A biodiversity evaluation framework for restoration of aquatic macrophyte communities in shallow lakes driven by hydrological process management

Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.     

混合式教学模式在中学地理课程中的探索——以人教版“行政区划”为例

混合式教学模式是既能发挥教师在课堂中的启发、引导与监控作用又能提高学生参与度、体现个性化学习的一种教学模式。目前中学地理课堂仍强调死记硬背,学生学习的主观能动性不强。为了实现地理课堂的高效性,笔者应用新型智慧教学工具——“雨课堂”,通过中学地理教学实例说明实现混合式教学的基本路径,包括课前准备、课堂应用以及课后统计。     

青州市表层土壤元素地球化学组合特征研究

聚类分析和因子分析可以获得土壤元素地球化学组合特征及其差异性。对青州市表层土壤样品数据进行分析研究,通过聚类分析,绘制表层土壤元素聚类谱系图,将23种元素或指标分为5个元素组合簇群及2个单元素簇,研究各元素间的组合特征,探讨其相关性、聚集性及其指示意义;通过因子分析,找出有代表性的因子,用其代表变量,绘制典型因子得分等值线图,并从中分析不同元素组合的区域分布基于何种因素,用11个代表性因子的分布特征就基本可以代表青州市表层土壤23项原始变量的分布特征,并对F1,F2,F3主因子进行了地质解释。聚类分析与因子分析相结合,利于表层土壤中元素的共生组合特征及其差异性研究,利于对研究区表层土壤异常进行归纳总结。     

Variations in glacier volume and snow cover and their impact on lake storage in the Paiku Co Basin,in the Central Himalayas

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat-2 altimetry data and long-term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree-day model and the ice and lake volume method. The interaction among the climate, ice-snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 10⁷ m³/a and 0.29 × 10⁷ m³/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 10⁸ m³ at a rate of −0.27 × 10⁸ m³/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.     

Benthic foraminiferal fauna and sediment provenance of Eocene syn-rift sequences in Taiwan: implication for onset of Asian epi-continental marginal seas off China coast

Marine Geophysical Research - Early Cenozoic rift basins developed commonly on the Mesozoic basement along the SE Asia Continent. However, Eocene–Oligocene sequences were only exposed widely...     

一种潜艇装备作战能力比较研究方法

对潜艇装备作战能力评估问题进行了研究，发现运用定量评估方法的过程中存在指标评价所需数据无法完全获取的问题。结合美弗吉尼亚潜艇改进情况，分析了影响潜艇作战能力的一些关键因素，重点基于潜艇武器装载能力，对国外典型潜艇进行了作战能力比较分析，最后对分析结果进行了评价，并讨论了未来潜艇作战能力评估方法的发展方向。     

毛乌素沙区1961-2016年降水特征

基于毛乌素沙区10个气象站1961-2016年观测资料，应用Mann-Kendall方法和t检验法对各气象站年降水量进行了突变检验，借助小波分析讨论了各气象站年降水量的周期特征，根据降水量等值线划分结果对整个研究区分区分析了年、季、月和日尺度上的降水变化特征，并在两个时段上分析了季节性降水的差异。结果表明：毛乌素沙区年降水量空间特征差异明显，东部亚区呈上升趋势，中西部亚区呈下降趋势，但变化趋势不显著且无突变发生；降水年内分配不均，干湿季分明，降水集中在5-9月，夏秋季降水占全年降水比重大，季、月和日尺度降水量存在梯度递减变化；年降水量的年际变化过程存在多重时间尺度的自相似结构；近26年的冬春季降水增加显著，但降水波动幅度小于前30年。     

徐闻南山镇红树林沉积物及红树植物中重金属的累积特征与迁移规律

为探究重金属在红树林沉积物及红树植物中的分布累积及迁移规律，选取了徐闻南山镇红树林为研究对象，通过测定红树林沉积物及红树植物不同部位（根、茎、叶）的重金属质量分数，运用富集因子、生物富集系数、转移系数及相关性分析等方法进行分析。结果表明：1）红树林沉积物重金属质量分数表现为铬（Cr）＞锌（Zn）＞镍（Ni）＞铜（Cu）＞铅（Pb）＞砷（As）＞汞（Hg）＞镉（Cd），为中等变异程度；除了镍（Ni）元素外，其余7种重金属未超过国家一级标准，除了铅（Pb）元素外，其余7种重金属均超过广东省土壤环境背景值，说明研究区沉积物中重金属具有一定的积累效应。2）沉积物中砷（As）、铜（Cu）、锌（Zn）、汞（Hg）、镍（Ni）、铬（Cr）富集因子值均＞1.5，说明受到轻微人为活动影响；各站位镍（Ni）富集因子值均＞5，结合研究区背景，反映了镍（Ni）受到自然和人为输入的共同影响。3）白骨壤体内重金属主要集中在根部，而红海榄体内重金属在根茎叶中分布相对均匀。白骨壤根茎叶部位的大多数重金属质量分数远高于红海榄，说明白骨壤对重金属的吸附能力比红海榄强。汞（Hg）集中分布在植物的叶片部位，且与其他重金属之间相关性不明显；推测汞（Hg）主要通过叶片吸收进入植物体内，与交通运输污染有关。4）不同红树植物对不同重金属富集能力各异，白骨壤对重金属的富集能力表现为：镉（Cd）＞砷（As）＞铜（Cu）＞锌（Zn）＞汞（Hg）＞铅（Pb）＞镍（Ni）＞铬（Cr），红海榄表现为：镉（Cd）＞铜（Cu）＞汞（Hg）＞锌（Zn）＞铅（Pb）＞砷（As）＞镍（Ni）＞铬（Cr）。白骨壤和红海榄对汞（Hg）的运移能力都较强；红海榄对镉（Cd）的富集能力和转运能力都较强，而白骨壤对镉（Cd）富集能力较强，转运能力却较弱，这说明红树植物对重金属元素的富集能力与转运能力不存在正比关系。     

Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.