Graduate Student Opportunities in the lab.

I am currently looking for a graduate student interested in problems related to the ocean carbon cycle and vertical flux. See this flyer for more details.

Other Links

Visit the Quantitative Thinking website

The activities of the NSF 2008 International Graduate Training Course in Antarctic Biology at this location.

Biological Oceanography Modeling at the University of Georgia

Map of NH4 concentrations in Florida Bay

Water column concentrations of NH4 in Florida Bay based on an empirical orthogonal function (EOF) analysis of data collected in the bay. This is an example of an empirical model.

Our research group uses mathematical and computer models to answer questions about marine systems. We have a diverse range of research interests, from trying to understand the small-scale interactions between particles in the oceans and how they affect large-scale biogeochemical cycles, to examining how seagrasses interact with their environment. In all these areas, we use mathematical and computer models, combined with field and laboratory data, to help us understand the interactions between the different processes occurring and to synthesize large, often disparate data sets. We work closely with other research groups, combining field and laboratory data with model results to gain a greater understanding of the systems we are studying. More detail about our research interests, and about specific research projects currently underway, can be found by following the Research link above. There is also information about the members of our group, the courses we teach, links to useful resources and publications.

What is a model?

Conceptual model of thorium scavenging and particle aggregation.

A schematic model of particle aggregation and thorium scavenging should the pathways that different size-fractions of particle mass (ni) and thorium (Thi) take.

Models can be thought of as the theoretical side of our science. In marine sciences, the models we develope are often applied models in that we use well-established mathematical descriptions to describe new phenomena. For example, rather than develop new theories of fluid motion, we use the well established Navier Stokes equations.

Models can be divided into three broad classes, though in practice, any model will contain elements of all of these. Conceptual models are constructions that provide a systematic desciption of the system one is interested in. They often represented as box-and-arrow diagrams, with the boxes representing compartments or variables and the arrows representing flows between the boxes. Conceptual models are most useful when intially looking at a problem, or when trying to organize ideas about the system you're studying. They are qualitativite descriptions that play the valuable role of organizing ideas and helping to design experiments and field studies.

Empirical models are statistical descriptions of data. These models are more quantitative than conceptual models, but can be limited by the data quality and range. For example, if you only make your measurements at one time of the year (maybe you don't like going to sea when the weather is bad in the winter), then any empirical model that uses only those data will be limited to making predictions only under those same conditions. You can of course extrapolate your measurements, but doing so dramatically increases the uncertainty in your model predictions.

Mechanistic models attempt to describe a system using our understanding of basic biological, physical and chemical processes. For example, we might use mathematical descriptions of diffusive and advective transport together with expressions for chemical kinetics to model trace metal adsorption onto sinking marine particles. If our models accurately describe observed phenomena, then we have increased confidence in our understanding of the system. Discrepencies between model results and observations would imply our understanding is incomplete.

What do you use models for?

Modeling is playing an increasing role in marine sciences and many major research programs have modelers on the team. Models are excellent tools for organizing data, looking for patterns and can be used to synthesize large and disparate data sets. Once we have devloped a model of how we think things are working, we can make qualitative and quantitative predictions of how it behaves. These predictions can be tested against observations. If the predictions and observations do not agree, then we know our model is not an accurate description of they system we are studying. We can now use the model itself to investigate which parts of it may need more work or a different approach.

Models are also useful for performing ''What if'' type experiments. They can be used to examine different hypotheses about how a particular system works, and these results can be used in the design of field and laboratory studies to test these ideas. Consequently, there is a very strong link between modeling and empirical studies.

Can I become a modeller?

Modellers are in short supply and high demand, and there is always a need for those who specialize in modeling biological and physical interactions. Modellers tend to have a good grounding in mathematics and computing, or have a mathematics-based background such as physics, engineering or computer science; however, there are many exceptions to this. Modelers tend to have an interest in using their skills to understand complex, interdisciplinary problems. Some of the types of mathematics you will find yourself needing include calculus and differential equations, linear algebra and numerical methods as well as a good dose of probability and statistics. Many of the models we develop are described in terms of differential equations and the techniques of linear algebra often form the basis of computational techniques we use to solve these equations.