Phytoplankton are critically important primary producers and mediators of biogeochemical fluxes in the coastal ocean. They form complex communities that respond to natural changes in the ecosystem at a variety of time and space scales. Despite decades of study, however, the current understanding of their regulation in natural systems and ability to predict their population dynamics remain inadequate. The principal impediments to improving this situation have been (1) the inability to sample adequately at the appropriate space and time scales and (2) the difficulty of deriving population-dynamic parameters (such as population growth rate) from the data that can be collected. In an attempt to overcome the first impediment, new sensor technologies for autonomous, in situ measurement of critical information about natural phytoplankton assemblages have been recently developed. To overcome the second impediment, a significant interdisciplinary collaboration, between mathematical scientists and biological oceanographers, is required. The PIs on this project will establish just such a collaboration.
Intellectual merit: The investigators-an applied mathematician, a statistician, and two biological oceanographers-will collaborate closely to develop a series of mathematical models for phytoplankton cell growth and division that reflect effects of changes in environmental conditions. The models will be validated by controlled growth experiments in the laboratory, with mono-specific phytoplankton cultures, that specify how diel patterns in population structure respond to changes in growth conditions (light, temperature, and nutrients). Rigorous statistical procedures will be developed to a) guide model refinement and evaluate model performance against results from laboratory cultures and b) estimate model parameters in order to predict population growth rates. The project will provide fundamental insights into the regulation of coastal ocean ecosystems.
Broader impacts: Beyond the inherent societal relevance, the proposed activities will have broader impacts within the scientific community. A new generation of coastal observatories, with the capability to generate long time-series similar to those that we propose to analyze, will soon be operational. By making the statistical model (and any software developed to run it) available to the oceanographic community, the investigators will contribute to broad and innovative ecological applications of ocean observatories and the data they generate. In addition, the research results will be integrated with teaching and other forms of education at several levels from the general public to graduate students. The combination of coastal sciences and innovative technologies that this proposed research involves will provide a compelling context for informal education efforts through public exhibits.
