Medium Scale Spatial Structures of Polycyclic Aromatic Hydrocarbons in the Topsoil of Tianjin Area

The spatial distribution patterns of Polycyclic Aromatic Hydrocarbons (PAHs) in soil are important to regional environmental assessment. In this paper, the spatial structural features of sixteen prior PAH compounds in the topsoil of Tianjin area, as well as soil properties, were studied. Results shown that medium scale spatial autocorrelations were well revealed. Spherical models with sills could be used to fit all experimental variograms. The spatial structures of PAHs contents demonstrated significant anisotropy. Air precipitation caused by the combustion of coal was the key factor in the formation of the spatial structural patterns of PAHs in the topsoil of Tianjin area.     

Contrasting home-range size and spatial partitioning in cryptic and sympatric pipistrelle bats

The extent of spatial partitioning in insectivorous bats, whose prey is patchily distributed and transient in nature, remains a contentious issue. The recent separation of a common Palaearctic bat, the pipistrelle, into Pipistrellus pipistrellus and Pipistrellus pygmaeus, which are morphologically similar and sympatric, provides an opportunity to examine this question. The present study used radio telemetry to address the spatial distribution and foraging characteristics of P. pipistrellus and P. pygmaeus in northeast Scotland, to test the hypothesis that coexistence between these species is facilitated through spatial segregation. We reveal large and significant differences in the spatial distribution and foraging characteristics of these two cryptic species. Individual P. pipistrellus home ranges were on average three times as large as that of P. pygmaeus, and they foraged for approximately an hour longer each night. Inter-specific spatial overlap was minimal (<5%) and core foraging areas of either species were essentially mutually exclusive despite the proximity of the two roosts. Inter-specific differences in range size were associated with the spatial dispersion of productive foraging sites within individual foraging ranges. P. pipistrellus foraging sites were highly dispersed, necessitating larger ranges. It is predicted that the spatial segregation revealed by the present study is a result of selection favouring the avoidance of competition in these species through differential habitat use.     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

固城湖春季水环境因子空间变异及其原因探讨

在现场采样数据的基础上,运用Arc GIS的地统计学组件分析了固城湖春季水环境因子空间变异规律,并从污粢源分布、水生植物密度差异方面探讨了水环境因子空间变异产生的原因.结果表明:(1)变异函教选择球状模型和高斯模型拟合水环境因子的变差曲线,从预测误差判断,拟合效果很好,因此,固城湖水环境因子的空同分布图比较真实地反映了固城湖水环境现状;(2)固城湖TN、NH4+-N基本上呈现从南到北逐渐升高的趋势,TN、NH4+-N、SD的空间变异主要是由人类活动引起,水革对TN、NH4+-N的吸收作用、SD的改善作用不明显;(3)太湖区TP基本上呈现从中西部向东逐渐升高的趋势,与大湖区水草密度的变化趋势相反;水革密度越大(大湖中南部)pH越高,而水草密度越小(航道、湖东部)pH越小;(4)中南部湖区围罔的全部拆除、港口河上游生活污水减少,都使得中南部湖区水质得到改善.     

Spatial distribution of perfluoroalkyl acids in the Pearl River of Southern China

An intensive campaign was conducted in September 2012 to collect surface water samples along the tributaries of the Pearl River in southern China. Thirteen perfluoroalkyl acids (PFAAs), including perfluorocarboxylates (PFCAs, C4–C11) and perfluorosulfonates (PFSAs, C4, C6–C8, and C10), were determined using high-performance liquid chromatography/negative electrospray ionization–tandem mass spectrometry (HPLC/(-)ESI–MS/MS). The concentrations of total PFAAs (ΣPFAAs) ranged from 3.0 to 52 ng L⁻¹, with an average of 19 ± 12 ng L⁻¹. The highest concentrations of ΣPFAAs were detected in the surface water of the Dong Jiang tributary (17–52 ng L⁻¹), followed by the main stream (13–26 ng L⁻¹) and the Sha Wan stream (3.0–4.5 ng L⁻¹). Perfluorooctanoate (PFOA), perfluorobutane sulfonate (PFBS), and perfluorooctane sulfonate (PFOS) were the three most abundant PFAAs and on average accounted for 20%, 24%, and 19% of ΣPFAAs, respectively. PFBS was the most abundant PFAA in the Dong Jiang tributary, and PFOA was the highest PFAA in the samples from the main stream of the Pearl River. A correlation was found between PFBS and PFOA, which suggests that both of these PFAAs originate from common source(s) in the region. Nevertheless, the slope of PFBS/PFOA was different in the different tributaries sampled, which indicates a spatial difference in the source profiles of the PFAAs.     

基于GIS空间插值方法的长湖水质评价

通过对各插值方法选择后取反距离加权法与克立格法对长湖的污染现状进行分析,结果表明大部分水域水质处于Ⅴ类和劣Ⅴ类,只有主要出水口习家口和刘岺闸附近的部分水域的水质可以达到Ⅳ类水质标准.同时发现反距离加权法与克立格法均具有较高的精度,但克立格法优于反距离加权法,这两种方法评价湖泊水质都能取得比较好的效果.     

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.     

矿山地质环境保护与治理恢复方案技术要求探讨

以《同煤集团梵王寺煤矿矿山地质环境保护与治理恢复方案》为例,根据相关工程实践,对"矿山地质环境保护与治理恢复方案"编制过程中需要特别注意的几个问题及争议问题进行了探讨,以期对该类项目起到指导作用。     

Slope,aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland

The slope and aspect of a vegetated surface strongly affects the amount of solar radiation intercepted by that surface. Solar radiation is the dominant component of the surface energy balance and influences ecologically critical factors of microclimate, including near-surface temperatures, evaporative demand and soil moisture content. It also determines the exposure of vegetation to photosynthetically active and ultra-violet wavelengths. Spatial variation in slope and aspect is therefore a key determinant of vegetation pattern, species distribution and ecosystem processes in many environments. Slope and aspect angle may vary considerably over distances of a few metres, and fine-scale species’ distribution patterns frequently follow these topographic patterns. The availability of suitable microclimate at such scales may be critical for the response of species distributions to climatic change at much larger spatial scales. However, quantifying the relevant microclimatic gradients is not straightforward, as the potential variation in solar radiation flux under clear-sky conditions is modified by local and regional variations in cloud cover, and interacts with long-wave radiation exchange, local meteorology and surface characteristics.     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.