Statistics on thin ice
![Arctic station, Qeqertarsuaq Arctic station, Qeqertarsuaq](/english/research/conferences/statistics-on-thin-ice/Arctic_Station.jpg)
An interdisciplinary course on Statistics, Arctic Ecology and Statistical Ecology
Greenland, 1-16 May 2023.
It is both exciting and daunting to understand and predict the ongoing ecosystem changes in the Arctic. Exciting because huge data sets are now collected by modern empirical methods, including detailed information on behavior of marine mammals together with extensive measurements on local characteristics. Information we could only dream of just a few years back. Daunting because over the past 30yrs sea ice and climate has changed rapidly in the Arctic generating unpredictable effects that are cascading through the marine food chains even affecting the top predators.
In this course, we study recent discoveries on Arctic ecology, climate changes and marine life in the Arctic, as well as modern concepts and theory behind statistical ecology and methods for doing statistical inference from biologging and environmental data. Biologging data collected from freely moving wild animals are giving rise to huge and complex data sets that require sophisticated statistical models and methodology to analyze. Combining the analysis with environmental data is essential for understanding the complex system and how it changes in the Arctic.
The course is interdisciplinary and aimed at PhD students in either statistics/data science with a keen interest in Arctic ecology and climate or in biology/geophysics with a keen interest in quantitative analysis and data science. It will be based on the participants own research data and problems.
The course content consists of:
- An advanced subject (selected by the participants, individual projects) in statistical modeling of a biological system
- Introduction to field work for collecting data on marine life in the Arctic
- Relationships between physical environment (climate, bathymetry) and ecological systems (marine life) in West Greenland
- Modelling states and developments of - and relationships between - individuals, populations and ecosystems in the waters around West Greenland
- Independent literature studies
- Theoretical work on models and methods relevant for data analysis
- Practical work on statistical modeling, implementation and/or data analysis
- Organization of a larger project and writing of reports
- Written presentation of methodology, data analysis and results
Recommended Academic Qualifications:
- Basic knowledge of probability theory (such as densities, conditional independence, conditional distributions and the Markov property) and standard statistical tools (such as likelihood theory, regression techniques and tests).
- Basic knowledge of programming in R.
Knowledge:
- aspects of applied statistics
- the specific subject as outlined in the description of the chosen project
- basic knowledge of Arctic ecology and data collection techniques
- simulation algorithms and R-packages for statistical ecological models
- statistical methods for parameter estimation in statistical ecological models
Skills: Ability to
- perform a statistical analysis of data from the chosen project
- compute parameter estimates and construct confidence intervals for these models
- interpret the output from the analyses and relate this to the scientific questions
- perform model diagnostics, statistical tests, model selection and model assessment for these models
- use R to be able to work with the above points for practical data analysis
- independently read scientific level statistics and ecology literature
- present models and methods studied in a concise way
- compare sampling strategies, assess their suitability, and select appropriate strategies for given natural resource contexts
Competences: Ability to
- document models, methods, implementation and/or data analysis in a coherent report
- be able to prioritize efforts in the studies, the practical implementations, the data analysis and the documentation so that the reader can assess and, if necessary, reproduce the results
- document that the goals of the project description are accomplished
- do statistical inference and simulate synthetic data sets from these models
- evaluate if a given model is adequate
- discuss the relevance, reliability, validity and interpretation of empirical data and results obtained in particular contexts
- evaluate empirical evidence, put results into perspective and discuss consequences in relation to sustainable management
- Lectures (a mixture of class room lectures, as well as a visit at Icefjods Center in Ilulissat and visit of bræ in Ilulissat): 24 hours
- Preparation of short synopsis of scientific problem and data (before course start, each student has to develop an individual scientific project): 10 hours
- Data science exercises: 10 hours
- Project work (with active interaction and discussion with teachers): 50 hours
Presentations by students: 10 hours - Field work (Including field trip on ship Porsild and whale tagging): 10 hours
Writing of final report (to hand-in two weeks after the course, feedback will be provided): 24 hours
Total: 138 hours
- Helle Sørensen (University of Copenhagen),
- Susanne Ditlevsen (University of Copenhagen),
- Uffe Høgsbro Thygesen (Danish Technical University),
- Mads Peter Heide-Jørgensen (Greenland Institute of Natural Resources),
- Eva Garde (Greenland Institute of Natural Resources).
The course will take place on the Disko Island at Arctic station, Qeqertarsuaq, Greenland. The first two days will be in Ilulissat, Greenland.
Transportation from Copenhagen and accommodation are included in the course fee.
If you are self-paying, the course free - DKK 22.000 - must be transferred to
Danske Bank, Holmens Kanal 2
1092 København K
Reg. nr. 0216, Konto 4069044336
IBAN DK73 0216 4069 0443 36
SWIFT DABADKKK
REF: Spec 5010924
Please register by 15 April 2023