In this module, students can deepen their theoretical and applied
knowledge of statistical methods in the context of behavioural,
population, and conservation ecology. Emphasising practical
implementation, students will learn to apply statistical techniques to
analyse diverse biological topics, such as individual behaviours,
population dynamics, and species distributions.

Students will select their own research topics and work individually or
in small groups to develop and address concrete research questions using
appropriate statistical methods. Possible topics include behavioural
studies of specific animal species, ecosystem population dynamics,
conservation strategies, and the effects of environmental variables on
animal behaviour and distributions.

The module begins with a two-week introductory phase, where a broad
spectrum of statistical approaches will be introduced in daily sessions.


Introductionary topics will be:

• Study design
• Data exploration & introduction to coding in R (1+2)
• How to select appropriate statistical methods (1+2)
• Variable distribution analysis
• Pair- and group-wise comparisons (parametric and non-parametric)
• Correlation and simple regression
• Selecting and understanding regression models (1+2)

Following the introductory phase, students will refine their research
questions and statistical approaches, incorporating peer feedback in
weekly meetings. The final sessions will be dedicated to presenting
their projects.

Bachelor's students can choose between:
A basic introduction (6 CP, pb193), which covers fundamental statistical
techniques but does not include advanced modelling methods.

A comprehensive course (12 CP, pb193), which extends to species
distribution models, various frequentist models (GLM, GLMM, GAM) and
clustering techniques.


For master’s students, we offer a 15-credit variant that requires the
application of more advanced statistical techniques, including Monte
Carlo Markov Chains (MCMC), advanced clustering techniques, MaxEnt and
Maximum Likelihood approaches on large datasets. This can be credited as
a research module (bio810, bio900).

Discussion and critical thinking are strongly encouraged throughout the
module. The course is conducted in English, but additional support in
German is available for bachelor’s students if needed.

Use of AI: Generative AI tools may be used to assist with coding, but
must not replace the fundamental understanding of statistical methods or
hinder knowledge acquisition. Students must transparently document any
AI assistance in their work, ensuring adherence to the university’s code
of conduct.