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Geometric Morphometrics in R
Provider: Department of Biology

Activity no.: 5146-24-01-31There are 8 available seats 
Enrollment deadline: 31/07/2024
PlaceBuilding 3, Room 239 (Kollokvierum 1)
Universitetsparken 15, DK-2100 København, 2100 København Ø
Date and time05.08.2024, at: 09:00 - 10.08.2024, at: 16:00
Regular seats30
ECTS credits3.50
Contact personChristy Anna Hipsley    E-mail address: christy.hipsley@bio.ku.dk
Enrolment Handling/Course OrganiserStefan Sommer    E-mail address: sommer@di.ku.dk
Written languageEnglish
Teaching languageEnglish
Exam detailsShort (10 minute) student presentations on the last day, showing some aspect of GM analysis covered in the course
Course workload
Course workload categoryHours
Class Instruction27.50
Practical exercises6.88
Theoretical exercises13.75



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.


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

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

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

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

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

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

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

• 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

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

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.

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

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.

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