This course will provide students with evidence-based core training in the fundamental concepts and quantitative tools used to estimate species distributions, habitat preferences and biodiversity patterns from field data.

The course will run over three weeks, made up of lectures and practical classes.

None

None

Students will be assessed based on completion and accuracy of practical assignments that will be initiated in class and completed independently. The first of the 9 assessments will be formative (i.e. marked but not used for assessment). Each of the remaining 8 assessed practicals will contribute 7.5% of the final course mark. The remaining 40% will require students to integrate practical and lecture work with independent reading in the generation of an analytical report based on application of the methods learned, to a new dataset.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Since the practical work cannot be redesigned for individual students, the practical component cannot be replicated.

This course will provide students with evidence-based core training in the fundamental concepts and quantitative tools used to estimate species distributions, habitat preferences and biodiversity patterns from field data. The main aim is to encourage students to think critically about the results of such analyses by highlighting the limitations of the current approaches and the contemporary primary literature that works to overcome them. All taught material will be demonstrated and consolidated with associated practicals in the programming language R.

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

■ Develop practical skills in the analysis of spatial data using R and associated libraries
Develop practical skills in the manipulation of GIS data sets within the R statistical framework

■ Develop practical skills in the analysis of species-habitat associations using statistical models

■ Critically discuss with respect to the evidence base the relative merits of estimating maps of species abundance and the strengths and pitfalls of different methods used to achieve this

■ Critically discuss with respect to the evidence base the challenges of developing and interpreting models of habitat preference

■ Critically discuss with respect to the evidence base methods for quantifying habitat preference and the use for prediction of special abundance

■ Critically discuss with respect to the evidence base techniques for importing and representing complex special data using geographic information systems

■ Extrapolate their learning from individual practicals to an integration across subjects in the preparation of an independent final project

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