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Our research is concerned with understanding the behaviour of biological systems in the marine environment. This involves developing means to understand how the interactions between biological, physical, chemical and geological influences affect these systems. We do this by formulating mathematical and computer models to help synthesize available information about these various influences and their effects on marine systems.If these models are successful, they should be able to accurately predict how the ecosystem will react to changes in external forcings (e.g., a change in nutrients resulting from changes in land usage). Developing models helps increase our understanding of how these systems work. The process of developing and testing the models relies heavily on the availability of data. Comparing the results of the model with data can have at least two important consequences. Firstly, the model can be refined so that agreement between the model predictions and data is improved. In addition, the model may help identify important gaps in the available data - e.g., parameters that have never been measured and which are crucial to improving our understanding of the system. So there is a two-way relationship between data and models. There are different types of models, each having their own advantages and disadvantages: Empirical Models: These are models that formed by fitting a simple function (e.g., a straight line) to a set of data. These models are relatively easy to develop, but their predictive power is restricted by the limitations of the data set. For example, the model cannot reliably predict the system's response to an influence greater than those appearing in the original data set. Mechanistic Models: These models are developed from first principles and are usually expressed as systems of differential and algebraic equations. These models are difficult to develop and rapidly become very complicated as more interactions are included. If they are successful, they are able to predict how a given system will respond to a very wide range of influences. Inverse Models: Frequently, the sheer complexity of marine biological systems limits the amount of data that can be collected. Inverse techniques allow the modeler to estimate parameter values, fluxes and other variables which cannot be measured in the field. The estimates one gets depend strongly on the structure of the inverse model and on the data used. Research Areas There are four broad classes of problem that we are presently working on. Coagulation and marine particles. Understanding the aggregation of marine particles and how it affects their vertical distribution and flux. None of these four problems is independent of the others. For example, particle aggregation affects the flux of material from the surface to the deep ocean. Changes in this flux affect the ecosystem throughout the water column. webmaster@halodule.tamu.edu Last modified: Wed Dec 20 11:12:37 CST 2000