淡水湖泊浮游藻类对富营养化和气候变暖的响应

水体富营养化和气候变暖是淡水生态系统面临的两大威胁。文章分别阐述了富营养化和气候变暖对淡水湖泊浮游藻类直接和间接效应, 并总结气候变暖可能通过影响水体理化性质、水生植物组成、食物链结构从而直接或间接改变浮游藻类生物量或群落结构。作者重点分析了气候变暖下湖泊生态系统蓝藻水华暴发机制, 比较了不同湖泊蓝藻对气候变暖和富营养化响应的异同点, 发现气候变暖和富营养化对湖泊生态系统影响存在相似性, 表现在均促进湖泊由清水-浊水稳态转变、增加蓝藻水华发生频率和强度。然而二者对湖泊浮游藻类影响的相对重要性取决于分层型湖泊和混合型湖泊的差异性、不同营养型湖泊和不同类群蓝藻组成差异性。作者认为, 开展气候变暖和富营养化下, 湖泊浮游藻类功能群响应研究亟待进行。     

洋河水库富营养化发展趋势及其关键影响因素

洋河水库作为秦皇岛和北戴河暑期办公的重要水源地,近年来富营养化逐年加重,水质恶化.通过收集历史资料和现场连续监测,综合分析了洋河水库富营养化发展趋势及其关键影响因素,结果表明1990-2011年期间,总氮、总磷常年超过国际公认的发生富营养化的浓度水平,每年8月是水华集中暴发期.以8月为例,自1990年以来,水体总氮、总磷和叶绿素a浓度逐年上升,透明度则呈明显下降趋势,富营养化状态从1990年的中营养状态变成2011年的超富营养状态.对2011年5-10月监测数据进行相关关系分析发现水温、透明度、溶解氧、pH值、总磷、正磷酸盐、高锰酸盐指数和总有机碳都与叶绿素a浓度明显相关,其中透明度、溶解氧、pH值是水华暴发的结果而非原因,表明透明度、溶解氧和pH值是水华暴发影响水质的主要表现因素;既然洋河水库地处北温带,四季分明,冬季存在2-3个月的冰封期,因此水温是水库水华暴发的驱动因子之一.2011年7月中旬至8月底洋河水库暴发了全库水域的蓝藻水华,优势种为铜绿微囊藻(Microystis aeruginosa)和鱼害微囊藻(M.ichthyoblabe),密度分别达到3.5×106和1.4×106个/mL.     

大型浅水富营养化湖泊中蓝藻水华形成机理的思考

湖泊富营养化依然是我国目前以及今后相当长一段时期内的重大水环境问题。研究蓝藻水华的形成机制 ,对于科学预测湖泊中蓝藻水华的产生 ,并采取相应措施减少其带来的影响具有重要的生态和环境意义。为探索富营养化湖泊中蓝藻水华形成机理 ,综述了目前对我国大型浅水湖泊蓝藻水华成因研究现状和对水华形成机理的一般认识。分析了导致蓝藻水华形成的化学、物理和生物等主要环境因素 ,论述了蓝藻 ,尤其是微囊藻成为水华优势种的可能原因。认为对水华的形成需要全面认识 ,营养盐浓度的升高可能仅是蓝藻水华形成、且人们可以加以控制的因素之一 ;在探索水华成因时 ,不能仅仅局限于夏季蓝藻水华发生时环境特征的研究与观察 ,而应该提前关注蓝藻的越冬生理生态特征、春季复苏的生态诱导因子及其阈值以及在复苏后 ,蓝藻如何在生长过程中形成群体 ,并逐步成为湖泊水生生态系统中的优势种乃至形成水华的过程。并需要对蓝藻越冬的生存对策、蓝藻群体的形成的条件、蓝藻在春季复苏的触发条件及其生态阈值、以及蓝藻在与其它藻类种群竞争中取胜的生理生化特征有足够的认识。蓝藻水华的“暴发”是表观现象 ,其前提还是藻类一定的生物量 ,且是一个逐渐形成的过程。根据生态学的基本理论和野外对水华形成过程的原位观测     

荧光原位杂交技术和流式细胞仪用于环境样品中产毒及非产毒微囊藻的定量监测

全球范围内,高频次、大范围暴发的蓝藻水华对淡水水体环境造成严重影响.微囊藻因其在生长特别是衰亡过程中向水体释放微囊藻毒素而威胁人类健康.因此,分析其产毒株及非产毒株在环境样品中的组成,建立产毒蓝藻的预报及评价体系显得极为重要.本文采用荧光原位杂交技术结合流式细胞技术实现对环境样品中产毒藻株的鉴别与定量.针对目标基因mcyA设计的、以地高辛标记的双链DNA探针可有效应用于产毒微囊藻FACHB905和PCC7806的鉴别.分别对来自滇池、太湖和关桥的11个样品进行分析显示,该方法与传统的形态学鉴定及PCR方法有较好的匹配.荧光原位杂交技术与流式细胞相结合可有效鉴别产毒与非产毒微囊藻,尤其可以对野外样品中产毒与非产毒藻株进行简便、可视化地鉴别,从而达到对产毒微囊藻水华早期预警的目的.     

有害藻华预警预测技术研究进展

近年来有害藻华频繁发生且危害严重,对有害藻华预警预测技术的研究可为有害藻华的预警预报、生态学防治及防灾减灾提供借鉴.本文从有害藻华的运动预测预警、指标临界值预警、数据驱动模型和生态数学模型4个方面介绍了国内外有害藻华的预警技术研究进展,分析了各类预警技术的优劣,并提出了基于细胞特征预测蓝藻生长速率以及基于藻类群落特征预警蓝藻水华的新思路.     

太湖湖岸带浮游植物初级生产力特征及影响因素

利用高频溶解氧监测,估算了太湖梅梁湾湖岸带浮游植物初级生产力的高频变化特征。结合同步气象监测及浮游植物、浮游动物和营养盐的周年逐周观测数据,分析了气象和环境条件对富营养化湖泊浮游植物初级生产力的影响。结果发现,高频溶解氧监测估算的初级生产力变化与浮游藻类生物量的变化一致,能够反映出浮游植物生产力的昼夜变化、季节变化等规律。统计分析表明,气温对太湖这一富营养化湖泊初级生产力影响很大;氮的供给与浮游动物的选择性牧食也是影响浮游植物初级生产力的重要因素。湖岸带的水华堆积过程对初级生产力影响巨大,气象、水文过程又加剧了蓝藻水华初级生产力的变化幅度,反映出富营养化湖泊初级生产力可能存在极大的时空不均一性。研究表明,溶解氧高频监测法估算初级生产力能够捕捉到湖泊初级生产力的快速变化过程,可以用于富营养化湖泊初级生产力监测、蓝藻水华灾害预警中。     

微囊藻毒素生物治理技术研究进展*

近年来,随着全球气候变暖和水体富营养化程度加深,蓝藻水华频繁暴发。微囊藻毒素是有害蓝藻产生及释放的危害最大的一类蓝藻毒素,对生态环境和公众健康造成了严重的威胁。因此,寻求有效的微囊藻毒素降解方法已成为全球科学领域的研究热点。针对微囊藻毒素生物治理技术展开综述,阐述了微囊藻毒素的产生、理化性质及生物毒性,总结了微生物、水生植物、浮游动物等自然生物降解微囊藻毒素的能力。在此基础上概述了生物滤池、人工湿地、生态浮床、膜生物膜反应器等生物治理技术对微囊藻毒素的去除效果,分析了现有微囊藻毒素生物处理方法的优势和局限性,并对今后的研究方向提出展望,为解决水环境中微囊藻毒素的污染问题提供思路。     

蓝藻毒素的类型及其产毒基因

水体富营养化及蓝藻水华的频繁发生,严重破坏水域生态系统,有毒水华蓝藻产生的藻毒素,污染水体且危害水生动物和人类健康。本文综述了水华蓝藻的次级代谢产物藻毒素及藻毒素合成基因的国内外研究进展,介绍了蓝藻毒素的类型、不同毒素毒性机理、产毒基因以及利用产毒基因进行产毒蓝藻的分子生物学检测等,并对研究中存在的问题和未来的研究方向进行了展望,目的是为建立有毒水华蓝藻的预警机制、科学评价产毒蓝藻及其藻毒素的生态、环境危害及生态环境治理提供理论和技术支撑。     

富营养湖泊滇池水华蓝藻的机械清除

富营养湖泊暴发蓝藻水华时可集聚浓度达n× 10 8— 9cell/L ,利用重力振动、旋振和离心等方法收集富藻水 ,逐次浓缩、脱水后得藻泥 ;根据需要可对藻泥进行干燥 ,得到干藻粉。 2 0 0 1年 4月至 2 0 0 2年 11月在云南滇池以上述方式清除蓝藻水华 35 1d ,共处理富藻水 4 2 6 4 8m3 ,折合清除水华蓝藻干重为 4 6 0 .83t。按所清除的蓝藻干粉的总氮、总磷、总钾、粗有机质及重金属的平均含量计算 ,相当于从试验区水体中去除了氮 (N) 37.33t、磷 (P) 2 .71t、钾 (K)2 .4 9t及粗有机质 2 0 0 .32t;重金属铅 (Pb) 2 .2 89kg、砷 (As) 2 .2 3kg、汞 (Hg) 2 .3kg、镉 (Cd) 0 .5 1kg。表明在富营养湖泊中水华蓝藻大量暴发时 ,采用机械除藻 ,对控制蓝藻水华污染 ,有效降低内源氮、磷等污染物负荷具有十分重要的作用     

草地螟的发生为害与监测预警技术研究进展

草地螟Loxostege sticticalis L.是我国北方农牧区重要的迁飞性害虫,具有突发性、暴发性和周期性危害的特点,随着第4个暴发周期的来临,将会给我国北方农牧业的安全生产带来很大的威胁.为准确掌握草地螟在我国的发生与迁飞为害规律,提高对重大迁飞性害虫的灾变预警能力,本文综述了草地螟在我国及周边国家的时空分布,发生为害、迁飞规律和监测预警等方面的研究进展,并对草地螟的监测预警技术进行了展望,以期为第4个暴发周期草地螟的监测和防控提供技术支持.     

Early detection of ecosystem regime shifts: a multiple method evaluation for management application

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change.     

Review on detection of critical transition in ecosystems

当一个存在多稳态的生态系统临近突变阈值点时, 外界条件即使发生一个微小变化, 也会引发生态系统的剧烈响应, 使之进入结构和功能截然不同的另一稳定状态, 这种现象称为重大突变(critical transition)。重大突变所导致的稳态转换总是伴随着生态系统服务的急剧变化, 可能对人类可持续发展产生重大影响。预测生态系统突变的发生非常困难, 但科学家在此领域的大量研究结果表明, 通过监测一些通用指标可以判断生态系统是否不断临近重大突变阈值点, 进而可以进行生态系统重大突变预警。该文对近年来生态系统重大突变检测领域所取得的成果进行总结与归纳, 论述了生态系统重大突变的产生机制及其后果, 介绍了生态系统突变预警信号提取的理论基础, 从时间和空间两个维度总结了近年来生态系统重大突变预警信号的提取方法, 概述了当前研究面临的挑战, 指出生态系统突变预警信号的检测应充分利用时空动态数据, 并且联合多个指标, 从多个角度进行综合预警, 此外, 还应重视生态系统结构与重大突变之间的关系, 增强生态系统突变预警能力。     

An integrated ecological security early-warning framework in the national nature reserve based on the gray model

Nature reserves (NRs) play a pivotal role in minimizing habitat loss and protecting wild animals and plants, which are critical for human ecological security. However, focusing only on the construction of ecological security patterns of NRs without understanding their ecological security early-warning situations and their driving factors may fail to achieve protection goals. This study constructed an ecological security early-warning framework and index system based on the Driving force-Pressure-State-Impact-Response (DPSIR) framework model. The gray model (GM) was used to predict the ecological security early-warning situation, and the Geo-detector model was applied to explore the driving factors of the ecological security early-warning system in the Baishuijiang National Nature Reserve (BNNR). The results showed that the average ecological security index (ESI) value increased from 0.2796 in 2005 to 0.3171 in 2017, with an average increase of 11.82%. The ecological security early-warning index (ESEWI) value increased from 0.3171 in 2018 to 0.3622 in 2030, which was an average increase of 12.46%. These results indicated that the ecological security situation continually improved from 2005 to 2030. By 2030, the number of towns with a “no warning” grade increased to four, the number of towns with an “extreme warning” grade was zero, and the proportion of areas with early-warnings decreased from 100% to 33%. The q values of per capita forest land areas and per capita grassland areas were both 0.9334, which indicated that environmental characteristic factors were the primary driving factors in ecological security early-warning. Our results demonstrated that the ecological security early-warning index system based on the DPSIR model and grey model can well prediction ecological security situation and provide scientific support for the ecological protection and management of NRs.     

基于可拓分析的区域生态安全预警模型及应用——以陕西省为例

生态安全问题是当前人类社会可持续发展面临的主要问题,预警是生态安全研究的重点。根据生态安全预警多层次、多维度和动态性的要求,利用可拓综合分析方法,建立了区域生态安全的"状态-胁迫-免疫"(State-Danger-Immunity,SDI)动态预警模型。运用该模型对陕西省历史年份(1990-2007年)生态安全进行定量评估,并对规划年份(2010年)生态环境进行动态预警。研究结果表明,陕西省1990年至2007年的生态环境从"不安全"状态到"较不安全"状态再到"较安全"状态,呈逐渐好转的趋势,但生态安全整体水平较低,均处于"安全"水平以下;2010年陕西省生态安全为"蓝色"预警,且具有向"黄色"预警变化的趋势,水资源出现"橙色"预警,是影响陕西生态安全的主要因素。     

能源预测预警理论与方法研究进展

能源作为一种稀缺性的战略资源是国民经济增长和社会进步的物质基础,但是随着化石能源耗竭及能源使用造成的环境问题日趋严重,能源安全问题逐渐成为关注的焦点,而能源预测预警也成为能源系统科学领域的新兴学科,其内容包含能源安全理论、基于模型的能源供需预测和基于安全评价指标体系的能源预警等方面内容。通过系统回顾能源安全的理论及其演变的历程,重点综述了自上而下、自下而上和混合建模3种建模思路的能源预测模型,探讨了三类模型的优点和局限性,并根据能源安全预警评价指标浓缩信息的程度,将现有预警评价体系划分单个型指标评价体系和聚合型指标评价体系两大类。通过对以上研究内容的总结分析,明确了当前能源预测预警研究各领域的研究进展,及其在理论和应用方面的优势与不足。在未来研究中,建议从供应链的角度出发,考虑能源系统内部各因素及与外部因素的相互作用,构建基于链式的预警体系,以有效弥补现有研究中的不足。     

Spatial correlation as leading indicator of catastrophic shifts

Generic early-warning signals such as increased autocorrelation and variance have been demonstrated in time-series of systems with alternative stable states approaching a critical transition. However, lag times for the detection of such leading indicators are typically long. Here, we show that increased spatial correlation may serve as a more powerful early-warning signal in systems consisting of many coupled units. We first show why from the universal phenomenon of critical slowing down, spatial correlation should be expected to increase in the vicinity of bifurcations. Subsequently, we explore the applicability of this idea in spatially explicit ecosystem models that can have alternative attractors. The analysis reveals that as a control parameter slowly pushes the system towards the threshold, spatial correlation between neighboring cells tends to increase well before the transition. We show that such increase in spatial correlation represents a better early-warning signal than indicators derived from time-series provided that there is sufficient spatial heterogeneity and connectivity in the system.     

青藏高原草地承载力空间演变特征及其预警

为研究青藏高原草地承载力的空间演变特征并对其进行预警，以已有的青藏高原净初级生产力数据为基础，核算了该地区的草地理论载畜量及演变趋势，并结合县域实际存栏量，划定了草地承载力的预警等级。结果表明：（1）青藏高原草地承载力整体呈东高西低的格局，其中高寒草原和高寒草甸是该地区草地承载力的主要组成部分；（2）2000-2015年，青藏高原理论载畜量由8614.89万羊单位增至9451.53万羊单位；（3）青藏高原整体处于超载状态，2000-2010年超载状况加剧，至2015年超载状况稍有缓解，草畜平衡指数由67.88%增至79.90%，再降至67.91%。目前亟需优先控制72个红色预警县（超载状态正在加剧）的牲畜存栏量，避免超载状况进一步恶化。未来需要通过控制牲畜存栏量、调整畜牧区发展布局和提高草地生产力等多项措施的结合来改善青藏高原地区的草地承载状况，维持草地生态系统的可持续发展。     

Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model

Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.     

Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron

In this work, a novel spatio-temporal air quality prediction framework is proposed, and its development and efficacy as a predictive tool are described. The framework exploits data from the suite of models of the Copernicus Atmosphere Monitoring Service (CAMS), fed to an artificial neural network for the removal of bias. The method inherently considers spatial and temporal correlations, because it is applied simultaneously to all monitoring stations of a given region, using past observations and past and future forecasts. The methodology is tested on twelve months of CAMS forecasts of daily surface particulate matter (PM₁₀) in 2017 and is verified against observations measured at 413 monitoring stations from the Italian air quality network.The raw data from the CAMS system, although they contain valuable information, show very poor performance, due to a large negative bias that does not allow the correct prediction of critical conditions. The model bias is found to have a strong seasonal dependency, with a large positive bias in winter and a small negative bias during summer months. The correction applied through the neural model allows to correct the original predictions and to practically eliminate the bias, increasing the performance forecast even to four days ahead. It is concluded that neural networks can be used to develop reliable air quality early-warning systems based on a network of automated monitoring stations and real-time ensemble predictions from deterministic models.