pb193 - Field of Work/Technique Biology III (Course overview)

Institute for Biology and Environmental Sciences

6 KP

Module components

Semester courses Summer semester 2025

Examination

Seminar

5.02.194 - Angewandte Statistik in Verhaltens- und Populationsökologie
- Dr. Simon Käfer
- Hana Tebelmann
- Dr. Bo Leberecht
Dates on Monday, 31.03.2025 - Friday, 04.04.2025, Monday, 07.04.2025 - Friday, 11.04.2025, Monday, 14.04.2025, Monday, 21.04.2025, Monda ...(more)
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.

Exercises

5.02.194 - Angewandte Statistik in Verhaltens- und Populationsökologie
- Dr. Simon Käfer
- Hana Tebelmann
- Dr. Bo Leberecht
Dates on Monday, 31.03.2025 - Friday, 04.04.2025, Monday, 07.04.2025 - Friday, 11.04.2025, Monday, 14.04.2025, Monday, 21.04.2025, Monda ...(more)
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.

Notes on the module

Module examination	1 written text (max 120 min) or 1 portfolio (2-8 achievements) or 1 assignment max 30 pages) or 1 oral examination (max 30 min) or 1 paper (max 30 min) or 1 poster presentation
Skills to be acquired in this module	Dieses Modul kann verschiedenen Veranstaltungen zugeordnet werden