Universität Wien

300141 VU Multivariate statistical methods in ecology (2026S)

data analysis and modelling

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work

This course belongs in module MNB2 to subject area Data Analysis and Modelling.
Mo 18.05. 09:45-16:30 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The preliminary meeting will take place on March 5, 2026, at 1:00 p.m. in seminar room 3.1, 3rd floor, UBB.

  • Tuesday 19.05. 09:45 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Wednesday 20.05. 09:45 - 13:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Wednesday 20.05. 13:15 - 16:30 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 21.05. 09:45 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 21.05. 15:00 - 16:30 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 22.05. 09:45 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 26.05. 09:45 - 16:30 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Wednesday 27.05. 09:45 - 13:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1

Information

Aims, contents and method of the course

Statistical data analysis in ecology: descriptive statistics, univariate statistical tests (t-tests, U-test, analysis of variance), bivariate data analysis (correlation, linear and nonlinear regression), selected regression models (multiple linear regression, ANCOVA, GLM, GAM), commonly used classic unconstrained and constrained ordination methods: principal component analysis (PCA), canonical correspondence analysis (CCA), redundancy analysis (RDA), distance/dissimilarity-based unconstrained and constrained ordination methods: metric and non-metric multi-dimensional scaling (MDS, NMDS), canonical analysis of principal coordinates (CAP), multivariate hypothesis tests (PERMANOVA, permutation tests based on ordinations).
We aim at providing an overview (including theoretical background) on statistical testing, in combination with a practical part during which students are to acquire the capacity to independently implement statistical testing strategies.
Lectures on theory and derivation of statistical methods are combined with hands-on R scripting to achieve this goal. Low-level understanding of R is advantageous.

Assessment and permitted materials

Presence throughout the course is compulsory.

A written exam of 2 h length, comprising a theoretical and a practical part, will be held at the end of the class. A second and a third opportunity will be offered in accordance with the participants

Minimum requirements and assessment criteria

Successful participants will gain a working understanding of the mathematical and computational mechanics behind the most commonly used statistical methods in ecology. They will understand how to interpret graphical and tabular output from univariate, bivariate and multivariate analyses of ecological datasets as presented in scientific papers and reports. The lecture also explains backgrounds of statistical thinking, hypothesis testing, study planning and experimental design.
Grading will be based on preparation of a course protocol and a written exam testing practical skills for which total of 10 points may be awarded. Failure to produce a coherent and comprehensive course protocol will result in the grade 5, independent of the results of the exam. Up to 3 points may be earned for the protocol, based on coherence, comprehensiveness and quality.
The grading scheme is as follows:
10-9: 1
8-7: 2
6-5: 3
4-3: 4
<3: 5
Points earned for the protocol will be added to points earned during the exam to produce the final score.

Examination topics

Successful participants will be capable of interpreting statistical testing theory, applying and justifying conventions as well as analyzing data sets. They can propose testing schemes for datasets/problems and develop and implement a testing approach based on an example data set.

Reading list

Literature will be provided in form of a course hand-out; additional literature will be presented during the class

Association in the course directory

MEC-5, UF MA BU 01, UF MA BU 04, MNB2, MMEI III

Last modified: Mo 23.02.2026 10:47