Overview

This module will provide proficiency in the quantitative skills needed to analyse complex ecological systems, predict impacts, and model future dynamics. It will also build skills for evidence-based decision-making, effective conservation planning, and implementation of adaptive management strategies.

• Introduction to the R environment as a tool for data analyses • Graphical tools for data exploration • General and generalized linear models • Multivariate techniques frequently used in environmental science • Big Data analyses • Modelling of ecological systems

Assessment Strategy

Excellent (Grade A: mark range 70% – 100%) An excellent student should show a nuanced and critical understanding of the latest advances in ecology and ecosystem function, drawing on extensive factual knowledge of the historical context and the most recent advances in these subjects. Written work should demonstrate an ability to synthesise and interpret data from the primary literature and construct original interpretations. Presentations should promote engaged and insightful discussion that spans both the specific findings of studies and their broader implications for global issues in biodiversity conservation and landscape management. In all aspects of their work students should be able to use their knowledge and understanding of issues to identify weaknesses in current theory and propose solutions to address major gaps in current knowledge. An excellent student should have a high level of detailed factual knowledge across all aspects of the module and be able to detail examples and case studies where appropriate. Written work should demonstrate an ability to think critically about the subject and to synthesise lecture material and information from extensive background reading in support of detailed, highly developed arguments.

Good (Grade B: mark range 60% – 69%) A good student should have thorough factual knowledge across all aspects of the module and be able to cite examples and case studies where appropriate. Written work should demonstrate an ability to think about the subject and to synthesise lecture material and some information from background reading into coherent arguments. A good student should be able to describe the significance of current debates in ecology and ecosystem function by showing an in-depth knowledge of both the historical context and the most recent advances in the fields. Written work should demonstrate an ability to synthesise and interpret data from the primary literature in a structured and logical manner, and all assessments should demonstrate advanced capacity to organise acquired knowledge.

Threshold (Grade C: mark range 50% – 59%) A threshold student should have knowledge of the essential facts and key concepts presented in the module. Written work should demonstrate an ability to synthesise and interpret data from the primary literature in a structured and logical manner, and all assessments should demonstrate the general capacity to organise acquired knowledge. Presentations should both elucidate important background concepts and promote original discussion of unfolding issues.

Learning Outcomes

• To know how to handle, plot, and describe different datasets in R

• To learn to work with data using reproducible workflows.

• To proficiently analyse large and complex datasets using various tools and technologies.

• To understand basic mathematical models and their use in the field of global change ecology.

• To understand the main techniques to analyse environmental data.