Sequence evolution of mitochondrial tRNA genes and deep-branch animal phylogenetics

Mitochondrial DNA sequences are often used to construct molecular phylogenetic trees among closely related animals. In order to examine the usefulness of mtDNA sequences for deep-branch phylogenetics, genes in previously reported mtDNA sequences were analyzed among several animals that diverged 20–600 million years ago. Unambiguous alignment was achieved for stem-forming regions of mitochondrial tRNA genes by virtue of their conservative secondary structures. Sequences derived from stem parts of the mitochondrial tRNA genes appeared to accumulate much variation linearly for a long period of time: nearly 100 Myr for transition differences and more than 350 Myr for transversion differences. This characteristic could be attributed, in part, to the structural variability of mitochondrial tRNAs, which have fewer restrictions on their tertiary structure than do nonmitochondrial tRNAs. The tRNA sequence data served to reconstruct a well-established phylogeny of the animals with 100% bootstrap probabilities by both maximum parsimony and neighbor joining methods. By contrast, mitochondrial protein genes coding for cytochrome b and cytochrome oxidase subunit I did not reconstruct the established phylogeny or did so only weakly, although a variety of fractions of the protein gene sequences were subjected to tree-building. This discouraging phylogenetic performance of mitochondrial protein genes, especially with respect to branches originating over 300 Myr ago, was not simply due to high randomness in the data. It may have been due to the relative susceptibility of the protein genes to natural selection as compared with the stem parts of mitochondrial tRNA genes. On the basis of these results, it is proposed that mitochondrial tRNA genes may be useful in resolving deep branches in animal phylogenies with divergences that occurred some hundreds of Myr ago. For this purpose, we designed a set of primers with which mtDNA fragments encompassing clustered tRNA genes were successfully amplified from various vertebrates by the polymerase chain reaction.Abbreviations AA stem amino acid-acceptor stem - AC stem anticodon stem - COI cytochrome oxidase subunit I - cytb cytochrome b - D stem dihydrouridine stem - MP maximum parsimony - mtDNA mitochondrial DNA - Myr million years - NJ neighbor joining - PCR polymerase chain reaction - Ti transition - T stem tC stem - Tv transversion Correspondence to: Y. Kumazawa     

Early evolutionary relationships among known life forms inferred from elongation factor EF-2/EF-G sequences: Phylogenetic coherence and structure of the archaeal domain

Summary Phylogenies were inferred from both the gene and the protein sequences of the translational elongation factor termed EF-2 (for Archaea and Eukarya) and EF-G (for Bacteria). All treeing methods used (distance-matrix, maximum likelihood, and parsimony), including evolutionary parsimony, support the archaeal tree and disprove the eocyte tree (i.e., the polyphyly and paraphyly of the Archaea). Distance-matrix trees derived from both the amino acid and the DNA sequence alignments (first and second codon positions) showed the Archaea to be a monophyletia-holophyletic grouping whose deepest bifurcation divides a Sulfolobus branch from a branch comprising Methanococcus, Halobacterium, and Thermoplasma. Bootstrapped distance-matrix treeing confirmed the monophyly-holophyly of Archaea in 100% of the samples and supported the bifurcation of Archaea into a Sulfolobus branch and a methanogen-halophile branch in 97% of the samples. Similar phylogenies were inferred by maximum likelihood and by maximum (protein and DNA) parsimony. DNA parsimony trees essentially identical to those inferred from first and second codon positions were derived from alternative DNA data sets comprising either the first or the second position of each codon. Bootstrapped DNA parsimony supported the monophyly-holophyly of Archaea in 100% of the bootstrap samples and confirmed the division of Archaea into a Sulfolobus branch and a methanogen-halophile branch in 93% of the bootstrap samples. Distance-matrix and maximum likelihood treeing under the constraint that branch lengths must be consistent with a molecular clock placed the root of the universal tree between the Bacteria and the bifurcation of Archaea and Eukarya. The results support the division of Archaea into the kingdoms Crenarchaeota (corresponding to the Sulfolobus branch and Euryarchaeota). This division was not confirmed by evolutionary parsimony, which identified Halobacterium rather than Sulfolobus as the deepest offspring within the Archaea.Offprint requests to: P. Cammarano     

Statistical properties of bootstrap estimation of phylogenetic variability from nucleotide sequences: II. Four taxa without a molecular clock

Summary The statistical properties of sample estimation and bootstrap estimation of phylogenetic variability from a sample of nucleotide sequences were studied by considering model trees of three taxa with an outgroup. The cases of constant and varying rates of nucleotide substitution were compared. From sequences obtained by simulation, phylogenetic trees were constructed by using the maximum parsimony (MP) and neighbor joining (NJ) methods. The effectiveness and consistency of the MP method were studied in terms of proportions of informative sites. The results of simulation showed that bootstrap estimation of the confidence level for an inferred phylogeny can be used even under unequal rates of evolution if the rate differences are not large so that the MP method is not misleading. The condition under which the MP method becomes misleading (inconsistent) is more stringent for slowly evolving sequences than for rapidly evolving ones, and it also depends on the length of the internal branch. If the rate differences are large so that the MP method becomes consistently misleading, then bootstrap estimation will reinforce an erroneous conclusion on topology. Similar conclusions apply to the NJ method with uncorrected distances. The NJ method with corrected distances performs poorly when the sequence length is short but can avoid the inconsistency problem if the sequence length is long and if the distances can be estimated accurately.Offprint requests to: W.-H. Li     

Graphical exploration of covariate effects on survival data through nonparametric quantile curves

Kaplan-Meier curves provide an effective means of presenting the distributional pattern in a sample of survival data. However, in order to assess the effect of a covariate, a standard scatterplot is often difficult to interpret because of the presence of censored observations. Several authors have proposed a running median as an effective way of indicating the effect of a covariate. This article proposes a form of kernel estimation, employing double smoothing, that can be applied in a simple and efficient manner to construct an estimator of a percentile of the survival distribution as a function of one or two covariates. Permutations and bootstrap samples can be used to construct reference bands that help identify whether particular features of the estimates indicate real features of the underlying curve or whether this may be due simply to random variation. The techniques are illustrated on data from a study of kidney transplant patients.     

A density-based approach for the modelling of root architecture: application to Maritime pine (Pinus pinaster Ait.) root systems

Root morphology influences strongly plant/soil interactions. However, the complexity of root architecture is a major barrier when analysing many phenomena, e.g. anchorage, water or nutrient uptake. Therefore, we have developed a new approach for the representation and modelling of root architecture based on branching density. A general root branching density in a space of finite dimension was used and enabled us to consider various morphological properties. A root system model was then constructed which minimizes the difference between measured and simulated root systems, expressed with functions which map root density in the soil. The model was tested in 2D using data from Maritime pine Pinus pinaster Ait. structural roots as input. We showed that simulated and real root systems had similar root distributions in terms of radial distance, depth, branching angle and branching order. These results indicate that general density functions are not only a powerful basis for constructing models of architecture, but can also be used to represent such structures when considering root/soil interaction. These models are particularly useful in that they provide a local morphological characterization which is aggregated in a given unit of soil volume.     

A Comparison of Nonparametric Error Rate Estimation Methods in Classification Problems

Assessment of the misclassification error rate is of high practical relevance in many biomedical applications. As it is a complex problem, theoretical results on estimator performance are few. The origin of most findings are Monte Carlo simulations, which take place in the “normal setting”: The covariables of two groups have a multivariate normal distribution; The groups differ in location, but have the same covariance matrix and the linear discriminant function LDF is used for prediction. We perform a new simulation to compare existing nonparametric estimators in a more complex situation. The underlying distribution is based on a logistic model with six binary as well as continuous covariables. To study estimator performance for varying true error rates, three prediction rules including nonparametric classification trees and parametric logistic regression and sample sizes ranging from 100‐1,000 are considered. In contrast to most published papers we turn our attention to estimator performance based on simple, even inappropriate prediction rules and relatively large training sets. For the major part, results are in agreement with usual findings. The most strikingly behavior was seen in applying (simple) classification trees for prediction: Since the apparent error rate Êrr.app is biased, linear combinations incorporating Êrr.app underestimate the true error rate even for large sample sizes. The .632+ estimator, which was designed to correct for the overoptimism of Efron's .632 estimator for nonparametric prediction rules, performs best of all such linear combinations. The bootstrap estimator Êrr.B0 and the crossvalidation estimator Êrr.cv, which do not depend on Êrr.app, seem to track the true error rate. Although the disadvantages of both estimators – pessimism of Êrr.B0 and high variability of Êrr.cv – shrink with increased sample sizes, they are still visible. We conclude that for the choice of a particular estimator the asymptotic behavior of the apparent error rate is important. For the assessment of estimator performance the variance of the true error rate is crucial, where in general the stability of prediction procedures is essential for the application of estimators based on resampling methods. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Population fluctuations and regulation in great snipe: a time-series analysis

1. During the last centuries, the breeding range of the great snipe Gallinago media has declined dramatically in the western part of its distribution. To examine present population dynamics in the Scandinavian mountains, we collected and analysed a 19-year time series of counts of great snipe males at leks in central Norway, 1987-2005. 2. The population showed large annual fluctuations in the number of males displaying at lek sites (range 45-90 males at the peak of the mating season), but no overall trend. 3. We detected presence of direct density-dependent mechanisms regulating this population. Inclusion of the density-dependent term in a Ricker-type model significantly improved the fit with observed data (evaluated with Parametric Bootstrap Likelihood Ratio tests and Akaike's Information Criterion for small sample size). 4. An analysis of (a number of a priori likely) environmental covariates suggests that the population dynamics were affected by conditions influencing reproduction and survival of offspring during the summer, but not by conditions influencing survival at the wintering grounds in Africa. This is in contrast to many altricial birds breeding in the northern hemisphere, and supports the idea that population dynamics of migratory nidifugous birds are more influenced by conditions during reproduction. 5. Inclusion of these external factors into our model improved the detectability of density dependence. This illustrates that allowing for external effects may increase statistical power of density dependence tests and thus be of particular importance in relatively short time series. 6. In our best model of the population dynamics, two likely density-independent offspring survival covariates explained 47.3% of the variance in great snipe numbers (predation pressure estimated by willow grouse reproductive success and food availability estimated by the amount of precipitation in June), whereas density dependence explained 35.5%. Demographic stochasticity and unidentified environmental stochasticity may account for the remaining 17.2%.     

Testing approaches for overdispersion in poisson regression versus the generalized poisson model

Overdispersion is a common phenomenon in Poisson modeling, and the negative binomial (NB) model is frequently used to account for overdispersion. Testing approaches (Wald test, likelihood ratio test (LRT), and score test) for overdispersion in the Poisson regression versus the NB model are available. Because the generalized Poisson (GP) model is similar to the NB model, we consider the former as an alternate model for overdispersed count data. The score test has an advantage over the LRT and the Wald test in that the score test only requires that the parameter of interest be estimated under the null hypothesis. This paper proposes a score test for overdispersion based on the GP model and compares the power of the test with the LRT and Wald tests. A simulation study indicates the score test based on asymptotic standard Normal distribution is more appropriate in practical application for higher empirical power, however, it underestimates the nominal significance level, especially in small sample situations, and examples illustrate the results of comparing the candidate tests between the Poisson and GP models. A bootstrap test is also proposed to adjust the underestimation of nominal level in the score statistic when the sample size is small. The simulation study indicates the bootstrap test has significance level closer to nominal size and has uniformly greater power than the score test based on asymptotic standard Normal distribution. From a practical perspective, we suggest that, if the score test gives even a weak indication that the Poisson model is inappropriate, say at the 0.10 significance level, we advise the more accurate bootstrap procedure as a better test for comparing whether the GP model is more appropriate than Poisson model. Finally, the Vuong test is illustrated to choose between GP and NB2 models for the same dataset.