- Students get acquainted with several methods for the description of vegetation and its characteristics, and with the subsequent data treatment.
- Students learn about the key features in plant community classification with an example from Flanders
- Students are able to describe and sample vegetation.
- Students learn various methods/techniques for vegetation description.
- Students can choose the best method for vegetation description taking into account the predetermined objectives.
- Students learn various methods/techniques of data preparation.
- Students can perform several data transformations and know why this is needed.
- Students know how to process the gathered data of vegetation description.
- Students learn various methods/techniques of data processing (from non parametric, bivariate statistics to multivariate data analysis).
- Students are able to choose the best data processing method taking into account the predetermined objectives.
Participation in this course requires basic courses in statistics, ecology and biology as offered e.g. in the Bachelor program "bio-science engineering".
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Slides, transparencies, courseware
Examples and samples
Toledo / e-platform
Articles and literature
Is also included in other courses
- Master in de bio-ingenieurswetenschappen: land- en bosbeheer (Major: Forestry and Nature Conservation) 120 ects.
- Master in de bio-ingenieurswetenschappen: land- en bosbeheer (Major: Production Forestry) 120 ects.
- Master in de bio-ingenieurswetenschappen: land- en bosbeheer (Major: Soil and Water) 120 ects.
- Master of Earth Observation 120 ects.
- Master of Biology 120 ects.
The course gives an introduction to vegetation science (formerly often called phytosociology) with a clear emphasis on techniques for the description of plant communities and their components and for the analysis of data collected from these descriptions. Various methods for vegetation description are given and illustrated with practical examples, e.g. physiognomic techniques (life forms, growth forms, leaf area indices (LAI), vegetation structure analysis), floristic methods (both destructive and non-destructive methods; the use of plots and plotless-measures, diversity indices, sampling design). An overview is given of the most important plant communities in Flanders/Belgium, where the emphasis is on the practical identification of the communities. The relation to the European systems (e.g. Corine classification) is given. After data description the full process of data treatment is presented. This includes a series of steps largely corresponding to: storage of data, data transformation, association and correlation measures, a selection of non-parametric statistics, trellis diagrams, and multivariate data analysis methods (ordination methods: WA, PCA, RA, DCA, CANOCO); polythetic classification techniques: Braun-Blanquet-tabulating method, TWINSPAN, UWPGM, group average,
The data treatment methods are approached from a spatial and practical aspect and are presented through a variety of examples. Particular attention is given to conditions, limits and application modalities of the methods. The relevance and correspondence with other disciplines is shown. In a practical part (currently in another course incorporated) the whole process of data treatment is executed. Exercises are part of the practical part and the examination procedure.
- Look at the course unit
Description of learning activities
- Interactive lectures about concepts and algorithms
- Look at Toledo for syllabus, slides, articles, extra information, etc.
- The evaluation of this course unit consists of an oral examination with written preparation during the examination period. Students get about 20 minutes preparation time and are not allowed to use any books (expect the formulas that will be given). The exam consists of several open-end question (For more information about the exam, consult Toledo).