Loïc Pellissier: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Loïc Pellissier |
| Field | Landscape Ecology |
| Address | Ökosysteme u. Landschaftsevolution ETH Zürich, CHN F 29.2 Universitätstrasse 16 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 32 03 |
| loic.pellissier@usys.ethz.ch | |
| Department | Environmental Systems Science |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||
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| 701-0553-00L | Landscape Ecology  | 3 credits | 2G | L. Pellissier, S. Gradinaru | ||||||||||||||||||||||||||||||||||||||
| Abstract | The course is an introduction to Landscape Ecology and Landscape Modelling and provides various practical applications of Landscape Ecology in nature and landscape management. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students are able - to explain and apply the concepts and methods of landscape analysis using examples, -to explain causes and effects of changes in landscape using examples and simulations, - to describe practical applications of Landscape Ecology in the management of nature and landscape. | |||||||||||||||||||||||||||||||||||||||||
| Content | Contents of the lecture: - important terms and concepts of Landscape Ecology, - analysis of landscape pattern (metrics), - landscape modelling, - perception of landscapes, - landscape inventories used for nature and landscape protection. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The course is offered via a MOOC (Edx) | |||||||||||||||||||||||||||||||||||||||||
| Literature | In the MOOC | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This lecture is coordinated with a MOOC. It is advantageous but not required to have some GIS knowledge for this lecture and the practical 'Praktikum Wald und Landschaft' (spring semester) which is loosely linked with this lecture. | |||||||||||||||||||||||||||||||||||||||||
| 701-1411-00L | Environmental DNA - Concepts and Applications for Biodiversity Monitoring at the Landscape Scale | 3 credits | 3G | L. Pellissier, K. Deiner, A. Frossard | ||||||||||||||||||||||||||||||||||||||
| Abstract | Environmental DNA (eDNA) allows the detection of organisms from traces of their DNA sampled from water, air or soil. Sampling eDNA instead of organisms makes monitoring fast, non-invasive, scalable and inexpensive. In this lecture, students will learn about eDNA and how it can be sampled, sequenced and analysed for biodiversity discovery and monitoring. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of this course, participants should be able to: - describe what eDNA is and how to harness the information in eDNA to turn it into a survey method for biodiversity - describe the eDNA analytical steps, from the sampling, laboratory, data analysis and interpretation. - summarise the common software and analytical tools for analysing eDNA data and be able to interpret the results. - apply eDNA methods to design programs for monitoring in conservation and restoration through case studies. Additionally, participants should be able to: - provide constructive feedback to peers and learn from feedback, - integrate concepts within and among disciplines of science. | |||||||||||||||||||||||||||||||||||||||||
| Content | The course is consisting of two pillars: Pillar 1: Theoretical background. The first pillar offers generals theoretical knowledge about the nature of eDNA and its use in biodiversity science. It is structured into theoretical blocks with video content about sampling design, laboratory and data processing, which offer fundamental knowledge to solve the practical case studies of pillar 2. Pillar 2: Data application on applied Case Studies. Each theory block will be associated with an exercise in which students are challenged to apply their knowledge from the theory. Students will collaborate on planning eDNA sampling design, visit the laboratory, run eDNA analysis (in R) following the best guidelines and interpret the results of analyses. These exercises will happen in person in the classroom. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - Basic understanding of genetics and molecular analyses. - Basic knowledge of R and Geographic Information Systems (GIS). - The analytic part of the lecture will rely on skills from “Environmental Systems Data Science” | |||||||||||||||||||||||||||||||||||||||||
| 701-1613-01L | Landscape Patterns and Processes | 5 credits | 3G | L. Pellissier, N. Bauer, D. Karger | ||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces landscapes as socially perceived, spatially and temporally dynamic entities that are shaped by natural and societal factors. Concepts and qualitative and quantitative methods to study landscapes from an ecological and societal perspective are presented. The course consists of a mixture of theoretical lectures and exercises or practical sessions. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will learn: - The use of spatial data and analyses for quantifying patterns and processes in landscapes - Concepts and methods to quantify functional connectivity in landscapes and seascapes. - The use of remote sensing (satellites images, drones) to extract information about landscape structure and change, with a focus on land-use. - The use of landscape genetics and its application to biodiversity conservation. - To computationally optimize land-use planning problems. - Concepts and methods in scenario-based land-use change modelling. - Concepts of social preference of landscapes and related measurement methods. - The role of landscape features in influencing human well-being. - Approaches of actively influencing attitudes and behavior toward landscapes as well as their scientific evaluation. | |||||||||||||||||||||||||||||||||||||||||
| Content | Thematic topics 1. Ecological quantification of landscape patterns: - Landscape resources and green infrastructure (e.g., ecological conservation areas). - Landscape and seascape connectivity. - Landscape genetics and conservation applications. - Concepts of spatial quantitative methods: least cost paths, resistance surfaces, Circuitscape, land-use change models, various statistical methods. - Image processing from remote sensing from satellites and drones. - Modelling future land-use. - Spatial optimization and trade-offs relative to biodiversity, agriculture and energy production. 2. Social perception and of landscapes: - Theories on landscape preference and place identity. - Role of landscapes for recreation, health and well-being - Methods of investigating the human-landscape relationship and evaluating interventions | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts will be available in the course and for download | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic Landscape Ecology courses at Bachelor level | |||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 701-3001-00L | Environmental Systems Data Science: Data Processing  | 2 credits | 2G | L. Pellissier, E. J. Harris, M. Volpi | ||||||||||||||||||||||||||||||||||||||
| Abstract | Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students are able to ● frame a data science problem and build a hypothesis ● describe the steps of a typical data science project workflow ● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models ● critically think about the limits and implications of a method ● visualise data and results throughout the workflow ● access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||
| Content | ● The data science workflow ● Access and handle (large) datasets ● Prepare and clean data ● Analysis: data exploratory steps ● Analysis: machine learning and computational methods ● Evaluate results and analyse uncertainty ● Visualisation and communication | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 252-0840-02L Anwendungsnahes Programmieren mit Python 401-0624-00L Mathematik IV: Statistik 401-6215-00L Using R for Data Analysis and Graphics (Part I) 401-6217-00L Using R for Data Analysis and Graphics (Part II) 701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften | |||||||||||||||||||||||||||||||||||||||||
| 701-3003-00L | Environmental Systems Data Science: Machine Learning | 3 credits | 2G | L. Pellissier, E. J. Harris, M. Volpi | ||||||||||||||||||||||||||||||||||||||
| Abstract | Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students are able to • select an appropriate model related to a research question and dataset • describe the steps from data preparation to running and evaluating models • prepare data for running machine learning with dependent and independent variable • build and validate regressions and neural network models • understand convolution and deep learning models • access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||
| Content | • The data science workflow • Data preparation for running and validating machine learning models • Get to know machine learning approaches including regression, random forest and neural network • Model complexity and hyperparameters • Model parameterization and loss • Model evaluations and uncertainty • Deep learning with convolutions | |||||||||||||||||||||||||||||||||||||||||
| Literature | Building on existing data science resources | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Math IV, VI (Statistics); R, Python; ESDS I | |||||||||||||||||||||||||||||||||||||||||