Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

701-1676-01L  Landscape Genetics

SemesterAutumn Semester 2017
LecturersR. Holderegger, J. Bolliger, F. Gugerli
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 14.

Prerequisites: good knowledge in population genetics and some experience in using GIS and R is required.


AbstractThis six-day winter school aims at teaching advanced Master students, PhD students and postdocs on landscape genetics. It provides both theoretical background as well as hands-on exercises on major topics of contemporary landscape genetics and landscape genomics such as landscape effects on gene flow and adpative genetic variation in a landscape context.
ObjectiveLandscape genetics is an evolving scientific field of both basic and applied interest. Researchers as well as conservation managers make increasing use of landscape genetic thinking and methods. Landscape genetics builds on concepts and methods from landscape ecology and population genetics. This winter school introduces advanced students to major concepts and methods of landscape genetics and genomics, i.e. (i) the study of landscape effects on dispersal and gene flow and (ii) the study of the interactions between the environment and adaptive genetic variation. The winter school focuses on currently used methods and hands-on exercises. It is specifically aimed at the needs of advanced students (Master, PhD and postdocs).
ContentThemes:
(1) Genetic data: estimates of gene flow; genetic distances; assignment tests and parentage analysis.
(2) Landscape data: landscape resistance; least cost paths; transects
(3) Landscape genetic analysis of gene flow: partial Mantel tests and causal modeling; multiple regression on distance matrices and mixed effects models.
(4) Networks and graph theory.
(5) Landscape genomics: adaptive genetic variation; outlier detection; environmental association.
(6) Overlays: Bayesian clustering; barrier detection; kriging.
Lecture notesHand-outs will be distributed.
LiteratureThe course requires 4 hours of preparatory reading of selected papers on landscape genetics. These papers will be distributed by e-mail.
Prerequisites / NoticeGrading will be according to a short written report (6-8 pages) on one of the themes of the course (workload: about 8 hours) and according to student contributions during the course.

Prerequisites: students should have good knowledge in population genetics and some experience in using GIS and R.