环境科学
生态系统
生态系统呼吸
分水岭
水文学(农业)
空间变异性
气候变化
初级生产
生态学
地质学
生物
数学
计算机科学
统计
机器学习
岩土工程
作者
Isabella A. Oleksy,Stuart Jones,Christopher T. Solomon
出处
期刊:Ecosystems
[Springer Science+Business Media]
日期:2021-10-29
卷期号:25 (6): 1328-1345
被引量:3
标识
DOI:10.1007/s10021-021-00718-5
摘要
Abstract Global change is influencing production and respiration in ecosystems across the globe. Lakes in particular are changing in response to climatic variability and cultural eutrophication, resulting in changes in ecosystem metabolism. Although the primary drivers of production and respiration such as the availability of nutrients, light, and carbon are well known, heterogeneity in hydrologic setting (for example, hydrological connectivity, morphometry, and residence) across and within regions may lead to highly variable responses to the same drivers of change, complicating our efforts to predict these responses. We explored how differences in hydrologic setting among lakes influenced spatial and inter annual variability in ecosystem metabolism, using high-frequency oxygen sensor data from 11 lakes over 8 years. Trends in mean metabolic rates of lakes generally followed gradients of nutrient and carbon concentrations, which were lowest in seepage lakes, followed by drainage lakes, and higher in bog lakes. We found that while ecosystem respiration (ER) was consistently higher in wet years in all hydrologic settings, gross primary production (GPP) only increased in tandem in drainage lakes. However, interannual rates of ER and GPP were relatively stable in drainage lakes, in contrast to seepage and bog lakes which had coefficients of variation in metabolism between 22–32%. We explored how the geospatial context of lakes, including hydrologic residence time, watershed area to lake area, and landscape position influenced the sensitivity of individual lake responses to climatic variation. We propose a conceptual framework to help steer future investigations of how hydrologic setting mediates the response of metabolism to climatic variability.
科研通智能强力驱动
Strongly Powered by AbleSci AI