Long-term changes in summer phytoplankton communities of the open northern Baltic Sea

Changes in the biomass and species composition of phytoplankton may reflect major shifts in environmental conditions. We investigated relationships between the late summer biomass of different phytoplankton taxa and environmental factors, and their long-term (1979–2003) trends in two areas of the Baltic Sea, the northern Baltic proper (NBP) and the Gulf of Finland (GF), with statistical analyses. An increasing trend was found in late summer temperature and chlorophyll a of the surface water layer (0–10 m) in both areas. There was also a significant decrease in summer salinity and an increase in winter dissolved inorganic nitrogen to phosphorus (DIN:DIP) ratio in the NBP, as well as increases in winter DIN concentrations and DIN:SiO₄ ratio in the GF. Simultaneously, the biomass of chrysophytes and chlorophytes increased in both areas. In the NBP, also the biomass of dinophytes increased and that of euglenophytes decreased, whereas in the GF, cyanobacteria increased and cryptophytes decreased. Redundancy analysis (RDA) indicated that summer temperature and winter DIN concentration were the most important factors with respect to changes in the phytoplankton community structure. Thus, the phytoplankton communities seem to reflect both hydrographic changes and the ongoing eutrophication process in the northern Baltic Sea.     

大型多元统计软件PRIMER的方法原理及其在底栖群落生态学中的应用

本文阐述大型多元统计分析软件 PRIMER的原理及其在底栖生态学中的应用。PRIMER是处理生态和环境多元数据的重要数值分析工具 ,本文从实用的角度对其中包括的技术方法和相应的子程序如 :等级聚类 (CLUSTER)、非度量多维标度 (MDS)、主分量分析 (PCA)、相似性分析检验(ANOSIM)、相似性百分比分析 (SIMPER)、生物 -环境连接的逐步分析和相关检验 (BIOENV/BVSTEP)等作了较为系统的介绍。     

渤海中、南部表层沉积硅藻的定量分析

对取自渤海中、南部海底表层的44个沉积物样品进行了硅藻分析。样品中共含硅藻20属41种,主要是潮间带、沿岸性底栖或浮游种类,其中尤以具槽直链藻Melosira sulc-ata和小环藻Cyclotella最多见。对本区的表层沉积硅藻用多元统计方法进行分区,共划分出三区、两亚区,井与其他海区进行了比较。     

土壤剖面的磁学特征及其对交通污染的指示意义——以北京首都机场高速公路为例

对北京首都机场高速公路旁采集的土壤柱状T01剖面的磁学参数和金属元素分析,探讨了研究区内现代交通导致的土壤磁学性质的变化及其对环境污染的响应.结果表明,磁参数(χ,ARM和SIRM)与重金属含量呈同步垂向变化趋势.来源于交通运输排放的污染物是土壤剖面上部(0～8 cm)磁性和金属含量增强的主要原因,8cm以下,土壤基本未受到污染,磁性矿物和重金属含量较低,磁性颗粒变化稳定,基本代表了该地区土壤的自然背景.尽管土壤岩石磁学分析表明剖面上下部受污染和未受污染样品的磁载体均是粒度较粗的多畴磁铁矿,但是结合磁参数比值曲线,说明底部样品的磁颗粒的粒度较顶部偏细.磁性矿物的含量变化没有影响磁颗粒的粒度特征.指标聚类等相关分析表明,土壤磁参数(χ,ARM和SIRM)与重金属元素(Pb,Zn和Cu)含量显著相关;结合模糊聚类分析,磁参数可用于追踪、识别交通污染物质在土壤剖面中的富集、迁移状态,揭示不同深度土壤的污染程度.     

层序地层学模拟研究进展及趋势

模拟层序地层学是层序地层学理论重要的研究手段之一,在国外该项技术较为成熟.层序地层计算机模拟软件采用的算法主要有扩散算法、流体流动算法、几何控制沉积算法、经验数值逼近算法、沉积物搬运的构造校正算法.目前,国外学者对以海平面升降、构造沉降和沉积物供应为主控因素的层序地层概念模拟的适用性提出了质疑,并对层序控制因素的多样性和不确定性进行了深入的研究.结合当前的研究进展,提出了层序控制因素的"多变量系统"观点,将层序的控制因素分为综合变量和独立变量,进一步指出如果假设综合变量为独立变量,则可以简化层序模拟,假设越多,模拟越简单,但模拟结果越粗略;要使模拟结果真实地反映实际地层特征,就要尽可能采用独立变量.     

Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula

The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd.     

The control of maturity and kerogen type on quantitative analytical pyrolysis data

Quantitative pyrolysis-gas chromatography has been performed on 96 kerogen samples isolated from 17 wells on the Norwegian Continental shelf. Petrographic and bulk geochemical measurements were also performed on the samples, and a combined data set of 117 variables for each sample was analysed using principal components analysis (PCA). This approach provides an objective and reproducible means of kerogen characterisation, which can be easily automated. In addition to objective kerogen characterisation and facile visualisation of facies and maturity related chemical trends, the method has the potential to allow objective prediction of key geochemical parameters such as maturity level from pyrogram data.     

An examination of the use of the logratio transformation for the testing of endmember hypotheses

Given a compositional dataset in the absence of any prior information on any mixing process which may have formed it, a complete analysis of mixtures determines three distinct types of estimates in order. These are: (i) the estimate of the number of endmembers or fixed source compositions, of which all the sample compositions of the dataset must be approximate mixtures; (ii) the estimated compositions for each of these chosen number of endmembers; and (iii) the estimated contributions of each of these endmember estimates to each sample. Traditionally, the estimate for the number of endmembers has been assessed either by mapping or by inspection of the coefficients of determination between the observed and estimated variables. Mapping entails the plotting on a map of the region from which the samples were taken, either the contours of the contributions of each endmember to each sample, or some other portrayal of the distribution of endmember abundances. Because it requires the complete analysis, assessment by this method is too elaborate except for final confirmation and display. Alternatively, choosing a number of endmembers, which result in suitability high coefficients of determination for all or most variables, may account for elements which are not part of the conjectured mixing process or, worse, may result in the identification of endmembers which may never in fact have existed. Such an error is similar to overspecifying a multiple regression model. So, the obvious starting point from which to assess the validity, or otherwise choice of endmember numbers, is to examine the matrix of residuals. The differences between the logratio-transformed observed and estimated data form an array of residual logratios. A linear combination of these may be formed for each sample, which, under a random perturbation assumption, should follow a univariate normal distribution. Whether or not this scalar is normal can be readily tested. It can also be examined graphically for such desirable qualities as symmetry when the test for normality may be too severe. This procedure is employed to assess the decompositions of the U.S.G.S. Mid-Pacific data and the Nazca Plate Surface sediments.This paper was presented at the 18th Geochautauqua, Newark, Delaware, 13–14 October 1989.     

Estimation of a multidimensional covariance function in case of anisotropy

A model of a multivariate covariance function with an ellipsoidal directional correlation scale has been developed. The axes of the ellipsoidal scale are related to the eigenvalues and eigenvectors of a matrix B which characterizes the ellipsoid of the range of influence. The matrix B is found to be related to a matrix T which can be estimated directly from sparse sampling data and can be used to determine estimates of the matrix B. The method has been applied to both two-dimensional and three-dimensional cases. The numerical results show that the satisfactory accuracy is obtained with sparse sampling data from an anisotropic random function.     

矿化富集中心地质因素多元统计分析及应用

本文导出了通过控矿地质因素逻辑信息法的研究来建立矿化富集中心数学模型的公式,使矿化富集中心数学模型与控矿地质因素定量地联系起来。还给出了单个矿化富集中心趋势面分析的数学模型和矿床空间分布模型。在这基础上,以某锡-多金属成矿带中某远景区的研究为例说明怎样应用矿化富集中心地质因素多元统计分析（逻辑信息法、趋势面分析、逐步回归分析和聚类分析）进行矿床统计预测。