Niklaus Zimmermann: Katalogdaten im Frühjahrssemester 2023 |
| Name | Herr Prof. Dr. Niklaus Zimmermann |
| Adresse | Ökosysteme u. Landschaftsevolution ETH Zürich, CHN F 77 Universitätstrasse 16 8092 Zürich SWITZERLAND |
| niklaus.zimmermann@usys.ethz.ch | |
| URL | http://www.wsl.ch/staff/niklaus.zimmermann |
| Departement | Umweltsystemwissenschaften |
| Beziehung | Titularprofessor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 701-1679-00L | Landscape Modelling of Biodiversity: From Global Changes to Conservation | 5 KP | 3G | L. Pellissier, C. Graham, N. Zimmermann | |
| Kurzbeschreibung | 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. | ||||
| Lernziel | 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 | ||||
| Inhalt | 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). | ||||
| Voraussetzungen / Besonderes | 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 | ||||