Frontiers in Marine Science (Feb 2018)
Modeling Net Growth of Phaeocystis antarctica Based on Physiological and Optical Responses to Light and Temperature Co-limitation
Abstract
Temperature and light are fundamental environmental variables which regulate phytoplankton growth rates when nutrients are in excess. For polar coastal oceans that are undergoing changes in sea ice cover and warming, light, and temperature are particularly important for bloom dynamics. Using colonial Phaeocystis antarctica cultures grown at steady-state, we assessed the combined effect of these two environmental controls on net growth rate (μn), chlorophyll-specific absorption of light (aph* (λ)), and quantum yields for growth (ϕμ). Specific net growth rates (μn) varied from 0.04 to 0.34 day−1 within a matrix of light and temperature ranging from 14 to 542 μmol quanta m−2 s−1 and −1.5 to 4°C. Values of aph* (λ) varied significantly with light but only slightly with temperature. Values of ϕμ ranged from 0.003 to 0.09 mol C (mol quanta absorbed)−1 with highest values at low light and 4°C. For excess irradiances or low temperatures where growth rate is inhibited, quantum yields were low. The low ϕμ values are attributed both to increased absorption by photoprotective pigments compared to photosynthetic pigments and thermodynamic control of dark reaction enzymes. The systematic changes in photophysiological properties of P. antarctica in relation to temperature and light were used to develop a series of nested light- and temperature-dependent models for μn, aph* (λ), and ϕμ. A model for aph* (300–700 nm) was developed that takes into account the systematic changes in aph* (λ) due to pigment packaging effects and cellular concentrations of chlorophylls and photoprotective pigments. Also, a model for ϕμ was developed based on a cumulative one-hit Poisson probability function. These model parameterizations for absorption and quantum yield are combined into an overall model of net growth that can be applied easily to P. antarctica bloom dynamics using remote sensing data for temperature, light, and chlorophyll a. Furthermore, modeling based on the biophysical variables aph* (λ), and ϕμ that are shown to regulate the growth rate provides a more fundamental mechanistic approach compared to other modeling methods that do not explicitly resolve photon flux into the cell or the quantum yield.
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