Time Series Analysis of Phytoplankton Populations at Two Stations Off Bermuda

该项目将使用流式细胞仪分析百慕大大西洋时间序列站和百慕大以南的S站的浮游植物种群动态。这些群落的结构影响系统产生的净群落生产量(新生产量)，因此，重要的是我们要了解调节它们的因素。这项工作将作为美国JGOFS时间序列研究(每月样本)的一部分，以及百慕大生物站在S站的定期样本计划(双周样本)的一部分。具体地说，我们建议测量浮游植物群落的下列组成部分的丰度、光散射特性(反映细胞大小和/或结构)和色素自发荧光：蓝藻-聚球藻、原绿藻、真核超浮游植物、球藻、羽状硅藻、隐芽植物、以及剩余细胞总数(除了那些太稀有而不能在100毫升样本中捕获的细胞)。这些时间序列数据集将在这些站点测量的其他物理、化学和生物参数的背景下进行解释。它们将帮助我们：-确定调节中纬度贫营养水域食物网络结构的关键环境因素；-确定这些社区变化的时间尺度；-确定人口动态的空间变异性，并估计BATS站和S站的“孤岛效应”；-解释已经在这些地点进行分析的高效液相色谱颜料数据；-完善遥感算法，以及-评估在非常长的时间范围内收集这些类型数据的可行性和实用性。

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.