The influence of key environmental variables on phytoplankton community structure in the estuary of tidal rivers around Luoyuan Bay,China

A total of 348 species belonging to 8 phyla and 125 genera were observed in seasonally sampled phytoplankton of tidal rivers from 13 sampling sites around Luoyuan Bay, and all field samplings were carried out in productive period(March/June/August/December) at ebb tide. Bacillariophyta species were the most abundant species, followed by Chlorophyta, Cyanophytes, Euglenophyta, Cryptophyta, Dinophyta, Xanthophyta and Chrysophytas. Seasonal distribution index(SDI) value ranged from 0.63 to 0.86, which meant that species found at those sites in 4 seasons tended to be largely different. Phytoplankton individuals ranged from 5.939×10~4 ind L~(-1) in winter to 75.31×10~4 ind L~(-1) in autumn. Phytoplankton biomass ranged from 0.620 mg L~(-1) in summer to 2.373 mg L~(-1) in autumn. The grey correlation analysis(GCA) showed that the nutrient variables played an important role in the influence on phytoplankton community in every season. The canonical correspondence analysis(CCA) revealed impact of environmental variables on the different species, most of Bacillariophyta species were negative correlation with nutrients(TP and NH_3-N) in the four seasons, Chlorophyta species and Cyanophyta species did not show obvious correlation with environment variables in every season. The combination of GRA analysis and CCA analysis provided a method to quantitatively reveal the correlation between phytoplankton community and environmental variables in water body of tidal rivers at this region.     

硅藻群落结构的差异:比较样本采集过程与空间梯度

硅藻种群的分布与其所处的生境条件密切相关,但在实际过程中的一些人为因素,例如取样方法、样本的制备以及藻种鉴定都可能会干扰到种群结构的分析结果.因此很有必要探究这些人为因素是否会对真实的硅藻群落生态学研究产生误导性的判断,以免干扰硅藻生物水质评价的客观性.选取采样方法及样本制备为代表的人为因素对硅藻群落生态分析结果引入的误差大小进行评估.结果显示,子样本之间(同一样品重复制作的玻片样本)和样品之间(同一样点重复取样)群落结构差异大小分别是1.26%和1.97%,同一条河流的样点之间则为3.38%,而所选定的跨河流研究区域的样点间群落结构差异最大(42.03%).生态学的排序结果和数理统计分析表明,在硅藻群落结构分析中,现场取样方法和样本制备过程相对于不同生境条件所引起的变化,并不会带来较大的差异.因此在河流附生硅藻的群落生态学研究中,可基本不考虑取样方法等因子的干扰,而是侧重于环境因子对硅藻生态分布的影响.     

基于Multipatch模型的冰凌三维可视化分析

冰凌作为一种复杂的自然景观与自然灾害,其可视化模拟是冰凌信息管理、监测和预警的重要手段,对防凌减灾工作意义重大。本文深入分析Multipatch数据模型的底层结构,探讨冰凌的三维表达技术途径,提出了复杂冰凌三维可视化的技术实现方法,即模拟冰面的自然形成机理,确定软件系统中冰面在河道中的范围,依据地形DEM数据插值确定冰面边界点高程,模拟冰凌的自然形成过程来切分冰面,采用随机方法生成凌锥和凌柱及其方位角等以体现无序性,以实际照片为纹理贴图以增加真实感,通过虚拟内存、纹理压缩和缓存技术完成三维场景中的冰凌叠加展示。最后,结合系统研究与开发,实现了冰凌三维模型的自动建立和可视化管理。     

案例推理的地学应用背景和方法

案例推理 (CBR)研究 ,是人类认知的一种合理性模型 ,接近于人类认识、解决问题最原始的思维方式 ,具有在无法获取机理模型、确定规则或统计模型时 ,采用简单的历史相似性推理实现问题的定量求解和预测 ,成为人工智能领域近年来的研究热点。     

中国南海区1992-2012年的中尺度涡旋演化过程表达与统计分析

Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea（SCS）. However, very few studies attempt to examine eddies＇ internal evolution processes. In this study, we reported a hybrid method to trace eddies＇ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies（DCEs） and the three long-lived anticyclonic eddies（ACEs） in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.