Numerical Ecology. Developments in Environmental Modelling, Volume 24, 20. Laying

Numerical Ecology. Developments in Environmental Modelling, Volume 24, 20. von Legendre, Pierre and Louis Legendre:Autor(en) Legendre, Pierre and Louis Legendre:Auflage Second impressionVerlag / Jahr Amsterdam : Elsevier Science, 2000.Format / Einband kart. 852 pgs.Sprache EnglischGewicht ca. 1597 gISBN 0444892508EAN 9780444892508Bestell-Nr 1253253Bemerkungen Good copy with small signs of wear, little corner bump. - Contents: Preface xi -- Complex ecological data sets -- Numerical analysis of ecological data -- Autocorrelation and spatial structure -- -Types of spatial structures -- - Tests of statistical significance in the presence of autocorrelation -- - Classical sampling and spatial structure -- Statistical testing by permutation -- -Classical tests of significance -- -Permutation tests -- -Numerical example -- - Remarks on permutation tests -- Computers -- Ecological descriptors -- - Mathematical types of descriptor -- - Intensive -- extensive -- additive -- and non- additive descriptors -- Coding -- - Linear transformation -- -Nonlinear transformations -- - Combining descriptors -- - Ranging and standardization -- - Implicit transformation in association coefficients -- -Normalization -- -Dummy variable (binary) coding -- Missing data -- - Deleting rows or columns -- -Accommodating algorithms to missing data -- - Estimating missing values -- Matrix algebra: a summary -- Matrix algebra -- The ecological data matrix -- Association matrices -- Special matrices -- Vectors and scaling -- Contents -- vi -- Matrix addition and multiplication -- Determinant -- The rank of a matrix -- Matrix inversion -- Eigenvalues and eigenvectors -- -Computation -- - Numerical examples -- Some properties of eigenvalues and eigenvectors -- Singular value decomposition -- Dimensional analysis in ecology -- Dimensional analysis -- Dimensions -- Fundamental principles and the Pi theorem -- The complete set of dimensionless products -- Scale factors and models -- Multidimensional quantitative data -- Multidimensional statistics -- Multidimensional variables and dispersion matrix -- Correlation matrix -- Multinormal distribution -- Principal axes -- Multiple and partial correlations -- -Multiple linear correlation -- -Partial correlation -- - -- statistical significance -- -Interpretation of correlation coefficien -- - Causal modelling using correlations -- Multinormal conditional distribution -- Tests of normality and multinormality -- Multidimensional semiquantitative data -- Nonparametric statistics -- Quantitative -- semiquantitative -- and qualitative multivariates -- One-dimensional nonparametric statistics -- Multidimensional ranking tests -- Multidimensional qualitative data -- General principles -- Information and entropy -- Two-way contingency tables -- Multiway contingency tables -- Contingency tables: correspondence -- Species diversity -- - Diversity -- - Evenness -- equitability -- Ecological resemblance -- The basis for clustering and ordination -- Q and R analyses -- Association coefficients -- Q mode: similarity coefficients -- - Symmetrical binary coefficients -- - Asymmetrical binary coefficients -- -Symmetrical quantitative coefficients -- -Asymmetrical quantitative coefficients -- - Probabilistic coefficients -- Q mode: distance coefficients -- - Metric distances -- -Semimetrics -- R mode: coefficients of dependence -- - Descriptors other than species abundances -- -Species abundances: biological associations -- Choice of a coefficient -- Computer programs and packages -- Cluster analysis -- A search for discontinuities -- Definitions -- The basic model: single linkage clustering -- Cophenetic matrix and ultrametric property -- - Cophenetic matrix -- - Ultrametric property -- The panoply of methods -- -Sequential versus simultaneous algorithms -- -Agglomeration versus division -- -Monothetic versus polythetic methods -- -Hierarchical versus non-hierarchical methods -- -Probabilistic versus non-probabilistic methods -- Hierarchical agglomerative clustering -- -Single linkage agglomerative clustering -- -Complete linkage agglomerative clustering -- -Intermediate linkage clustering -- -Unweighted arithmetic average clustering (UPGMA) -- -Weighted arithmetic average clustering (WPGMA) -- - Unweighted centroid clustering (UPGMC) -- -Weighted centroid clustering (WPGMC) -- -Ward\'s minimum variance method -- - General agglomerative clustering model -- -Flexible clustering -- - Information analysis -- Reversals -- Hierarchical divisive clustering -- -Monothetic methods -- -Polythetic methods -- ordination space -- -TWINSPAN -- -Division in -- Partitioning by K-means -- Species clustering: biological associations -- - Probabilistic -- -Non-hierarchical complete linkage clustering -- - Indicator species -- clustering -- Seriation -- Clustering statistics -- -Connectedness and isolation -- -Cophenetic correlation and related measures -- Cluster validation -- Cluster representation and choice of a method -- Ordination in reduced space -- Projecting data sets in a few dimensions -- Principal component analysis (PCA) -- -Computing the eigenvectors -- - Computing and representing the principal components -- -Contributions of descriptors -- - Biplots -- -Principal components of a correlation matrix -- -The meaningful components -- -Misuses of principal components -- -Ecological applications -- -Algorithms -- Principal coordinate analysis (PCOA) -- -Computation -- -Numerical example -- -Rationale of the method -- -Negative eigenvalues -- -Ecological applications -- - Algorithms -- Nonmetric multidimensional scaling (MDS) -- Correspondence analysis (CA) -- -Computation -- -Numerical example -- - Interpretation -- -Site x species data tables -- -Arch effect -- -Ecological applications -- -Algorithms -- Factor analysis -- Interpretation of ecological structures -- Ecological structures -- Clustering and ordination -- The mathematics of ecological interpretation -- Regression -- -Simple linear regression: model I -- -Simple linear regression: model II -- - Multiple linear regression -- - Polynomial regression -- -Partial linear regression -- -Nonlinear regression -- -Logistic regression -- - Splines and LOWESS smoothing -- Path analysis -- Matrix comparisons -- -Mantel test -- -More than two matrices -- -ANOSIM test -- - Procrustes analysis -- The -- th-corner problem -- - Comparing two qualitative variables -- -Test of statistical significance -- -Permutational models -- -Other types of comparison among variables -- Canonical analysis -- Principles of canonical analysis -- Redundancy analysis (RDA) -- -The algebra of redundancy analysis -- -Numerical examples -- -Algorithms -- Canonical correspondence analysis (CCA) -- -The algebra of canonical correspondence analysis -- -Numerical example -- - Algorithms -- Partial RDA and CCA -- - Applications -- - Tests of significance -- Canonical correlation analysis (CCorA) -- Discriminant analysis -- - The algebra of discriminant analysis -- - Numerical example -- Canonical analysis of species data -- Ecological data series -- Ecological series -- Characteristics of data series and research objectives -- Trend extraction and numerical filters -- Periodic variability: correlogram -- - Autocovariance and autocorrelation -- -Cross-covariance and cross- correlation -- Periodic variability: periodogram -- - Periodogram of Whittaker and Robinson -- - Contingency periodogram of Legendre et al. -- -Periodograms of Schuster and Dutilleul -- -Harmonic regression -- Periodic variability: spectral analysis -- -Series of a single variable -- - Multidimensional series -- - Maximum entropy spectral analysis -- Detection of discontinuities in multivariate series -- -Ordinations in reduced space -- -Segmenting data series -- -Webster\'s method -- - Chronological clustering -- Box-Jenkins models -- Computer programs -- Spatial analysis -- Spatial patterns -- Structure functions -- -Spatial correlograms -- - Interpretation of all-directional correlograms -- -Variogram -- -Spatial covariance -- semi-variance -- correlation -- cross-correlation -- - Multivariate Mantel correlogram -- Maps -- -Trend-surface analysis -- -Interpolated maps -- -Measures of fit -- Patches and boundaries -- -Connection networks -- -Constrained clustering -- - Ecological -- boundaries -- - Dispersal -- Unconstrained and constrained ordination maps -- Causal modelling: partial canonical analysis -- -Partitioning method -- - Interpretation of the fractions -- Causal modelling: partial Mantel analysis -- -Partial Mantel correlations -- - Multiple regression approach -- - Comparison of methods -- Computer programs -- Bibliography -- Tables -- Subject index. 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