Morphometric Analysis:
Schedule of Lectures and Readings
Week |
Topic |
Material |
|
1 |
1: Introduction and History of Morphometrics |
Zel: Ch 1 #5 , #26, #8, |
|
1: Primary Data: Considerations |
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1 |
2: The Data of Morphometrics |
Morphometric features, homology, linear distances, the truss, landmarks, outlines |
Zel: Ch 2 |
2 |
3: Univariate Statistics and Matrix Algebra |
Matrix algebra, bias, efficiency, consistency, correlation, ANOVA, regression, ANCOVA |
|
4: The Basics of Multivariate Statistics |
The jump to multivariate GLM, confirmatory methods (MANOVA, regression) |
#15 |
|
3 |
5: Multivariate Statistics II |
Exploratory multivariate methods (PCA, UPGMA), CVA, resampling methods |
Zel: Ch 8 |
6: Data Acquisition |
2-D systems (digitizing pads, digital cameras, scanners), 3-D systems (handheld digitizers, 3-D scanners, 3-D stereomicroscopes), data acquisition software |
#11 |
|
2: Generate Shape Variables |
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4 |
7: Preliminary Concepts & Traditional Morphometrics |
What is shape, shape variables, a general morphometric protocol, Dean’s principle of classification, Traditional Morphometrics, size standardization, Allometry and CPCA, the Truss |
#14 |
8: Landmark Methods I: Superimposition |
Why bother with geometric methods, centroid size, differences between shapes, reflections, Gaussian model of shape variation, Goodall’s F-test, Bookstein’s shape coordinates for triangles |
Zel: Ch 3 |
|
5 |
9: Landmark Methods II: Superimposition cont. |
Shape coordinates for objects other than triangles, 3-D shape coordinates, least-squares (Procrustes) superimposition (GPA) |
Zel: Ch 5 #25 |
10: Landmark Methods III: Superimposition cont. 2 |
Full vs. partial GPA fitting, affine parameters and GALS, resistant-fit methods |
#25 Dryden and Mardia, 98 |
|
6 |
11: Landmark Methods IV: Visualization as Displacements |
GPA displacements, GRF displacements, variation at each landmark (and PCA) |
#25 |
12: Landmark Methods V: Visualization as Deformations |
D’Arcy Thompson and the history of deformations, computation of the thin-plate spline & U-function, principle and partial warps, uniform and non-uniform shape |
Zel: Ch 6 |
|
7 |
13: Landmark Methods VI: Visualization as Deformations cont. |
Meaning of partial warps, relative warps, principle axes, strain and anisotropy, image unwarping |
|
Midterm Exam |
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8 |
14: Shape Spaces for Landmark Data |
Distances between objects, Procrustes Distance formulations, figure space, perform space, form space, |
Zel: Ch 4 #19, #21, #29 |
15: Shape Spaces for Landmark Data cont. |
#21 |
||
9 |
16: Outline Methods: Open and Closed Contours |
polynomials, cubic splines, Fourier of radii, tangent angles, and EFA, Eigenshape analysis |
#12, #17 |
17: Semi-landmarks, combining Landmarks and Outlines |
Procrustean relaxation methods for outlines |
#7, #9 Zel: Ch 15 |
|
10 |
Spring Break |
||
Spring Break |
|||
11 |
18: Other Approaches for Landmark Data |
EDMA I, EDMA II, EDMA III, FESA, Distances (Rao and Suryawanshi), triangle interior angles (Rao and Suryawanshi) |
#22 |
19: Statistical Comparison of Landmark Methods |
Data spaces, Type I error rates, statistical power, bias, and MSE, why this is important |
#16, #18,#21, #22, #23, #24 |
|
3: Biological Shape Variation: Analysis and Interpretation |
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12 |
20: Statistical Analysis of Shape I: Exploratory Methods |
Shape variables, Ordination: PCA, RWA (alpha parameter), Clustering: UPGMA |
Zel: Ch 7 |
21: Statistical Analysis of Shape II: Confirmatory Methods |
MANOVA, regression, multi-factor MANOVA, CVA, PLS |
Zel: 9-11 |
|
13 |
22: Methodological Extensions |
Applications to asymmetry, quantitative genetics, analysis of motion and shape of articulated structures |
#2, #3 |
23: Shape Data and Phylogenies |
Mapping shape onto a phylogeny, using shape to generate phylogenies, shape and the comparative method |
Zel: Ch 14 |
|
14 |
24: Variation and Disparity |
Evolutionary shape variation, variation within and among taxa, |
Zel: Ch 12 |
25: Applications and Examples |
Taxonomy and group comparisons, ecomorphology (shape vs. other variables), accounting for phylogeny in data analysis, morphometrics and bioinrformatics |
||
15 |
26: Other Morphological Data |
Morphological color, fractals, morphological textures and patterns |
|
27: The Future of Morphometrics |
3-D data, surfaces, phylogeny, covariance structure, absent/missing data |
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16 |
Review and Conclusions |
#5, #6 |
|
Student Projects |
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16 |
Final Exam |
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*NOTE: Lab periods are also an opportunity for students to collect and analyze data for their project.
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