Courses taught:
FW3320: Fundamentals of Forest Genetics and Genomics (3 credits, 2019-2023)
This course will teach fundamental and applied genetics principles that are essential for management of forests and other ecosystems to maintain their long-term health and sustainability. The class will cover the following topics: structure and function of DNA, inheritance, molecular evolution, population and quantitative genetics, gene conservation, genomics and the application of genetic techniques, and approaches to various fields of biotechnology.
FW5340: Population Genetics and Applied Forest Genetics (3 credits, graduate level)
The course will highlight population and quantitative genetic topics and deals with the effects of evolutionary factors (e.g. mutation, migration, gene flow, genetic drift, selection and adaptation) on genetic diversity and adaptation. In the first part of the course, basics of genetics and DNA marker techniques are introduced. Basic principles in population genetics (measurement of genetic variation, the Hardy Weinberg Model, changes in genetic variation patterns by mutation, gene flow, genetic drift and natural selection) will be presented by means of case studies in temperate and tropical forest trees. The relevance of genetic variation patterns for the future management and conservation of forests is stressed. Genetic variation is affected directly or indirectly by human activities. It is important to understand how these activities affect the ability of forest tree populations to adapt to temporarily varying and heterogeneous environmental conditions. The course contains lectures, practical exercises and computational analyses of real datasets.
FW5411: Applied Data Analysis (3 credits, graduate level)
Using statistic tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical and grouped data and mixed effect models. Emphasis is placed on application of tools to real-world data using R.
FW5412: Data Analysis in R (1 credit, graduate level)
Use of R for data manipulation, statistical summary and statistical analysis. Topics include: Data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed effect models.