Time Series Analysis of Phytoplankton Population Dynamics atTwo Stations off Bermuda

本项目将研究浮游植物种群动态 百慕大大西洋时间序列站（BATS） S，百慕大南部使用流式细胞仪分析。的 这些社区的结构影响了净的数量， 由系统产生的社区生产（新生产）， 因此我们必须了解 规范他们。 这项工作将作为美国JGOFS时间序列的一部分进行 研究（每月样本）和百慕大生物站的 S站的定期取样程序（每两周取样一次）。 具体来说，我们建议测量的丰度，光 散射特性（反映细胞大小和/或结构）， 和色素自发荧光的下列组分的 浮游植物群落：蓝藻-聚球藻， 原绿藻，真核超浮游生物，颗石藻， 羽状硅藻，隐藻，和剩余的细胞总数 （除了那些太罕见而无法在100毫升中捕获的 样品）。 这些时间序列数据集将在上下文中解释， 其他物理化学和生物学参数 在这些车站进行测量。 他们将帮助我们： - 确定调节食品的关键环境因素 web 中纬度贫营养沃茨的结构; - 确定这些社区变化的时间尺度; - 确定种群动态的空间变异性， 并估算了BATS站的“孤岛效应”， S站; - 解释已经在进行的HPLC颜料数据 在这些地点进行分析; - 完善遥感算法， - 评估收集这些类型的可行性和实用性 的数据。

This project will examine the phytoplankton population dynamics at the Bermuda Atlantic Time Series Station (BATS) and Station S, south of Bermuda using flow cytometric analysis. The structure of these communities influences the amount of net community production (new production) produced by the system, and thus it is important that we understand the factors that regulate them. This work will be done as a part of the US JGOFS Time Series Study (monthly samples), and the Bermuda Biological Station's regular sampling program at Stations S (biweekly samples). Specifically, we propose to measure the abundance, light scatter properties (which reflect cell size and/or structure), and pigment autofluorescence of the following components of the phytoplankton community: the cyanobacteria - Synechococcus, prochlorophytes, eukaryotic ultraplankton, coccolithophores, pennate diatoms, cryptophytes, and the total remaining cells (except for those that are too rare to be caught in a 100 ml sample). These time series data sets will be interpreted in the context of the other physical, chemical, and biological parameters being measured at these stations. They will help us: - Identify the key environmental factors that regulate food web structure in mid-latitude oligotrophic waters; - Determine the time scales of change in these communities; - Determine the spatial variability in population dynamics, and estimate the "island effect" at the BATS station and Station S; - Interpret the HPLC pigment data that is already being analyzed at these sites; - Refine remote sensing algorithms, and - Assess the feasibility and utility of collecting these types of data over very long time frames.