溪流
环境科学
栖息地
分水岭
生态系统
水质
恢复生态学
河流恢复
生态系统健康
底栖区
环境资源管理
生态学
水文学(农业)
生态系统服务
工程类
计算机科学
机器学习
生物
岩土工程
计算机网络
作者
Hehuan Liao,Emily Sarver,Leigh‐Anne Krometis
标识
DOI:10.1016/j.watres.2017.11.065
摘要
Ecological degradation of streams remains a major environmental concern worldwide. While stream restoration has received considerable attention, mitigation efforts focused on the improvement of physical habitat have not proven completely effective. Several small-scale studies have emphasized that effective restoration strategies require a more holistic understanding of the variables at play, although the generalization of the findings based on the small-scale studies remains unclear. Using a comprehensive statewide stream monitoring database from West Virginia (WV), a detailed landscape dataset, and a machine learning algorithm, this study explores the interactive impacts of water quality and physical habitat on stream ecosystem health as indicated by benthic macroinvertebrate scores. Given the long history of energy extraction in this region (i.e., coal mining and oil/gas production), investigation of energy extraction influences is highlighted. Our results demonstrate that a combination of good habitat and low specific conductance is generally associated with favorable benthic macroinvertebrate scores, whereas poor habitat combined with water quality conditions typically indicative of high ionic strength are associated with impaired stream status. In addition, streams impacted by both energy extraction and residential development had a higher percentage of impairment compared to those impacted predominantly by energy extraction or residential development alone. While water quality played a more important role in the ecosystem health of streams impacted primarily by energy extraction activities, habitat seems to be more influential in streams impacted by residential development. Together, these findings emphasize that stream restoration strategies should consider interactive effects of multiple environmental stressors tailored to specific sites or site types - as opposed to considering a single stressor or multiple stressors separately.
科研通智能强力驱动
Strongly Powered by AbleSci AI