Paleontological Data Analysis

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Format: Paperback
Pub. Date: 2005-11-04
Publisher(s): Wiley-Blackwell
List Price: $135.00

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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, phylogenetic 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. The book and the accompanying PAST software package available through www.blackwellpublishing.com/hammer are important investigative tools in a rapidly developing field characterized by many exciting new discoveries and innovative techniques. Paleontological Data Analysis is an invaluable tool for all students and researchers involved in quantitative paleontology.

Author Biography

Dr Øyvind Hammer is currently a Researcher in Paleontology at the Geological Museum in Oslo, and in Geobiology at the research center “Physics of Geological Processes”. In addition to a number of research publications, he is the author of the popular data-analysis software PAST.


David Harper is Professor of Paleontology and Deputy Director of Geology at the Natural History Museum of Denmark, University of Copenhagen. His research interests include the history of biodiversity; fossil brachiopods from the Palaeozoic rocks of NW Europe, North America, and China, and from the Cretaceous and Cenozoic rocks of the Caribbean; computer-based methods for the analysis and modeling of fossils and their distributions.

Table of Contents

Preface ix
Acknowledgments xi
Introduction
1(7)
The nature of paleontological data
1(3)
Advantages and pitfalls of paleontological data analysis
4(3)
Software
7(1)
Basic statistical methods
8(53)
Introduction
8(4)
Statistical distributions
12(7)
Shapiro-Wilk test for normal distribution
19(3)
F test for equality of variances
22(1)
Student's t test and Welch test for equality of means
23(4)
Mann-Whitney U test for equality of medians
27(3)
Kolmogorov--Smirnov test for equality of distribution
30(3)
Bootstrapping and permutation
33(2)
One-way ANOVA
35(4)
Kruskal--Wallis test
39(3)
Linear correlation
42(4)
Non-parametric tests for correlation
46(2)
Linear regression
48(5)
Reduced major axis regression
53(4)
Chi-square test
57(4)
Introduction to multivariate data analysis
61(17)
Approaches to multivariate data analysis
61(1)
Multivariate distributions
62(1)
Parametric multivariate tests -- Hotelling's T2
63(3)
Non-parametric multivariate tests -- permutation test
66(1)
Hierarchical cluster analysis
67(8)
K-means cluster analysis
75(3)
Morphometrics
78(79)
Introduction
78(1)
The allometric equation
79(4)
Principal components analysis (PCA)
83(8)
Multivariate allometry
91(5)
Discriminant analysis for two groups
96(4)
Canonical variate analysis (CVA)
100(3)
MANOVA
103(2)
Fourier shape analysis in polar coordinates
105(3)
Elliptic Fourier analysis
108(4)
Eigenshape analysis
112(3)
Landmarks and size measures
115(2)
Procrustes fitting
117(4)
PCA of landmark data
121(1)
Thin-plate spline deformations
122(6)
Principal and partial warps
128(4)
Relative warps
132(2)
Regression of warp scores
134(2)
Disparity measures and morphospaces
136(5)
Point distribution statistics
141(4)
Directional statistics
145(12)
Case study: The ontogeny of a Silurian trilobite
148(9)
Phylogenetic analysis
157(26)
Introduction
157(3)
Characters
160(1)
Parsimony analysis
161(5)
Character state reconstruction
166(2)
Evaluation of characters and trees
168(1)
Consensus tree
168(2)
Consistency index
170(1)
Retention index
171(1)
Bootstrapping
172(2)
Bremer support
174(1)
Stratigraphic congruency indices
175(3)
Phylogenetic analysis with maximum likelihood
178(5)
Case study: The systematics of heterosporous ferns
179(4)
Paleobiogeography and paleoecology
183(71)
Introduction
183(3)
Biodiversity indices
186(7)
Taxonomic distinctness
193(3)
Comparison of diversity indices
196(2)
Abundance models
198(4)
Rarefaction
202(4)
Diversity curves
206(2)
Size-frequency and survivorship curves
208(3)
Association similarity indices for presence/absence data
211(5)
Association similarity indices for abundance data
216(5)
ANOSIM and NPMANOVA
221(2)
Correspondence analysis
223(10)
Principal coordinates analysis (PCO)
233(3)
Non-metric multidimensional scaling (NMDS)
236(4)
Seriation
240(14)
Case study: Ashgill brachiopod-dominated paleocommunities from East China
244(10)
Time series analysis
254(25)
Introduction
254(1)
Spectral analysis
255(5)
Autocorrelation
260(3)
Cross-correlation
263(3)
Wavelet analysis
266(3)
Smoothing and filtering
269(2)
Runs test
271(8)
Case study: Sepkoski's generic diversity curve for the Phanerozoic
273(6)
Quantitative biostratigraphy
279(38)
Introduction
279(2)
Parametric confidence intervals on stratigraphic ranges
281(2)
Non-parametric confidence intervals on stratigraphic ranges
283(3)
Graphic correlation
286(5)
Constrained optimization
291(7)
Ranking and scaling
298(8)
Unitary associations
306(8)
Biostratigraphy by ordination
314(1)
What is the best method for quantitative biostratigraphy?
315(2)
Appendix A: Plotting techniques 317(11)
Appendix B: Mathematical concepts and notation 328(5)
References 333(12)
Index 345

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