Quantitative Paleontology is the practical application of quantitative analysis to paleontology, including the analysis of diversity through time, analysis of diversity in space, analysis of morphological disparity, and reconstruction of phylogenetic relationships. Skills include Monte Carlo statistical tests, analysis of large data sets, use of relational SQL databases, and the application of finite element analysis to paleontological problems.
Module 1 - Introduction
Course overview and software installation.Â
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Module 2 - MySQL and Mathematica
Introduction to software, SQL queries, Mathematica interface, programming, and data queries. See handouts in right column of this page.
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Module 3 - Time series and random walks
Discussion of papers, discussion of properties of time series and random walks, discussion of methods for assessing trends and correlations within and between time series.
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Module 4 - Rates and modes of evolution
Discussion of papers, review of properties of time series and random walks, discussion of directional and stabilizing modes of evolution, discussion of step rates and net rates, discussion of expected scaling of net rates to directional, random, and stabilizing modes of evolution
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Module 5 - Rates of evolution within lineages
Review discussion of the LineageEvolution function; modes of evolution, rates of evolution, thinking of step rate as a statistical distribution, LRI method for estimating step rate and mode, time series properties, serial correlation, McKinney's equation for time series process, correlation between time series. Introduction to evolution of quantitative trait on a phylogenetic tree.
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Module 6 - Quantitative traits and phylogeny
Review of properties of random walks; discussion of assignment; star phylogenies and their properties; Simulating star phylogenies; expected covariances from phylogeny; C matrix; birth-death models; reconstructing ancestral nodes.
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Module 7 - Phylogeny, parsimony, and tree support
Review assignment; trees; continuous versus discrete characters; parsimony; character polarity; tree support, consistency index, retention index, bootstrapping; ML and Bayesian approaches; stratigraphy and phylogeny.
Module 8 - Tree support and disparity
Review assignment; tree support, and bootstrapping; ML phylogenetics; disparity; taxon distances
Module 9 - Empirical diversity patterns
Review assignment; what is diversity or taxonomic richness; how is it estimated; first and last occurrences; rates or probabilities of origination and extinction.
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Module 10 - Simulated diversity patterns
Discusison of Raup (1985); Diversity curves; extinction and speciation rates; cohort analysis; paraclade dynamics.
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Module 11 - Introduction to Finite Element Analysis (FEA)
Lecture on FEA, introduction to FARO Arm laser scanner.
Module 12 - Climate
Introduction to climate and global climate models.