输水情景下白洋淀好氧反硝化菌群落对溶解性有机物的响应

溶解性有机物（DOM）是影响微生物群落演变的重要因素,而生态输水是白洋淀的一个重要特征,为了探究输水情境下DOM对好氧反硝化菌的影响,本文结合水体DOM的组分解析和好氧反硝化高通量测序技术,进行了水体好氧反硝化菌对DOM的响应研究.结果显示,白洋淀水体DOM的相对浓度存在显著差异,河口区要低于内部区;DOM呈现出较强自生源特征,河口区具有更高的分子量和更强的腐殖化;平行因子法解析出3种类蛋白组分和1种类腐殖质组分,类蛋白组分占比达到35.64%~96.38%,与荧光区域积分得到类蛋白占主体的结果相一致.与此同时,该时期水体好氧反硝化菌主要属于变形菌门（Protebacterice）,主要包括Cupriavidus、Aeromonas、Thauera、Shewanella和Pseudomonas,与随机森林筛选出的指示物种相一致;网络分析得到35个网络关键节点,主要隶属于Thauera、Cupriavidus以及Unclassified_bacteria;冗余分析（RDA）显示类腐殖质物质是影响水体整体好氧反硝化菌群落组成的因子,类蛋白物质是影响指示物种群落和关键节点群落结构分布的重要因素.综上可知,水体溶解性有机物中类蛋白组分可以作为筛选适于生态输水期水体特征的耐低温高效好氧反硝化菌的碳源选择.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Application of a temperature-dependent von Bertalanffy growth model to bullhead (Cottus gobio)

The most studied and commonly applied model of fish growth is the von Bertalanffy model. However, this model does not take water temperature into account, which is one of the most important environmental factors affecting the life cycle of fish, as many physiological processes that determine growth, e.g. metabolic rate and oxygen supply, are directly influenced by temperature. In the present study we propose a version of the von Bertalanffy growth model that includes mean annual water temperatures by correlating the growth coefficient, k, explicitly and the asymptotic length, L_∞, implicitly to water temperature. All relationships include parameters with an obvious biological relevance that makes them easier to identify. The model is used to fit growth data of bullhead (Cottus gobio) at different locations in the Bez River network (Drme, France). We show that temperature explains much of the growth variability at the different sampling sites of the network.     

Multiple structural modifications to dendritic ecological networks produce simple responses

Structural modifications to landscapes affect the ability of organisms to access different habitat patches. There exist, however, very few general methods by which to relate modifications to expectations of effects, and even fewer that enable understanding of how multiple modifications may interact. In the absence of any guiding principles, ecologists have assumed that interactions will result in complex landscape-scale effects. One way of understanding such effects is through rendering a landscape as a graph or network, among the simplest of which are dendritic networks typified by stream systems. Yet even for stream networks, there are no known general principles concerning the nature of interactions between multiple modifications. We developed a model to describe the ability of fish to access and use different habitat patches within dendritic networks. We used mathematical and numerical analyses of the model to investigate how the habitat value of a network is affected by changes in connectivity and habitat quality, and then to examine interactions between multiple modifications. Rather than showing complex interactions, our analytic and simulation-based results show that the combined effect of multiple modifications approximately equals the sum of individually predicted effects. Dendritic networks thus appear to respond far more simply to multiple modifications than has previously been assumed. These results have implications for stream management planning, and offer a firm foundation from which to better understand population processes within dendritic networks.     

基于人工神经网络法的绿色建筑评价

以实际建筑物为例,介绍了用层次分析法建立绿色建筑评价模型的过程,并分别用层次分析法和人工神经网络法对实际建筑物进行了评价。评价结果显示,人工神经网络法与层次分析法相对误差不到0.5%,表明人工神经网络法作为一种客观科学的评价方法,应用于绿色建筑的评价,能有效降低主观因素带来的影响,会使结果更具有客观性。     

Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems

This paper presents a novel quantitative risk analysis process for urban natural gas pipeline networks using geographical information systems (GIS). The process incorporates an assessment of failure rates of integrated pipeline networks, a quantitative analysis model of accident consequences, and assessments of individual and societal risks. Firstly, the failure rates of the pipeline network are calculated using empirical formulas influenced by parameters such as external interference, corrosion, construction defects, and ground movements. Secondly, the impacts of accidents due to gas leakage, diffusion, fires, and explosions are analyzed by calculating the area influenced by poisoning, burns, and deaths. Lastly, based on the previous analyses, individual risks and social risks are calculated. The application of GIS technology helps strengthen the quantitative risk analysis (QRA) model and allows construction of a QRA system for urban gas pipeline networks that can aid pipeline management staff in demarcating high risk areas requiring more frequent inspections.     

Social capital networks for achieving sustainable development

There have been few empirical studies investigating whether and how social capital (SC) relates to better achievement of sustainability goals and, if so, how. This research investigates the roles three types of SC, namely, bonding, bridging, and bracing SC, play in achieving sustainable development (SD), using a case study of the Japanese region to explore the process of SC accumulation leading to collective action. The research question as to whether SC accumulation makes a difference in the progress towards sustainability is addressed qualitatively and quantitatively using a case study, and network and regression analysis; in particular the impact and functions of bracing SC are closely investigated. The study concludes that SC accumulation can indeed make a difference in achieving sustainability and that bracing SC plays an essential role in expediting the processes of goal sharing and resource flow by connecting various networks across sectors and scales, thereby making collective action possible. These findings suggest that creating an environment in which the generation of all three types of SC is encouraged may help local governments to achieve their desired policy goals for SD.     

Climate change in mountain regions: how local communities adapt to extreme events

This paper examines how local communities adapt to climate change and how governance structures can foster or undermine adaptive capacity. Climate change policies, in general, and disaster risk management in mountain regions, in particular, are characterised by their multi-level and multi-sectoral nature during formulation and implementation. The involvement of numerous state and non-state actors at local to national levels produces a variety of networks of interaction and communication. The paper argues that the structure of these relational patterns is critical for understanding adaptive capacity. It thus proposes an expanded concept of adaptive capacity that incorporates (horizontal and vertical) actor integration and communication flow between these actors. The paper further advocates the use of formal social network analysis to assess these relational patterns. Preliminary results from research on adaptation to climate change in a Swiss mountain region vulnerable to floods and other natural hazards illustrate the conceptual and empirical significance of the main arguments.     

大型公园道路网络关键节点脆弱性评价*

为更加科学有效地辨识景区道路网络中的客流关键节点,以节点脆弱性为度量指标,提出1种基于FIM模型的关键节点脆弱性评价方法。以某大型公园为例,首先通过ArcGIS软件将该公园的道路网络信息转换成可编译的文本信息,经过Java枚举可行路径,然后利用嵌入FIM算法的Lingo进行优化,得出网络节点的重要度。最后综合节点容量、流量、吸引度等信息加权得出网络节点的脆弱性。结果表明:该方法能够有效地对道路网络节点进行脆弱性度量并排序,景区可据此制定相应的客流导向方案以提高景区资源的利用率,提高游览的安全性。     

基于多智能体建模方法的人群应急疏散模型构建研究——以腾龙芳烃（漳州）有限公司“4·6”爆炸着火重大事故疏散为例*

为从更微观角度分析人群疏散过程中疏散行为及路网设计对疏散效果的影响,基于腾龙芳烃（漳州）有限公司“4·6”爆炸着火重大事故,构建多智能体人群应急疏散模型,模拟人群中个体群组、惯性、就近、从众、信息传播的行为决策及相互交互影响。结果表明:群组行为会严重影响应急疏散效果,在疏散路网两端避难所附近会出现明显拥堵现象,在疏散路网设计和避难所选择时,应尽可能避免出现极端汇流路段或节点;在应急培训中,应告知群众减少群组行为。研究结果可为人群应急疏散提供借鉴。