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Project properties |
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| Title | The distribution of genetic diversity of main tree species over Europe |
| Group | Forest Ecology and Forest Management Group |
| Project type | thesis |
| Credits | 18-39 |
| Supervisor(s) | FEM group: Koen Kramer, Frits Mohren
Other organisation: prof. Dr. K�rkk�inen Katri (Luke) katri.karkkainen@luke.fi; Dr. Vakkari Pekka (Luke) <pekka.vakkari@luke.fi> |
| Examiner(s) | Prof. Dr. Ir. G.M.J. (Frits) |
| Contact info | Koen.kramer@wur.nl |
| Begin date | 2018/01/01 |
| End date | |
| Description | Genetic diversity (GD) is the base of adaptation to environmental changes such as climate change. To use and conserve GD, Gene Conservation Units (GCUs) are installed throughout Europe (see: www.eufgis.org; www.euforgen.org). The question is if these GCUs are installed at locations where GD is highest or that important hotspots of GD are missed.
The research questions of this project are: � what is the current distribution of GD of main European tree species (beech, oak, Norway spruce, Scots pine)? � Do the CGUs represent the distribution of GD? � Where best to place GCUs? � Are there gaps in our knowledge in the distribution of GD? � What are the best DNA sources (nuclear, chloroplasts, mitochondria) and genetic markers to characterize GD for an efficient measuring and monitoring? A large database with information on GD is available on hundreds of sites throughout Europe, including environmental data for these sites which needs to be analyzed to answer these questions. BSC/MSC Thesis or internship Best period: any moment Combination: - Co-operation: project can be done in a group of 2-3 students Type of work: emphasis on literature study and statistical analyses. This research is part of the FORGER project (�Towards the Sustainable Management of Forest Genetic Resources in Europe�: www.fp7-forger.eu) Biodiversity and functional diversity / Sustainable forest management/ Europe/ Temperate zone/ |
| Used skills | Sound background in multivariate statistical analyses, interest and to learn and apply advanced machine-learning techniques.
Basic skills in open-source scripting languages such as R or Python (preferred). |
| Requirements | FEM-30306 Forest Ecology and Forest Management; REG-31806 Ecological Methods I;
Standard for BSc: minimal 120 credits |