ADVANCED COURSE:MUTLIVARIATE STATISTICS FOR ECOLOGY AND EVOLUTION IN R

Many of the questions concerning the fields of ecology and evolution are better represented by multivariate datasets. The complex relationships observed across an ecological community, the association among different sets of phenotypic traits and the interaction between the phenotype and its environment are all fundamental questions in evolutionary ecology. The description and comprehension of complex traits is often better achieved through the quantification and analysis of multiple, frequently interdependent, phenotypic and ecological variables. The R-language for statistical computing has been increasingly used by evolutionary ecologists for statistical inference and hypothesis testing. Being a comprehensive statistical package with excellent graphical capabilities and freely available, it has become an indispensable tool in ecological and evolutionary studies: many new statistical methods have been developed in recent years using R and numerous top-rank journals nowadays favor its use for the publication of scientific results.

This course is directed towards PhD students interested in exploring the potential of R language for multivariate analyses in ecology and evolution. The course will provide a general presentation of major statistical tools for multivariate analyses, including e.g. exploratory methods, multivariate GLM, methods for controlling for evolutionary and ecological non-independence, and it will provide the participants with the skills for implementing these tools using R.

The course will include a morning and an afternoon session. During the morning session, the instructors will discuss major themes in ecology and evolution and associate them to the statistical tools available for exploring specific scientific questions. The afternoon session will begin with a short (1h) demonstration of R code based on a worked, biological example and followed by practical training by the participants. At the end of the course, participants will each give a presentation on their research system, potentially including some analyses carried out during the course, or ideas of how they might incorporate the knowledge acquired during the course to their research.

PROGRAMME
MONDAY
9:30-13:00 | 14:30-17:00

1: Preliminary concepts: Why bother with multivariate analyses? How do we do this? Review of matrix algebra. What's in a distance: multivariate data spaces and metrics
2: General introduction to programming and the R environment

TUESDAY

9:30-13:00 | 14:30-17:00
3a: Inferential Methods I: Resampling (randomization, bootstrap, jackknife, Monte Carlo statistics)
3b: Inferential Methods II: General linear models in matrix form: multivariate GLM (MANOVA, regression, MANCOVA)

WEDNESDAY

9:30-13:00 | 14:30-17:00
4: Exploratory Methods: PCA, PCoA, MDS, clustering
5: Association methods: PLS, CanCor, Mantel tests, etc., why NOT to use CVA)

THURSDAY

9:30-13:00 | 14:30-17:00
6a: Methods for evolutionary and ecological non-independence I: phylogenetic comparative method (PGLS, PIC, rates of change)
6b: Methods for evolutionary and ecological non-independence II: spatial considerations (spatial autocorrelation, spatial GLS)

FRIDAY

9:30-13:00 | 14:30-17:00
Student presentations

COURSE INSTRUCTORS
Pedro Tarroso - CIBIO - InBIO | BIODESERTS
Jesús Muñoz - CIBIO - InBIO | PLANTBIO
Antigoni Kaliontzopoulou - CIBIO - InBIO | AP

INTENDED AUDIENCE
The course will be open to a maximum number of 20 participants.

Priority will be given to:

• 1st year and other PhD students attending the BIODIV Doctoral Program;
• PhD students attending other courses;
• Other post-graduate students and researchers.

REGISTRATION
Registration deadline: June 16, 2017

Participation is free of charge for BIODIV students | 95 € (students) / 200 € (other participants). CIBIO members will have an additional discount of 20%. Does not include lunch nor coffee breaks.
To register, please send an e-mail accompanied by your short CV (max. two A4 pages) to post.graduation@cibio.up.pt. Please refer your status (PhD student, MSc Student, Other) and the University to which you are affiliated.