校园绿地系统景观生态异质性分析与质量评价

针对南京大学浦口新校园的生态景观实际，依据景观生态异质性原理，对校园绿地系统做出了景观生态定量分析和质量评价，结果表明绿地斑块丰富度不足，景观多样性较差和绿地廊道密度不够是存在的主要生态环境问题，是导致新建校园生态质量不高的主要因子，为了有效解决这些生态问题，本文对南京大学校园绿地系统今后进行优化和改建，提出了建设性的意见和修改方案。     

A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data

Environmental pollution of urban areas is one of key factors that state authorities and local agencies have to consider in the decision-making process. To find a compromise among many criteria, spatial analysis extended by geostatistical methods and dynamic models has to be carried out. In this case, spatial analysis includes processing of a wide range of air, water and soil pollution data and possibly noise assessment and waste management data. Other spatial inputs consist of data from remote sensing and GPS field measurements. Integration and spatial data management are carried out within the framework of a geographic information system (GIS). From a modeling point of view, GIS is used mainly for the preprocessing and postprocessing of data to be displayed in digital map layers and visualized in 3D scenes. Moreover, for preprocessing and postprocessing, deterministic and geostatistical methods (IDW, ordinary kriging) are used for spatial interpolation; geoprocessing and raster algebra are used in multi-criteria evaluation and risk assessment methods. GIS is also used as a platform for spatio-temporal analyses or for building relationships between the GIS database and stand-alone modeling tools. A case study is presented illustrating the application of spatial analysis to the urban areas of Prague. This involved incorporating environmental data from monitoring networks and field measurements into digital map layers. Extra data inputs were used to represent the 3D concentration fields of air pollutants (ozone, NO₂) measured by differential absorption LIDAR. ArcGIS was used to provide spatial data management and analysis, extended by modeling tools developed internally in the ArcObjects environment and external modules developed with MapObjects. Ordinary kriging methods were employed to predict ozone concentrations in selected 3D locations together with estimates of variability. Higher ozone concentrations were found above crossroads with their heavy traffic than above the surrounding areas. Ozone concentrations also varied with height above the digital elevation model. Processed data, spatial analysis and models are integrated within the framework of the GIS project, providing an approach that state and local authorities can use to address environmental protection issues.     

Direct-push geochemical profiling for assessment of inorganic chemical heterogeneity in aquifers

Discrete-depth sampling of inorganic groundwater chemistry is essential for a variety of site characterization activities. Although the mobility and rapid sampling capabilities of direct-push techniques have led to their widespread use for evaluating the distribution of organic contaminants, complementary methods for the characterization of spatial variations in geochemical conditions have not been developed. In this study, a direct-push-based approach for high-resolution inorganic chemical profiling was developed at a site where sharp chemical contrasts and iron-reducing conditions had previously been observed. Existing multilevel samplers (MLSs) that span a fining-upward alluvial sequence were used for comparison with the direct-push profiling. Chemical profiles obtained with a conventional direct-push exposed-screen sampler differed from those obtained with an adjacent MLS because of sampler reactivity and mixing with water from previous sampling levels. The sampler was modified by replacing steel sampling components with stainless-steel and heat-treated parts, and adding an adapter that prevents mixing. Profiles obtained with the modified approach were in excellent agreement with those obtained from an adjacent MLS for all constituents and parameters monitored (Cl, NO(3), Fe, Mn, DO, ORP, specific conductance and pH). Interpretations of site redox conditions based on field-measured parameters were supported by laboratory analysis of dissolved Fe. The discrete-depth capability of this approach allows inorganic chemical variations to be described at a level of detail that has rarely been possible. When combined with the mobility afforded by direct-push rigs and on-site methods of chemical analysis, the new approach is well suited for a variety of interactive site-characterization endeavors.     

A trait specific model of competition in a spatially structured plant community

The non-random dispersal of plant propagules is thought to counter competitive exclusion and thus promote the survival of competitively inferior species. We investigated this process by modelling the outcome of interactions between species with competitive ability defined as a function of both life-history traits and the environment with both random and clustered dispersal strategies and in environmentally homogeneous and heterogeneous environments. Four main results emerged: (1) environmental heterogeneity was seen to promote co-existence in conjunction with associated trait variation for tolerance to the environmental variable and where this trait variation was effectively limited by ‘trade-off’ such that no single species had an overall competitive advantage, (2) consistent with theory, random dispersal appeared to enhance the likelihood of competitive exclusion, whereas clustering favoured co-existence, (3) the ecological outcome of interactions between dispersal and competitive relationships varied as a function of the trait determining the competitive advantage within a particular environment, and (4) promotion of co-existence by clustered dispersal was most marked when associated with environmental heterogeneity. It is argued that these results suggest that current ecological models of species interactions may need to be modified to incorporate a more realistic understanding of competitive ability if we are to better understand the factors effecting species co-existence.     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

城市绿地生态效果评价方法的改进

城市绿地在改善城市环境、为居民提供游憩服务等方面具有重要作用。目前衡量绿地效果的指标从总量上规定城市绿地应达到的指标，但缺乏绿地空间分布合理性的表征。本文采用景观生态学中的异质性指数和均一性指数，提出判断城市绿地分布合理程度的两种新指标。     

Analysis on sensitivity and landscape ecological spatial structure of site resources

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长江经济带城市扩张与经济增长脱钩关系及其空间异质性

论文基于GIS平台及探索性空间数据分析方法（Exploratory Spatial Data Analysis, ESDA）和脱钩模型,以地级行政单元为对象,分析2000—2013年长江经济带建设用地扩张、经济增长的时空差异及脱钩关系。研究表明：1）近15 a来,长江经济带建设用地扩张与经济增长具有明显的阶段性和区域性。热点分析显示,建设用地增长热点区呈扩散效应,冷点区逐渐缩小。而经济增长的冷、热点区演变格局与建设用地并不同步。2）2000—2013年,长江经济带建设用地扩张与经济增长耦合关系经历了“增长负脱钩、弱脱钩”为主—“弱脱钩”为主—“弱脱钩、增长连结”为主的变化过程。分析表明,近15 a来长江经济带建设用地扩张与经济增长脱钩程度整体趋于优化,其中,负脱钩类型的城市数量降低17.76%,而脱钩类型数量上升11.22%。3）脱钩程度的空间异质性分析显示,长江经济带各时期的城市扩张与经济增长脱钩关系具有弱空间集聚性。总体上,长三角城市群较长江中游、成渝及滇、黔城市群的脱钩程度表现更优。脱钩程度变化机制与城市产业结构、用地效率和用地模式等密切相关。加强土地利用规划管控力度、严格用途管制规则,引导城市精明增长,走内涵式新型城镇化道路是改变经济发展对建设用地扩张依赖的重要途径。     

滦河流域鱼类群落结构空间异质性与影响因子分析

掌握鱼类群落结构与环境因子的关系是河流鱼类多样性保护与恢复的基础.基于2016年10月对滦河流域58个采样点的调查数据,通过聚类分析法及NMS（非度量多维标度法,non-matric multidimentionalscaling）分析了滦河鱼类群落空间格局特征,并采用典范对应分析（CCA,canonical correspondence analysis）探讨了影响鱼类群落结构的环境因子.调查期间共采集鱼类15科41属49种,鲤科鱼类最多,为23种.研究表明:①聚类分析发现鱼类空间分布可分为3组,第1组集中在滦河干流中下游及支流汇入干流附近河段,第2组集中在冀东部独流入海的河流,第3组集中在滦河干支流的中上游河段.②NMS结果也证明3组在空间分布上差异明显,第2组与第3组之间没有重叠.③物种数、Shannon-Wiener多样性指数、Margalef丰富度指数和Simpson多样性指数在第1组与第3组之间存在显著差异,而Simpson多样性在第2组与第3组之间也存在显著差异.④单因素方差分析（one-way ANOVA）显示,第1组与第2组、第3组相比,水温与两组之间都有显著性差异,第2组与第1组、第3组之间在电导率、TDS（总溶解固体）、栖境复杂性、速度-深度结合、堤岸稳定性和河水水量状况有显著性差异.⑤典范对应分析发现,影响滦河流域的环境因子包括TDS、浊度、pH与水温等4个水体理化参数以及底质与堤岸稳定性2个栖息地质量参数.研究显示,基于对滦河流域鱼类空间异质性和相关环境影响因子的理解,可为该流域鱼类多样性保护及管理对策制订提供方向.