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.

The course will consist of 10 4 hour sessions that will combine lectures to explain the theory and background, accompanied by computer practicals to demonstrate analytical techniques. A detailed schedule will be provided prior to the start of the course. For this course, it is recommended that you should plan on 1 hour of own time to prepare for and reflect on lectures and 2 hours per hour of practical to complete and reflect on practical skills assessments assigned during each session. This means that you should plan on devoting about 45 hours to the final assignment, including review of the practical skills required to complete it and background reading.

None

Students will submit practical exercises in connection to each of the practicals and these together will be the in-course assessment part of the course grade (30%). There will also be a more substantial own-time assignment after the teaching sessions which will require integration of the knowledge and skills learned across the range of methods and concepts during the course (70% of the course grade).

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 to critically discuss with reference to the evidence base and primary literature:

• The applications, limitations and assumptions of the range of currently used single-species 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

In addition, they will be able to:

• Implement a range of different population models in R, and be able to conduct comprehensive numerical analysis of these models

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

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