四种浮游植物生物量计算方法的比较分析

浮游植物是水生生态系统中重要的初级生产者,其生物量是反映其现存量的关键指标.本文利用具有3个处理组的围隔实验中的浮游植物数据,对文献中常见的计算浮游植物种群生物量和群落生物量的4种方法:标准法、细分法、粗分法和资料法进行比较,并分析采用这4种不同方法得到的浮游植物生物量与叶绿素a浓度的相关性.结果表明:粗分法是计算浮游植物生物量的高效方法,能够保证准确度和节省时间;提高浮游植物生物量计算准确度不是影响浮游植物生物量与叶绿素a浓度相关性显著程度的关键.通过比较剔除稀有种(生物量不超过群落生物量5%的种类)前后浮游植物生物量的差异,发现忽略稀有种会导致种类均匀度较高的浮游植物群落生物量严重偏低,建议浮游植物生物量的计算不能一概忽略稀有种.     

滆湖水生植物生物量、演替规律及合理利用

用五种不同的方法估算了滆湖水生植物的生物量,比较分析了全湖断面生物量和群丛生物量．分三个方面对滆湖水生植物的演替规律进行了讨论,对草型湖泊水生植物的合理利用进行了探讨．初步提出了草型湖泊估算渔产潜力的公式．在分析引起水生植物生物量变化的原因的基础上,提出了若干合理利用植物资源的建议．     

长江中下游典型湖泊营养盐历史变化模拟

湖泊营养盐变化在自然条件下受到气候水文因素控制,同时受到湖泊生态系统生物群落作用和反馈.作为动力机制探讨,本文试图基于水文和生态动力学方法,分别构建气候-流域水文作用于湖泊营养盐的外源模式和湖泊生物群落作用于湖泊营养盐的內源模式.针对长江中下游典型湖泊,经过控制实验和率定,发现营养盐模拟与观测数据在时间序列上达到90%百分位的正相关,因此用来模拟1640 1840 A.D.期间的营养盐演变历史.研究表明:(1)模拟的湖泊营养盐变化与沉积钻孔揭示的历史营养盐变化基本一致,沉积记录与模式模拟的7个湖泊的营养盐变化均显著相关;(2)气候因素是湖泊营养盐历史演变的主控因子,来自于湖泊生物群落的反馈作用贡献约占40%;(3)在温度和降水因子的驱动下,湖泊营养盐历史变化主要受降水控制,在极端干旱时期,60%的营养盐变化同步响应于降水变化.同时,面积在400 km2以下的湖泊营养盐对气候变化的响应比2000 km2以上的大湖更为敏感.研究结果对长江中下游湖泊营养状态的长期变化机理认识和趋势控制提供科学依据.     

不同温度培养下莕菜(Nymphoides peltata)生长与光合作用特征

以太湖东山与西山之间水域的莕菜根茎为实验材料,在室内分别进行3种温度(28、30、32℃)处理,利用Li-6400便携式光合作用测量系统,分别测定3种温度处理下莕菜的光响应曲线和CO2响应曲线,并且利用非直角双曲线法进行拟合,得到相关参数.结果表明,3种温度下,莕菜的光合能力大小顺序表现为30℃>32℃>28℃,羧化效率为32℃>30℃>28℃,蒸腾速率、气孔导度均为30℃>28℃>32℃;28、30和32℃下的单位叶片生物量分别为0.0019±0.0002、0.0021±0.0003、0.0020±0.0003 g(DW)/cm2,叶片及其单位面积生物量为30℃>28℃>32℃,但差异不显著;30℃下的根、茎干重显著高于另外2个温度下的.在低于30℃的温度条件下,随着温度的升高,莕菜的光合能力均明显升高,促进了植物的生长;而当温度超过30℃后,光合作用效率降低,植物的生长则受到抑制.在适宜温度范围内(<30℃),小幅度的升温(2℃)产生了显著的积温效应(200℃.d),促进了莕菜的生长.因此推断,1998 2006年太湖地区平均气温升高约1.0℃的现象对近年来太湖莕菜的大面积扩张可能具有促进作用.     

基于水声学方法的太湖鱼类空间分布和资源量评估

利用BioSonics DT-X科学回声探测仪(208 kHz)在太湖2011年开捕前的8月对东部和北部湖区的鱼类进行了走航式水声学调查,并结合地理信息系统(GIS)模型对调查湖区的鱼类大小组成、空间分布和资源量进行了评估.结果表明,调查湖区的鱼类平均目标强度(目标鱼类对声波的反射能力)为-51.85±0.02 dB,平均体长在6 cm左右,体长范围为2.35～89.33 cm,不同区域间的鱼类目标强度差异性显著,表明不同区域间鱼类大小存在差异,其中鱼类的最小平均目标强度(-53.94±0.10 dB)出现在洞庭东西山之间,最大平均目标强度(-50.27±0.14 dB)出现在光福湾.调查湖区的鱼类密度在0.43～3.90 ind./m3之间,采用地理信息系统(GIS)对调查湖区进行建模得到鱼类密度均值为2.27±0.57 ind./m3,不同区域间鱼类密度差异性显著,鱼类密度在敞水区较高.基于建模的栅格化数据评估调查湖区鱼类资源量约为5.3×109ind.,其中目标强度在-45 dB(体长约13 cm)以下的鱼类占98.49%.本文对水声学方法在大型浅水湖泊中的应用进行了初步探索,水声学方法可在一定程度上突破传统鱼类资源调查方法在较大空间尺度上的局限性,但在调查时易受风浪、水生植物、船速的影响.     

鄱阳湖国家级自然保护区湿地植被的干旱响应及影响因素

近年来鄱阳湖干旱事件频发,干旱导致的气象水文要素变化直接影响植被生长状况,尤其是对于地上植被生物量的影响极为显著.研究鄱阳湖干旱事件对于湿地植被的影响,对于保护鸟类栖息地,认识湿地生态功能和结构的变化具有重要的现实意义.利用长期卫星遥感数据,结合植被生物量野外调查,以2003和2006年极端干旱年份为出发点,从湿地植被面积、生物量密度和总生物量的角度分析了鄱阳湖湿地植被生物量对于极端干旱的响应.研究表明:湿地植被面积、生物量密度以及总生物量均呈现双峰分布特征,在4和11月分别达到上、下半年的峰值.2003年植被生物量与多年均值一致,2006年下半年植被面积、生物量密度以及总生物量均明显超出多年均值.影响湿地植被面积的主要因素为鄱阳湖水位变化;而影响植被生物量密度的主要因素为气温和水位,退水时间提前对于生物量密度影响最大;总生物量同时受到植被面积与植被生物量密度的综合影响,其中植被面积的影响更大,植被面积对于总生物量的影响在2006年表现得比2003年更加显著.总之,2006年湿地植被对水文干旱的响应要比气象干旱强烈得多.     

Biomorphological scaling laws from convectively accelerated streams

Worldwide convectively accelerated streams flowing in downstream-narrowing river sections show that riverbed vegetation growing on alluvial sediment bars gradually disappears, forming a front beyond which vegetation is absent. We revise a recently proposed analytical model able to predict the expected longitudinal position of the vegetation front. The model was developed considering the steady state approximation of 1-D ecomorphodynamics equations. While the model was tested against flume experiments, its extension and application to the field is not trivial as it requires the definition of proper scaling laws governing the observed phenomenon. In this work, we present a procedure to calculate vegetation parameters and flow magnitude governing the equilibrium at the reach scale between hydromorphological and biological components in rivers with converging boundaries. We collected from worldwide rivers data of section topography, hydrogeomorphological and riparian vegetation characteristics to perform a statistical analysis aimed to validate the proposed procedure. Results are presented in the form of scaling laws correlating biological parameters of growth and decay from different vegetation species to flood return period and duration, respectively. Such relationships demonstrate the existence of underlying selective processes determining the riparian vegetation both in terms of species and cover. We interpret the selection of vegetation species from ecomorphodynamic processes occurring in convectively accelerated streams as the orchestrated dynamic action of flow, sediment and vegetation characteristics. © 2019 John Wiley & Sons, Ltd.     

天文大潮对南海北部浮游动物生物量的影响

基于2020年8月至11月在南海北部获取的声学多普勒流速剖面仪观测数据,利用后向散射强度数据估算得到相对体积散射强度并用其表征浮游动物生物量的相对大小,对相对体积散射强度的半月变化与具有半月周期的潮流动能进行相关性分析,进而分析天文大潮对声学估算的浮游动物生物量的影响。结果表明：半月周期的相对体积散射强度与潮流动能之间呈负相关关系,即天文大潮时,潮流动能较强,水体相对体积散射强度较低,浮游动物生物量则较小,天文小潮时则情况相反。初步推测其原因为：天文大潮时,强潮流一方面导致浮游动物生存环境恶化,使其生物量下降,另一方面也改变了浮游动物垂直迁移特性,浮游动物迁移到近海底处使其难以被观测。     

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.