Andreas Hamann
Contact
Professor, Faculty of Agricultural, Life and Environmental Sci - Renewable Resources Dept
- ahamann@ualberta.ca
- Phone
- (780) 492-6429
- Address
-
733 General Services Building
9007 - 116 St NWEdmonton ABT6G 2H1
Director Academic & Communicat, Faculty of Agricultural, Life and Environmental Sci - Renewable Resources Dept
- ahamann@ualberta.ca
Overview
About
My primary research fields are global change biology, genetics, and climate change adaptation with applications in forest ecology and management. I ask: How are tree species and their populations adapted to the environments in which they occur? How is the growth and health of natural tree populations and planted forests affected by climate change? How should we manage our forest resources under changing environments?
Graduate students and staff in my lab work on climate change adaptation strategies for the forestry sector, ecological and conservation genetics, forest growth, tree improvement, dendrochronology, ecophysiology, and phenology of tree species in boreal, temperate and tropical ecosystems (research). We further maintain GIS and climate databases for North America, South America and Europe (data).
Research
Opportunities for Graduate Students, Postdocs & Research Associates
If you are interested in joining the research group as graduate student, postdoc or research associate, see these opportunities and positions.
Courses
REN R 480 - Applied Statistics for Environmental Sciences
Focuses on problem formulation, method selection, and interpretation of statistical analysis. Covers data management and data visualization, statistical tests for parametric, non-parametric and binomial data, linear and non-linear regression approaches. Participants will gain general statistical literacy and learn how to visualize and analyze data with open-source software packages. Prerequisite: 60 units. 3 units in introductory statistics recommended.
REN R 580 - Applied Statistics for Environmental Sciences
Focuses on problem formulation, method selection, and interpretation of statistical analysis. Covers data management and data visualization, statistical tests for parametric, nonparametric and binomial data, linear and non-linear regression approaches. Participants engage in problem-based learning by analyzing data from their thesis research project. Students without a suitable dataset should enroll in two or more 1 unit REN R 58X courses instead. Prerequisite: 3 units in introductory statistics recommended.
REN R 690 - Multivariate Statistics and Machine Learning for the Environmental Sciences
Covers methods for visualization, analysis and prediction for complex biological or environmental data. Includes classical and modern approaches to ordination and classification, analysis of multivariate relationships, and the application of deep neural networks and other machine learning tools for prediction. Participants engage in problem-based learning by analyzing data from their thesis research project. Students without a suitable dataset should enroll in two or more 1 unit REN R 58X courses instead. Prerequisite: 3 units introductory statistics recommended.
REN R 906 - Research Project
The final research project that comprises REN R 906 is a final capping exercise for the degrees of MAg and MF. Its practical and professional focus should integrate the core areas of study in the program. The successful completion of the project entails (1) a research topic approved by the supervisor; (2) the presentation of a draft research proposal; and (3) the presentation of the research as a written document to the supervisor. The project may take the form of any of the following: (1) a formal analysis of management practice, organizational processes or policy; (2) a formative or summative evaluation of a research project or program; (3) a case study, using secondary documents, survey data, or interviews; or (4) replication of a previous study, with either the introduction of a new variable or an analysis in a changed context.