Search result: Catalogue data in Autumn Semester 2017

Biology Master Information
Elective Major Subject Areas
Elective Major: Ecology and Evolution
Elective Compulsory Master Courses
NumberTitleTypeECTSHoursLecturers
751-4801-00LSystem-Oriented Management of Herbivore Insects IW2 credits2GD. Mazzi
AbstractThe focus is on the potential to assess strategies and tactics of pest management, taking into account the demands from the economy, the environment and the society. Significant agricultural approaches will be explained using practical examples, including prevention using natural resources, surveillance and forecasting, resistance management, as well as product registration, incl. ecotoxicology.
ObjectiveThe students gain a good understanding of fundamental aspects of pest management in agroecosystems. They will have the ability to assess options for action in view of requirements from the economy, the ecology and the society. Further, they will learn to perform searches on relevant issues in pest management, and to critically evaluate case studies.
701-1409-00LResearch Seminar: Ecological Genetics
Minimum number of participants is 4.
W2 credits1SA. Widmer, S. Fior
AbstractIn this research seminar we will critically discuss current topics in Ecological Genetics using publications from the leading scientific journals in this field.
ObjectiveIt is our aim that participants gain insight into the current research topics and knowledge available in Ecological Genetics and learn to critically assess and appreciate scientific publications in this field.
Lecture notesnone
Literaturewill be distributed
Prerequisites / NoticeActive participation in the discussions is a prerequisite for this course.
551-1703-00LEcology of Anthropogenic HabitatsW2 credits1VD. Ramseier
AbstractThe focus will be on agro-ecology and ecology of urban habitats. Both experience frequent disturbances, specific chemical influences, and extreme climatic conditions. Additionally, in urban habitats edaphic conditions are difficult as well. Turnover of species diversity and composition are higher, both locally and temporary, compared to natural conditions at comparable sites.
ObjectiveKnowledge of agro-ecosystems and urban ecosystems; their origin, ecosystem services, mechanisms and importance for the maintenance of biodiversity.
701-1441-00LAlpine Ecology and Environments Information W2 credits2GS. Dietz, D. Ramseier
AbstractThe online course ALPECOLe provides a global overview of the complex ecosystems of mountain regions, and of their great diversity of habitats and organisms. The course is interdisciplinary and the various approaches are designed to help understand the past, present and future of mountain ecosystems.
ObjectiveKnowledge of alpine environments worldwide and their ecology
ContentThe online course is subdivided into
- 5 lessons on abiotic factors: geology, soils and their forming processes, climate, and disturbance factors
- 12 lessons on plants: diversity, patterns and processes, treelines, water & nutrients, carbon cycle, atmospheric influences, sexual and clonal reproduction, and one specific lesson on aquatic environments
- 5 lessons on animals: habitats and adaptations, origin of species, food ecology and impact of domestic livestock
- 3 lessons on landscape evolution: quarternary paleoenvironments, methods like radiocarbon dating, pollen records, dendrochronology, stable isotopes, and historical data
- 1 lesson on global change

Students can also follow a virtual walk through alpine areas where context-based information on alpine environments can be accessed. Moreover, all mayor alpine areas of the world can be selected on a map and then informative pictures of those landscapes and faunistic and floristic inhabitants will be shown.
Online exercises and tests allow to test the learned matter.
Prerequisites / NoticeOnline course and seminar
Students prepare for the seminar by working through particular lessons. Each student has to present some special aspects of one lesson. The seminar contribution is part of the performance assessment.
Course language is English
751-5121-00LInsect EcologyW2 credits2VC. De Moraes, M. Mescher, N. Stanczyk
AbstractThis is an introductory course in insect ecology. Students will learn about the ways in which insects interact with and adapt to their abiotic & biotic environments and their roles in diverse ecosystems. The course will entail lectures, outside readings, and critical analysis of contemporary literature.
ObjectiveStudents completing this course should become familiar with the application of ecological principles to the study of insects, as well as major areas of inquiry in this field. Highlighted topics will include insect behavior, chemical and sensory ecology, physiological responses to biotic and abiotic stressors, plant-insect interactions, community and food-web dynamics, and disease ecology. The course will emphasize insect evolution and adaptation in the context of specific interactions with other organisms and the abiotic environment. Examples from the literature incorporated into lectures will highlight the methods used to study insect ecology.
Lecture notesProvided to students through ILIAS
LiteratureSelected required readings (peer reviewed literature, selected book chapters). Optional recommended readings with additional information.
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 credits2V + 1UL. Meier
AbstractPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
ObjectiveParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
ContentPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteratureG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Prerequisites / NoticeThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-0649-00LApplied Statistical RegressionW5 credits2V + 1UM. Dettling
AbstractThis course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis.
ObjectiveThe students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling.
ContentThe course starts with the basics of linear modeling, and then proceeds to parameter estimation, tests, confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, multicollinearity problems and model interpretation, as well as general modeling strategies.

The last third of the course is dedicated to an introduction to generalized linear models: this includes the generalized additive model, logistic regression for binary response variables, binomial regression for grouped data and poisson regression for count data.
Lecture notesA script will be available.
LiteratureFaraway (2005): Linear Models with R
Faraway (2006): Extending the Linear Model with R
Draper & Smith (1998): Applied Regression Analysis
Fox (2008): Applied Regression Analysis and GLMs
Montgomery et al. (2006): Introduction to Linear Regression Analysis
Prerequisites / NoticeThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held.

In the Mathematics Bachelor and Master programmes, the two course units 401-0649-00L "Applied Statistical Regression" and 401-3622-00L "Regression" are mutually exclusive. Registration for the examination of one of these two course units is only allowed if you have not registered for the examination of the other course unit.
701-0301-00LApplied Systems EcologyW3 credits2VA. Gessler
AbstractThis course provides the ecological systems` knowledge needed to question applied solutions to current environmental issues. Our central aim is to balance participants' respect for complexity with a sense of possibility by providing examples from the vast solution space offered by ecological systems, such as e.g. green infrastructure to manage water.
ObjectiveAt the end of the course...
...you know how to structure your inquiry and how to proceed the analysis when faced with a complex environmental issue. You can formulate the relevant questions, find answers (supported by discussions, input from the lecturers and the literature), and you are able to present your conclusions clearly and cautiously.
...you understand the complexity of interactions and structures in ecosystems. You know how ecosystem processes, functions and services interact and feed back across multiple spatio-temporal scales (in general, plus in depth case examples).
...you understand that biodiversity and the interaction between organisms are an integral part of ecosystems. You are aware that the link between biodiversity and process/function/service is rarely fully understood. You know how to honestly deal with this lack of understanding and can nevertheless find, critically analyse and communicate solutions.
...you understand the importance of ecosystem services for society.
...you have an overview of the methods of ecosystem research and have a deeper insight into some of them, e.g. ecosystem observation, manipulation and modelling.
...you have reflected on ecology as a young discipline at the heart of significant applied questions.
ContentThis course provides the ecological systems' knowledge needed to question applied sustainability solutions. We will critically assess the complexity of current environmental issues, illustrating basic ecological concepts and principles. Our central aim is to balance participants' respect for complexity with a sense of possibility by providing examples from the vast solution space offered by ecological systems, such as e.g. green infrastructure to manage water.

The course is structured around four larger topical areas: (1) Integrated Water Management -- Green infrastructure (land management options) as an alternative to engineered solutions (e.g. large reservoirs) in flood and drought management; (2) Fire dynamics, the water cycle and biodiversity -- The surprising dynamics of species life cycles and populations in arid landscapes; (3) Rewilding, e.g. re-introducing apex predators (e.g. wolves), or large ungulates (e.g. bisons) in protected areas -- A nature conservation trend with counterintuitive effects; (4) Coupling of aquatic and terrestrial systems: carbon, nitrogen and phosphorus transfers of global importance on landscape scale.
Lecture notesCase descriptions, commented glossary and a list of literature and further resources per case.
LiteratureIt is not essential to borrow/buy the following books. We will continuously provide excerpts and other literature during the course.

Agren GI and Andersson FO (2012) Principles of Terrestrial Ecosystem Ecology, Cambridge University Press.

Chapin et al. (2011), Principles of Terrestrial Ecosystem Ecology, Springer.

Schulze et al. (2005) Plant Ecology; Springer.
Prerequisites / NoticeThe course combines elements of a classic lecture, group discussions and problem based learning. It is helpful, but not essential to be familiar with the "seven stages" method (see e.g. course 701-0352-00L "Analysis and Assessment of Environmental Sustainability" by Christian Pohl et al.).
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 credits1GA. Drewek, M. Mächler
AbstractThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
ObjectiveThe students will be able to use the software R for simple data analysis.
ContentThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: Link

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
Lecture notesAn Introduction to R. Link
Prerequisites / NoticeThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
Link
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
401-6217-00LUsing R for Data Analysis and Graphics (Part II) Information W1.5 credits1GA. Drewek, M. Mächler
AbstractThe course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.
Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.
ObjectiveThe students will be able to use the software R efficiently for data analysis.
ContentThe course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part II of the course builds on part I and covers the following additional topics:
- Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects;
- More on R functions;
- Applying functions to elements of vectors, matrices and lists;
- Object oriented programming with R: classes and methods;
- Tayloring R: options
- Extending basic R: packages

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: Link
Lecture notesAn Introduction to R. Link
Prerequisites / NoticeBasic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course.

The course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
Link
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
751-4504-00LPlant Pathology IW2 credits2GB. McDonald
AbstractPlant Pathology I will focus on pathogen-plant interactions, epidemiology, disease assessment, and disease development in agroecosystems. Themes will include: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems.
ObjectiveStudents will understand: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems as a basis for implementing disease management strategies in agroecosystems.
ContentCourse description: Plant Pathology I will focus on pathogen-plant interactions, epidemiology, disease assessment, and disease development in agroecosystems. Themes will include: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems. Topics under the first theme will include pathogen life cycles, disease cycles, and an overview of plant pathogenic nematodes, viruses, bacteria, and fungi. Topics under the second theme will include plant defense strategies, host range, passive and active defenses, and chemical and structural defenses. Topics under the third theme will include the disease triangle and cultural control strategies.

Lecture Topics and Tentative Schedule

Week 1 No Lecture: First day of autumn semester

Week 2 The nature of plant diseases, symbiosis, parasites, mutualism, biotrophs and necrotrophs, disease cycles and pathogen life cycles. Nematode attack strategies and types of damage.

Week 3 Viral pathogens, classification, reproduction and transmission, attack strategies and types of damage. Examples TMV, BYDV, plum pox virus. Bacterial pathogens and phytoplasmas, classification, reproduction and transmission. Bacterial attack strategies and symptoms. Example bacterial diseases: fire blight, Agrobacterium crown gall, soft rots.

Week 4 Fungal pathogens, classification, growth and reproduction, sexual and asexual spores, transmission. Fungal life cycles, disease cycles, infection processes, colonization, phytotoxins and mycotoxins. Attack strategies of fungal necrotrophs and biotrophs.

Week 5 Symptoms and signs of fungal infection. Example fungal diseases: potato late blight, wheat stem rust, grape powdery mildew, wheat Septoria leaf blotch.

Week 6 Plant defense mechanisms, host range and non-host resistance. Passive structural and chemical defenses, preformed chemical defenses. Active structural defense, papillae, active chemical defense, hypersensitive response, pathogenesis-related (PR) proteins, phytoalexins and disease resistance.

Week 7 Pisatin and pisatin demethylase. Local and systemic acquired resistance, signal molecules.

Week 8 Pathogen effects on food quality and safety.

Week 9 Epidemiology: historical epidemics, disease pyramid, environmental effects on epidemic development. Plant effects on development of epidemics, including resistance, physiology, density, uniformity.

Week 10 Disease assessment: incidence and severity measures, keys, diagrams, scales, measurement errors. Correlations between incidence and severity.

Week 11 Molecular detection and diagnosis of pathogens. Host indexing, serology, monoclonal and polyclonal antibodies. ELISA, PCR, rDNA and rep-PCR.

Week 12 Strategies for minimizing disease risks: principles of disease control and management.

Week 13 Disease control strategies: economic thresholds, physical control methods.

Week 14 Cultural control methods: avoidance, tillage practices, crop sanitation, fertilizers, crop rotation.
Lecture notesDetailed lecture notes (~160 pages) will be available for purchase at the cost of reproduction at the start of the semester.
636-0017-00LComputational Biology Information W6 credits3G + 2AC. Magnus, T. Stadler, T. Vaughan
AbstractThe aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.
ObjectiveAttendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* epidemiology
* pathogen evolution
* macroevolution of species
ContentThe course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics. The aim of phylodynamics is to understand and quantify the population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.
Lecture notesLecture slides will be available on moodle.
LiteratureThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
Prerequisites / NoticeBasic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the "Central Element". We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills.
701-1419-00LAnalysis of Ecological DataW3 credits2GS. Güsewell
AbstractThis class provides students with an overview of techniques for data analysis used in modern ecological research, as well as practical experience in running these analyses with R and interpreting the results. Topics include linear models, generalized linear models, mixed models, model selection and randomization methods.
ObjectiveStudents will be able to:
- describe the aims and principles of important techniques for the analysis of ecological data
- choose appropriate techniques for given problems and types of data
- evaluate assumptions and limitations
- implement the analyses in R
- represent the relevant results in graphs, tables and text
- interpret and evaluate the results in ecological terms
Content- Linear models for experimental and observational studies
- Model selection
- Introduction to likelihood inference and Bayesian statistics
- Analysis of counts and proportions (generalised linear models)
- Models for non-linear relationships
- Grouping and correlation structures (mixed models)
- Randomisation methods
Lecture notesLecture notes and additional reading will be available electronically a few days before the course
LiteratureSuggested books for additional reading (available electronically)
Zuur A, Ieno EN & Smith GM (2007) Analysing ecological data. Springer, Berlin.
Zuur A, Ieno EN, Walker NJ, Saveliev AA & Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York.
Faraway JJ (2006) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis.
Prerequisites / NoticeTime schedule
The course takes place on Mondays 12:45-15:00 from 25 September until 27 November, with the final exam on Monday 4 December. The last two weeks of the semester are free.

Prerequisites
- Basic statistical training (e.g. Mathematik IV in D-USYS): Data distributions, descriptive statistics, hypothesis testing, linear regression, analysis of variance
- Basic experience in data handling and data analysis in R

Individual preparation
Students without the required knowledge are asked to contact the lecturer before the first lecture date for support with individual preparation.
701-1471-00LEcological Parasitology Restricted registration - show details
Number of participants limited to 20. A minimum of 6 students is required that the course will take place.

Waiting list will be deleted on September 29th, 2017.
W3 credits1V + 1PO. E. Seppälä, H. Hartikainen, J. Jokela
AbstractCourse focuses on the ecology and evolution of macroparasites and their hosts. Through lectures and practical work, students learn about diversity and natural history of parasites, adaptations of parasites, ecology of host-parasite interactions, applied parasitology, and human macroparasites in the modern world.
Objective1. Identify common macroparasites in aquatic organisms.
2. Understand ecological and evolutionary processes in host-parasite interactions.
3. Conduct parasitological research
ContentLectures:
1. Diversity and natural history of parasites (i.e. systematic groups and life-cycles).
2. Adaptations of parasites (e.g. evolution of life-cycles, host manipulation).
3. Ecology of host-parasite interactions (e.g. parasite communities, effects of environmental changes).
4. Applied parasitology (e.g. aquaculture and fisheries).
5. Human macroparasites (schistosomiasis, malaria).

Practical exercises:
1. Examination of parasites in fish (identification of species and description of parasite communities).
2. Examination of parasites in molluscs (identification and examination of host exploitation strategies).
3. Examination of parasites in amphipods (identification and examination of effects on hosts).
Prerequisites / NoticeThe three practicals will take place at the 10.10.2017, the 24.10.2017 and the 7.11.2017 at Eawag Dübendorf from 08:15 - 12:00.
701-1427-00LExperimental EvolutionW4 credits2SG. Velicer, A. Hall, S. Wielgoss, Y.‑T. N. Yu
AbstractStudents will analyze experimental evolution literature covering a wide range of questions, species and types of analysis and will lead discussions of this literature. Students will develop a written project proposal for a novel evolution experiment (or a novel analysis of a published experiment) to address an unanswered question and will also deliver an oral presentation of the project proposal.
ObjectiveCourse objectives:
i) become familiar with a diverse sample of experimental evolution literature,
ii) gain understanding of the strengths and limitations of experimental evolution for addressing evolutionary questions relative to other forms of evolutionary analysis, and
iii) gain the ability to effectively design and analyze evolution experiments that address fundamental or applied questions in evolutionary biology.
ContentExperimental evolution is a powerful and increasingly prominent approach to investigating evolutionary processes. Students will analyze experimental evolution literature covering a diverse range of topics, species and types of analysis and will lead discussions of this literature. Students will develop a written project proposal for a novel evolution experiment (or a novel analysis of a published experiment) to address an unanswered question and will also deliver an oral presentation of the project proposal. Evaluation will be based on a combination of participation in and leadership of literature discussions, in-class exams, and oral and written presentations of the project proposal.
LiteraturePrimary research papers and review articles.
Prerequisites / Notice701-0245-00 Introduction to Evolutionary Biology (or equivalent).
701-1703-00LEvolutionary Medicine for Infectious DiseasesW3 credits2GA. Hall
AbstractThis course explores infectious disease from both the host and pathogen perspective. Through short lectures, reading and active discussion, students will identify areas where evolutionary thinking can improve our understanding of infectious diseases and, ultimately, our ability to treat them effectively.
ObjectiveStudents will learn to (i) identify evolutionary explanations for the origins and characteristics of infectious diseases in a range of organisms and (ii) evaluate ways of integrating evolutionary thinking into improved strategies for treating infections of humans and animals. This will incorporate principles that apply across any host-pathogen interaction, as well as system-specific mechanistic information, with particular emphasis on bacteria and viruses.
ContentWe will cover several topics where evolutionary thinking is relevant to understanding or treating infectious diseases. This includes: (i) determinants of pathogen host range and virulence, (ii) dynamics of host-parasite coevolution, (iii) pathogen adaptation to evade or suppress immune responses, (iv) antimicrobial resistance, (v) evolution-proof medicine. For each topic there will be a short (< 20 minutes) introductory lecture, before students independently research the primary literature and develop discussion points and questions, followed by interactive discussion in class.
LiteratureThe focus is on primary literature, but for some parts the following text books provide good background information:

Schmid Hempel 2011 Evolutionary Parasitology
Stearns & Medzhitov 2016 Evolutionary Medicine
Prerequisites / NoticeA basic understanding of evolutionary biology, microbiology or parasitology will be advantageous but is not essential.
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