Pre-inscription: Ecological applications of biomarkers in aquatic food webs: A climate-change perspective
Specialized course planned from February 21st to 25th, 2022, on campus. As the number of participants is limited, please express your interest by completing this form. Participants will be informed on their selection by email by mid January 2022.
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Humans activities combined with climate change may constraint the provision of aquatic goods and services. As a consequence, there is growing demand for a sustainable use of aquatic resources. This has stimulated research interest in untangling the functional relationships between aquatic organisms, including interactions at the basis of food webs i.e. between primary producers and primary consumers. Knowledge of these (trophic) interactions is a prerequisite to understand and to protect the carrying capacity of aquatic ecosystems. The use of trophic biomarkers (stable isotopes, fatty acids) has proven to be a useful tool in reconstructing diets, characterizing trophic relationships, elucidating patterns of resource allocation, constructing food webs but can also be used as indicators of organisms' response to climate change. Consequently, the number of studies using biomarkers in trophic ecology has increased exponentially over the past decade. Several sub-disciplines have developed, including isotope mixing models, incorporation dynamics models, lipid-extraction and correction methods, and compound-specific isotope analysis. This course aims to provide advanced knowledge on the use of stable isotopes and fatty acids as tracers in ecological research. More specifically, next to theory on the up-to-date techniques, analyzing and implementing biomarker and tracer data in marine ecology and aquaculture will be included in the training.
Topics include:
• Energy flow in marine food webs
• Trophic biomarkers: stable isotopes and fatty acids
• mass spectrometry: GC-MS and EA-IRMS
• Bioconversion of fatty acids
• Compound-specific isotope analysis (CSIA)
• Fatty acid profiling: data analysis
Lectures will be given in the mornings and students are expected to lead seminar discussions in the evenings.