Estimating phytoplankton growth rates from compositional data
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I build on the deterministic phytoplankton growth model of Sosik et al. by introducing process error, which simulates real variation in population growth and inaccuracies in the structure of the matrix model. Adding a stochastic component allows me to use maximum likelihood methods of parameter estimation. I lay out the method used to calculate parameter estimates, confidence intervals, and estimated population growth rates, then use a simplified three-stage model to test the efficacy of this method with simulated observations. I repeat similar tests with the full model based on Sosik et al., then test this model with a set of data from a laboratory culture whose population growth rate was independently determined. In general, the parameter estimates I obtain for simulated data are better the lower the levels of stochasticity. Despite large confidence intervals around some model parameter estimates, the estimated population growth rates have relatively small confidence intervals. The parameter estimates I obtained for the laboratory data fell in a region of the parameter space that in general contains parameter sets that are difficult to estimate, although the estimated population growth rate was close to the independently determined value.
Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008
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