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Michael Neubert and Heidi Sosik. Thomas, Lorraine (Lorraine Marie) Woods Hole Oceanographic Institution. Joint Program in Biological Oceanography. Woods Hole Oceanographic Institution Massachusetts Institute of Technology. Department of Biology 2008-12-11T18:22:05Z 2008-12-11T18:22:05Z 2008 http://hdl.handle.net/1721.1/43755 232607185 Thesis (S.M.)--Joint Program in Biological Oceanography (Massachusetts Institute of Technology, Dept. of Biology; and the Woods Hole Oceanographic Institution), 2008. "February 2008." Includes bibliographical references (p. 133). 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. by Lorraine Thomas. S.M. 133 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Joint Program in Biological Oceanography. Biology. Woods Hole Oceanographic Institution. Phytoplankton Primary productivity (Biology) Computer simulation Estimating phytoplankton growth rates from compositional data Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="bcf1ca3d-8fb9-40c0-9965-88de98a7a047"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Estimating phytoplankton growth rates from compositional data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2008</PublicationDate> <Authors> <Author> <DisplayName>Thomas, Lorraine (Lorraine Marie)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Joint Program in Biological Oceanography.</Keyword> <Keyword>Biology.</Keyword> <Keyword>Woods Hole Oceanographic Institution.</Keyword> <Abstract>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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>