The Experts below are selected from a list of 63813 Experts worldwide ranked by ideXlab platform
Chong Chu - One of the best experts on this subject based on the ideXlab platform.
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clades a classification based machine learning method for species Delimitation from population genetic data
Molecular Ecology Resources, 2018Co-Authors: Jingwen Pei, Chong ChuAbstract:Species are considered to be the basic unit of ecological and evolutionary studies. As multilocus genomic data are increasingly available, there have been considerable interests in the use of DNA sequence data to delimit species. In this study, we show that machine learning can be used for species Delimitation. Our method treats the species Delimitation problem as a classification problem for identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purposes. Each pair of known populations is combined to form training samples with a label of "same species" or "different species". We use support vector machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: "same species" or "different species". Given multilocus genomic data of multiple related organisms or populations, our method (called CLADES) performs species Delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species Delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in Delimitation, for example with short species divergence time and gene flow.
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clades a classification based machine learning method for species Delimitation from population genetic data
bioRxiv, 2018Co-Authors: Jingwen Pei, Chong ChuAbstract:Species are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are becoming increasingly available, there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species Delimitation. There exists no species Delimitation methods that are based on machine learning. Our method treats the species Delimitation problem as a classification problem. It is a problem of identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purpose. Each pair of known populations are combined to form training samples with a label of "same species" or "different species". We use Support Vector Machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: "same species" or "different species". Given multi-locus genomic data of multiple related organisms or populations, our method (called CLADES) performs species Delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species Delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in Delimitation, e.g. with short species divergence time and gene flow.
Matthew K Fujita - One of the best experts on this subject based on the ideXlab platform.
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species Delimitation using genome wide snp data
Systematic Biology, 2014Co-Authors: Adam D Leache, Matthew K Fujita, Vladimir N Minin, Remco R BouckaertAbstract:The multispecies coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species Delimitation models. However, Bayesian species Delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species Delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species Delimitation models using Bayes factors. We test this approach, which we call Bayes factor Delimitation (*with genomic data; BFD*), using common species Delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size θ has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data. This new Bayesian method for species Delimitation builds on a growing trend for objective species Delimitation methods with explicit model assumptions that are easily tested. [Bayes factor; model testing; phylogeography; RADseq; simulation; speciation.].
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species Delimitation using genome wide snp data
bioRxiv, 2014Co-Authors: Adam D Leache, Matthew K Fujita, Vladimir N Minin, Remco R BouckaertAbstract:The multi-species coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species Delimitation models. However, Bayesian species Delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species Delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species Delimitation models using Bayes factors. We test this approach, which we call Bayes factor Delimitation (*with genomic data; BFD*), using common species Delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size θ has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data data. This new Bayesian method for species Delimitation builds on a growing trend for objective species Delimitation methods with explicit model assumptions that are easily tested.
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coalescent based species Delimitation in an integrative taxonomy
Trends in Ecology and Evolution, 2012Co-Authors: Matthew K Fujita, Adam D Leache, Frank T Burbrink, Jimmy A Mcguire, Craig MoritzAbstract:The statistical rigor of species Delimitation has increased dramatically over the past decade. Coalescent theory provides powerful models for population genetic inference, and is now increasingly important in phylogenetics and speciation research. By applying probabilistic models, coalescent-based species Delimitation provides clear and objective testing of alternative hypotheses of evolutionary independence. As acquisition of multilocus data becomes increasingly automated, coalescent-based species Delimitation will improve the discovery, resolution, consistency, and stability of the taxonomy of species. Along with other tools and data types, coalescent-based species Delimitation will play an important role in an integrative taxonomy that emphasizes the identification of species limits and the processes that have promoted lineage diversification.
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bayesian species Delimitation in west african forest geckos hemidactylus fasciatus
Proceedings of The Royal Society B: Biological Sciences, 2010Co-Authors: Adam D Leache, Matthew K FujitaAbstract:Genealogical data are an important source of evidence for delimiting species, yet few statistical methods are available for calculating the probabilities associated with different species Delimitations. Bayesian species Delimitation uses reversible-jump Markov chain Monte Carlo (rjMCMC) in conjunction with a user-specified guide tree to estimate the posterior distribution for species Delimitation models containing different numbers of species. We apply Bayesian species Delimitation to investigate the speciation history of forest geckos (Hemidactylus fasciatus) from tropical West Africa using five nuclear loci (and mtDNA) for 51 specimens representing 10 populations. We find that species diversity in H. fasciatus is currently underestimated, and describe three new species to reflect the most conservative estimate for the number of species in this complex. We examine the impact of the guide tree, and the prior distributions on ancestral population sizes (u) and root age (t0), on the posterior probabilities for species Delimitation. Mis-specification of the guide tree or the prior distribution for u can result in strong support for models containing more species. We describe a new statistic for summarizing the posterior distribution of species Delimitation models, called speciation probabilities, which summarize the posterior support for each speciation event on the starting guide tree.
Xingyu Yan - One of the best experts on this subject based on the ideXlab platform.
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the jurisdictional Delimitation in the chinese anti monopoly law public enforcement regime the inevitable overstepping of authority and the implications
Journal of Antitrust Enforcement, 2017Co-Authors: Xingyu YanAbstract:Following the adoption of the Anti-Monopoly Law (AML) in 2007, China established a public enforcement regime that has three equal-ranking authorities. The legislative history of the AML suggests that this was a backward-looking compromise reached between the three central administrative agencies (the Ministry of Commerce (MOFCOM), the National Development and Reform Commission (NDRC), and the State Administration for Industry and Commerce (SAIC)), instead of a forward-looking design choice. This article focuses on the jurisdictional Delimitation between the NDRC and the SAIC, a Delimitation assigning the enforcement responsibilities based on whether an allegedly anticompetitive conduct is price related or not. This article first describes the jurisdictional Delimitation as defined in the relevant legal documents. On that basis, it examines the legal and the economic rationales behind this Delimitation. Subsequently, this article investigates to what extent this Delimitation has been adhered to in practice, and there it identifies three problematic scenarios, which indicate the inevitability of the two agencies overstepping their respective authority in practice. This article finds that this Delimitation is likely to induce the following problems: uncertainty on supplementary enforcement and follow-on civil actions, uncontrolled agency discretion, and distortive theories of harm. Therefore, it suggests that this jurisdictional Delimitation should be removed.
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the jurisdictional Delimitation in the chinese anti monopoly law public enforcement regime the inevitable overstepping of authority and the implications
Social Science Research Network, 2017Co-Authors: Xingyu YanAbstract:Following the adoption of the Anti-Monopoly Law (AML) in 2007, China established a public enforcement regime that has three equal-ranking authorities. The legislative history of the AML suggests that this was a backward-looking compromise reached between the three central administrative agencies (the MOFCOM, the NDRC, and the SAIC), instead of a forward-looking design choice. This article focuses on the jurisdictional Delimitation between the NDRC and the SAIC, a Delimitation assigning the enforcement responsibilities based on whether allegedly anti-competitive conduct is price-related or not. This article first describes the jurisdictional Delimitation as defined in the relevant legal documents. On that basis, it examines the legal and the economic rationales behind this Delimitation. Subsequently, this article investigates to what extent this Delimitation has been adhered to in practice, and there it identifies three problematic scenarios, which indicate the inevitability of the two agencies overstepping their respective authority in practice. This article finds that this Delimitation is likely to induce the following problems: uncertainty on supplementary enforcement and follow-on civil actions, uncontrolled agency discretion, and distortive theories of harm. Therefore, it suggests that this jurisdictional Delimitation should be removed.
Carla M Penz - One of the best experts on this subject based on the ideXlab platform.
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species limits in butterflies lepidoptera nymphalidae reconciling classical taxonomy with the multispecies coalescent
Systematic Entomology, 2019Co-Authors: Pavel Matosmaravi, Niklas Wahlberg, Alexandre Antonelli, Carla M PenzAbstract:Species Delimitation is at the core of biological sciences. During the last decade, molecular-based approaches have advanced the field by providing additional sources of evidence to classical, morphology-based taxonomy. However, taxonomy has not yet fully embraced molecular species Delimitation beyond threshold-based, single-gene approaches, and taxonomic knowledge is not commonly integrated into multilocus species Delimitation models. Here we aim to bridge empirical data (taxonomic and genetic) with recently developed coalescent-based species Delimitation approaches. We use the multispecies coalescent model as implemented in two Bayesian methods (dissect/stacey and bp&p) to infer species hypotheses. In both cases, we account for phylogenetic uncertainty (by not using any guide tree) and taxonomic uncertainty (by measuring the impact of using a priori taxonomic assignments to specimens). We focus on an entire Neotropical tribe of butterflies, the Haeterini (Nymphalidae: Satyrinae). We contrast divergent taxonomic opinion – splitting, lumping and misclassifying species – in the light of different phenotypic classifications proposed to date. Our results provide a solid background for the recognition of 22 species. The synergistic approach presented here overcomes limitations in both traditional taxonomy (e.g. by recognizing cryptic species) and molecular-based methods (e.g. by recognizing structured populations, and not raising them to species). Our framework provides a step forward towards standardization and increasing reproducibility of species Delimitations. (Less)
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species limits in butterflies lepidoptera nymphalidae reconciling classical taxonomy with the multispecies coalescent
bioRxiv, 2018Co-Authors: Pavel Matosmaravi, Niklas Wahlberg, Alexandre Antonelli, Carla M PenzAbstract:Species Delimitation is at the core of biological sciences. During the last decade, molecular-based approaches have advanced the field by providing additional sources of evidence to classical, morphology-based taxonomy. However, taxonomy has not yet fully embraced molecular species Delimitation beyond threshold-based, single-gene approaches, and taxonomic knowledge is not commonly integrated to multi-locus species Delimitation models. Here we aim to bridge empirical data (taxonomic and genetic) with the latest coalescent-based species Delimitation approaches. We use the multispecies coalescent model as implemented in two recently developed Bayesian methods (DISSECT/STACEY and BP&P) to infer species hypotheses. In both cases, we account for phylogenetic uncertainty (by not using any guide tree) and taxonomic uncertainty (by measuring the impact of using or not a priori taxonomic assignment to specimens). We focus on an entire Neotropical tribe of butterflies, the Haeterini (Nymphalidae: Satyrinae). We contrast divergent taxonomic opinion-splitting, lumping and misclassifying species-in the light of different phenotypic classifications proposed to date. Our results provide a solid background for the recognition of 22 species. The synergistic approach presented here overcomes limitations in both traditional taxonomy (e.g., by recognizing cryptic species) and molecular-based methods (e.g., by recognizing structured populations, and not raise them to species). Our framework provides a step forward towards standardization and increasing reproducibility of species Delimitations.
Adam D Leache - One of the best experts on this subject based on the ideXlab platform.
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species Delimitation using genome wide snp data
Systematic Biology, 2014Co-Authors: Adam D Leache, Matthew K Fujita, Vladimir N Minin, Remco R BouckaertAbstract:The multispecies coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species Delimitation models. However, Bayesian species Delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species Delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species Delimitation models using Bayes factors. We test this approach, which we call Bayes factor Delimitation (*with genomic data; BFD*), using common species Delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size θ has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data. This new Bayesian method for species Delimitation builds on a growing trend for objective species Delimitation methods with explicit model assumptions that are easily tested. [Bayes factor; model testing; phylogeography; RADseq; simulation; speciation.].
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species Delimitation using genome wide snp data
bioRxiv, 2014Co-Authors: Adam D Leache, Matthew K Fujita, Vladimir N Minin, Remco R BouckaertAbstract:The multi-species coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species Delimitation models. However, Bayesian species Delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species Delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species Delimitation models using Bayes factors. We test this approach, which we call Bayes factor Delimitation (*with genomic data; BFD*), using common species Delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size θ has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data data. This new Bayesian method for species Delimitation builds on a growing trend for objective species Delimitation methods with explicit model assumptions that are easily tested.
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coalescent based species Delimitation in an integrative taxonomy
Trends in Ecology and Evolution, 2012Co-Authors: Matthew K Fujita, Adam D Leache, Frank T Burbrink, Jimmy A Mcguire, Craig MoritzAbstract:The statistical rigor of species Delimitation has increased dramatically over the past decade. Coalescent theory provides powerful models for population genetic inference, and is now increasingly important in phylogenetics and speciation research. By applying probabilistic models, coalescent-based species Delimitation provides clear and objective testing of alternative hypotheses of evolutionary independence. As acquisition of multilocus data becomes increasingly automated, coalescent-based species Delimitation will improve the discovery, resolution, consistency, and stability of the taxonomy of species. Along with other tools and data types, coalescent-based species Delimitation will play an important role in an integrative taxonomy that emphasizes the identification of species limits and the processes that have promoted lineage diversification.
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bayesian species Delimitation in west african forest geckos hemidactylus fasciatus
Proceedings of The Royal Society B: Biological Sciences, 2010Co-Authors: Adam D Leache, Matthew K FujitaAbstract:Genealogical data are an important source of evidence for delimiting species, yet few statistical methods are available for calculating the probabilities associated with different species Delimitations. Bayesian species Delimitation uses reversible-jump Markov chain Monte Carlo (rjMCMC) in conjunction with a user-specified guide tree to estimate the posterior distribution for species Delimitation models containing different numbers of species. We apply Bayesian species Delimitation to investigate the speciation history of forest geckos (Hemidactylus fasciatus) from tropical West Africa using five nuclear loci (and mtDNA) for 51 specimens representing 10 populations. We find that species diversity in H. fasciatus is currently underestimated, and describe three new species to reflect the most conservative estimate for the number of species in this complex. We examine the impact of the guide tree, and the prior distributions on ancestral population sizes (u) and root age (t0), on the posterior probabilities for species Delimitation. Mis-specification of the guide tree or the prior distribution for u can result in strong support for models containing more species. We describe a new statistic for summarizing the posterior distribution of species Delimitation models, called speciation probabilities, which summarize the posterior support for each speciation event on the starting guide tree.