A model of phytoplankton acclimation to iron-light colimitation
A model of phytoplankton acclimation to iron-light colimitation
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DOI：
10.4319/lo.2009.55.2.0714
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发表时间：
2010-03-01
影响因子：
4.5
通讯作者：
Geider, Richard J.
中科院分区：
文献类型：
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作者：
Buitenhuis, Erik T.;Geider, Richard J.
我们开发并校准了一个动态模型的细胞碳，叶绿素（Chl），铁下铁光共刺激。该模型允许生长速率和其他两个状态变量（Fe-p：C和Chl：C）被描述为光强度和游离铁浓度（Fe '）的函数。该模型需要指定9个参数的值。我们获得了这些参数的值，使用发表的实验结果海链藻使用的随机参数初始化和黄金分割搜索的组合，以最大限度地减少成本函数。调整后的模型解释了95%的生长率观测值的变异性，94%的Chl：C和90%的Fe-p：C。虽然该模型适用于平衡和不平衡的增长条件下，数据只可用于平衡增长，因此，在不平衡的增长条件下，状态变量的动态无法调查。校准模型的一个限制是缺乏适当的实验数据集下定义明确的环境强迫。这表明需要对铁限制培养物进行新的实验工作，包括测量光合作用-光曲线和对改变的Fe'和光强度的动态响应。这一浮游植物生长模型提供了一个生理治疗的铁光colimitation内海洋生态地球化学模型的实施。通过包括生长速率和元素化学计量（例如，Fe-p：C）作为状态变量，该模型可用于评价速率和产量限制。
We developed and calibrated a dynamic model for cellular carbon, chlorophyll (Chl), and iron under iron-light colimitation. The model allows growth rate and two other state variables (Fe-p : C and Chl : C) to be described as functions of light intensity and the free iron concentration (Fe'). The model requires specification of the values of nine parameters. We obtained values for these parameters using published experimental results for Thalassiosira pseudonana using a combination of a random parameter initialization and a golden section search to minimize the cost function. The tuned model explained 95% of the variability in the observations of growth rate, 94% in Chl : C, and 90% in Fe-p : C. Although the model is applicable to both balanced and unbalanced growth conditions, data were only available for balanced growth; thus, the dynamics of state variables during unbalanced growth conditions could not be investigated. A limitation in calibrating the model was in the scarcity of suitable experimental data sets under well-defined environmental forcing. This points to the need for new experimental work on iron-limited cultures, including measurements of photosynthesis-light curves and the dynamic responses to changed Fe' and light intensity. This phytoplankton growth model provides a physiological treatment of iron-light colimitation for implementation within ocean biogeochemical models. By including both growth rate and elemental stoichiometry (e.g., Fe-p : C) as state variables, the model can be applied to assess both rate and yield limitation.