine species, including several cryptic ones, for which no clear geographical or morphological patterns are correlated. However, the limited data and the possibility of unmodelled processes mean there is still much uncertainty about the true number of MSC species, and for taxonomic purposes, other criteria might be relevant. Nevertheless, we argue that the approach signifies an important step towards objective and testable species delimitations in any organismal group. In particular, it makes it possible to avoid biologically irrelevant species classifications. (C) 2016 Elsevier Inc. All rights reserved.