生物多样性
中国
生态系统
生物多样性保护
土地利用
环境科学
土地利用、土地利用的变化和林业
环境资源管理
地理
环境保护
生态学
生物
考古
作者
Huiyu Xie,Xiaowei Jin,Wenpan Li,Kun Cai,Guangli Yang,Kai Chen,Jian Xu,Andrew C. Johnson
标识
DOI:10.1021/acs.est.4c09911
摘要
Aquatic biodiversity loss, particularly in rapidly developing nations, continues to raise concerns, prompting urgent debates on reconciling economic growth with environmental preservation through land use planning. While spatial variations in aquatic communities along land use gradients are well-documented, precise ecological thresholds for land use impacts on freshwater lakes remain elusive, hindering sustainable development efforts. This study investigated six representative freshwater lakes in China between 2019 and 2020, all significantly impacted by anthropogenic activities. We utilized macroinvertebrate communities as bioindicators and employed four categories of aquatic ecological metrics─taxonomic diversity, functional diversity, pollution tolerance, and water quality─to assess their responses to local land use patterns. Macroinvertebrate community composition varied significantly among the studied lakes, with pollution-tolerant taxa predominating in highly urbanized and eutrophic systems. Notably, benthic communities exhibited greater sensitivity to urban land use (ecological thresholds: 2-10%) compared to agricultural land use (thresholds: 15-40%). The most pronounced responses were observed within 1-5 km of the lakeshore, with circular buffers yielding more significant effects than fan-shaped buffers, excluding water areas. A novel land use intensity indicator─the ratio of nonecological to ecological land (NEL/EL = area of nonecological land/area of ecological land)─proved effective in predicting ecological shifts. Smaller or heavily urbanized lakes showed marked changes at NEL/EL ratios between 0 and 0.6, while larger or river-connected lakes exhibited shifts at ratios exceeding 1.5. These findings underscore the profound ecological footprint of human activities on lake ecosystems with urban land cover emerging as the most deleterious factor.
科研通智能强力驱动
Strongly Powered by AbleSci AI