Geometric Morphometrics is the analysis of shape using Cartesian geometric coordinates rather than linear, areal, or volumetric variables. Geometric morphometric methods (GMM) include 2D and 3D points representing landmarks, curves, outlines, or surfaces. This course is a practical, applied introduction to GMM. Students learn to collect, analyze, and interpret geometric morphometric data. Shape theory and methods are covered, including Procrustes superimposition and its statistical implications, analysis of curves and outlines, and Monte Carlo modeling of shape.
[Course Syllabus 2014]

Overview of principles of geometric morphometric methods (GMM), including definitions of landmarks, outlines and surfaces, differentiation between traditional morphometrics and GMM, short history of morphometrics, components of a morphometric study, and examples of equipment needed for GMM studies.
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Learn basics of Mathematica, including basic operations, using cells and styles, writing short programs, defining functions, and making simple graphs.
[Download slides] [Assignment 1]
[Assignment 1 Example Answer]


Basics of Procrustes analysis, principal components analysis of shape, and morphospace. Introduction to the morphometrics functions tpsImport[], Procrustes[], PrincipalComponentsOfShape[]. Analysis of faces.
[Download slides] [Assignment 2]


Details of Procrustes, Procrustes distances, Principal Components Analysis, Eigenvalues, Eigenvectors, Scores, and shape modelling.
[Download slides] [Assignment 3]


Review of PCA, introduction to statistical tests, introduction to Rsquared values, univariate regression, multivariate regression, ANOVA, MANOVA.
[Download slides] [Assignment 4]
[tarsal associated data]


Session 6  Bootstrapping, Randomization, and Monte Carlo methods
This session is based on readings by Adams & Anthony (1996), Kowalewski & NovackGotshall (2010), and Efron & Tibshirani (1986). What are "statistics"? What is a statistical test? What is a statistical null hypothesis? How can resamppling and randomizations of real samples be used to construct customized statistical tests. Bootstrapping, jackknifing, randomization, Monte Carlo.
[Assignment 5]


Continues previous topic.
[Assignment 5 with tips]


Intro to semilandmarks, Fourier analysis, semilandmark analysis, sliding semilandmarks, eigenshape, "Pinocchio effect", alternatives to Procrustes, EDMA.
[Download slides] [Assignment 6]


Phylogenetic signal in morphometric data, "factor thinking", tree building algorithms and terminology, building morphometric trees in Phylip, phylogenetic comparative statistics, trees in morphospace.
[Download slides] [Assignment 7]


Random walks, their properties and statistics, random walks in more than one dimension, random walks in morphospace, modeling random evolution.
[Download slides] [Assignment 8]


Mathematica Morphometrics Package
Download Geometric Morphometrics for Mathematica and other Mathematica packages here
Handouts
Mathematica Quick Guide
Manipulating Data In Mathematica
How to collect landmarks from photos
Collecting 3D landmarks with MicroScribe
Collecting outline points in ImageJ
Sample Data Files
Associated data from Caumul and Polly, 2005 [Excel format]
Onyx turtle images for outlines
Marmot molar TPS file
Mammal tree [Newick format]
Morphometrics Resources
Jim Rohlf's morphometrics site
MorphoJ software from Klingenberg
GMM tools for
Mathematica and R
EVAN toolbox
Online Morphometric, Image, and Scan Data
Digital Morphology Site (DigiMorph)
MorphoBrowser
Virtual Zooarchaeology of the Arctic
IU Natural History Collections
IU Paleontology Collection
Adams Zooarchaeological Collection
Glenn Black Lab of Archaeology
IU Herbarium
IU Paleo Collection on
FaceBook
Online Phylogenetic Data
CladeStore
TreeBase
MorphoBank
NCBI Taxonomy
Molecular Data from Extinct Species
