REG-31806 Ecological Methods I

Course

Credits 6.00

Language of instruction:

English

Continuation courses:

REG-32306 Ecological Methods II; REG-30306 Animal Ecology; Thesis FEM, PEN, REG.

Contents:

Students learn how to analyse hypothesis-centred ecological field research projects on plants, animals, and their environment, with emphasis on the appropriate choice and application of statistical techniques for the analysis of ecological data. The course aims at providing the necessary statistical skills for the data analysis of BSc and MSc thesis projects and will enable the students to be able to understand and critically evaluate the analysis of ecological projects, such as described in scientific publications.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- formulate appropriate null hypotheses for ecological research questions;
- outline the possibilities, limitations and constraints of the different univariate and multivariate statistical tests, and are able to identify alternative solutions;
- select the best statistical tool to test the ecological data at hand;
- analyse ecological data using appropriate statistical procedures;
- interpret the statistical results in an ecologically meaningful sense;
- perform both univariate and multivariate analyses.

Activities:

This course deals with the choice and application of univariate and multivariate statistical techniques and tests, for the interpretation of ecological field data. The choice of appropriate test will be given attention in relation to the type of data under investigation. The necessary theoretical statistical background is expected to be present. The use of software (Excel, SPSS, Canoco, Ecological packages) for the analysis of field data will be highlighted and exercised. Attention will be paid to the sequence: hypothesis, choice of tests, interpretation of statistical results, and ecological meaning of outcome with respect to the hypothesis;
- students will attend lectures explaining the general approach of a sound ecological research proposal based on hypothesis testing, and on each of the main statistical techniques. Emphasis will be placed on the use of data from plant-animal interactions and studies on individual plant or animal species;
- introductory lectures will be followed by PC-based exercises to practice the application of statistical techniques to ecological data, where students work in pairs. Some exercises will be discussed interactively with the entire classroom at the end of each practical session, and correct answers will be made available then;
- the 5th week of the course is reserved for applied statistics: dendrogram analysis, survival analysis, transect data analysis, species richness analysis, geo-statistics, and capture-recapture data analysis;
- once the main techniques are mastered and tested, student groups will each analyse a large, real-life dataset, using both univariate and multivariate statistical techniques to obtain an ecological interpretation. The ecological datasets are supplied by the lecturers of the course, in co-operation with PhD-students, covering plant, forestry, and animal topics.

Examination:

The final exam consists of three parts that will be marked separately; exemption for individual parts of this exam can be obtained by passing the respective three test during the course. The knowledge of univariate (test 1), multivariate (test 2) and applied statistical tests and their applications (test 3) will be examined separately and individually in three multiple choice tests.
The results of the analyses of the large ecological dataset will be presented in a plenary session using PowerPoint. The presentation will be evaluated based on the clarity and balance of the various components, the appropriateness of the statistical techniques applied, the data analyses, and the ecological interpretation of the results.
Each of the four marks contributes equally (25%) to the final mark, and the minimum mark for a pass is 5.5. for all four components.

Literature:

Syllabus and course website.