JGOFS-Synthesis: The Role of Phytoplankton Community Structure in Determining Particulate Elemental Composition, Carbon Fixation and Bio-Optical Properties

浮游植物群落结构和生物地理学已被确定为从目前的海洋生物地球化学模型（具有一般浮游植物参数化）发展到更明确的模型所需知识的关键空白。特别是，与一般浮游植物描述的偏差可能在少数物种的偶发华期间最大，并且在某些区域，这些偶发事件与颗粒碳向海底的快速通量、不同的生物地球化学过程（例如，固氮、碳酸盐沉淀、二甲基硫化物产生）或颗粒物质元素组成的变化有关。因此，未来的生物地球化学模型可能需要改变参数化，以反映生物地理区域和/或季节性水华。在JGOFS时代，高效液相色谱（HPLC）方法被广泛用于表征浮游植物色素，这些数据集可以用来对浮游植物群落结构进行一级近似。该项目将分析全球范围内收集的HPLC色素数据集，以开发估算浮游植物群落结构的算法（在事件、区域和全球尺度上）。通过HPLC色素估算的浮游植物群落结构将与显微镜和流式细胞术数据进行比较，以测试这些新算法的有效性。Drs。利佐特和迪图里奥将编纂和综合数据集，研究浮游植物的生物地理分布。他们还将提取以单一分类类群（如硅藻、球藻、花青菌、Phaeocystis spp）为主的样本的数据子集，以确定其他与浮游植物相关的参数是否与群落结构相关。为浮游植物相关参数编制的辅助数据集将是对开发SMP生物地球化学模型和浮游植物生物量遥感最有用的数据集（例如，颗粒碳、氮和吸光度；碳与叶绿素a比值；光合速率；光合作用-辐照度参数和量子产率；养分利用指数和比值；光衰减；反射率比；和水柱稳定性）。

DiTullioPhytoplankton community structure and biogeography have been identified as key gaps in the knowledge needed to progress from current ocean biogeochemistry models (with generic phytoplankton parameterizations) to more explicit models. In particular, deviations from generic phytoplankton descriptions may be greatest during episodic blooms of a few species, and in some regions these episodic events are associated with rapid flux of particulate carbon to the seafloor, distinct biogeochemical processes (e.g., N-fixation, carbonate precipitation, dimethylsulfide production), or shifts in elemental composition of particulate matter. Thus, future biogeochemical models may need to shift parameterizations to reflect biogeographic regions and/or seasonal blooms. During the JGOFS era, High-Performance-Liquid-Chromatography (HPLC) methods for characterizing phytoplankton pigments were used widely, and these data sets can be used to make a first-order approximation of phytoplankton community structure. This project will analyze HPLC pigment data sets collected worldwide to develop algorithms (on event, regional, and global scales) for estimating phytoplankton community structure. The estimates of phytoplankton community structure from HPLC pigments will be compared to microscopy and flow-cytometry data, when available, to test the validity of these new algorithms. Drs. Lizotte and DiTullio will compile and synthesize data sets to study biogeographical distributions of phytoplankton. They will also extract subsets of the data for samples dominated by single taxonomic groups (e.g., diatoms, coccolithophorids, picocyanobacteria, Phaeocystis spp.) to determine whether other phytoplankton-related parameters are correlated with community structure. The ancillary data sets compiled for phytoplankton-related parameters will be those most useful for developing SMP biogeochemical models and for remote sensing of phytoplankton biomass (e.g. particulate carbon, nitrogen, and absorbance; carbon:chlorophyll a ratios; photosynthetic rates; photosynthesis-irradiance parameters and quantum yields; nutrient utilization indices and ratios; light attenuation; reflectance ratios; and water column stability).