The aim of the course is to provide the student with an evidence-based founding in the basic theory and practice of spatial ecology and species habitat associations.

Course offered over four weeks, on alternating days, with a 2-hour lecture and a 2-hour computer laboratory per day. 

Students will submit ten practical exercise reports to gauge their depth of understanding and engagement with the skills learned in each of the practical sessions. Of these ten, 8 will be summative and 2 will be formative. The work will be assessed not only on completion of the assigned tasks but on interpretation and self-reflection of the theories learned (60%). The remaining 40% will be a take-home problem-based assignment that will require integration of the knowledge and skills learned in this module, in the analysis and discussion of an independent dataset.

It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

This course will give the student the conceptual foundations and software tools to allow them to process spatial data and interpret the results in terms of the ecological mechanisms driving the spatial distribution of species and biodiversity. Emphasis will be placed on avoiding pitfalls of model specification and interpretation.

You will learn how to think about the biological drivers and constraints of species-habitat associations, approach statistical questions on single-species distributions in a spatial setting, take into account the challenges posed by different types of spatial data, confront challenges and deploy methods for robust predictions, and connect spatial ecology to its dynamical context.

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

■ Show a practical awareness of the types of data and necessary checks for spatial analysis.

■ Critically discuss, evaluate and reflect on the limitations of empirical statistical models for capturing complex ecological processes in space and deploy associated solutions.

■ Recognise and discuss questions posed in environmental versus geographical spaces.

■ Critically discuss and develop applications for data originating in the areas of conservation or epidemiology.