铅胁迫对斜纹夜蛾生长发育与生殖的影响

在植食性昆虫斜纹夜蛾幼虫标准人工饲料中添加不同浓度的重金属铅(Pb),研究Pb胁迫对其生长发育与生殖的影响.结果表明:斜纹夜蛾不同发育阶段(幼虫、蛹和成虫)的存活率和体质量随着饲料中Pb浓度的增加而下降,引起存活率显著下降的最低Pb胁迫浓度是100 mg·kg-1,引起体质量显著减少的最低Pb胁迫浓度是50 mg·kg-1.在取食Pb浓度为25～ 200 mg·kg-1的人工饲料后,斜纹夜蛾成虫的产卵天数显著减少;产卵力和生育力随着饲料中Pb浓度的增加而显著下降;1000粒卵的平均质量显著低于对照;卵孵化率显著下降.重金属Pb胁迫对斜纹夜蛾生长发育和生殖具有显著的抑制作用.     

镉胁迫对草地贪夜蛾生长发育及松毛虫赤眼蜂寄生的影响

【目的】为探究镉胁迫对植食性昆虫草地贪夜蛾Spodoptera frugiperda的影响以及是否通过“bottom-up”级联效应影响赤眼蜂Trichogramma的寄生能力。【方法】在室内调查了孵化24 h内的草地贪夜蛾幼虫取食添加了不同浓度(0,0.2和51.2 mg/kg)Cd²⁺的饲料后,草地贪夜蛾生长发育和繁殖力(单雌产卵量)和子一代卵内镉含量,并调查了松毛虫赤眼蜂T.dendrolimi成蜂对低浓度(0.2 mg/kg) Cd²⁺胁迫的草地贪夜蛾卵的寄生能力及偏好性。【结果】与对照(正常人工饲料)相比,人工饲料中0.2和51.2 mg/kg Cd²⁺均导致草地贪夜蛾幼虫历期显著延长、雌蛹重显著降低;高浓度(51.2 mg/kg)Cd²⁺处理下草地贪夜蛾的化蛹率、成虫羽化率、成虫寿命和单雌产卵量均显著降低;而低浓度(0.2 mg/kg)Cd²⁺处理下草地贪夜蛾的单雌产卵量略高于对照,且低浓度Cd²⁺胁迫下草地贪夜蛾卵的镉含量为1.03 mg/k...     

染料标记对斜纹夜蛾生长发育及成虫飞行能力的影响

以斜纹夜蛾Spodoptera litura为研究对象,将染料添加到半人工饲料中饲喂幼虫,评价其对该虫生长发育和成虫飞行能力的影响,以期为开展该虫的田间诱集和扩散行为研究提供标记方法。结果表明,添加不同浓度苏丹红7B、苏丹蓝Ⅱ和龙胆紫的半人工饲料饲养斜纹夜蛾幼虫,含0.006%苏丹红7B的半人工饲料幼虫存活率高,成虫标记效果好。进一步研究显示,取食含0.006%苏丹红7B半人工饲料的斜纹夜蛾幼虫发育历期、蛹重以及成虫寿命、体长、翅展及飞行能力等与对照相比均无显著差异。含0.006%苏丹红7B半人工饲料饲料对斜纹夜蛾的生物学无显著影响,标记效果显著,操作简单,适合于大规模标记斜纹夜蛾成虫。     

Pb2+在甜菜夜蛾体内的积累及对种群的影响

重金属的污染及其治理是目前倍受关注的生态学问题之一.甜菜夜蛾是我国长江流域及以南地区蔬菜、棉花等作物上的主要害虫,近几年突发趋势明显.本文以甜菜夜蛾为靶标对象,设计5个浓度(0.3、1.2、4.8、19.2、76.8 mg · kg^-1)重金属Pb²⁺处理,探讨处理后甜菜夜蛾各虫态体内Pb²⁺的积累和排泄,并根据甜菜夜蛾各虫态的生长与存活情况,构建了该虫连续3个世代的种群生命表.结果表明: 取食含有不同浓度Pb²⁺的人工饲料后,各虫态甜菜夜蛾体内Pb²⁺的浓度在同一世代不同处理间存在显著差异,且呈显著的剂量 反应关系.各虫态体内Pb²⁺的浓度基本上随胁迫世代数的延长而增加.受Pb²⁺胁迫后,各虫态体内Pb²⁺的浓度顺序为5龄幼虫＞成虫＞蛹;该虫可通过粪便、预蛹蜕和蛹壳排泄体内部分重金属,且粪便和预蛹蜕中的Pb²⁺浓度远大于蛹蜕中的浓度;根据Pb²⁺不同浓度条件下甜菜夜蛾各虫态的存活率、雌性比和产卵量,构建的种群生命表表明,低浓度Pb²⁺胁迫可促进甜菜夜蛾的种群增长,而高浓度胁迫使种群增长衰退;且随着重金属的持续胁迫,其种群衰退更明显.研究结果为长期、客观评价重金属的危害性以及对重要农业害虫的种群影响提供了借鉴.     

叉角厉蝽对斜纹夜蛾生殖行为的干扰胁迫

为探索叉角厉蝽Eocanthecona furcellate (Wolff)对斜纹夜蛾Spodoptera litura (Fabricius)生殖行为的干扰胁迫作用,实验设置了3种处理来观察这种干扰效应,处理Ⅰ：2对斜纹夜蛾成虫,2头叉角厉蝽雌成虫;处理Ⅱ：2对斜纹夜蛾成虫,2头叉角厉蝽雌成虫,2头处于四龄的斜纹夜蛾幼虫;处理Ⅲ：两对斜纹夜蛾成虫,作为对照。将斜纹夜蛾成虫放置在上述3种条件下,饲养至斜纹夜蛾停止产卵为止,对斜纹夜蛾产卵行为跟踪观察。结果表明,当斜纹夜蛾与叉角厉蝽同处狭小的空间时,叉角厉蝽对斜纹夜蛾正常的生殖活动有一定的干扰作用,斜纹夜蛾采取逃离或躲避的措施来摆脱叉角厉蝽的接触。受叉角厉蝽的干扰胁迫,斜纹夜蛾产卵量减少,处理Ⅰ与处理Ⅱ的平均产卵量均明显少于对照。叉角厉蝽对斜纹夜蛾卵块数量、结构、大小也构成一定的影响。在处理Ⅰ与处理Ⅱ中,卵块主要以单层卵和双层卵卵块为主,卵块数量低于对照,且卵块体积较小。从斜纹夜蛾卵的空间格局来看,处理Ⅰ中的卵块主要分布于产卵笼的上部;处理Ⅱ中的卵块分散于产卵笼的中上部;对照中斜纹夜蛾的卵块则分布于产卵笼的各个区域。叉角厉蝽对斜纹夜蛾成虫产卵的干扰胁迫作用明显。     

Pb2+在甜菜夜蛾体内的积累及对种群的影响

重金属的污染及其治理是目前倍受关注的生态学问题之一.甜菜夜蛾是我国长江流域及以南地区蔬菜、棉花等作物上的主要害虫,近几年突发趋势明显.本文以甜菜夜蛾为靶标对象,设计5个浓度(0.3、1.2、4.8、19.2、76.8 mg · kg^-1)重金属Pb²⁺处理,探讨处理后甜菜夜蛾各虫态体内Pb²⁺的积累和排泄,并根据甜菜夜蛾各虫态的生长与存活情况,构建了该虫连续3个世代的种群生命表.结果表明: 取食含有不同浓度Pb²⁺的人工饲料后,各虫态甜菜夜蛾体内Pb²⁺的浓度在同一世代不同处理间存在显著差异,且呈显著的剂量 反应关系.各虫态体内Pb²⁺的浓度基本上随胁迫世代数的延长而增加.受Pb²⁺胁迫后,各虫态体内Pb²⁺的浓度顺序为5龄幼虫＞成虫＞蛹;该虫可通过粪便、预蛹蜕和蛹壳排泄体内部分重金属,且粪便和预蛹蜕中的Pb²⁺浓度远大于蛹蜕中的浓度;根据Pb²⁺不同浓度条件下甜菜夜蛾各虫态的存活率、雌性比和产卵量,构建的种群生命表表明,低浓度Pb²⁺胁迫可促进甜菜夜蛾的种群增长,而高浓度胁迫使种群增长衰退;且随着重金属的持续胁迫,其种群衰退更明显.研究结果为长期、客观评价重金属的危害性以及对重要农业害虫的种群影响提供了借鉴.     

在受重金属Zn2+胁迫的斜纹夜蛾幼虫寄主上双斑侧沟茧蜂的生存与发育

通过在植食性昆虫斜纹夜蛾Spodoptera litura Fabricius幼虫人工饲料中添加不同浓度的Zn²⁺,测定了连续3代取食含不同Zn²⁺浓度饲料幼虫的血淋巴中Zn²⁺含量,并研究了被Zn²⁺胁迫的斜纹夜蛾幼虫对寄生性天敌双斑侧沟茧蜂Microplitis bicoloratus Chen生存与发育的影响。结果表明,斜纹夜蛾幼虫血淋巴中Zn²⁺含量随饲料中Zn²⁺浓度和幼虫取食世代数的增加而增加;Zn²⁺胁迫斜纹夜蛾幼虫对双斑侧沟茧蜂的寄生率、从寄生到结茧的时间、蛹期长短、成虫羽化率和成虫寿命等都有不同程度的影响,其中以第2代的寄生率和第1代的羽化率受影响最大,均随着Zn²⁺浓度的增大而降低。因此,寄主饲料中高浓度的Zn²⁺能通过食物链影响双斑侧沟茧蜂的生存和发育。     

镉在土壤-蔬菜-昆虫食物链的传递特征

通过温室盆栽试验,以两种蔬菜(小白菜和苋菜)和一种昆虫(斜纹夜蛾)幼虫为对象,研究了重金属镉(Cd)在土壤-蔬菜-昆虫食物链的传递特征以及两种蔬菜中Cd化学形态分布特征.结果表明： 随着土壤Cd处理浓度的升高,两种蔬菜生物量均显著下降(P<0.05),而其各器官中的Cd含量均显著上升(P<0.05),两种蔬菜各器官Cd含量大小顺序为苋菜茎>根>叶,小白菜茎>叶>根;食用小白菜和苋菜的斜纹夜蛾幼虫体内Cd含量随着土壤Cd处理含量的升高而升高,最高分别为36.7和46.3 mg·kg^-1,但粪便中Cd含量分别高达190.0和229.8 mg·kg^-1,表明斜纹夜蛾幼虫食入的Cd大部分通过粪便排泄出体外.小白菜和苋菜各器官Cd化学形态均以氯化钠提取态为主(>70%),其次为无机态和有机水溶态Cd(乙醇提取态和去离子水提取态),而不溶性Cd磷酸盐(醋酸提取态)、草酸盐结合态Cd(盐酸提取态)和残渣态均极低,这有利于重金属Cd在食物链中传递.斜纹夜蛾幼虫通过粪便排泄大量Cd以缓解Cd对自身的毒害,可有效限制Cd向下一营养级传递.小白菜和苋菜对Cd的富集性均较高,不宜在Cd高污染土壤种植.     

苦瓜叶乙酸乙酯提取物对斜纹夜蛾实验种群的抑制作用

应用生命表方法评价了苦瓜Momordica charantia叶乙酸乙酯提取物与人工饲料混合饲喂斜纹夜蛾Spodoptera litura 3龄幼虫后对其实验种群增长的影响,旨在为探明苦瓜叶提取物的作用方式和作用机理以及田间应用提供科学依据。结果表明,苦瓜叶乙酸乙酯提取物对斜纹夜蛾幼虫的生长发育有显著的抑制作用。随着处理浓度的增加,幼虫体重的增长也随着减慢,发育历期明显延长,死亡率也随着提高。用提取物浓度为0.032%、0.04%、0.08%和0.16%的人工饲料饲喂3龄幼虫,2d后的体重增长抑制率分别为76.3%、79.9%、97.6%和111.2%。0.16%浓度处理的幼虫化蛹率明显降低,成虫的羽化率和产卵量也明显下降。苦瓜叶乙酸乙酯提取物能显著降低斜纹夜蛾的种群趋势指数值(I),与对照相比,0.032%、0.04%、0.08%和0.16%浓度处理的种群控制指数(IIPC)分别是0.59、0.56、0.29和0.20。说明苦瓜叶乙酸乙酯提取物不仅对斜纹夜蛾的生长发育有明显的抑制作用,而且对斜纹夜蛾的繁殖及其种群增长也有明显的控制作用。     

紫茎泽兰粗提物对草地贪夜蛾生长发育及繁殖的影响

为明确紫茎泽兰粗提物对草地贪夜蛾生长发育及繁殖的影响,在实验室条件下分别利用4个不同稀释浓度（20、15、10、5倍）的紫茎泽兰粗提物处理玉米叶片后喂养草地贪夜蛾初孵幼虫,记录各虫态的发育历期、存活率、重量、成虫寿命以及后代性比、F2代卵孵化率、F2代卵的发育历期。结果表明,随着紫茎泽兰粗提物浓度的升高,草地贪夜蛾各虫态的发育历期延长,5倍稀释液处理下蛹的发育历期最长,达20.27 d,10倍稀释液处理下幼虫的发育历期最长,为21.46 d;随着处理浓度的增加,各虫态的重量逐渐降低,且存活率呈下降趋势,其中粗提物对蛹存活率的影响最大,对1~3龄幼虫存活率的影响较小;随着处理浓度的升高成虫的寿命均缩短,且雌成虫寿命长于雄成虫;随着处理浓度的升高,草地贪夜蛾的产卵前期和F2代卵发育历期延长,产卵期缩短;单雌产卵量、后代性比和卵的孵化率降低。总的来说,紫茎泽兰粗提物对草地贪夜蛾的生长发育及繁殖均造成消极影响。5倍稀释液对草地贪夜蛾的生长发育及繁殖的影响最显著,可作为草地贪夜蛾防治研究中的重要植物源杀虫剂材料。     

Selection of likelihood parameters for complex models determines the effectiveness of Bayesian calibration

Assessing the parameter uncertainty of complex ecosystem models is a key challenge for improving our understanding of real world abstractions, such as those for explaining carbon and nitrogen cycle at ecosystem scale and associated biosphere-atmosphere-hydrosphere exchange processes. The lack of data about the variance of measurements forces scientists to revisit assumptions used in estimating the parameter distribution of complex ecosystem models.An increasingly used tool for assessing parameter uncertainty of complex ecosystem models is Bayesian calibration. In this paper, we generate two data sets which may represent a seasonal temperature curve or the seasonality of soil carbon dioxide flux and a single high peak put on a low background signal as is e.g. typical for soil nitrous oxide emission. Based on these examples we illustrate that commonly used assumptions for measurement uncertainty can lead to a sampling of wrong areas in the parameter space, incorrect parameter dependencies, and an underestimation of parameter uncertainties. This step needs particular attention by modelers as these issues lead to erroneous model simulations a) in present and future domains, b) misinterpretations of process feedback and functioning of the model, and c) to an underestimation of model uncertainty (e.g. for soil greenhouse gas fluxes).We also test the extension of the Bayesian framework with a model error term to compensate the effects caused by the false assumption of a perfect model and show that this approach can alleviate the observed problems in estimating the model parameter distribution.     

Use of randomized sampling for analysis of metabolic networks

Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology.     

Estimating Material and Energy Flows in Life Cycle Inventory with Statistical Models

Data (un)availability and uncertainty are recurring problems in life cycle assessment, and particularly inventory analysis. Advances in life cycle inventory have focused on the propagation and management of uncertainty, but this article addresses the question of how to account for unavailable data and corresponding uncertainty. Large and complicated systems often lack complete data due to confidential practices or the efforts required in the data collection process. Electricity production with multiple processes generating a single product is a classic example. Instead of the conventional process‐based models to estimate missing data, the approach developed in this article divides systems based on functionally equivalent objects. Each one of these objects is then described in terms of characteristic variables, such as power capacity. Kriging, a flexible statistical estimator, allows for the estimation of unknown material and energy flows based on the objects’ characteristic variables. Both univariate and multivariate kriging are tested and compared to regression analysis. It is found that kriging performs better than linear regression, according to the mean absolute error criterion. Multivariate kriging provides an even more accurate joint estimation method to bridge data gaps scattered across inventories and when observable values of material and energy flows differ from one object to the next. Parameters of the underlying models are interpreted in terms of data uncertainty.     

Evidence for strong seasonality in the carbon storage and carbon use efficiency of an Amazonian forest

The relative contribution of gross primary production and ecosystem respiration to seasonal changes in the net carbon flux of tropical forests remains poorly quantified by both modelling and field studies. We use data assimilation to combine nine ecological time series from an eastern Amazonian forest, with mass balance constraints from an ecosystem carbon cycle model. The resulting analysis quantifies, with uncertainty estimates, the seasonal changes in the net carbon flux of a tropical rainforest which experiences a pronounced dry season. We show that the carbon accumulation in this forest was four times greater in the dry season than in the wet season and that this was accompanied by a 5% increase in the carbon use efficiency. This seasonal response was caused by a dry season increase in gross primary productivity, in response to radiation and a similar magnitude decrease in heterotrophic respiration, in response to drying soils. The analysis also predicts increased carbon allocation to leaves and wood in the wet season, and greater allocation to fine roots in the dry season. This study demonstrates implementation of seasonal variations in parameters better enables models to simulate observed patterns in data. In particular, we highlight the necessity to simulate the seasonal patterns of heterotrophic respiration to accurately simulate the net carbon flux seasonal tropical forest.     

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.     

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.     

Machine reasoning about anomalous sensor data

We describe a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific domain to which the data belongs. Our software solution can produce a variety of different outcomes when a data anomaly or unexpected event is detected, ranging from simple flagging of data points, to data augmentation, to validation of proposed hypotheses that could explain the phenomenon. Hosted on the Jena Semantic Web Framework, the tool is completely domain-agnostic and is made domain-aware by reference to an ontology and Knowledge Base (KB) that together describe the key resources of the system being observed. The KB comprises ontologies for the sensor packages and for the domain; historical data from the network; concepts designed to guide discovery of internet resources unavailable in the local KB but relevant to reasoning about the anomaly; and a set of rules that represent domain expert knowledge of constraints on data from different kinds of instruments as well as rules that relate types of ecosystem events to properties of the ecosystem. We describe an instance of such a system that includes a sensor ontology, some rules describing coastal storm events and their consequences, and how we relate local data to external resources. We describe in some detail how a specific actual event—an unusually high chlorophyll reading—can be deduced by machine reasoning to be consistent with being caused by benthic diatom resuspension, consistent with being caused by an algal bloom, or both.     

植物多样性监测研究进展

监测是植物保护工作的重要基础,也是管理者决策的重要依据,对生物资源的可持续利用和保护至关重要。本研究综述了植物多样性监测的进展,就未来的热点和方向进行了探讨。植物多样性监测正以全新的局面飞速发展,已经进入了智能化、宏观与微观结合、联网监测、大数据、大尺度、多学科、全方位、立体化、由物种水平扩展到科属水平、群落或生态系统层面的监测时代;生物多样性监测网络的建设促进了联网监测与核心监测指标体系的统一,网络资源成为植物多样性监测的大数据源。数据的标准化与有效利用、大数据共享、遗传多样性和个体物种水平监测是监测植物多样性的机遇和挑战。未来的生态监测是大尺度、智能化、统一化的监测,完善监测网络体系,寻找新方法、构建新模型,开展热点区域和优先物种监测,并兼顾群落或更大尺度,重视植物基础本底数据的收集,是今后监测的重点。     

基于植被羰基硫通量估算陆地生态系统总初级生产力研究进展

羰基硫(COS)是大气中的长周期痕量气体，其分子结构、对流层大气混合比的昼夜和季节动态类似于二氧化碳(CO₂)。植物光合作用及其水解过程中，受扩散通路导度和酶活性影响，气孔的COS与CO₂吸收紧密相关，同时，植物自养呼吸并不释放COS。最新研究中，采用植被COS通量直接指示生态系统总初级生产力(GPP)。综述了植被COS通量与光合作用中碳固定过程的关联机制，以及采用涡度相关观测、整合大气COS监测和生态系统过程模型等方法开展植被COS通量与GPP研究的最新进展，探讨了关键生态过程和参数，发现方法存在以下瓶颈：(1)生理过程、尺度效应和解析效应影响了COS与CO₂的叶片相对吸收率，(2)观测与模拟手段有待进一步耦合，(3)全球COS观测密度限制了方法验证，(4)硫循环过程影响了多区域模拟精度。方法发展的前沿领域包括：(1)开展重点地区植被COS通量观测，(2)提高COS卫星柱浓度的覆盖范围，(3)完善生态系统过程模型的COS吸收机理。展望未来研究关注的科学问题是：对于亚热带等尚待开展COS连续观测的区域，采用植被COS通量...