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题名: 红树林湿地生态退化综合评价
作者: 刘静
学位类别: 博士
答辩日期: 2015
授予单位: 中国科学院研究生院
授予地点: 北京
导师: 马克明
关键词: 红树林湿地 ; Zhanjiang mangrove swamp ; 重金属 ; heavy metals ; 群落特征 ; vegetation community characteristics ; 显著性影响因子 ; significant variables ; 生态退化 ; ecological degradation ; 指标体系 ; indicator systems
其他题名: Study on a Comprehensive Assessment of Ecosystem Degradation in Mangrove Swamp
中文摘要:       红树林湿地是热带、亚热带海岸潮间带或河流入海口处重要的生态系统,在防风抗浪、净化水质、保护生物多样性、提供食物和栖息地等方面具有重要的环境功能和生态效益。随着经济快速发展,人类不合理的开发活动使红树林遭到了严重的破坏。中国自20世纪90年代末启动了大规模的红树林湿地生态恢复工程,至2009年,中国红树林面积从1990s的22752 hm2缓升至2.4×104 hm2。然而红树林湿地仍面临红树林资源退化、环境污染、生物资源过度开发等问题。 本研究以湛江红树林湿地为对象,通过对“植物-水体-土壤-景观”间的互作过程,详细探明了湛江红树林湿地景观格局、水质、沉积物及红树群落特征,揭示了湿地植被和沉积物重金属分布的主要影响因子。与此前湿地研究多强调单一尺度、单个因素的重要作用相比,具有更深入的研究意义。在此基础上,依据评价指标筛选原则,参考压力-状态-响应模型构建了红树林湿地生态系统退化评价指标体系。主要结论如下: 1)基于2010年湛江遥感影像解译分析得知,湛江各区县红树林景观特征差异比较大,其中雷州市红树林面积最大,廉江和遂溪县红树林景观破碎度较低。红树林湿地周边2000m范围内土地利用类型以养殖塘和农田为主。 2)湛江红树林湿地水体为富营养化状态,水环境中重金属污染程度比较低。霞山特呈岛、雷州和廉江等区县的重金属含量相对较高,Cu、Ni、Pb、Hg在个别点存在污染现象,需要重点监控管理。水环境中重金属来源比较复杂,Hg、Cu、Pb、Ni、Mn和Cd主要来源于人类活动。水体中TOC和pH是影响重金属浓度的关键因子。 3)红树林湿地沉积物中TS、TK和TOC以及重金属Cu、Cr、Cd、 Ni、As、Mn、Fe浓度受红树林群落类型的影响。湛江红树林湿地沉积物中首要污染物是As、Cr、Ni。 4)湿地周边土地利用类型、沉积物性质对湿地重金属浓度有显著性影响。沉积物中TS、TP、TK浓度及湿地周边2000m内的不透水面、稻田、森林及泥滩是影响红树林湿地沉积物重金属含量的显著性因子。 5)湛江红树林湿地由白骨壤(Avicnnia marina)、红海榄(Rhizophora stylosa)、木榄(Bruguiera gymnorrhiza)、秋茄(Kandelia candel)、桐花树(Aegiceras corniculatum)、无瓣海桑(Sonneratia apetala)和海漆(Excoecaria agallocha)等7种真红树组成的优势乔木群落共有12个类型。无瓣海桑群落分布最广,其胸径、高度和冠幅面积显著大于其他物种,但多度相对较低。白骨壤和秋茄群落的多样性指数较高。优势乔木群落林下层物种组成简单、数量较少;桐花树、秋茄和白骨壤是林下层植被的优势物种。 6)湛江红树林物种分布的变化是栖息地环境因子和景观格局共同作用的结果。红树林湿地沉积物中的TOC、TP、盐度与海岸线的距离,沉积物As浓度,湿地周边2000m范围内的养殖塘、不透水面、红树林湿地和泥滩都是影响林上层物种分布的显著性因子,而针对林下层植被,与海岸线距离、沉积物电导率、沉积物As和Pb浓度以及2000m范围内水稻田、养殖塘等是显著性影响因子。 7)在分析大量国内外湿地评价指标体系的基础上,根据红树林湿地生态系统特点,构建了一套生态系统退化评价指标体系。该体系定性指标与定量指标充分结合,可以较完整的描述红树林湿地主要生态特征,对湿地退化程度进行判定,适用于我国红树林湿地生态系统退化评价。 8)湛江7个县区红树林湿地进行退化程度评价结果表明:廉江、遂溪、霞山、徐闻、麻章、雷州、坡头红树林湿地的退化指数及退化程度分别为0.88(未退化)、0.78(未退化)、0.71(轻度退化)、0.70(轻度退化)、0.68(轻度退化)、0.67(轻度退化)、0.59(轻度退化).
英文摘要:        Mangrove forests are diverse vegetation communities that commonly thrive in the intertidal zones of tropical and subtropical coastal rivers, estuaries and bays. Mangrove ecosystems provide food sources, diverse ideal habitats for residents and migratory organisms, and maintain stabilization for adjacent coastal landforms. However, with the rapid development of economy, environment pollution and human activities make mangrove swamp degrated. In China, mangrove ecological restoration projects launched in the late 1990s and the area of mangrove swamp slowly rose from 22752 hm2 in 1990s to 24000hm2 in 2009. While vegetation degradation, metal pollution, overexploitation and so on are still common phenomenons in mangarove swamp. Basing on the stduy of landscape, water evnironment, sediment and vegetation characteristics in Zhanjiang mangrove swamp, significant variables from multi-scales, espeically for mangrove vegetation distribution and sediment heavy metals, were founded. With these results, the evaluation index system of ecological degradation in mangrove swamp was constructed, refering to pressure-state-response model. The main conclusions are as follows: 1) Results of analysis on remote sensing images in 2010 showed that mangrove landscape features are different among seven districts in Zhanjiang. Leizhou has the largest mangrove area and mangrove landsacpe fragmentation is very low in Lianjiang and Suixi. Aquaculture ponds and crop lands are the main types of land use in 2000m around mangrove swamp. 2) Water eutrophication is common in Zhanjiang mangrove swamp, while the level of heavy-metal pollution is low. Some sites, especially in Xiashan, Leizhou and Lianjiang, have high concentration of Cu、Ni、Pb、Hg, which need to strengthen detection. The source of heavy metals in water is complex. Hg, Cu, Pb, Ni, Mn and Cd are mainly from human activities. TOC and pH could be mainly responsible for heavy metal concentration in water body. 3)The concentration of Cu, Cr, Cd, Ni, As, Mn and Fe in sediment of mangrove swamp did significantly differ with mangrove communities, which is similar to TS, TK and TOC. The primary heavy-metal pollutants in Zhanjiang are As, Cr and Ni. 4)Variables from landscape in 2000m around sampling sites and sediment characteristics all have significant effects on sediment heavy metals. Total sulfur, total phosphorus and total potassium in sediment and imperative area, paddy field and forest land in 2000 m around sampling sites are significant variables for heavy metals. 5) There are twelve types of dominant mangrove tree communities, belonging to seven true mangrove speices, including Avicnnia marina, Rhizophora stylosa, Bruguiera gymnorrhiza, Kandelia candel, Aegiceras corniculatum, Sonneratia apetala and Excoecaria agallocha. Multiple comparison of species showed DBH, height and crown areas of Sonneratia apetala were obviously larger than other species, while diversity index of Avicennia marina and Kandelia candel dominant communities were higher. There are samll number of species in understory of dominant mangrove tree communities, with dominant species Aegiceras corniculatum, Kandelia candel and Avicennia marina. 6) Results indicate that explanatory variables at the landscape and habitat scales are strongly related to mangrove species composition. The independent contribution of landscape variables to species composition was greatest in overstory and understory vegetation. TOC, TP and salinity of the sediment; distance from coastline; concentration of As; and land use area within 2000 m surrounding the sampling sites, such as aquaculture, mangrove swamp, mud flat and imperative area, were limiting factors of species composition in overstory vegetation, while understory vegetation may be more affected by the distance from the coastline, electrical conductivity, concentration of As and Pb in the sediment, and areas of paddy field and aquaculture within 2000 m surrounding the sampling sites. 7) According
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/35204
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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Recommended Citation:
刘静. 红树林湿地生态退化综合评价[D]. 北京. 中国科学院研究生院. 2015.
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