Sabine Güsewell: Katalogdaten im Herbstsemester 2017

NameFrau PD Dr. Sabine Güsewell

701-0323-00LPlant Ecology3 KP2VS. Güsewell, J. Levine
KurzbeschreibungThis class focuses on ecological processes involved with plant life, mechanisms of plant adaptation, plant-animal and plant-soil interactions, plant strategies and implications for the structure and function of plant communities. The discussion of original research examples familiarises students with research questions and methods; they learn to evaluate results and interpretations.
LernzielStudents will be able to:
- propose methods to study ecological processes involved with plant life, and how these processes depend on internal and external factors;
- analyse benefits and costs of plant adaptations;
- explain plant strategies with relevant traits and trade-offs;
- explain and predict the assembly of plant communities;
- explain implications of plant strategies for animals, microbes and ecosystem functions;
- evaluate studies in plant ecology regarding research questions, assumptions, methods, as well as the reliability and relevance of results.
InhaltPlants represent the matrix of natural communities. The structure and dynamics of plant populations drives the function of ecosystems. This course presents essential processes and plant traits involved with plant life. We focus on research questions that have been of special interest to plant ecologists as well as current topical questions. We use original research examples to discuss how ecological questions are studied and how results are interpreted.
- Growth: what determines the production of a plant?
- Nutrients: consumption or recycling: opposite strategies and feedbacks on soils;
- Clonality: collaboration and division of labour in plants;
- Plasticity: benefits and costs of plant intelligence;
- Flowering and pollination: how expensive is sex?
- Seed types, dispersal, seed banks and germination: strategies and trade-offs in the persistence of plant populations;
- Development and structure of plant populations;
- Stress, disturbance and competition as drivers of different plant strategies;
- Herbivory: plant-animal feedbacks and functioning of grazing ecosystems
- Fire: impacts on plants, vegetation and ecosystems.
- Plant functional types and rules in the assembly of plant communities.
SkriptHandouts and further reading will be available electronically at the beginning of the semester.
Voraussetzungen / BesonderesPrerequisites
- General knowledge of plant biology
- Basic knowledge of plant sytematics
- General ecological concepts
701-1419-00LAnalysis of Ecological Data3 KP2GS. Güsewell
KurzbeschreibungThis class provides students with an overview of techniques for data analysis used in modern ecological research, as well as practical experience in running these analyses with R and interpreting the results. Topics include linear models, generalized linear models, mixed models, model selection and randomization methods.
LernzielStudents will be able to:
- describe the aims and principles of important techniques for the analysis of ecological data
- choose appropriate techniques for given problems and types of data
- evaluate assumptions and limitations
- implement the analyses in R
- represent the relevant results in graphs, tables and text
- interpret and evaluate the results in ecological terms
Inhalt- Linear models for experimental and observational studies
- Model selection
- Introduction to likelihood inference and Bayesian statistics
- Analysis of counts and proportions (generalised linear models)
- Models for non-linear relationships
- Grouping and correlation structures (mixed models)
- Randomisation methods
SkriptLecture notes and additional reading will be available electronically a few days before the course
LiteraturSuggested books for additional reading (available electronically)
Zuur A, Ieno EN & Smith GM (2007) Analysing ecological data. Springer, Berlin.
Zuur A, Ieno EN, Walker NJ, Saveliev AA & Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York.
Faraway JJ (2006) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis.
Voraussetzungen / BesonderesTime schedule
The course takes place on Mondays 12:45-15:00 from 25 September until 27 November, with the final exam on Monday 4 December. The last two weeks of the semester are free.

- Basic statistical training (e.g. Mathematik IV in D-USYS): Data distributions, descriptive statistics, hypothesis testing, linear regression, analysis of variance
- Basic experience in data handling and data analysis in R

Individual preparation
Students without the required knowledge are asked to contact the lecturer before the first lecture date for support with individual preparation.