6 research outputs found

    The Main Community Structure Characteristics of Fishes in Zhangjiang Estuary Mangrove National Nature Reserve

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    本研究于2013年8月、11月、2014年3-4月、5-6月在漳江口红树林保护区三条断面进行了夏、秋、冬、春四个季节的采样,分析了漳江口红树林区鱼类的种类组成、优势种、生态类群和物种多样性及其季节变化和空间分布,探讨了主要优势种四季的体长变化特征和代表种在不同断面的体长特征,并用多元统计分析方法(聚类分析、非度量多维标度分析nMDS、相似性分析检验ANOSIM、相似性比例分析SIMPER)分析了鱼类群落结构的时空变化特征。此外,本文还初步探讨了漳江口红树林区做为鱼类产卵场、索饵场和庇护所的可能性和价值。主要研究结果如下: (1)漳江口红树林区鱼类共90种,隶属于10目34科,均为硬骨鱼类。其...The study was based on the fish surveys in Zhangjiang Estuary Mangrove National Nature Reserve. The samplings were held in August, November 2013 and march-April, May-June 2014. The fish species composition, dominant species, ecological group and biodiversity characteristics and seasonal variation and spatial dynamics were described. The body length distribution of main dominant species in 4 season...学位:理学硕士院系专业:海洋与地球学院_海洋生物学学号:2232012115135

    Taxonomic diversity of fish community in the waters surrounding the Taishan Islands of Fujian Province

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    根据2012年~2013年在台山列岛周边海域进行的4个航次底拖网调查资料,并结合该海域的历史资料系统整理了台山列岛周边海域鱼类总名录,使用PRIMER 5.0软件计算了该海域鱼类的分类多样性指数,研究了分类多样性指数的季节变化。结果显示,台山列岛周边海域共记录鱼类2纲20目81科150属208种,以鲈形目种类占绝对优势;2012年~2013年调查记录到鱼类2纲11目40科63属77种。台山列岛周边海域鱼类的平均分类差异指数(△~+)和分类差异变异指数(∧~+)的理论平均值分别为63.14和378.4。鱼类分类多样性指数(△)秋季最高,春、夏季次之,冬季最低;鱼类分类差异指数(△~*)秋季最高,春季次之,夏、冬季较低。研究结果表明,春季鱼类种类数最多,夏、秋季次之,冬季最少,水温和饵料决定了研究海域鱼类种类组成。According to the data collected from four seasonal trawling surveys between 2012 and 2013 in the waters surrounding the Taishan Islands,we made a list of fish species in this area and calculated its taxonomic diversity by PRIMER 5. 0. We identified 208 fish species including 2 classes,20 orders,81 families and 150 genera in the waters around Taishan Islands,with Perciformes being the major order. According to the survey data from 2012 to 2013,77 fish species including 2 classes,11 orders,40 families and 63 genera were identified. The average taxonomic distinctness( △~+) and variation in taxonomic distinctness( ∧~+) of fish species listed in that area were 63. 14 and 378. 4,respectively. The taxonomic diversity( △) and taxonomic distinctness( △~*) were higher in autumn and spring than in summer and winter. It is concluded that the species in spring was more than that in summer and autumn,and was the least in winter. Water temperature and food were the main factors which affect fish species composition.国家自然科学基金项目(41106073);; 福建省科技计划项目(2012Y0072);; 福建省海洋与渔业厅科技项目[(2012)013号

    Assessment on the vulnerability of Luoyuan Bay ecosystem, Fujian

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    本研究以"暴露程度-敏感性-适应能力"为框架,构建了罗源湾海湾生态系统脆弱性评价指标体系,并运用模糊综合评价法对罗源湾海湾生态系统在1986~1; 990、2004~2006、2010~2012年三个阶段的脆弱性状况开展了定量评价,结果显示,罗源湾海湾生态系统在三个阶段均处于"中度脆弱"水平; ,综合评价得分逐年上升,呈现渐脆弱趋势.三个子系统的模糊评价结果表明,罗源湾海湾生态系统面临的暴露程度不断增大,其主要人为胁迫因子为滩涂围垦面积; 、临港工业发展和海水养殖密度;其次,罗源湾海湾生态系统的敏感性也越来越强,主要的敏感因子有海洋生物质量综合指数、浮游动物和潮间带底栖生物多样性指; 数和鱼卵仔稚鱼种类与密度;与此同时,适应能力则呈较好态势.研究结果可为相似区域海湾生态系统脆弱性评价提供参考.Based on the "Exposure- Sensitivity- Ability to adapt" framework, the; vulnerability assessment index system was established for Luoyuan Bay; ecosystem. The ecosystem vulnerabilities in 3 periods (1986~1990,; 2004~2006, 2010~2012) were studied used the fuzzy comprehensive; evaluation method. The results showed that all 3 periods were in; "mild-vulnerability". The scores increased in years and Luoyuan Bay; ecosystem tended to be increasingly vulnerable. The evaluate results of; 3 subsystems showed that the exposure intensity of Luoyuan Bay ecosystem; was increasing and the major artificial stress factors were reclamation; areas, port industry developments and aquaculture density. And the; sensitivity of Luoyuan Bay ecosystem was obviously intense, the main; sensitive factors were comprehensive index of marine life quality,; biological diversity indexes of zooplankton and intertidal benthic, and; species and density of fish larvae and eggs. Meanwhile, the adaptive; capacity of Luoyuan Bay ecosystem presented a good tendency. This; research can provide a reference for the vulnerability assessment of bay; ecosystem in similar regions.农业部2014年物种资源保护(渔业)资助项

    JUNO Sensitivity on Proton Decay pνˉK+p\to \bar\nu K^+ Searches

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in pνˉK+p\to \bar\nu K^+ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+p\to \bar\nu K^+ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is 9.6×10339.6 \times 10^{33} years, competitive with the current best limits on the proton lifetime in this channel

    JUNO sensitivity on proton decay pνK+p → νK^{+} searches

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    JUNO sensitivity on proton decay p → ν K + searches*

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the pνˉK+ p\to \bar{\nu} K^+ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+ p\to \bar{\nu} K^+ is 36.9% ± 4.9% with a background level of 0.2±0.05(syst)±0.2\pm 0.05({\rm syst})\pm 0.2(stat) 0.2({\rm stat}) events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 9.6 \times 10^{33} years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies
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