Testing alternative phylogenetic hypotheses for the tent tortoise species complex (Reptilia, Testudinidae) using multiple data types and methods

AbstractWe examined genetic differentiation in the highly polymorphic and taxonomically confusing tent tortoise (Psammobates tentorius) species complex in southern Africa, using three types of molecular markers (nDNA, mtDNA and microsatellite DNA) and morphological data. The Approximate Bayesian Computation based simulation analyses advocated an alternative phylogenetic hypothesis for the tent tortoise species complex, which was better and more inclusive in explaining its genealogical history. Based on the evidence derived from the sequence, microsatellite and morphology data, a four species scheme (among the seven mtDNA clades) appears to be the best taxonomic solution for the systematic puzzle of the P. tentorius species complex, namely, “C1+C4+C5+C7”, “C3”, “C2” and “C6”. The microsatellite datasets yielded similar genetic structure and gene flow patterns among the seven mtDNA clades in comparison to the sequence DNA. Evidence was found of possible hybridization between C1 and C2 in their intergradation zone, but not between C2 and C4. Results of the inbreeding analyses provided strong evidence of inbreeding in the eastern population of C1 and southern population of C2, which may be indicative of a bottleneck effect.

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Phylogeny of beech in western Eurasia as inferred by approximate Bayesian computation

Acta Societatis Botanicorum Poloniae ◽

10.5586/asbp.3582 ◽

2018 ◽

Vol 87 (2) ◽

Cited By ~ 4

Author(s):

Dušan Gömöry ◽

Ladislav Paule ◽

Vladimír Mačejovský

Keyword(s):

Species Complex ◽

Approximate Bayesian Computation ◽

Demographic History ◽

Sensu Stricto ◽

Early Pleistocene ◽

Hybrid Origin ◽

Bayesian Computation ◽

Western Asia ◽

Approximate Bayesian ◽

Western Eurasia

The Fagus sylvatica L. species complex in Europe and Western Asia comprises two commonly recognized subspecies, F. sylvatica subsp. sylvatica [= F. sylvatica sensu stricto (s. str.)] and F. sylvatica subsp. orientalis (= F. orientalis), and two putatively hybridogenous or intermediate taxa, “F. moesiaca” and “F. taurica”. The present study aimed to examine the demographic history of this species complex using 12 allelic loci of nine allozymes scored in 279 beech populations in western Eurasia. Three sets of phylogenetic scenarios were tested by approximate Bayesian computation: one dealing with the divergence of subspecies and/or regional populations within the whole taxonomical complex, and two others focusing on the potential hybrid origin of “F. moesiaca” and “F. taurica”. The best-supported scenario within the first set placed the time of divergence of regional populations of F. orientalis in the Early Pleistocene (1.18–1.87 My BP). According to this scenario, the Iranian population was the ancestral lineage, whereas F. sylvatica s. str. was the lineage that diverged most recently. “Fagus taurica” was found to have originated from hybridization between the Caucasian population of F. orientalis and F. sylvatica s. str. at 144 ky BP. In contrast, there was no evidence of a hybrid origin of “F. moesiaca”. The best-supported scenario suggested that the Balkan lineage is a part of F. sylvatica s. str., which diverged early from F. orientalis in Asia Minor (817 ky BP), while both the Italian and Central-European lineages diverged from the Balkan one later, at the beginning of the last (Weichselian) glacial period.

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Phylogeny and divergence times of suckers (Cypriniformes: Catostomidae) inferred from Bayesian total-evidence analyses of molecules, morphology, and fossils

PeerJ ◽

10.7717/peerj.5168 ◽

2018 ◽

Vol 6 ◽

pp. e5168 ◽

Cited By ~ 3

Author(s):

Justin C. Bagley ◽

Richard L. Mayden ◽

Phillip M. Harris

Keyword(s):

Dna Sequences ◽

Phylogenetic Relationships ◽

Fossil Record ◽

Total Evidence ◽

Morphological Data ◽

Divergence Times ◽

Data Types ◽

Multiple Data ◽

Phylogenetic Informativeness ◽

Divergence Dating

Catostomidae (“suckers”) is a diverse (76 species) and broadly distributed family of Holarctic freshwater fishes with a rich fossil record and a considerable number (∼35%) of threatened and imperiled species. We integrate DNA sequences (three mitochondrial genes, three nuclear genes), morphological data, and fossil information to infer sucker phylogenetic relationships and divergence times using Bayesian “total-evidence” methods, and then test hypotheses about the temporal diversification of the group. Our analyses resolved many nodes within subfamilies and clarified Catostominae relationships to be of the form ((Thoburniini, Moxostomatini), (Erimyzonini, Catostomini)). Patterns of subfamily relationships were incongruent, but mainly supported two placements of the Myxocyprininae; distinguishing these using Bayes factors lent strongest support to a model with Myxocyprininae sister to all remaining sucker lineages. We improved our Bayesian total-evidence dating analysis by excluding problematic characters, using a clock-partitioning scheme identified by Bayesian model selection, and employing a fossilized birth-death tree prior accommodating morphological data and fossils. The resulting chronogram showed that suckers evolved since the Late Cretaceous–Eocene, and that the Catostomini and Moxostomatini clades have accumulated species diversity since the early to mid-Miocene. These results agree with the fossil record and confirm previous hypotheses about dates for the origins of Catostomide and catostomine diversification, but reject previous molecular hypotheses about the timing of divergence of ictiobines, and between Asian–North American lineages. Overall, our findings from a synthesis of multiple data types enhance understanding of the phylogenetic relationships, taxonomic classification, and temporal diversification of suckers, while also highlighting practical methods for improving Bayesian divergence dating models by coupling phylogenetic informativeness profiling with relaxed-clock partitioning.

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Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis

Journal of Computational Physics ◽

10.1016/j.jcp.2020.109999 ◽

2020 ◽

pp. 109999

Author(s):

Agnieszka Borowska ◽

Diana Giurghita ◽

Dirk Husmeier

Keyword(s):

Differential Equation ◽

Stochastic Differential Equation ◽

Gaussian Process ◽

Approximate Bayesian Computation ◽

Equation System ◽

Bayesian Computation ◽

Differential Equation System ◽

Parameter Inference ◽

Approximate Bayesian

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Weighted approximate Bayesian computation via Sanov’s theorem

Computational Statistics ◽

10.1007/s00180-021-01093-4 ◽

2021 ◽

Author(s):

Cecilia Viscardi ◽

Michele Boreale ◽

Fabio Corradi

Keyword(s):

Large Deviations ◽

Posterior Distribution ◽

Approximate Bayesian Computation ◽

Bayesian Computation ◽

Information Theoretic ◽

Discrete Random Variables ◽

Positive Weights ◽

Approximate Bayesian ◽

Information Theoretic Method ◽

Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

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Inference of Brain Networks with Approximate Bayesian Computation – assessing face validity with an example application in Parkinsonism

NeuroImage ◽

10.1016/j.neuroimage.2021.118020 ◽

2021 ◽

pp. 118020

Author(s):

Timothy O. West ◽

Luc Berthouze ◽

Simon F. Farmer ◽

Hayriye Cagnan ◽

Vladimir Litvak

Keyword(s):

Approximate Bayesian Computation ◽

Brain Networks ◽

Face Validity ◽

Bayesian Computation ◽

Approximate Bayesian

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Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

Experiments in Fluids ◽

10.1007/s00348-020-03122-2 ◽

2021 ◽

Vol 62 (2) ◽

Author(s):

Jason D. Christopher ◽

Olga A. Doronina ◽

Dan Petrykowski ◽

Torrey R. S. Hayden ◽

Caelan Lapointe ◽

...

Keyword(s):

Parameter Estimation ◽

Absorption Spectroscopy ◽

Approximate Bayesian Computation ◽

Flow Parameter ◽

Bayesian Computation ◽

Laser Absorption Spectroscopy ◽

Laser Absorption ◽

Approximate Bayesian

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Approximate Bayesian Computation for Discrete Spaces

Entropy ◽

10.3390/e23030312 ◽

2021 ◽

Vol 23 (3) ◽

pp. 312

Author(s):

Ilze A. Auzina ◽

Jakub M. Tomczak

Keyword(s):

Approximate Bayesian Computation ◽

Likelihood Function ◽

Real Life ◽

Random Variables ◽

Bayesian Computation ◽

Inference Problem ◽

Markov Kernel ◽

Inference Problems ◽

Binary Neural Network ◽

Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

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