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Model selection in ecology and evolution.

by Jerald B Johnson , Kristian S Omland , jerry.johnson@noaa Jerald B Johnson , ) Gov - Trends in Ecology and Evolution , 2004
"... Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypo ..."
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Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and markrecapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible. Science is a process for learning about nature in which competing ideas about how the world works are evaluated against observations Two basic approaches have been used to draw biological inferences. The dominant paradigm is to generate a null hypothesis (typically one with little biological meaning How model selection works Generating biological hypotheses as candidate models Model selection is underpinned by a philosophical view that understanding can best be approached by simultaneously weighing evidence for multiple working hypotheses

Bayesian phylogenetic analysis of combined data

by Johan A. A. Nylander, Fredrik Ronquist, John P. Huelsenbeck, Jose ́ Luis Nieves-aldrey - Syst. Biol , 2004
"... Abstract. — The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new typ ..."
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Abstract. — The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5 % of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more

RAxML Version 8: A tool for Phylogenetic Analysis and PostAnalysis of Large Phylogenies. Bioinformatics

by Alexandros Stamatakis , 2014
"... Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popu ..."
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Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as, a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX, and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date, 50 page user manual covering all new RAxML options is available.
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Bayesian estimation of ancestral character states on phylogenies

by Mark Pagel, Andrew Meade, Daniel Barker - Syst. Biol , 2004
"... Abstract.—Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree a ..."
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Abstract.—Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors. [Ancestral states; comparative methods; maximum likelihood; MCMC; phylogeny.] Given a collection of species, information on their at-tributes, and a phylogeny that describes their shared hi-erarchy of descent, the prospect is raised of inferring the

A review of long-branch attraction

by Johannes Bergsten - CLADISTICS , 2005
"... The history of long-branch attraction, and in particular methods suggested to detect and avoid the artifact to date, is reviewed. Methods suggested to avoid LBA-artifacts include excluding long-branch taxa, excluding faster evolving third codon positions, using inference methods less sensitive to LB ..."
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The history of long-branch attraction, and in particular methods suggested to detect and avoid the artifact to date, is reviewed. Methods suggested to avoid LBA-artifacts include excluding long-branch taxa, excluding faster evolving third codon positions, using inference methods less sensitive to LBA such as likelihood, the Aguinaldo et al. approach, sampling more taxa to break up long branches and sampling more characters especially of another kind, and the pros and cons of these are discussed. Methods suggested to detect LBA are numerous and include methodological disconcordance, RASA, separate partition analyses, parametric simulation, random outgroup sequences, long-branch extraction, split decomposition and spectral analysis. Less than 10 years ago it was doubted if LBA occurred in real datasets. Today, examples are numerous in the literature and it is argued that the development of methods to deal with the problem is warranted. A 16 kbp dataset of placental mammals and a morphological and molecular combined dataset of gall wasps are used to illustrate the particularly common problem of LBA of problematic ingroup taxa to outgroups. The preferred methods of separate partition analysis, methodological disconcordance, and long branch extraction are used to demonstrate detection methods. It is argued that since outgroup taxa almost always represent long branches and are as such a hazard towards misplacing long branched ingroup taxa, phylogenetic analyses should always be run with and without the outgroups included. This will detect whether only the outgroup roots the ingroup or if it simultaneously alters the ingroup topology, in which case previous studies have shown that the latter is most often the worse. Apart from that LBA to outgroups is the major

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst. Biol

by Mark Pagel, Andrew Meade , 2004
"... Abstract.—We describe a general likelihood-based ‘mixture model ’ for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of t ..."
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Abstract.—We describe a general likelihood-based ‘mixture model ’ for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites “pattern-heterogeneity ” to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate-variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program. [Bayesian inference; MCMC; mixture model; phylogeny; rate-heterogeneity; secondary structure; sequence evolution] The conventional likelihood-based approach to infer-ring phylogenetic trees from aligned gene-sequence or other data is to apply a single substitutional model to

Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. Mol Biol Evol,

by Itay Mayrose , Dan Graur , Nir Ben-Tal , Tal Pupko , 2004
"... The degree to which an amino acid site is free to vary is strongly dependent on its structural and functional importance. An amino acid that plays an essential role is unlikely to change over evolutionary time. Hence, the evolutionary rate at an amino acid site is indicative of how conserved this s ..."
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The degree to which an amino acid site is free to vary is strongly dependent on its structural and functional importance. An amino acid that plays an essential role is unlikely to change over evolutionary time. Hence, the evolutionary rate at an amino acid site is indicative of how conserved this site is and, in turn, allows evaluation of its importance in maintaining the structure/function of the protein. When using probabilistic methods for site-specific rate inference, few alternatives are possible. In this study we use simulations to compare the maximum-likelihood and Bayesian paradigms. We study the dependence of inference accuracy on such parameters as number of sequences, branch lengths, the shape of the rate distribution, and sequence length. We also study the possibility of simultaneously estimating branch lengths and sitespecific rates. Our results show that a Bayesian approach is superior to maximum-likelihood under a wide range of conditions, indicating that the prior that is incorporated into the Bayesian computation significantly improves performance. We show that when branch lengths are unknown, it is better first to estimate branch lengths and then to estimate site-specific rates. This procedure was found to be superior to estimating both the branch lengths and sitespecific rates simultaneously. Finally, we illustrate the difference between maximum-likelihood and Bayesian methods when analyzing site-conservation for the apoptosis regulator protein Bcl-x L .
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Stochastic mapping of morphological characters

by P. Huelsenbeck, Rasmus Nielsen, Jonathan P. Bollback, John P. Huelsenbeck, Rasmus Nielsen, Jonathan P. Bollback - Syst. Biol , 2003
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at. ..."
Abstract - Cited by 36 (3 self) - Add to MetaCart
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.

Learning domain structures

by Charles Kemp, Amy Perfors, Joshua B. Tenenbaum - In Proceedings of the 26th Annual Conference of the Cognitive Science Society , 2004
"... How do people acquire and use knowledge about domain structures, such as the tree-structured taxonomy of folk biology? These structures are typically seen either as consequences of innate domain-specific knowledge or as epiphenomena of domain-general associative learning. We present an alternative: ..."
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How do people acquire and use knowledge about domain structures, such as the tree-structured taxonomy of folk biology? These structures are typically seen either as consequences of innate domain-specific knowledge or as epiphenomena of domain-general associative learning. We present an alternative: a framework for statistical inference that discovers the structural principles that best account for different domains of objects and their properties. Our approach infers that a tree structure is best for a biological dataset, and a linear structure (“left”–“right”) is best for a dataset of people and their political views. We compare our proposal with unstructured associative learning and argue that our structured approach gives the better account of inductive
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Bayesian Analysis of Molecular Evolution using MrBayes

by John P. Huelsenbeck, Fredrik Ronquist , 2004
"... Stochastic models of evolution play a prominent role in the field of molecular evolution; they are used in applications as far ranging as phylogeny estimation, uncovering the pattern of DNA substitution, identifying amino acids under directional selection, and in inferring the history of a populatio ..."
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Stochastic models of evolution play a prominent role in the field of molecular evolution; they are used in applications as far ranging as phylogeny estimation, uncovering the pattern of DNA substitution, identifying amino acids under directional selection, and in inferring the history of a population using