黑水河流域生态系统完整性评价

Assessment of ecosystem integrity in Heishui River basin

为评估黑水河流域受人为干扰的实际情况,依托流域范围内的基础地理、生物多样性、环境数据、其他威胁等调查资料,构建包含水文情势、水质、连通性、栖息地、生物群落5项关键属性的淡水生态系统完整性评价指标体系。通过变化范围法RVA、综合连通系数、栖息地比例相似PSI指数、入侵指标和捕捞指标等对黑水河流域生态系统完整性进行评价,黑水河流域水文情况属性现状为差、连通性属性现状为差、栖息地属性现状为差、生物群落属性现状为一般;河流水质现状较优,但黑水河支流存在一定富营养化风险,评估为一般。结果表明:流域生态系统完整性评价指标体系,应围绕淡水生态系统五大关键生态属性,并结合流域生态特征及生态现状来确定;RVA、综合连通系数、PSI指数等的综合运用能够科学地确定指标阈值,进而正确进行流域生态完整性评价;受不同流域鱼类生态学习性差异等因素影响,还需通过长期监测不断完善评价理论和方法。

In order to evaluate the actual situation of human interference in the Heishui River basin,based on the basic geography,biodiversity,environmental data,threats and other survey data within the basin,the freshwater ecosystem integrity assessment index system was constructed,which included five key attributes,namely hydrological regime,water quality,connectivity,habitat,and biological community.The ecosystem integrity of the Heishui River basin was assessed by range of variability approach,comprehensive connectivity coefficient,habitat proportion similarity PSI index,invasion index and fishing index.The hydrological status attribute of Heishui River basin is poor,the connectivity attribute is poor,the habitat attribute is poor,and the biological community attribute is general;The current water quality of the river is relatively good,but Heishui River Sub reservoir has a certain risk of eutrophication,which is evaluated as average.The results show that:1.The assessment index system of watershed ecosystem integrity should be determined by centering on the five key ecological attributes of freshwater ecosystem and combining the ecological characteristics and ecological status of the watershed;2.The comprehensive application of RVA,comprehensive connectivity coefficient,PSI index,etc.can scientifically determine the indicator threshold,and then correctly assess the ecological integrity of the watershed;3.Influenced by factors such as differences in fish ecological learning in different watersheds,assessment theories and methods need to be constantly improved through long-term monitoring.