Paleontological data analysis / Øyvind Hammer and David A.T. Harper.

Format:

Book

Author/Creator:

Hammer, Øyvind, 1968-

Contributor:

Harper, D. A. T.

Language:

English

Subjects (All):

Paleontology--Data processing.

Paleontology.

Physical Description:

x, 351 pages : illustrations ; 25 cm

Place of Publication:

Malden, MA : Blackwell Pub., 2006.

Summary:

During the last 10 years numerical methods have begun to dominate paleontology. These methods now reach far beyond the fields of morphological and phylogenetic analyses to embrace biostratigraphy, paleobiogeography, and paleoecology. The availability of cheap computing power, together with a wide range of software products, have made increasingly complex algorithms accessible to the vast majority of paleontologists.

Paleontological Data Analysis explains the key numerical techniques in paleontology, and the methodologies employed in the software packages now available. Following an introduction to numerical methodologies in paleontology, and to univariate and multivariate techniques (including inferential testing), are chapters on morphometrics, phylogentic analysis, paleobiogeography and paleoecology, time series analysis, and quantitative biostratigraphy. Each chapter describes a range of techniques in detail, with worked examples, illustrations, and appropriate case histories. The purpose, type of data required, functionality, and implementation of each technique are described, together with notes of caution where appropriate

Contents:

1.1 The nature of paleontological data 1

1.2 Advantages and pitfalls of paleontological data analysis 4

1.3 Software 7

2 Basic statistical methods 8

2.2 Statistical distributions 12

2.3 Shapiro-Wilk test for normal distribution 19

2.4 F test for equality of variances 22

2.5 Student's t test and Welch test for equality of means 23

2.6 Mann-Whitney U test for equality of medians 27

2.7 Kolmogorov-Smirnov test for equality of distribution 30

2.8 Bootstrapping and permutation 33

2.9 One-way ANOVA 35

2.10 Kruskal-Wallis test 39

2.11 Linear correlation 42

2.12 Non-parametric tests for correlation 46

2.13 Linear regression 48

2.14 Reduced major axis regression 53

2.15 Chi-square test 57

3 Introduction to multivariate data analysis 61

3.1 Approaches to multivariate data analysis 61

3.2 Multivariate distributions 62

3.3 Parametric multivariate tests - Hotelling's T[superscript 2] 63

3.4 Non-parametric multivariate tests - permutation test 66

3.5 Hierarchical cluster analysis 67

3.6 K-means cluster analysis 75

4 Morphometrics 78

4.2 The allometric equation 79

4.3 Principal components analysis (PCA) 83

4.4 Multivariate allometry 91

4.5 Discriminant analysis for two groups 96

4.6 Canonical variate analysis (CVA) 100

4.7 MANOVA 103

4.8 Fourier shape analysis in polar coordinates 105

4.9 Elliptic Fourier analysis 108

4.10 Eigenshape analysis 112

4.11 Landmarks and size measures 115

4.12 Procrustes fitting 117

4.13 PCA of landmark data 121

4.14 Thin-plate spline deformations 122

4.15 Principal and partial warps 128

4.16 Relative warps 132

4.17 Regression of warp scores 134

4.18 Disparity measures and morphospaces 136

4.19 Point distribution statistics 141

4.20 Directional statistics 145

Case study: The ontogeny of a Silurian trilobite 148

5 Phylogenetic analysis 157

5.2 Characters 160

5.3 Parsimony analysis 161

5.4 Character state reconstruction 166

5.5 Evaluation of characters and trees 168

5.6 Consensus tree 168

5.7 Consistency index 170

5.8 Retention index 171

5.9 Bootstrapping 172

5.10 Bremer support 174

5.11 Stratigraphic congruency indices 175

5.12 Phylogenetic analysis with maximum likelihood 178

Case study: The systematics of heterosporous ferns 179

6 Paleobiogeography and paleoecology 183

6.2 Biodiversity indices 186

6.3 Taxonomic distinctness 193

6.4 Comparison of diversity indices 196

6.5 Abundance models 198

6.6 Rarefaction 202

6.7 Diversity curves 206

6.8 Size-frequency and survivorship curves 208

6.9 Association similarity indices for presence/absence data 211

6.10 Association similarity indices for abundance data 216

6.11 ANOSIM and NPMANOVA 221

6.12 Correspondence analysis 223

6.13 Principal coordinates analysis (PCO) 233

6.14 Non-metric multidimensional scaling (NMDS) 236

6.15 Seriation 240

Case study: Ashgill brachiopod-dominated paleocommunities from East China 244

7 Time series analysis 254

7.2 Spectral analysis 255

7.3 Autocorrelation 260

7.4 Cross-correlation 263

7.5 Wavelet analysis 266

7.6 Smoothing and filtering 269

7.7 Runs test 271

Case study: Sepkoski's generic diversity curve for the Phanerozoic 273

8 Quantitative biostratigraphy 279

8.2 Parametric confidence intervals on stratigraphic ranges 281

8.3 Non-parametric confidence intervals on stratigraphic ranges 283

8.4 Graphic correlation 286

8.5 Constrained optimization 291

8.6 Ranking and scaling 298

8.7 Unitary associations 306

8.8 Biostratigraphy by ordination 314

8.9 What is the best method for quantitative biostratigraphy? 315

Appendix A Plotting techniques 317

Appendix B Mathematical concepts and notation 328.

Notes:

Includes bibliographical references (pages [333]-344) and index.

ISBN:

1405115440

OCLC:

58431669

Publisher Number:

9781405115445