SP16: Geometric Morphometrics (29940)

Meetings:                 Tuesday and Thursday, 2:30-3:45 pm, Geology Building 221

Course website:     http://www.indiana.edu/~g562/ Links to an external site.

Description: 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.

Objectives: The primary goal of this course is to learn to competently carry out a geometric morphometric study and to evaluate other studies intelligently. No prior background in morphometrics, advanced statistics, or programming is required, but interest in learning these is fundamental. Secondary goals of the course are to gain competency in data manipulation, programming, and graphics using the Mathematica® statistical language, as well as to gain literacy in basic multivariate statistics.

Projects: The focal assignment for the course is to carry out an original morphometric study that you will present as a written project paper. Ideally your project will be related to your broader research interests. During the first half of the course you will develop your study, with advice if you need/want it.

Project proposals are due mid semester. Proposals consist of a one to two page description of the aim and subject of your project, the source of your data, the question your study will try to answer, and the tools you’ll need to collect your data. Please feel free to consult me as you develop your proposal.

The final project report should be modelled on a scientific journal paper, with introduction to the problem, materials and methods, results, discussion, conclusion, references (cited properly in the journal format of your choice). Target length is 10-15 pages, including figures, tables, and references.

Grading      

Participation                            30%
Assignments                            20%
Project proposal                      10%   (due 8 March)
Project report                           40%   (due 5 May)

Computers and Mathematica®

Laptops should be brought to class every week. The software we will use is compatible with both Mac and Microsoft operating systems.

General software: If possible, please install the following on your system: ImageJ (http://rsbweb.nih.gov/ij Links to an external site. ), Adobe Creative Suite (especially Photoshop and Illustrator, http://iuware.iu.edu/ Links to an external site. ), Microsoft Office or Mac iWork or OpenOffice (for spread sheet programs, http://iuware.iu.edu/ Links to an external site. ).

Mathematica®: Most of our work will be done in the Mathematica which is a mathematical and statistical application that does efficient computations, is flexible for customized analysis, has powerful graphics capabilities, and is easy to learn (compared to R, for example, which is an equivalent application). You can purchase a student license for Mathematica 9 at the Research Analytics office in 200 Woodburn Hall for $30 (I think they prefer cash or check).

Supplementary texts

Hammer, Ø. and D.A.T. Harper. 2006. Palaeontological Data Analysis. Blackwell Publishing, Oxford, United Kingdom.

Zelditch, M.L., D.L. Swiderski, H.D. Sheets, and W.L. Fink. 2004. Geometric Morphometrics for Biologists: a Primer. Elsevier Academic Press, San Diego, California.

MacLeod, N. 2012. Palaeo Math 101. A series of essays published in the Palaeontological Association Newsletter. http://www.palass.org/modules.php?name=palaeo_math Links to an external site.

Discussion Sessions

Each discussion session will focus on a set of readings, which will be posted to Canvas. We will focus on: (1) who are the authors and what is their background; (2) where applicable, identifying the paper’s main focus or research question; (3) where applicable, what data are used; (4) how the methods work and how they transform the data; and (5) how interpretations drawn from the analyses.