A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.

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The neighbor-joining method: a new method for reconstructing phylogenetic trees.

The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved in making estimates from the original data. In the case of phylogenies, it is argued that the proper method of resampling is to keep all of the original species while sampling characters with replacement, under the assumption that the characters have been independently drawn by the systematist and have evolved independently. Majority-rule consensus trees can be used to construct a phylogeny showing all of the inferred monophyletic groups that occurred in a majority of the bootstrap samples. If a group shows up 95% of the time or more, the evidence for it is taken to be statistically significant. Existing computer programs can be used to analyze different bootstrap samples by using weights on the characters, the weight of a character being how many times it was drawn in bootstrap sampling. When all characters are perfectly compatible, as envisioned by Hennig, bootstrap sampling becomes unnecessary; the bootstrap method would show significant evidence for a group if it is defined by three or more characters.

Confidence limits on phylogenies: an approach using the bootstrap.

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special

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Some methods for classification and analysis of multivariate observations

genalex is a user-friendly cross-platform package that runs within Microsoft Excel, enabling population genetic analyses of codominant, haploid and binary data. Allele frequency-based analyses include heterozygosity, F statistics, Nei's genetic distance, population assignment, probabilities of identity and pairwise relatedness. Distance-based calculations include amova, principal coordinates analysis (PCA), Mantel tests, multivariate and 2D spatial autocorrelation and twogener. More than 20 different graphs summarize data and aid exploration. Sequence and genotype data can be imported from automated sequencers, and exported to other software. Initially designed as tool for teaching, genalex 6 now offers features for researchers as well. Documentation and the program are available at http://www.anu.edu.au/BoZo/GenAlEx/

genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.

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Arlequin (version 3.0): An integrated software package for population genetics data analysis

les caminalcules sont un groupe «d'organisme» cree artificiellement selon des principes ressemblant aux organismes reels

A Phylogenetic Analysis of the Caminalcules. I. the Data Base

THE PURPOSE of the study reported in this paper is to investigate whether multiple factor analysis could be used to describe and evaluate taxonomic relationships among organisms. Recent efforts to quantify taxonomic procedure have been termed "numerical taxonomy" by Sokal and Sneath (Sokal, 1960). Numerical methods are being developed for (1) estimating the relative degrees of similarity or affinity among organisms, based on many equally weighted characters, and (2) arranging organisms into taxa in which there is a greater degree of similarity between any two organisms within a taxon than there is between two organisms in different taxa at the same hierarchic level. There are several ways in which each of these two problems may be attacked. Sokal and Michener (1958) have used correlation coefficients as an index of similarity and the weighted variable-group method for constructing a tree-like diagram of relationships (dendrogram). Sneath (1957a, 1957b) has used a coefficient of association as an index of similarity and also a computer program which sorts the matrix of similarity coefficients into a form such that the relationships become immediately evident. Rogers and Tanimoto (1960) have developed a coefficient of association and yet another method of finding clumps or clusters from their similarity ratios. Sokal (1961) has proposed using geometric distance as a

https://www.jstor.org/stable/pdfplus/2411444.pdf

The Description of Taxonomic Relationships by Factor Analysis

Publisher Summary This chapter discusses the nature and purpose of clustering and classification The classification is one of the fundamental processes in science It is important to get all the facts and phenomena straight before understanding and developing the unifying principles that explain the reasons for the occurrence of classification and its apparent order As classification is the ordering of objects by their similarities, it is important to recognize that classification transcends human intellectual endeavor and is a fundamental property of living organisms The attempts to develop techniques for automatic classification necessitates the quantification of similarity Therefore, the ability to perceive any two objects as more similar to each other than either is to a third is sure to have been present in the ancestors of the human species In much classificatory work, it is impractical to obtain estimates of taxonomic similarity in an assemblage of objects from a sample of subjects

Clustering and Classification: Background and Current Directions

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading convergent issues in genetics and demography is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

Convergent issues in genetics and demography

Tests for differences among regional means are typically carried out by analysis of variance (ANOVA). When such data are spatially autocorrelated (SA), the assumptions of ANOVA are not met, giving rise to excessive type I error rates. Two spatially adjusted ANOVA methods, Griffith's and COCOPAN, have been proposed to overcome this problem. In this study we show, by means of extensive simulations, the magnitude of the error rates introduced by SA induced in isolation-by-distance models typical of those used in population genetics. For data suspected of exhibiting such SA, we propose a strategy for distinguishing between inherent SA, generated within the data by a contagious process, and spurious SA, introduced by regional differences in means. The approach adopted is that of restricted randomization of distance matrices. We also furnish error rates and power estimates for both Griffith's method and COCOPAN. In addition to the simulated data, the methods are applied to an actual example from plant population biology.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1993.tb00291.x

Robert R. Sokal

Papers

A Phylogenetic Analysis of the Caminalcules. I. the Data Base

The Description of Taxonomic Relationships by Factor Analysis

Clustering and Classification: Background and Current Directions

Convergent issues in genetics and demography

Testing for Regional Differences in Means: Distinguishing Inherent from Spurious Spatial Autocorrelation by Restricted Randomization