Direct comparison of four bacterial source tracking methods and use of composite data sets

AIMS: Four bacterial source tracking (BST) methods, enterobacterial repetitive intergenic consensus sequence polymerase chain reaction (ERIC-PCR), automated ribotyping using HindIII, Kirby-Bauer antibiotic resistance analysis (KB-ARA) and pulsed-field gel electrophoresis (PFGE) were directly compared using the same collection of Escherichia coli isolates. The data sets from each BST method and from composite methods were compared for library accuracy and their ability to identify water isolates. METHODS AND RESULTS: Potential sources of faecal pollution were identified by watershed sanitary surveys. Domestic sewage and faecal samples from pets, cattle, avian livestock, other nonavian livestock, avian wildlife and nonavian wildlife sources were collected for isolation of E. coli. A total of 2275 E. coli isolates from 813 source samples were screened using ERIC-PCR to exclude clones and to maximize library diversity, resulting in 883 isolates from 745 samples selected for the library. The selected isolates were further analysed using automated ribotyping with HindIII, KB-ARA and PFGE. A total of 555 E. coli isolates obtained from 412 water samples were analysed by the four BST methods. A composite data set of the four BST methods gave the highest rates of correct classification (RCCs) with the fewest unidentified isolates than any single method alone. RCCs for the four-method composite data set and a seven-way split of source classes ranged from 22% for avian livestock to 83% for domestic sewage. Two-method composite data sets were also found to be better than individual methods, having RCCs similar to the four-method composite and identification of the same major sources of faecal pollution. CONCLUSIONS: The use of BST composite data sets may be more beneficial than the use of single methods. SIGNIFICANCE AND IMPACT OF THE STUDY: This is one of the first comprehensive comparisons using composite data from several BST methods. While the four-method approach provided the most desirable BST results, the use of two-method composite data sets may yield comparable BST results while providing for cost, labour and time savings.     

The importance of data partitioning and the utility of Bayes factors in Bayesian phylogenetics

As larger, more complex data sets are being used to infer phylogenies, accuracy of these phylogenies increasingly requires models of evolution that accommodate heterogeneity in the processes of molecular evolution. We investigated the effect of improper data partitioning on phylogenetic accuracy, as well as the type I error rate and sensitivity of Bayes factors, a commonly used method for choosing among different partitioning strategies in Bayesian analyses. We also used Bayes factors to test empirical data for the need to divide data in a manner that has no expected biological meaning. Posterior probability estimates are misleading when an incorrect partitioning strategy is assumed. The error was greatest when the assumed model was underpartitioned. These results suggest that model partitioning is important for large data sets. Bayes factors performed well, giving a 5% type I error rate, which is remarkably consistent with standard frequentist hypothesis tests. The sensitivity of Bayes factors was found to be quite high when the across-class model heterogeneity reflected that of empirical data. These results suggest that Bayes factors represent a robust method of choosing among partitioning strategies. Lastly, results of tests for the inclusion of unexpected divisions in empirical data mirrored the simulation results, although the outcome of such tests is highly dependent on accounting for rate variation among classes. We conclude by discussing other approaches for partitioning data, as well as other applications of Bayes factors.     

Multilocus phylogenetics of a rapid radiation in the genus Thomomys (Rodentia: Geomyidae)

Species complexes undergoing rapid radiation present a challenge in molecular systematics because of the possibility that ancestral polymorphism is retained in component gene trees. Coalescent theory has demonstrated that gene trees often fail to match lineage trees when taxon divergence times are less than the ancestral effective population sizes. Suggestions to increase the number of loci and the number of individuals per taxon have been proposed; however, phylogenetic methods to adequately analyze these data in a coalescent framework are scarce. We compare two approaches to estimating lineage (species) trees using multiple individuals and multiple loci: the commonly used partitioned Bayesian analysis of concatenated sequences and a modification of a newly developed hierarchical Bayesian method (BEST) that simultaneously estimates gene trees and species trees from multilocus data. We test these approaches on a phylogeny of rapidly radiating species wherein divergence times are likely to be smaller than effective population sizes, and incomplete lineage sorting is known, in the rodent genus, Thomomys. We use seven independent noncoding nuclear sequence loci (total approximately 4300 bp) and between 1 and 12 individuals per taxon to construct a phylogenetic hypothesis for eight Thomomys species. The majority-rule consensus tree from the partitioned concatenated analysis included 14 strongly supported bipartitions, corroborating monophyletic species status of five of the eight named species. The BEST tree strongly supported only the split between the two subgenera and showed very low support for any other clade. Comparison of both lineage trees to individual gene trees revealed that the concatenation method appears to ignore conflicting signals among gene trees, whereas the BEST tree considers conflicting signals and downweights support for those nodes. Bayes factor analysis of posterior tree distributions from both analyses strongly favor the model underlying the BEST analysis. This comparison underscores the risks of overreliance on results from concatenation, and ignoring the properties of coalescence, especially in cases of recent, rapid radiations.     

Classification tree method for bacterial source tracking with antibiotic resistance analysis data

Various statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used with antibiotic resistance analysis (ARA) data to construct models for bacterial source tracking (BST). We applied the statistical method known as classification trees to build a model for BST for the Anacostia Watershed in Maryland. Classification trees have more flexibility than other statistical classification approaches based on standard statistical methods to accommodate complex interactions among ARA variables. This article describes the use of classification trees for BST and includes discussion of its principal parameters and features. Anacostia Watershed ARA data are used to illustrate the application of classification trees, and we report the BST results for the watershed.     

An unconditional exact test for the Hardy-Weinberg equilibrium law: sample-space ordering using the Bayes factor

Much forensic inference based upon DNA evidence is made assuming that the Hardy-Weinberg equilibrium (HWE) is valid for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, each having advantages and limitations. The limitations become more obvious when testing for deviation within multiallelic DNA loci is attempted. Here we present an exact test for HWE in the biallelic case, based on the ratio of weighted likelihoods under the null and alternative hypotheses, the Bayes factor. This test does not depend on asymptotic results and minimizes a linear combination of type I and type II errors. By ordering the sample space using the Bayes factor, we also define a significance (evidence) index, P value, using the weighted likelihood under the null hypothesis. We compare it to the conditional exact test for the case of sample size n = 10. Using the idea under the method of chi(2) partition, the test is used sequentially to test equilibrium in the multiple allele case and then applied to two short tandem repeat loci, using a real Caucasian data bank, showing its usefulness.     

A test for heterotachy using multiple pairs of sequences

Heterotachy is a general term to describe positions that evolve at different rates in different lineages. Heterotachy also can generally be viewed as multivariate rates-across-sites variation, which can be described as randomly drawing rates (or branch lengths) from a multivariate distribution for each branch at each site (Wu J, Susko E. 2009. General heterotachy and distance method adjustments. Mol Biol Evol. 26:2689-2697). Motivated by this result, we propose three new distance-based tests: a heterogeneity test, a heterotachy test, and a within-gene heterotachy test and demonstrate with simulations that they perform well under a wide range of conditions. We also applied the first two tests to two real data sets and found that although all these data sets showed significant evidence of heterotachy, there were subtrees for which the data were consistent with an equal rates or rates-across-sites model.heterogeneity, heterotachy, within-gene heterotachy, covarion model, distance method, hypothesis test.     

An analysis of mating biases in trees

Assortative mating is a deviation from random mating based on phenotypic similarity. As it is much better studied in animals than in plants, we investigate for trees whether kinship of realized mating pairs deviates from what is expected from the set of potential mates and use this information to infer mating biases that may result from kin recognition and/or assortative mating. Our analysis covers 20 species of trees for which microsatellite data is available for adult populations (potential mates) as well as seed arrays. We test whether mean relatedness of observed mating pairs deviates from null expectations that only take pollen dispersal distances into account (estimated from the same data set). This allows the identification of elevated as well as reduced kinship among realized mating pairs, indicative of positive and negative assortative mating, respectively. The test is also able to distinguish elevated biparental inbreeding that occurs solely as a result of related pairs growing closer to each other from further assortativeness. Assortative mating in trees appears potentially common but not ubiquitous: nine data sets show mating bias with elevated inbreeding, nine do not deviate significantly from the null expectation, and two show mating bias with reduced inbreeding. While our data sets lack direct information on phenology, our investigation of the phenological literature for each species identifies flowering phenology as a potential driver of positive assortative mating (leading to elevated inbreeding) in trees. Since active kin recognition provides an alternative hypothesis for these patterns, we encourage further investigations on the processes and traits that influence mating patterns in trees.     

Molecular phylogeny of the hominoids: inferences from multiple independent DNA sequence data sets

Consensus on the evolutionary relationships of humans, chimpanzees, and gorillas has not been reached, despite the existence of a number of DNA sequence data sets relating to the phylogeny, partly because not all gene trees from these data sets agree. However, given the well-known phenomenon of gene tree-species tree mismatch, agreement among gene trees is not expected. A majority of gene trees from available DNA sequence data support one hypothesis, but is this evidence sufficient for statistical confidence in the majority hypothesis? All available DNA sequence data sets showing phylogenetic resolution among the hominoids are grouped according to genetic linkage of their corresponding genes to form independent data sets. Of the 14 independent data sets defined in this way, 11 support a human- chimpanzee clade, 2 support a chimpanzee-gorilla clade, and one supports a human-gorilla clade. The hypothesis of a trichotomous speciation event leading to Homo; Pan, and Gorilla can be firmly rejected on the basis of this data set distribution. The multiple-locus test (Wu 1991), which evaluates hypotheses using gene tree-species tree mismatch probabilities in a likelihood ratio test, favors the phylogeny with a Homo-Pan clade and rejects the other alternatives with a P value of 0.002. When the probabilities are modified to reflect effective population size differences among different types of genetic loci, the observed data set distribution is even more likely under the Homo-Pan clade hypothesis. Maximum-likelihood estimates for the time between successive hominoid divergences are in the range of 300,000-2,800,000 years, based on a reasonable range of estimates for long-term hominoid effective population size and for generation time. The implication of the multiple-locus test is that existing DNA sequence data sets provide overwhelming and sufficient support for a human-chimpanzee clade: no additional DNA data sets need to be generated for the purpose of estimating hominoid phylogeny. Because DNA hybridization evidence (Caccone and Powell 1989) also supports a Homo-Pan clade, the problem of hominoid phylogeny can be confidently considered solved.      

Changing the landscape: a new strategy for estimating large phylogenies

In this paper we describe a new heuristic strategy designed to find optimal (parsimonious) trees for data sets with large numbers of taxa and characters. This new strategy uses an iterative searching process of branch swapping with equally weighted characters, followed by swapping with reweighted characters. This process increases the efficiency of the search because, after each round of swapping with reweighted characters, the subsequent swapping with equal weights will start from a different group (island) of trees that are only slightly, if at all, less optimal. In contrast, conventional heuristic searching with constant equal weighting can become trapped on islands of suboptimal trees. We test the new strategy against a conventional strategy and a modified conventional strategy and show that, within a given time, the new strategy finds trees that are markedly more parsimonious. We also compare our new strategy with a recent, independently developed strategy known as the Parsimony Ratchet.     

Testing and estimating the non-disjunction fraction in Meiosis I using reference priors

In this paper we analyze the fraction of non-disjunction in Meiosis I assuming reference (non-informative) priors. We consider Jeffreys's approach to built a non-informative prior (Jeffreys's prior) for the fraction of non-disjunction in Meiosis I. We prove that Jeffreys's prior is a proper distribution. We perform Monte Carlo studies in order to compare Bayes estimates obtained assuming Jeffreys's and uniform priors. We consider full Bayesian significance test (FBST) and Bayes factor (BF) for testing precise hypothesis on the fraction of non-disjunction in Meiosis I. The ultimate goal of this paper is to compare these two test procedures through simulation studies using both prior specifications. An application to Down Syndrome data is also presented.     

Plug‐in tests for nonequivalence of means of independent normal populations

In this paper, we consider the problem of testing nonequivalence of several independent normal population means. It is a well‐known problem to test the equality of several means using the analysis of variance (ANOVA). Instead of determining the equality, one may consider more flexible homogeneity, which allows a predetermined level of difference. This problem is known as testing nonequivalence of populations. We propose the plug‐in statistics for two different measures of variability: the sum of the absolute deviations and the maximum of the absolute deviations. For each test, the least favorable configuration (LFC) to ensure the maximum rejection probability under the null hypothesis is investigated. Furthermore, we demonstrate the numerical studies based on both simulation and real data to evaluate the plug‐in tests and compare these with the range test.     

Least and most powerful phylogenetic tests to elucidate the origin of the seed plants in the presence of conflicting signals under misspecified models

Several tests of molecular phylogenies have been proposed over the last decades, but most of them lead to strikingly different P-values. I propose that such discrepancies are principally due to different forms of null hypotheses. To support this hypothesis, two new tests are described. Both consider the composite null hypothesis that all the topologies are equidistant from the true but unknown topology. This composite hypothesis can either be reduced to the simple hypothesis at the least favorable distribution (frequentist significance test [FST]) or to the maximum likelihood topology (frequentist hypothesis test [FHT]). In both cases, the reduced null hypothesis is tested against each topology included in the analysis. The tests proposed have an information-theoretic justification, and the distribution of their test statistic is estimated by a nonparametric bootstrap, adjusting P-values for multiple comparisons. I applied the new tests to the reanalysis of two chloroplast genes, psaA and psbB, and compared the results with those of previously described tests. As expected, the FST and the FHT behaved approximately like the Shimodaira-Hasegawa test and the bootstrap, respectively. Although the tests give overconfidence in a wrong tree when an overly simple nucleotide substitution model is assumed, more complex models incorporating heterogeneity among codon positions resolve some conflicts. To further investigate the influence of the null hypothesis, a power study was conducted. Simulations showed that FST and the Shimodaira-Hasegawa test are the least powerful and FHT is the most powerful across the parameter space. Although the size of all the tests is affected by misspecification, the two new tests appear more robust against misspecification of the model of evolution and consistently supported the hypothesis that the Gnetales are nested within gymnosperms.     

Phylogenetic congruence and discordance among one morphological and three molecular data sets from Pontederiaceae

A morphological data set and three sources of data from the chloroplast genome (two genes and a restriction site survey) were used to reconstruct the phylogenetic history of the pickerelweed family Pontederiaceae. The chloroplast data converged towards a single tree, presumably the true chloroplast phylogeny of the family. Unrooted trees estimated from each of the three chloroplast data sets were identical or extremely similar in shape to each other and mostly robustly supported. There was no evidence of significant heterogeneity among the data sets, and the few topological differences seen among unrooted trees from each chloroplast data set are probably artifacts of sampling error on short branches. Despite well-documented differences in rates of evolution for different characters in individual data sets, equally weighted parsimony permits accurate reconstructions of chloroplast relationships in Pontederiaceae. A separate morphology-based data set yielded trees that were very different from the chloroplast trees. Although there was substantial support from the morphological evidence for several major clades supported by chloroplast trees, most of the conflicting phylogenetic structure on the morphology trees was not robust. Nonetheless, several statistical tests of incongruence indicate significant heterogeneity between molecules and morphology. The source of this apparent incongruence appears to be a low ratio of phylogenetic signal to noise in the morphological data.     

The Rooting of the Universal Tree of Life Is Not Reliable

Several composite universal trees connected by an ancestral gene duplication have been used to root the universal tree of life. In all cases, this root turned out to be in the eubacterial branch. However, the validity of results obtained from comparative sequence analysis has recently been questioned, in particular, in the case of ancient phylogenies. For example, it has been shown that several eukaryotic groups are misplaced in ribosomal RNA or elongation factor trees because of unequal rates of evolution and mutational saturation. Furthermore, the addition of new sequences to data sets has often turned apparently reasonable phylogenies into confused ones. We have thus revisited all composite protein trees that have been used to root the universal tree of life up to now (elongation factors, ATPases, tRNA synthetases, carbamoyl phosphate synthetases, signal recognition particle proteins) with updated data sets. In general, the two prokaryotic domains were not monophyletic with several aberrant groupings at different levels of the tree. Furthermore, the respective phylogenies contradicted each others, so that various ad hoc scenarios (paralogy or lateral gene transfer) must be proposed in order to obtain the traditional Archaebacteria–Eukaryota sisterhood. More importantly, all of the markers are heavily saturated with respect to amino acid substitutions. As phylogenies inferred from saturated data sets are extremely sensitive to differences in evolutionary rates, present phylogenies used to root the universal tree of life could be biased by the phenomenon of long branch attraction. Since the eubacterial branch was always the longest one, the eubacterial rooting could be explained by an attraction between this branch and the long branch of the outgroup. Finally, we suggested that an eukaryotic rooting could be a more fruitful working hypothesis, as it provides, for example, a simple explanation to the high genetic similarity of Archaebacteria and Eubacteria inferred from complete genome analysis.     

Does choice in model selection affect maximum likelihood analysis?

In order to have confidence in model-based phylogenetic analysis, the model of nucleotide substitution adopted must be selected in a statistically rigorous manner. Several model-selection methods are applicable to maximum likelihood (ML) analysis, including the hierarchical likelihood-ratio test (hLRT), Akaike information criterion (AIC), Bayesian information criterion (BIC), and decision theory (DT), but their performance relative to empirical data has not been investigated thoroughly. In this study, we use 250 phylogenetic data sets obtained from TreeBASE to examine the effects that choice in model selection has on ML estimation of phylogeny, with an emphasis on optimal topology, bootstrap support, and hypothesis testing. We show that the use of different methods leads to the selection of two or more models for approximately 80% of the data sets and that the AIC typically selects more complex models than alternative approaches. Although ML estimation with different best-fit models results in incongruent tree topologies approximately 50% of the time, these differences are primarily attributable to alternative resolutions of poorly supported nodes. Furthermore, topologies and bootstrap values estimated with ML using alternative statistically supported models are more similar to each other than to topologies and bootstrap values estimated with ML under the Kimura two-parameter (K2P) model or maximum parsimony (MP). In addition, Swofford-Olsen-Waddell-Hillis (SOWH) tests indicate that ML trees estimated with alternative best-fit models are usually not significantly different from each other when evaluated with the same model. However, ML trees estimated with statistically supported models are often significantly suboptimal to ML trees made with the K2P model when both are evaluated with K2P, indicating that not all models perform in an equivalent manner. Nevertheless, the use of alternative statistically supported models generally does not affect tests of monophyletic relationships under either the Shimodaira-Hasegawa (S-H) or SOWH methods. Our results suggest that although choice in model selection has a strong impact on optimal tree topology, it rarely affects evolutionary inferences drawn from the data because differences are mainly confined to poorly supported nodes. Moreover, since ML with alternative best-fit models tends to produce more similar estimates of phylogeny than ML under the K2P model or MP, the use of any statistically based model-selection method is vastly preferable to forgoing the model-selection process altogether.     

Comparison of Nonparametric Statistics for Detection of Linkage in Nuclear Families: Single-Marker Evaluation

We have evaluated 23 different statistics, from a total of 10 popular software packages for model-free linkage analysis of nuclear-family data, by applying them to single-marker data simulated under several two-locus disease models. The statistics that we examined fall into two broad categories: (1) those that test directly for increased identity-by-state or identity-by-descent sharing (by use of the programs APM, Genetic Analysis System [GAS] SIBSTATE and SIBDES, SAGE SIBPAL, ERPA, SimIBD, and Genehunter NPL) and (2) those that are based on likelihood-ratio tests and that report LOD scores (by use of the programs Splink, SIBPAIR, Mapmaker/Sibs, ASPEX, and GAS SIBMLS). For each of eight two-locus disease models, we analyzed six data sets; the first three data sets consisted of two-child families with both sibs affected and zero, one, or both parents typed, whereas the other three data sets consisted of four-child families with at least two affected sibs and zero, one, or both parents typed. We report false-positive rates, overall rank by power, and the power for each statistic. We give rough recommendations regarding which programs provide the most powerful tests for linkage, as well as the programs to be avoided under certain conditions. For the likelihood-ratio-based statistics, we examined the effects of various treatments of sibships with multiple affected individuals. Finally, we explored the use of some simple two-of-three composite statistics and found that such tests are of only marginal benefit over the most powerful single statistic.     

Model misspecification and probabilistic tests of topology: evidence from empirical data sets

Probabilistic tests of topology offer a powerful means of evaluating competing phylogenetic hypotheses. The performance of the nonparametric Shimodaira-Hasegawa (SH) test, the parametric Swofford-Olsen-Waddell-Hillis (SOWH) test, and Bayesian posterior probabilities were explored for five data sets for which all the phylogenetic relationships are known with a very high degree of certainty. These results are consistent with previous simulation studies that have indicated a tendency for the SOWH test to be prone to generating Type 1 errors because of model misspecification coupled with branch length heterogeneity. These results also suggest that the SOWH test may accord overconfidence in the true topology when the null hypothesis is in fact correct. In contrast, the SH test was observed to be much more conservative, even under high substitution rates and branch length heterogeneity. For some of those data sets where the SOWH test proved misleading, the Bayesian posterior probabilities were also misleading. The results of all tests were strongly influenced by the exact substitution model assumptions. Simple models, especially those that assume rate homogeneity among sites, had a higher Type 1 error rate and were more likely to generate misleading posterior probabilities. For some of these data sets, the commonly used substitution models appear to be inadequate for estimating appropriate levels of uncertainty with the SOWH test and Bayesian methods. Reasons for the differences in statistical power between the two maximum likelihood tests are discussed and are contrasted with the Bayesian approach.     

Empirical evaluation of two-sample statistical tests for differences of stepping phase during insect walking

In order to determine which statistical tests can validly be applied to data that describe a temporal relationship between two or more repetitive movements by an animal, we evaluated empirically seven two-sample tests that seemed potentially useful: Student's t test, the Watson Williams test for means, the variance-ratio F test, the Watson Williams test for the concentration parameter k, the Wallraff test, the Mann Whitney test and the Watson U² test. Evaluations were carried out on the timing (phases) of bursts of muscular activity in one leg relative to those in another during free walking in cockroaches. Each statistical test was evaluated by dividing randomly a single parent set of data into two subsets, each subset containing about half the original data set. This division was repeated 400 times, thus generating 400 different pairs of subsets. Each statistical test was used separately on the pairs of subsets to test the null hypothesis that the two samples of each pair came from the same population;; this procedure generated 400 statistics for each test, one for each pair of subsets. An estimate of the reliability of each statistical test was obtained by comparing the number of times the test actually indicated a significant difference between subsets to the number of times it might be expected to do so out (20 out of 400 when tested at the 5% level of significance). This procedure was repeated on ten different sets of data. The outcome of the evaluation suggested that, from an empirical point of view, Student's t, the Mann Whitney, the Wallraff and the Watson U² tests may be useful in assessing differences among the data we analyzed. The variance-ratio F test and the Watson Williams test for the concentration parameter k were clearly not usable. The Watson Williams test for means might be useful in some circumstances. Performing an arcsine transformation of the data did not significantly alter these results. Possible causes of the inapplicability of some of these tests to phase data are discussed.     

Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level

Detecting positive Darwinian selection at the DNA sequence level has been a subject of considerable interest. However, positive selection is difficult to detect because it often operates episodically on a few amino acid sites, and the signal may be masked by negative selection. Several methods have been developed to test positive selection that acts on given branches (branch methods) or on a subset of sites (site methods). Recently, Yang, Z., and R. Nielsen (2002. Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol. Biol. Evol. 19:908-917) developed likelihood ratio tests (LRTs) based on branch-site models to detect positive selection that affects a small number of sites along prespecified lineages. However, computer simulations suggested that the tests were sensitive to the model assumptions and were unable to distinguish between relaxation of selective constraint and positive selection (Zhang, J. 2004. Frequent false detection of positive selection by the likelihood method with branch-site models. Mol. Biol. Evol. 21:1332-1339). Here, we describe a modified branch-site model and use it to construct two LRTs, called branch-site tests 1 and 2. We applied the new tests to reanalyze several real data sets and used computer simulation to examine the performance of the two tests by examining their false-positive rate, power, and robustness. We found that test 1 was unable to distinguish relaxed constraint from positive selection affecting the lineages of interest, while test 2 had acceptable false-positive rates and appeared robust against violations of model assumptions. As test 2 is a direct test of positive selection on the lineages of interest, it is referred to as the branch-site test of positive selection and is recommended for use in real data analysis. The test appeared conservative overall, but exhibited better power in detecting positive selection than the branch-based test. Bayes empirical Bayes identification of amino acid sites under positive selection along the foreground branches was found to be reliable, but lacked power.     

An empirical test of the midpoint rooting method

The outgroup method is widely used to root phylogenetic trees. An accurate root indication, however, strongly depends on the availability of a proper outgroup. An alternate rooting method is the midpoint rooting (MPR). In this case, the root is set at the midpoint between the two most divergent operational taxonomic units. Although the midpoint rooting algorithm has been extensively used, the efficiency of this method in retrieving the correct root remains untested. In the present study, we empirically tested the success rate of the MPR in obtaining the outgroup root for a given phylogenetic tree. This was carried out by eliminating outgroups in 50 selected data sets from 33 papers and rooting the trees with the midpoint method. We were thus able to compare the root position retrieved by each method. Data sets were separated into three categories with different root consistencies: data sets with a single outgroup taxon (54% success rate for MPR), data sets with multiple outgroup taxa that showed inconsistency in root position (82% success rate), and data sets with multiple outgroup taxa in which root position was consistent (94% success rate). Interestingly, the more consistent the outgroup root is, the more successful MPR appears to be. This is a strong indication that the MPR method is valuable, particularly for cases where a proper outgroup is unavailable.  © 2007 The Linnean Society of London, Biological Journal of the Linnean Society , 2007, 92 , 669–674.