Fredrik Ronquist

Bio: Fredrik Ronquist is an academic researcher from Swedish Museum of Natural History. The author has contributed to research in topics: Monophyly & Markov chain Monte Carlo. The author has an hindex of 54, co-authored 122 publications receiving 76188 citations. Previous affiliations of Fredrik Ronquist include Uppsala University & Florida State University.

Evolution of parasitism among closely related species: phylogenetic relationships and the origin of inquilinism in gall wasps (hymenoptera, cynipidae).

Abstract: A new term, agastoparasitism, is proposed for parasitism among closely related species. Cynipid inquilines are typical agastoparasites. They cannot induce galls; instead their larvae live inside the galls formed by other cynipids. As in many other groups of agastoparasites, there are two competing hypotheses for the evolutionary origin of cynipid inquilines: either they arose from one of their cynipid hosts, and later radiated to exploit other gall-inducing cynipids (monophyletic origin), or they arose repeatedly, each inquiline from its host (polyphyletic origin). These hypotheses for the origin of cynipid inquilines were tested by a phylogenetic analysis of representative species of cynipid gall inducers and inquilines based on adult morphological characters. The analysis supported the monophyly of the inquilines and indicated an origin from gall inducers related to the genus Diastrophus, one of the current host groups. To examine whether the result of the analysis was influenced by convergent similarities among inquilines because of their similar mode of life, all putative apomorphies shared by some or all of the inquilines but not occurring in any of the gall inducers were removed. Despite this, the phylogenetic conclusions essentially remained the same, that is, the support for inquiline monophyly was not caused by convergent evolution. Based on these results, adaptive aspects of the evolutionary origin and maintenance of cynipid inquilinism are discussed, as well as general patterns in the evolution of agastoparasitism.

A phylogenetic analysis of higher-level gall wasp relationships (Hymenoptera: Cynipidae)

Abstract: We present the most comprehensive analysis of higher-level relationships in gall wasps conducted thus far. The analysis was based on detailed study of the skeletal morphology of adults, resulting in 164 phylogenetically informative characters, complemented with a few biological characters. Thirty-seven cynipid species from thirty-one genera, including four genera of the apparently monophyletic Cynipini and almost all of the genera in the other tribes, were examined. The outgroup included exemplar species from three successively more distant cynipoid families: Figitidae (the sister group of the Cynipidae), Liopteridae and Ibaliidae. There was considerable homoplasy in the data, but many groupings in the shortest tree were nonetheless well supported, as indicated by bootstrap proportions and decay indices. Partitioning of the data suggested that the high level of homoplasy is characteristic of the Cynipidae and not the result of the amount of available phylogenetically conservative characters being exhausted. The analysis supported the monophyly of the Cynipini (oak gall wasps) which, together with the Rhoditini (the rose gall wasps), Eschatocerini and Pediaspidini formed a larger monophyletic group of gall inducers restricted to woody representatives of the eudicot subclass Rosidae. The inquilines (Synergini) were indicated to be monophyletic, whereas the Aylacini, primarily herb gall inducers, appeared as a paraphyletic assemblage of basal cynipid groups. The shortest tree suggests that the Cynipidae can be divided into three major lineages: one including the inquilines, the Aylacini genera associated with Rosaceae, and Liposthenes; one consisting entirely of Aylacini genera, among them Aulacidea, Isocolus and Neaylax; and one comprising the woody rosid gallers (the oak and rose gall wasps and allies), the Phanacis- Timaspis complex and the Aylacini genera associated with Papaveraceae.

Phylogeny and early evolution of the Cynipoidea (Hymenoptera)

Abstract: Based on several structural and biological characteristics, the Cynipoidea can be divided into two groups, ‘macrocynipoids’ and ‘microcynipoids’ The macrocynipoids (ie the family Liopteridae and the genera Austrocynips, Eileenella, Heteribalia and Ibalia) are generally large insects that parasitize wood- or cone-boring insect larvae The microcynipoids are smaller insects that are either phytophagous gall inducers and inquilines (Cynipidae) or parasitoids of larvae of Hymenoptera, Neuroptera or Diptera (Figitidae sensu lato, including the families Eucoilidae, Charipidae and Anacharitidae) The phylogenetic relationships among genera of macrocynipoids and between these and a sample of four genera representing the Figitidae and Cynipidae were examined by parsimony analysis of 110 external morphological characters of adults Within the macrocynipoids, three monophyletic lineages emerged, classified here as different families: the Austrocynipidae, with a single species, Austrocynips mirabilis, the only cynipoid with a true pterostigma; the Ibaliidae, including the genera Eileenella, Ibalia and Heteribalia; and the Liopteridae, comprising the remaining genera of macrocynipoids The analysis further supported the monophyly of the microcynipoids and indicated that the macrocynipoids form a paraphyletic group relative to the microcynipoids, with the shortest tree suggesting the relationship (Austrocynipidae, (Ibaliidae, (Liopteridae, microcynipoids))) These results imply that cynipoids were originally parasitoids of wood-boring insect larvae and that the other modes of life evolved secondarily within the group

MrBayes 3: Bayesian phylogenetic inference under mixed models

Abstract: Summary: MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types—e.g. morphological, nucleotide, and protein— and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.

MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space

Abstract: Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d(N)/d(S) rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

Abstract: The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/. (Algorithm; computer simulations; maximum likelihood; phylogeny; rbcL; RDPII project.) The size of homologous sequence data sets has in- creased dramatically in recent years, and many of these data sets now involve several hundreds of taxa. More- over, current probabilistic sequence evolution models (Swofford et al., 1996 ; Page and Holmes, 1998 ), notably those including rate variation among sites (Uzzell and Corbin, 1971 ; Jin and Nei, 1990 ; Yang, 1996 ), require an increasing number of calculations. Therefore, the speed of phylogeny reconstruction methods is becoming a sig- nificant requirement and good compromises between speed and accuracy must be found. The maximum likelihood (ML) approach is especially accurate for building molecular phylogenies. Felsenstein (1981) brought this framework to nucleotide-based phy- logenetic inference, and it was later also applied to amino acid sequences (Kishino et al., 1990). Several vari- ants were proposed, most notably the Bayesian meth- ods (Rannala and Yang 1996; and see below), and the discrete Fourier analysis of Hendy et al. (1994), for ex- ample. Numerous computer studies (Huelsenbeck and Hillis, 1993; Kuhner and Felsenstein, 1994; Huelsenbeck, 1995; Rosenberg and Kumar, 2001; Ranwez and Gascuel, 2002) have shown that ML programs can recover the cor- rect tree from simulated data sets more frequently than other methods can. Another important advantage of the ML approach is the ability to compare different trees and evolutionary models within a statistical framework (see Whelan et al., 2001, for a review). However, like all optimality criterion-based phylogenetic reconstruction approaches, ML is hampered by computational difficul- ties, making it impossible to obtain the optimal tree with certainty from even moderate data sets (Swofford et al., 1996). Therefore, all practical methods rely on heuristics that obtain near-optimal trees in reasonable computing time. Moreover, the computation problem is especially difficult with ML, because the tree likelihood not only depends on the tree topology but also on numerical pa- rameters, including branch lengths. Even computing the optimal values of these parameters on a single tree is not an easy task, particularly because of possible local optima (Chor et al., 2000). The usual heuristic method, implemented in the pop- ular PHYLIP (Felsenstein, 1993 ) and PAUP ∗ (Swofford, 1999 ) packages, is based on hill climbing. It combines stepwise insertion of taxa in a growing tree and topolog- ical rearrangement. For each possible insertion position and rearrangement, the branch lengths of the resulting tree are optimized and the tree likelihood is computed. When the rearrangement improves the current tree or when the position insertion is the best among all pos- sible positions, the corresponding tree becomes the new current tree. Simple rearrangements are used during tree growing, namely "nearest neighbor interchanges" (see below), while more intense rearrangements can be used once all taxa have been inserted. The procedure stops when no rearrangement improves the current best tree. Despite significant decreases in computing times, no- tably in fastDNAml (Olsen et al., 1994 ), this heuristic becomes impracticable with several hundreds of taxa. This is mainly due to the two-level strategy, which sepa- rates branch lengths and tree topology optimization. In- deed, most calculations are done to optimize the branch lengths and evaluate the likelihood of trees that are finally rejected. New methods have thus been proposed. Strimmer and von Haeseler (1996) and others have assembled four- taxon (quartet) trees inferred by ML, in order to recon- struct a complete tree. However, the results of this ap- proach have not been very satisfactory to date (Ranwez and Gascuel, 2001 ). Ota and Li (2000, 2001) described

RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

Abstract: Summary: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Γ yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets ≥4000 taxa it also runs 2--3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25 057 (1463 bp) and 2182 (51 089 bp) taxa, respectively. Availability: icwww.epfl.ch/~stamatak Contact: Alexandros.Stamatakis@epfl.ch Supplementary information: Supplementary data are available at Bioinformatics online.

New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0

Abstract: PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.