大型水母爆发对东海生态系统中上层能量平衡的影响

通过建立东海生态系统ECOPATH模型,并将大型水母作为一个独立的功能组,从能量平衡的角度探讨近年来东海大型水母爆发对生态系统的影响,并在此基础上提出抑制大型水母爆发加剧的控制机制的假说。模型分析结果表明:大型水母对中上层生物资源普遍具有显著不利影响;在大型水母、浮游动物和鲳鱼等小型中上层鱼类之间可能存在一个由大型水母爆发引发的生态系统中上层能量反馈循环;大型水母爆发初期将破坏生态系统中上层能量平衡;浮游动物生物量的波动可能是抑制大型水母爆发加剧的自然控制机制之一。     

流域生态过程与水环境效应研究进展

以Web of Science数据库及CNKI(中国知网)的文献资料为数据源,利用CiteSpace文献计量工具,对流域生态过程和水环境效应研究领域的相关文献从发文量、发文期刊、关键词、作者及论文被引频次等方面进行分析,以此探讨国内外该领域研究现状、研究热点及未来发展趋势.结果 表明,流域生态过程和水环境效应研究文献数...     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.     

Age and spatial distribution of the world's oldest trees

Extremely old trees have important roles in providing insights about historical climatic events and supporting cultural values, yet there has been limited work on their global distribution and conservation. We extracted information on 197,855 tree cores from 4854 sites and combined it with other tree age (e.g., the OLDLIST) data from a further 156 sites to determine the age of the world's oldest trees and quantify the factors influencing their global distribution. We found that extremely old trees >1000 years were rare. Among 30 individual trees that exceeded 2000 years old, 27 occurred in high mountains. We modeled maximum tree age with climatic, soil topographic, and anthropogenic variables, and our regression models demonstrated that elevation, human population density, soil carbon content, and mean annual temperature were key determinants of the distribution of the world's oldest trees. Specifically, our model predicted that many of the oldest trees will occur in high-elevation, cold, and arid mountains with limited human disturbance. This pattern was markedly different from that of the tallest trees, which were more likely to occur in relatively more mesic and productive locations. Global warming and expansion of human activities may induce rapid population declines of extremely old trees. New strategies, including targeted establishment of conservation reserves in remote regions, especially those in western parts of China and the United States, are required to protect these trees.