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Numerical ecology /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford :
Elsevier,
2012.
|
| Edición: | 3rd ed. |
| Colección: | Developments in environmental modelling ;
24. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Numerical Ecology; Copyright; Contents; Preface; Chapter 1: Complex ecological data sets; 1.0 Numerical analysis of ecological data; 1.1 Spatial structure, spatial dependence, spatial correlation; 1.2 Statistical testing by permutation; 1.3 Computer programs and packages; 1.4 Ecological descriptors; 1.5 Coding; 1.6 Missing data; 1.7 Software; Chapter 2: Matrix algebra: a summary; 2.0 Matrix algebra; 2.1 The ecological data matrix; 2.2 Association matrices; 2.3 Special matrices; 2.4 Vectors and scaling; 2.5 Matrix addition and multiplication; 2.6 Determinant; 2.7 Rank of a matrix.
- 2.8 Matrix inversion2.9 Eigenvalues and eigenvectors; 2.10 Some properties of eigenvalues and eigenvectors; 2.11 Singular value decomposition; 2.12 Software; Chapter 3: Dimensional analysis in ecology; 3.0 Dimensional analysis; 3.1 Dimensions; 3.2 Fundamental principles and the Pi theorem; 3.3 The complete set of dimensionless products; 3.4 Scale factors and models; Chapter 4: Multidimensional quantitative data; 4.0 Multidimensional statistics; 4.1 Multidimensional variables and dispersion matrix; 4.2 Correlation matrix; 4.3 Multinormal distribution; 4.4 Principal axes.
- 4.5 Multiple and partial correlations4.6 Tests of normality and multinormality; 4.7 Software; Chapter 5: Multidimensional semiquantitative data; 5.0 Nonparametric statistics; 5.1 Quantitative, semiquantitative, and qualitative multivariates; 5.2 One-dimensional nonparametric statistics; 5.3 Rank correlations; 5.4 Coefficient of concordance; 5.5 Software; Chapter 6: Multidimensional qualitative data; 6.0 General principles; 6.1 Information and entropy; 6.2 Two-way contingency tables; 6.3 Multiway contingency tables; 6.4 Contingency tables: correspondence; 6.5 Species diversity; 6.6 Software.
- Chapter 7: Ecological resemblance7.0 The basis for clustering and ordination; 7.1 Q and R analyses; 7.2 Association coefficients; 7.3 Q mode: similarity coefficients; 7.4 Q mode: distance coefficients; 7.5 R mode: coefficients of dependence; 7.6 Choice of a coefficient; 7.7 Transformations for community composition data; 7.8 Software; Chapter 8: Cluster analysis; 8.0 A search for discontinuities; 8.1 Definitions; 8.2 The basic model: single linkage clustering; 8.3 Cophenetic matrix and ultrametric property; 8.4 The panoply of methods; 8.5 Hierarchical agglomerative clustering; 8.6 Reversals.
- 8.7 Hierarchical divisive clustering8.8 Partitioning by K-means; 8.9 Species clustering: biological associations; 8.10 Seriation; 8.11 Multivariate regression trees (MRT); 8.12 Clustering statistics; 8.13 Cluster validation; 8.14 Cluster representation and choice of a method; 8.15 Software; Chapter 9: Ordination in reduced space; 9.0 Projecting data sets in a few dimensions; 9.1 Principal component analysis (PCA); 9.2 Correspondence analysis (CA); 9.3 Principal coordinate analysis (PCoA); 9.4 Nonmetric multidimensional scaling (nMDS); 9.5 Software.