95th ESA Annual Meeting (August 1 -- 6, 2010)

PS 104-117 - Coastal Synechococcus growth rates from cell size distributions and a matrix population model

Friday, August 6, 2010
Exhibit Hall A, David L Lawrence Convention Center
Kristen R. Hunter-Cevera1, Heidi M. Sosik1, Michael Neubert2, Rob Olson2 and Andrew Solow3, (1)Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, (2)Biology, Woods Hole Oceanographic Institution, Woods Hole, MA, (3)Marine Policy Center, Woods Hole Oceanographic Institution, Woods Hole, MA
Background/Question/Methods

Synechococcus populations in temperate coastal waters often undergo dramatic seasonal variations. At Martha’s Vineyard Coastal Observatory (MVCO), abundance can vary from less than 100 per ml in winter to over 100,000 per ml in summer. Information about changes in the population growth rate over the course of the year is crucial for understanding the top-down and bottom-up processes and environmental factors that control these large changes in abundance.  Using long term deployments of a custom-built automated submersible flow cytometer (FlowCytobot) at MVCO, we have collected multi-year observations of the Synechococcus population, including high resolution (hourly) changes in cell size distribution.  With these data we have estimated the parameters in a matrix population model and from the model we have derived daily population growth rates.

Results/Conclusions

When applied over an annual cycle, the model shows a range of population growth rates from near 0 to 1 per day, with the lowest rates being in winter and highest in early summer. Low growth rates indicate physiological limitation, while time series of growth rates and abundance allow investigation of loss processes (e.g., viral lysis, grazing, advection). At present, the matrix model is deterministic, which limits the statistical methods that can be used for parameter estimation and model evaluation.  We are currently incorporating both sampling error and intrinsic biological randomness in the model. Using likelihood-based methods we expect to be able to better quantify the uncertainty in our growth rate estimates.