Book Details
| Print ISBN | 9780444538680 |
| eText ISBN | 9780444538680 |
| Publisher | Elsevier |
| Publishing Year | 2012 |
| Edition | 3rd Edition |
| Language | English |
| Pages | 1006 |
Numerical Ecology, 3rd Edition, is a comprehensive textbook that addresses quantitative analysis techniques for multivariate ecological data. The volume connects ecological research questions to statistical analysis methods through clear descriptions of complex numerical techniques.
The text presents numerical methods drawn from disciplines including mathematical physics, information theory, numerical taxonomy, and both parametric and nonparametric statistics. Broad areas of coverage include the mathematical foundations of ecological resemblance, multidimensional clustering and ordination, and spatial structures.
All analytical calculations presented across the volume are accessible through R language functions to aid research workflows. This textbook is designed for practicing ecologists, supporting graduate students as well as professional researchers across oceanography, environmental science, and ecology.
Table of Contents
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
- • 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 inversion
- • 2.9 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 correlations
- • 4.6 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 resemblance
- • 7.0 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 clustering
- • 8.8 Partitioning by K-means
- • 8.9 Species clustering: biological associations
- • 8.10 Seriation
- • 8.11 Multivariate regression trees (MRT)
- • 8.12 Clustering statistics
- • 1 Connectedness and isolation
- • 2 Cophenetic correlation and related measures
- • 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
Chapter 10: Interpretation of ecological structures
- • 10.0 Ecological structures
- • 10.1 Clustering and ordination
- • 10.2 The mathematics of ecological interpretation
- • 10.3 Regression
- • 10.4 Path analysis
- • 10.5 Matrix comparisons
- • 10.6 The fourth-corner problem
- • 4 Other types of comparisons among variables
- • 10.7 Software
Chapter 11: Canonical analysis
- • 11.0 Principles of canonical analysis
- • 11.1 Redundancy analysis (RDA)
- • 11.2 Canonical correspondence analysis (CCA)
- • 11.3 Linear discriminant analysis (LDA)
- • 11.4 Canonical correlation analysis (CCorA)
- • 11.5 Co-inertia (CoIA) and Procrustes (Proc) analyses
- • 11.6 Canonical analysis of community composition data
- • 11.7 Software
Chapter 12: Ecological data series
- • 12.0 Ecological series
- • 12.1 Characteristics of data series and research objectives
- • 12.2 Trend extraction and numerical filters
- • 12.3 Periodic variability: correlogram
- • 12.4 Periodic variability: periodogram
- • 12.5 Periodic variability: spectral and wavelet analyses
- • 12.6 Detection of discontinuities in multivariate series
- • 12.7 Box-Jenkins models
- • 12.8 Software
Chapter 13: Spatial analysis
- • 13.0 Spatial patterns
- • 13.1 Structure functions
- • 13.2 Maps
- • 13.3 Patches and boundaries
- • 13.4 Unconstrained and constrained ordination maps
- • 13.5 Spatial modelling through canonical analysis
- • 13.6 Software
Chapter 14: Multiscale analysis
- • 14.0 Introduction to multiscale analysis
- • 14.1 Distance-based Moran’s eigenvector maps (dbMEM)
- • 14.2 Moran’s eigenvector maps (MEM), general form
- • 14.3 Asymmetric eigenvector maps (AEM)
- • 14.4 Multiscale ordination (MSO)
- • 14.5 Other eigenfunction-based methods of spatial analysis
- • 14.6 Multiscale analysis of beta diversity
- • 14.7 Software
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