This course will introduce students to the different ways in which it is possible to formulate single and multi-species population models, how data can inform them and how existing models be can fitted to data. The sessions will cover models formulated in discrete and continuous time, deterministic and stochastic models, their dynamical behaviour, their local and global stability analysis, and model inference. Different model formulations and their analysis are all implemented in R.

This course is made up of seminars and practical classes in semester 2.

None

Moodle quizzes (ILOs 1-3), 30% 

Modelling assignment where the students solve set problems utilising skills learnt in the course, and involves R programming and short written answers (ILOs 1-3), 70%

This course will introduce students to the theory and practice of single and multi-species population models, their construction and relationship with data. It will aim to introduce students to the different ways these models can be formulated in theory, implemented in practice, their behaviour and stability analysed, and linked with data. The course material will be provided in the R programming environment.

By the end of this course students will be able to:

1. Critically discuss, with reference to the evidence base and primary literature:

■ The applications, limitations and assumptions of the range of currently used single and multi-species population models

■ The key features of a range of commonly used population models, and evaluate the various assumptions that each make

■ Examples of when these different models have been applied to particular situations, and what different sorts of predictions such models are most appropriate for current issues and controversies in this area of ecological modelling

2. Utilise data to inform model formulations, and their parameter values, and understand how models can be fitted to data

3. Implement a range of different single and multi-species models in R, and be able to conduct comprehensive numerical analysis of these models

4. Estimate critical parameters contained within these different formulations, and critically evaluate the sensitivity of model outputs to these parameters

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.