CSA-34306 Ecological Modelling and Data Analysis in R

Course

Credits 6.00

Language of instruction:

English.

Assumed knowledge on:

PEN-10503 Ecology I and PEN-20503 Ecology II;
MAT-15303 Statistics 1 and MAT-15403 Statistics 2;
MAT-14803 Mathematics 1 and MAT-14903 Mathematics 2;
REG-31806 Ecological Methods I or MAT-20306 Advanced Statistics.

Continuation courses:

MSc Thesis.

Contents:

Ecological modelling, based on field data, has become an indispensable tool in ecological research to analyse data and propose plausible mechanistic models and mechanistic explanations for observed phenomena. This course presents a conceptual framework and the hands-on skills for ecological modelling, covering elementary functions and probability distributions needed to mathematically model processes and data, and confront models with the data, using state of the art statistical methods.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- fit a model to data, using likelihood methods and the programming language R;
- choose appropriate mathematical functions for describing ecological phenomena;
- choose appropriate probability models for describing biological variation;
- use Akaike's criterion to select among competing models;
- describe key commonalities and differences between classical statistics, Bayesian statistics, and statistics with likelihood methods;
- translate an ecological problem into a project that addresses this problem by fitting one or models to data, and interpreting the results;
- develop skills in using R as a platform for ecological modelling;
- develop an active learning style, that helps to acquire skills and insight in a broad sense. Those skills include, but are not limited to articulating questions, engaging in class discussion, define known unknowns.

Activities:

This course is not the usual course in which teachers present theory and students follow lectures and make assignments. On the contrary, we use a reverse format where students present theory during lectures, participate actively in the group discussion, apply the concepts in assignments and case studies, according to the student's design. The teachers give guidance in the learning process by explaining modelling techniques and data analysis techniques and programming approaches, as needed. The course starts with a crash course in R.

Examination:

A mark is given on the basis of three components:
- a written test on theory and concepts (30%);
- observations of active and in depth engagement during the presentation of theory and class discussions (30%);
- a group paper/design on the application of the concepts and the elaboration of computer codes in R in a case study (40%).
The required minimum mark for each component for a final pass of the course is 5.0.

Literature:

Ecological Models and Data in R by Ben Bolker, Princeton University Press, 2008. Mixed Effects Models and Extensions in Ecology with R by Alain Zuur et al., Springer, 2009.