701-1679-00L Landscape Modelling of Biodiversity: From Global Changes to Conservation
| Semester | Spring Semester 2024 |
| Lecturers | L. Pellissier, C. Graham, N. Zimmermann |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 701-1679-00 G | Landscape Modelling of Biodiversity: From Global Changes to Conservation | 3 hrs |
| L. Pellissier, C. Graham, N. Zimmermann |
Catalogue data
| Abstract | The course provides the student with the spatial tools to address societal challenges toward ensuring the sustainable use of terrestrial ecosystems and the conservation of biodiversity. Students learn theory, tools and models during a few introductory sessions and apply this knowledge to solve a practical problem in groups related to climate change, land use change and biodiversity conservation. |
| Learning objective | Students learn: - Theoretical foundations of the species ecological niche - Biodiversity concepts and global change impacts - Basic concepts of spatial (& macro-) ecology - Environmental impact assessment and planning - Advanced statistical methods (GLM and RF) in the statistical environment R. - The use of GIS functionality in R |
| Content | 1. The basics: Introduction to the concept of the ecological niche, and biodiversity theories. Overview of the knowledge on expected biodiversity response to global changes and conservation planning methods. Introduction to the statistical methods of Generalized Linear (GLM) and Random Forest (RF). Introduction to basic GIS and programming elements in the statistical environment R. This part will be evaluated by a written exam after the first half of the semester. 2. The class project: In groups of 3-4, students solve a conservation planning problem independently in R using the techniques taught in the introductory classes. The students then prepare a presentation of the obtained results that will be discussed during a mini-symposium (graded). |
| Prerequisites / Notice | Basic knowledge in statistics (OLS regression, test statistics), basic knowledge in geographic information science, and basic knowledge in R (data processing, functions, loops). Students should be familiar with the content of the following lectures: 701-3001-00L Environmental Systems Data Science: Data Processing 701-3003-00L Environmental Systems Data Science: Machine Learning |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 5 credits |
| Examiners | L. Pellissier, C. Graham, N. Zimmermann |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Additional information on mode of examination | There will be a written exam after the first half of the semester. The final presentation will be graded. Both grades count equally. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
Offered in
| Programme | Section | Type | |
|---|---|---|---|
| Environmental Sciences Master | Quantitative and Computational Expertise | W |  |
| Environmental Sciences Master | Methods and Tools | W | |