Analysis of Ecological Data 2 - Introduction to Multivariate Methods
| Lecturer (assistant) | |
|---|---|
| Number | 0000001972 |
| Type | exercise |
| Duration | 2 SWS |
| Term | Wintersemester 2025/26 |
| Language of instruction | German |
| Position within curricula | See TUMonline |
| Dates | See TUMonline |
- 11.03.2026 09:00-17:00 Ort/Zeit nicht bekannt
- 12.03.2026 09:00-17:00 Ort/Zeit nicht bekannt
- 13.03.2026 09:00-17:00 Ort/Zeit nicht bekannt
- 16.03.2026 09:00-17:00 Ort/Zeit nicht bekannt
Admission information
See TUMonline
Note: Course is in German, but slides and tutorials are written in English
Note: Course is in German, but slides and tutorials are written in English
Objectives
After successfully completing this module, students will be able to understand and apply multivariate evaluation methods and the analysis of functional diversity. They have an important basis for scientific work. In addition, students will be able to statistically evaluate their own ecological data sets and interpret the evaluation results.
Description
Block course:
Thematically, the module course is particularly suitable for students who deal with the evaluation of ecological data sets from the fields of vegetation ecology and animal ecology and their interactions with abiotic environmental factors as part of internships, project work and theses (e.g. Bachelor's, Master's and doctoral theses).
Monday (introduction to the tidyverse)
- Handling of the statistical software R with RStudio (packages of the 'tidyverse' such as 'tidyr', 'dplyr', 'stringr', 'forcats', etc.).
- Graphical representation of analysis results with 'ggplot2'
- Short introduction to Open Science
Tuesday (ordinations)
- PCA
- NMDS
- RDA
Wednesday (Functional Diversity)
- Harmonize species names with database
- Selection and download of functional plant traits
- Calculation Community-Weighted Mean (CWM)
- Calculation of Functional Dispersion (FDis)
Thursday (Environment-plant community-trait relationship)
- Statistical analysis of the relationship between species composition and environmental variables
- Statistical analysis of the relationship between environmental variables and functional plant traits using species compositions (CWM regression, Fourth-corner approach)
Friday
- Presentation of the results (= examination)
Thematically, the module course is particularly suitable for students who deal with the evaluation of ecological data sets from the fields of vegetation ecology and animal ecology and their interactions with abiotic environmental factors as part of internships, project work and theses (e.g. Bachelor's, Master's and doctoral theses).
Monday (introduction to the tidyverse)
- Handling of the statistical software R with RStudio (packages of the 'tidyverse' such as 'tidyr', 'dplyr', 'stringr', 'forcats', etc.).
- Graphical representation of analysis results with 'ggplot2'
- Short introduction to Open Science
Tuesday (ordinations)
- PCA
- NMDS
- RDA
Wednesday (Functional Diversity)
- Harmonize species names with database
- Selection and download of functional plant traits
- Calculation Community-Weighted Mean (CWM)
- Calculation of Functional Dispersion (FDis)
Thursday (Environment-plant community-trait relationship)
- Statistical analysis of the relationship between species composition and environmental variables
- Statistical analysis of the relationship between environmental variables and functional plant traits using species compositions (CWM regression, Fourth-corner approach)
Friday
- Presentation of the results (= examination)
Prerequisites
No (Fundamentals in R are helpful)
Teaching and learning methods
The module course is held as an exercise with an alternation of lecture, exercise and the processing of an own data set.
The short lecture units provide a basic overview of the evaluation methods, the exercise shows their application and the participants' own data set should enable them to apply these methods independently.
Participants are also welcome to bring their own data sets from Bachelor's, Master's or project work. It is advisable to check the suitability of this data in advance.
The short lecture units provide a basic overview of the evaluation methods, the exercise shows their application and the participants' own data set should enable them to apply these methods independently.
Participants are also welcome to bring their own data sets from Bachelor's, Master's or project work. It is advisable to check the suitability of this data in advance.
Examination
Gruppenpräsentation (20 Min.):
Bei der Vorstellung der im Kurs erarbeiteten Ergebnisse zeigen die Studierenden in Form einer Präsentation, dass sie die Auswertungsmethoden verstanden haben und selbstständig anwenden können. Da jeder Kursteilnehmer einen eigenen Datensatz mit eigener Fragestellung bearbeitet hat, bietet die Präsentation vor der Gruppe auch für die
anderen Kursteilnehmer die Möglichkeit, verschiedene Anwendungsmöglichkeiten der gezeigten Methoden kennenzulernen.
Bei der Vorstellung der im Kurs erarbeiteten Ergebnisse zeigen die Studierenden in Form einer Präsentation, dass sie die Auswertungsmethoden verstanden haben und selbstständig anwenden können. Da jeder Kursteilnehmer einen eigenen Datensatz mit eigener Fragestellung bearbeitet hat, bietet die Präsentation vor der Gruppe auch für die
anderen Kursteilnehmer die Möglichkeit, verschiedene Anwendungsmöglichkeiten der gezeigten Methoden kennenzulernen.
Recommended literature
Borcard D, Gillet F & Legendre P (2018) Numerical ecology with R (2. ed). Springer, Cham. https://doi.org/10.1007/978-1-4419-7976-6
De Bello F, Carmona CP, Dias AT, Götzenberger L, Moretti M & Berg MP (2021) Handbook of trait-based ecology: from theory to R tools. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108628426
Leyer I & Wesche C (2007) Multivariate Statistik in der Ökologie. Springer, Berlin-Heidelberg. https://doi.org/10.1007/b137219
De Bello F, Carmona CP, Dias AT, Götzenberger L, Moretti M & Berg MP (2021) Handbook of trait-based ecology: from theory to R tools. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108628426
Leyer I & Wesche C (2007) Multivariate Statistik in der Ökologie. Springer, Berlin-Heidelberg. https://doi.org/10.1007/b137219