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.
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Chapter 1 Complex ecological data sets
Chapter 3 Dimensional analysis in ecology
Chapter 4 Multidimensional quantitative data
Chapter 5 Multidimensional semiquantitative data
Chapter 6 Multidimensional qualitative data
Chapter 7 Ecological resemblance
Chapter 8 Cluster analysis
Chapter 9 Ordination in reduced space
algorithm association axes axis binary biplot calculated canonical analysis causal centroids Chapter columns computed contingency table correlation coefficient correlogram correspondence analysis covariance data series data set data table dendrogram described diagonal dimensional dimensionless distance classes distribution Ecological application ecological data ecologists eigenvalues eigenvectors environmental variables equation estimate Euclidean distance explanatory variables factors fraction frequencies function gradient groups Legendre linear linkage clustering Mantel test measure multidimensional multiple regression multivariate normal null hypothesis number of species numerical example objects observations obtained ordination orthogonal pairs parameters partial correlation partition PCoA periodogram permutation phytoplankton plot polynomial principal component analysis problem procedure programs qualitative descriptors quantitative random reduced space regression coefficients relationships residuals response variable rows scale scores Section semiquantitative similarity matrix slope spatial autocorrelation species abundance standard Subsection sum of squares transformation values variance variation variogram vector zero