An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

This paper presents a new and simple method to find indicator species and species assemblages characterizing groups of sites The novelty of our approach lies in the way we combine a species relative abundance with its relative frequency of occurrence in the various groups of sites This index is maximum when all individuals of a species are found in a single group of sites and when the species occurs in all sites of that group; it is a symmetric indicator The statistical significance of the species indicator values is evaluated using a randomization procedure Contrary to TWINSPAN, our indicator index for a given species is independent of the other species relative abundances, and there is no need to use pseudospecies The new method identifies indicator species for typologies of species releves obtained by any hierarchical or nonhierarchical classification procedure; its use is independent of the classification method Because indicator species give ecological meaning to groups of sites, this method provides criteria to compare typologies, to identify where to stop dividing clusters into subsets, and to point out the main levels in a hierarchical classification of sites Species can be grouped on the basis of their indicator values for each clustering level, the heterogeneous nature of species assemblages observed in any one site being well preserved Such assemblages are usually a mixture of eurytopic (higher level) and stenotopic species (characteristic of lower level clusters) The species assemblage approach demonstrates the importance of the ''sampled patch size,'' ie, the diversity of sampled ecological combinations, when we compare the frequencies of core and satellite species A new way to present species-site tables, accounting for the hierarchical relationships among species, is proposed A large data set of carabid beetle distributions in open habitats of Belgium is used as a case study to illustrate the new method

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Species assemblages and indicator species:the need for a flexible asymmetrical approach

Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.

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The worldwide leaf economics spectrum

Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.

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Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation

For the last 25 years species delimitation in prokaryotes (Archaea and Bacteria) was to a large extent based on DNA-DNA hybridization (DDH), a tedious lab procedure designed in the early 1970s that served its purpose astonishingly well in the absence of deciphered genome sequences. With the rapid progress in genome sequencing time has come to directly use the now available and easy to generate genome sequences for delimitation of species. GBDP (Genome Blast Distance Phylogeny) infers genome-to-genome distances between pairs of entirely or partially sequenced genomes, a digital, highly reliable estimator for the relatedness of genomes. Its application as an in-silico replacement for DDH was recently introduced. The main challenge in the implementation of such an application is to produce digital DDH values that must mimic the wet-lab DDH values as close as possible to ensure consistency in the Prokaryotic species concept. Correlation and regression analyses were used to determine the best-performing methods and the most influential parameters. GBDP was further enriched with a set of new features such as confidence intervals for intergenomic distances obtained via resampling or via the statistical models for DDH prediction and an additional family of distance functions. As in previous analyses, GBDP obtained the highest agreement with wet-lab DDH among all tested methods, but improved models led to a further increase in the accuracy of DDH prediction. Confidence intervals yielded stable results when inferred from the statistical models, whereas those obtained via resampling showed marked differences between the underlying distance functions. Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty. It is thus crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes. Such methodological advancements, easily accessible through the web service at http://ggdc.dsmz.de
 , are crucial steps towards a consistent and truly genome sequence-based classification of microorganisms.

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Genome sequence-based species delimitation with confidence intervals and improved distance functions

1. Introduction 2. Data in Biology 3. Computers and Data Analysis 4. Descriptive Statistics 5. Introduction to Probability Distributions 6. The Normal Probability Distribution 7. Hypothesis Testing and Interval Estimation 8. Introduction to Analysis of Variance 9. Single-Classification Analysis of Variance 10. Nested Analysis of Variance 11. Two-Way and Multiway Analysis of Variance 12. Statistical Power and Sample Size in the Analysis of Variance 13. Assumptions of Analysis of Variance 14. Linear Regression 15. Correlation 16. Multiple and Curvilinear Regression 17. Analysis of Frequencies 18. Meta-Analysis and Miscellaneous Methods

Biometery: The principles and practice of statistics in biological research

Biometry: The Principles and Practice of Statistics in Biological Research

Superimposition methods for comparing configurations of landmarks in two or more specimens are reviewed. These methods show differences in shape among specimens as residuals after rotation, translation, and scaling them so that they align as well as possible. A new method is presented that generalizes Siegel and Benson's (1982) resistant-fit theta-rho analysis so that more than two objects can be compared at the same time. Both least-squares and resistant-fit approaches are generalized to allow for affine transformations (uniform shape change). The methods are compared, using artificial data and data on 18 landmarks on the wings of 127 species of North American mosquitoes. Graphical techniques .are also presented to help sum- marize the patterns of differences in shape among the objects being compared. (Morphometrics; resistant-fit; least-squares; theta-rho analysis; rotational fit; affine transformations.) An important problem in morphomet- is now easy to display a transformation grid rics is that of comparing configurations of that maps the configuration of landmarks landmarks in two or more specimens. of one organism exactly into those of Thompson (1917) suggested an elegant ap- another. proach, using "transformation grids," that An alternative approach to fitting a mod- depicts the overall form of one organism el that completely describes the differences as a distortion in the shape of a reference between two organisms is to fit a very sim- organism. The basic idea was to place a ple model that only takes into consider- Cartesian coordinate grid over the refer- ation global parameters such as differences ence organism and then distort the image in rotation, translation, and scale. Geo- of the organism (including the grid) in var- metrically, this corresponds to superim- ious ways until the form of the second or- posing one organism on top of another so ganism was achieved. The differences in that its landmarks align as well as possible shapes of the two organisms are shown by (in some sense) with the positions of the the deviations of the fitted grid (usually corresponding landmarks on the second. bent and stretched in various ways) from Differences in shape are then shown by the original simple square grid. Thompson differences in positions of corresponding (1917) sketched the grids subjectively landmarks. Shape differences between two without an explicit specification of which organisms are found by studying these re- landmarks were used. Not all landmarks siduals. These methods are the subject of shown in pairs of drawings are located ex- the present paper. actly where the superimposed grids would Sneath (1967) investigated the problem imply they should be. This means that the of finding the optimal translation, rota- grids should be more complex than those tion, and size change of one object in order shown in Thompson (1917) in order to ac- for it to be superimposed on another. The curately show the differences between two two objects were represented as sets of x,y- organisms. Bookstein (1978) developed the coordinates of landmarks. A least-squares method of biorthogonal grid analysis criterion was used to measure the goodness which quantifies Thompson's approach and of fit of one object to another. Gower (1971) makes it objective. But it is complex and further developed Sneath's (1967) method has not been applied very often. A recent and expressed the operations in terms of b~ak-through (Bookstein, 1989) is the use matrix algebra. Siegel and Benson (1982) of methods based on thin-plate splines. It made the important observation that a

https://www.jstor.org/stable/pdfplus/2992207.pdf

Extensions of the Procrustes Method for the Optimal Superimposition of Landmarks

The analysis of shape is a fundamental part of much biological research. As the field of statistics developed, so have the sophistication of the analysis of these types of data. This lead to multivariate morphometrics in which suites of measurements were analyzed together using canonical variates analysis, principal components analysis, and related methods. In the 1980s, a fundamental change began in the nature of the data gathered and analyzed. This change focused on the coordinates of landmarks and the geometric information about their relative positions. As a by-product of such an approach, results of multivariate analyses could be visualized as configurations of landmarks back in the original space of the organism rather than only as statistical scatter plots. This new approach, called “geometric morphometrics”, had benefits that lead Rohlf and Marcus (1993) to proclaim a “revolution” in morphometrics. In this paper, we briefly update the discussion in that paper and summarize the advances in the ten years since the paper by Rohlf and Marcus. We also speculate on future directions in morphometric analysis.

Geometric morphometrics: Ten years of progress following the ‘revolution’

Introduction to Biostatistics , Introduction to Biostatistics , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

F. James Rohlf

Papers

Biometery: The principles and practice of statistics in biological research

Biometry: The Principles and Practice of Statistics in Biological Research

Extensions of the Procrustes Method for the Optimal Superimposition of Landmarks

Geometric morphometrics: Ten years of progress following the ‘revolution’

Introduction to Biostatistics