Techniques and methodology
Introductory text under construction...
Modeling and data assimilation
We use numerical models to interpret data sets, analyze ecosystem functioning and answer our scientific questions. The numerical models are strongly coupled to data from experiments or field observations. Via this data – model merge, formally called “data assimilation”, we try to squeeze as much information out of data as possible and we apply this in different types of models:
Diagenetic models
Diagenetic models describe the mineralization of organic matter in the sediment surface and how this is impacted by e.g. faunal activity and organic matter quality. [Filip Meysman, Karline Soetaert]
Food web models
We use and develop food web models – linear inverse models – to reconstruct real, complex food webs, such as soft-sediments and cold-water coral reefs based on isotope, biomass and physiological data. [Dick van Oevelen, Karline Soetaert]
Physiological models
A physiological model describes the utilization of food by an organism. We use physiological models e.g. to study oxygen utilization in nematodes and to quantify sedimentation stress on corals and sponges. [Dick van Oevelen, Karline Soetaert]
Ecosystem models
An ecosystem model describes the dynamics in the ecology (e.g. phytoplankton) and biogeochemistry (e.g. nutrient cycling) of an ecosystem. We usually focus on the pelagic realm and how this is linked to the seafloor. [Dick van Oevelen, Filip Meysman, Karline Soetaert]
Modeling and visualisation in R
At YES we not only share our scientific results via scientific papers, but we also freely share our scientific computing tools in the form of R-packages.
Computing is an essential tool for scientists that want to extract the maximum of information out of their data. A good way to perform such data analysis is by writing and executing computer code. At YES, we use R as the problem solving environment for our scientific computing and environmental modeling. R is a user-friendly, open source, flexible computer language, that has recently gained a lot of popularity in academia, where it is often used for statistical analysis and graphical representation of data. Recent developments (to which we contributed) have also made it an ideal tool for scientific computing. The really nice thing about R is that it is extendible by R-enthusiasts in the form of user packages, that perform specific tasks and which can be easily shared with the entire R-community. One YES member in particular, Karline Soetaert, is such and R-enthusiast and has been very active in R package development. She wrote R packages to solve complex mathematical problems (differential equations, constrained linear problems, nonlinear roots), several packages to aid in the visualization of oceanographic data, an R-package with statistical functions to aid in model-data comparison, and some packages in the field of marine chemistry.
Isotope techniques
Under construction...
Eddy correlation
Under construction...
Digital holographic microscopy
Digital holographic microscopy is a promising tool for the aquatic sciences! With a much larger depth of focus than a classical light microscope (up to 100x more) and the ability to obtain quantitative information on optical density (so-called phase information) we can explore many different aquatic organisms and substances. Plankton cell morphology can be characterized, interactions studied, classification work carried out and even ‘invisible’ substances such as mucoids and polymers can be visualized. DHM thus presents itself as an innovative and unique tool for novel applications in the aquatic sciences. [Eva-Maria Zetsche, Filip Meysman]
Micro-profiling
Under construction...