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 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 classiﬁcation columns computed contingency table correlation coefﬁcient correlogram correspondence analysis covariance data series data sets data table deﬁned dendrogram described diagonal dimensional distance classes distribution Ecological application ecological data ecologists eigenvalues eigenvectors environmental variables equation estimate Euclidean distance explanatory variables factors ﬁeld ﬁnd ﬁrst ﬁtted fraction frequencies function gradient groups identiﬁed Legendre linear linkage clustering Mantel test measure modiﬁed multidimensional multiple regression multivariate normal null hypothesis numerical example objects observations obtained ordination orthogonal pairs parameters partition PCoA periodogram permutation phytoplankton plot polynomial principal component analysis principal coordinate problem procedure programs qualitative descriptors quantitative random reduced space regression coefﬁcients relationships residuals response variable rows scale scores Section signiﬁcantly similarity matrix slope spatial autocorrelation species abundance speciﬁc Subsection tests of signiﬁcance transformation values variance variation variogram vector zero
Page xvi - Whenever we say that the probability of an event E with respect to an experiment (£ is equal to P, the concrete meaning of this assertion will thus simply be the following: In a long series of repetitions of...