School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China; Jiangsu Research Center for Ocean Survey Technology, NUIST, Nanjing, China.
School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing, China.
Water Res. 2019 Jun 15;157:119-133. doi: 10.1016/j.watres.2019.03.081. Epub 2019 Mar 30.
Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages' dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE, are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately < 1 month) of phytoplankton to the changing external environment conditions is found by the wavelet analysis. This study concludes that phytoplankton community structures in the marginal seas remain stable and are year-independent over the past two decades, together with a short-term in-year cycle; this change rule need to be considered in future oceanographic studies.
海洋浮游植物约占地球初级生产力的一半，对海洋生态系统功能、物理和生物地球化学过程以及气候变化都具有重要作用。因此，记录浮游植物群集的动态，特别是它们的群落结构特性，是一项至关重要且具有挑战性的任务。大量原位和星载观测数据集被收集，涵盖了西太平洋的边缘海，包括渤海、黄海和东海。在这里，开发了一个定制的特定区域的半分析模型，以检测浮游植物群落结构特性（使用浮游植物大小类群，PSC，作为其一阶代表），并进行了反复测试以确保其可靠的性能。独立的原位验证数据集生成相对较低且可接受的预测误差（例如，微、纳米和微微浮游植物估算的平均绝对百分比误差（MAPE）分别为 38.4%、22.7%和 34.4%）。卫星同步验证也产生了可比的预测误差。通过将该模型应用于长时间序列的卫星数据，我们记录了过去二十年（即 1997 年至 2017 年）PSC 的变化。卫星衍生记录揭示了一个一般的空间分布规律，即微浮游植物在近岸地区占大部分变化，而纳米浮游植物则主导近海海域，同时微微浮游植物也有一定的高贡献。二十年的数据记录表明，在过去的二十年中，大致呈现出稳定的趋势，而在短期的一年尺度上存在周期性变化。进一步发现海洋环境因素和 PSC 之间存在高度相关性，结果表明水下光场和海面温度是两个主导气候变量，它们能够很好地多元统计模型化这些边缘海的 PSC 变化。具体而言，可以概括为三种水下光场和海面温度引起的影响，以覆盖不同的水条件和区域，同时通过小波分析发现浮游植物对不断变化的外部环境条件的快速响应时间（约 <1 个月）。本研究得出结论，在过去的二十年中，边缘海的浮游植物群落结构保持稳定且与年份无关，同时存在短期的年内周期；在未来的海洋学研究中需要考虑这种变化规律。
