The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others. Compared to the first edition of Numerical Ecology, this second edition includes three new chapters, dealing with the analysis of semiquantitative data, canonical analysis and spatial analysis. New sections have been added to almost all other chapters. There are sections listing available computer programs and packages at the end of several chapters. As in the previous English and French editions, there are numerous examples from the ecological literature, and the choice of methods is facilitated by several synoptic tables.
For practicing ecologists, graduate students and professional researchers.
Chapter headings and selected parts: Preface. Complex Ecological Data Sets. Numerical analysis of ecological data. Statistical testing by permutation. Ecological descriptors. Matrix Algebra: A Summary. The ecological data matrix. Vectors and scaling. Eigenvalues and eigenvectors. Dimensional Analysis in Ecology. Fundamental principles and the Pi theorem. Scale factors and models. Multidimensional Quantitative Data. Multidimensional variables and dispersion matrix. Multinormal distribution. Tests of normality and multinormality. Multidimensional Semiquantitative data. Nonparametric statistics. Quantitative, semiquantitative, and qualitative multivariates. Multidimensional Qualitative Data. Multiway contingency tables. Species diversity. Ecological Resemblance. The basis for clustering and ordination. Association coefficients. R mode: coefficients of dependence. Cluster Analysis. The basic model: single linkage clustering. Cophenetic matrix and ultrametric property. Hierarchical divisive clustering. Ordination in Reduced Space. Projecting data sets in a few dimensions. Principal component analysis (PCA). Nonmetric multidimensional scaling (MDS). Interpretation of Ecological Structures. Ecological structures. The mathematics of ecological interpretation. The 4th-corner problem. Canonical Analysis. Redundancy analysis (RDA). Canonical correspondence analysis (CCA). Canonical analysis of species data. Ecological Data Series. Characteristics of data series and research objectives. Trend extraction and numerical filters. Periodic variabilty: spectral analysis. Detection of discontinuities on multivariate series. Box-Jenkins models. Spatial Analysis. Unconstrained and constrained ordination maps. Causal modelling: partial canonical analysis
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@qu:...The text is well written and very clear, it is so clear that the reader is seduced to read further; I found myself happily reading page after page. The authors are masters of their subject and as they delineate it, from chapter to chapter, they define it. At every corner of the text there is an 'ecological application' and this is useful in revealing the limits and advantages of a method.
@source:Journal of Plankton Research, Volume 21, Number 7
@qu:...The first English edition of Numerical Ecology appeared in 1983. It was already an important reference for multi-dimensional analysis in ecology, providing the most comprehensive explanations of matrix algebra, eigenanalysis, measures of association (similarity and dissimilarity), cluster analysis and ordination for ecology. The new edition is much more than this. The last 15 years have seen an explosion of new statistical non-parametric methods, particularly permutation methods, and computer programs that have made the analysis of 'misbehaving' data more possible than ever before.
This new edition of Numerical Ecology provides a very impressive overview of these complex new methods. It still provides an (up-dated) summary of the fundamentals of measures of association, clustering and ordination, but it also provides a plethora of new material, consistent with the recent explosion of new methods. It is comprehensive in the sheer quantity of the new methods it covers, such as cluster analysis with spatial contiguity constraints, multivariate Mantel tests for spatial autocorrelation, redundancy analysis and permutation tests for complex linear models. It is, without a doubt, the most extensive current review of the most up-to-date multivariate numerical techniques in experimental ecology.
Along with providing understandable introductions to and examples of these new methods, it is a great source