Content

The field of geometric morphometrics (GM) is concerned with the 
quantification and analysis of patterns of shape variation and its 
covariation with other variables. Advances in the statistical treatment of 
morphometric data, made largely possible by the course instructors, have 
led to a revolution in GM that can be seen across scientific disciplines, 
including paleontology, ecology, evolutionary biology, evo-devo, and more.

Program:

Monday
Digital Image Acquistion: GBIF, MorphoSource
Image Naming & Organisation
Data Covariables
Landmark Export & Formats

Tuesday
Morphometrics: History, Introduction and Data Types
Linear Algebra and Linear Models
Superimposition: Generalized Procrustes Analysis (GPA)
Laboratory Tutorial-Individual Research

Wednesday
Shape Spaces, Shape Variables, & PCA
GPA with Semilandmarks
Introduction to gmShiny
Laboratory Tutorial-Individual Research

Thursday
Shape Statistics I
Allometry
Shape Statistics II o Laboratory Tutorial-Individual Research

Friday
Phylogenetics and Shape Variation
Symmetry and Asymmetry
Disparity
Integration and Modularity
Laboratory Tutorial-Individual Research

Saturday
Missing Data
Future Directions and Prospectus
Student Presentations
Social Event (Group Dinner

Aim and content

The goal of this workshop is to provide participants with both working
knowledge of the theory of geometric morphometrics, as well as practical
training in applying these methods using R.
Course content over 6 days consists of morning lectures (Digitisation,
Superimposition, Shape space, Shape statistics, Phylogenetic integration,
Future directions) followed by afternoon hands-on practical exercises and
opportunities for one-on-one training. The final day will end with student
presentations and a social event (group dinner).
See attached Course Content for more details.

Formel requirements

Enrolled in or completed PhD program in Natural or Computer Sciences.
It is assumed that participants have some working knowledge in R, as the
practical sessions will focus on geometric morphometric analyses and not
basic R use. It is therefore strongly recommended that participants refresh
their R skills prior to attending the workshop

Learning outcome

Knowledge:
• Distinguish between data types used in shape analysis
• Understand linear algebra and models underlying GM

Skills:
• Collect digital shape data from online sources
• Place landmarks on 2D and 3D images
• Perform Procrustes Superimposition
• Visualise shape differences using PCA and other tools
• Perform shape statistics in R environment

Competences:
• Use geomorph software in R
• Produce statistical models for analysing and comparing shapes

Literature

Software Packages:
Baken, E.K., M.L. Collyer, A. Kaliontzopoulou, and D.C. Adams. 2021.
geomorph v4.0 and gmShiny: enhanced analytics and a new graphical
interface for a comprehensive morphometric experience. Methods in
Ecology and Evolution. 12:2355–2363.
Collyer, M.L, and D.C. Adams. 2018. RRPP: An R package for fitting linear
models to high-dimensional data using residual randomization. Methods in
Ecology and Evolution. 9:1772-1779.
General Overview of Geometric Morphometrics:
Adams, D.C., F. J. Rohlf, and D.E. Slice. 2013. A field comes of age:
Geometric morphometrics in the 21st century. Hystrix. 24:7-14.

Target group

The course is aimed at students working with digital image data who wish
to learn the latest methods for quantifying and comparing 2D and 3D
shapes in a statistical framework. Applications include, but are not limited
to, medical image analysis, ecology, morphology, evolutionary biology,
paleontology, anthropology and phylogenetics.

Teaching and learning methods

The course is organized in morning theoretical and afternoon practical
sessions. The lectures provide a solid theoretical understanding of the
mathematical underpinnings of the procedures for proper analysis of
shape data from landmark coordinates. Laboratory sessions put these
concepts into practice, and include worked examples, giving participants
the opportunity to gain hands-on experience in the treatment of shape
data using the R package geomorph.

Lecturers

The two guest lecturers are the founding authors of the R package
geomorph, cited over 3000 times in the scientific literature (Dean Adams,
Iowa State University; Michael Collyer, Chatham University, USA). They
have taught 20 versions of this sought-after workshop since 2001 in
countries around the world. Guest lecturer/collaborator Christy Hipsley
(Biology, KU) is an expert in 3D digitisation and will teach Day 1 on image
acquisition and landmark placement

Remarks

No fee for students completing the course. Student participation must be
in agreement with the principal supervisor.

Signing up: Please note that in addition to your online registration at this page, we also need you to send your name, address, title of your PhD Project including start and end date, and a few lines describing the relevance of this course to your studies to christy.hipsley@bio.ku.dk before May 20, 2024.

Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.