Evaluation of uncalibrated preferential flow models against data for isoproturon movement to drains through a heavy clay soil

The uncalibrated predictive ability of four preferential flow models (CRACK‐NP, MACRO/MACRO_DB, PLM, SWAT) has been evaluated against point rates of drainflow and associated concentrations of isoproturon from a highly structured and heterogeneous clay soil in the south of England. Data were available for four plots for a number of storm events in each of three successive growing seasons. The mechanistic models CRACK‐NP and MACRO generally gave reasonable estimates of drainflow over the three seasons, but under‐estimated concentrations of isoproturon over a prolonged period in the first season and over‐estimated them in the two remaining seasons. CRACK‐NP simulated maximum concentrations of isoproturon over the first two events of each of the three seasons of 156, 527 and 24.4 µg litre^?1, respectively, and matched the observed data (465, 65.1 and 0.65 µg litre^?1) slightly better than MACRO (69.1, 566 and 58.5 µg litre^?1). Automatic selection of parameters from soils information within MACRO_DB reduced the emphasis on preferential flow relative to the stand‐alone version of MACRO. This gave a poor simulation of isoproturon breakthrough and simulated maximum concentrations were 0, 50.1 and 35.1 µg litre^?1, respectively. The capacity model PLM gave the best overall simulation of total drainflow for the first two events in each season, but over‐estimated concentrations of isoproturon (967, 808 and 51.3 µg litre^?1). The simple model SWAT represented total drainflow reasonably well and gave the best simulation of maximum isoproturon concentrations (140, 80.2 and 8.2 µg litre^?1). There was no clear advantage here in using the mechanistic models rather than the simpler models. None of the models tested was able to simulate consistently the data set, and uncalibrated modelling cannot be recommended for such artificially drained heavy clay soils. © 2001 Society of Chemical Industry     

Anticipating the species jump: surveillance for emerging viral threats

Zoonotic disease surveillance is typically triggered after animal pathogens have already infected humans. Are there ways to identify high‐risk viruses before they emerge in humans? If so, then how and where can identifications be made and by what methods? These were the fundamental questions driving a workshop to examine the future of predictive surveillance for viruses that might jump from animals to infect humans. Virologists, ecologists and computational biologists from academia, federal government and non‐governmental organizations discussed opportunities as well as obstacles to the prediction of species jumps using genetic and ecological data from viruses and their hosts, vectors and reservoirs. This workshop marked an important first step towards envisioning both scientific and organizational frameworks for this future capability. Canine parvoviruses as well as seasonal H3N2 and pandemic H1N1 influenza viruses are discussed as exemplars that suggest what to look for in anticipating species jumps. To answer the question of where to look, prospects for discovering emerging viruses among wildlife, bats, rodents, arthropod vectors and occupationally exposed humans are discussed. Finally, opportunities and obstacles are identified and accompanied by suggestions for how to look for species jumps. Taken together, these findings constitute the beginnings of a conceptual framework for achieving a virus surveillance capability that could predict future species jumps.     

BODIUM—A systemic approach to model the dynamics of soil functions

The increasing demand for biomass for food, animal feed, fibre and bioenergy requires optimization of soil productivity, while at the same time, protecting other soil functions such as nutrient cycling and buffering, carbon storage, habitat for biological activity and water filter and storage. Therefore, one of the main challenges for sustainable agriculture is to produce high yields while maintaining all the other soil functions. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological and chemical processes of soils that generate those functions. We developed a soil model to simulate the impact of various agricultural management options and climate change on soil functions by integrating the relevant processes mechanistically and in a systemic way. As a special feature, we include the dynamics of soil structure induced by tillage and biological activity, which is especially relevant in arable soils. The model operates on a 1D soil profile consisting of a number of discrete layers with dynamic thickness. We demonstrate the model performance by simulating crop growth, root growth, nutrient and water uptake, nitrogen cycling, soil organic matter turnover, microbial activity, water distribution and soil structure dynamics in a long-term field experiment including different crops and different types and levels of fertilization. The model is able to capture essential features that are measured regularly including crop yield, soil organic carbon, and soil nitrogen. In this way, the plausibility of the implemented processes and their interactions is confirmed. Furthermore, we present the results of explorative simulations comparing scenarios with and without tillage events to analyse the effect of soil structure on soil functions. Since the model is process-based, we are confident that the model can also be used to predict quantities that have not been measured or to estimate the effect of management measures and climate states not yet been observed. The model thus has the potential to predict the site-specific impact of management decisions on soil functions, which is of great importance for the development of a sustainable agriculture that is currently also on the agenda of the ‘Green Deal’ at the European level.     

土壤微生物学实验模块化的开设研究

高校微生物学实验教学注重培养学生的兴趣和科学探究技能,然而目前实验教学模式单一,容易导致学生学习兴趣丧失,自主学习能力下降,造成学习效果不佳。本文根据微生物学实验教学特点,对土壤微生物学实验设计了3个相互关联、前后衔接的实验模块,分别为：土壤微生物的采集与培养基制作,微生物的分离与纯化,微生物的鉴定和保藏。本文结合微生物学实验教程将独立的实验内容连成整体,并通过学生自主研究结合教师指导的模式,以达到加深学生对土壤微生物学知识理解及提高实验操作技能目的。     

利用BIOLOG系统对不同种类细菌鉴定的研究

为完善细菌鉴定工作,本试验通过利用BIOLOG细菌自动微生物分析系统(简称BIOLOG系统)对13株大肠杆菌、15株巴氏杆菌和7株金黄色葡萄球菌进行了鉴定,并与传统方法[1]包括形态学观察、革兰氏染色、生化实验等的鉴定结果进行了比对.实验结果表明,与传统方法相比BIOLOG系统具有准确、快捷、简便的优点,同时也有一定的局限性,从而为BIOLOG系统的更好使用提供了实验依据.     

SPF金黄地鼠核心种群的微生物质量控制

SPF金黄地鼠微生物质量控制对保持动物种群的质量稳定性十分重要。介绍了SPF金黄地鼠核心种群微生物质量控制的具体做法和经验体会,以期为提高隔离器饲养条件下SPF金黄地鼠的种群质量稳定性提供参考和借鉴。     

微生物学实验教学中革兰氏染色三步法应用试验

通过实验室应用试验证明,革兰氏染色三步法虽操作简单、染色快、成功率高、效果好.但要严格控制相关环节,即革兰氏阳性菌菌龄16-20h,革兰氏阴性菌菌龄22-24h;取菌量要少于1/3环,涂菌要稀薄、分散均匀;在结晶紫和碘液染色时间各1 min条件下,第三步以0.4%碱性品红乙醇液处理时间60-90s,或以0.3%蕃红乙醇液处理时间90-120s,效果较佳.     

An ecosystem‐scale predictive model of coastal seagrass distribution

• 1. Maintaining ecological processes that underpin the functioning of marine ecosystems requires planning and management of marine resources at an appropriate spatial scale.
• 2. The Great Barrier Reef World Heritage Area (GBR) is the world's largest World Heritage Area (approximately 348 000 km²) and second largest marine protected area. It is difficult to inform the planning and management of marine ecosystems at that scale because of the high cost associated with collecting data. To address this and to inform the management of coastal (approximately 15 m below mean sea level) habitats at the scale of the GBR, this study determined the presence and distribution of seagrass by generating a Geographic Information System (GIS)‐based habitat suitability model.
• 3. A Bayesian belief network was used to quantify the relationship (dependencies) between seagrass and eight environmental drivers: relative wave exposure, bathymetry, spatial extent of flood plumes, season, substrate, region, tidal range and sea surface temperature. The analysis showed at the scale of the entire coastal GBR that the main drivers of seagrass presence were tidal range and relative wave exposure. Outputs of the model include probabilistic GIS‐surfaces of seagrass habitat suitability in two seasons and at a planning unit of cell size 2 km×2 km.
• 4. The habitat suitability maps developed in this study extend along the entire GBR coast, and can inform the management of coastal seagrasses at an ecosystem scale. The predictive modelling approach addresses the problems associated with delineating habitats at the scale appropriate for the management of ecosystems and the cost of collecting field data. Copyright © 2010 John Wiley & Sons, Ltd.

     

Fine‐scale habitat partitioning of Chilean and Peale's dolphins and their overlap with aquaculture

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Fish movement and habitat use depends on water body size and shape

Abstract –  Home ranges are central to understanding habitat diversity, effects of fragmentation and conservation. The distance that an organism moves yields information on life history, genetics and interactions with other organisms. Present theory suggests that home range is set by body size of individuals. Here, we analyse estimates of home ranges in lakes and rivers to show that body size of fish and water body size and shape influence home range size. Using 71 studies including 66 fish species on five continents, we show that home range estimates increased with increasing water body size across water body shapes. This contrasts with past studies concluding that body size sets home range. We show that water body size was a consistently significant predictor of home range. In conjunction, body size and water body size can provide improved estimates of home range than just body size alone. As habitat patches are decreasing in size worldwide, our findings have implications for ecology, conservation and genetics of populations in fragmented ecosystems.