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.     

平原河网地区滨岸带外来植物入侵现状及影响研究——以上海青浦区为例

河流滨岸带作为一种典型的开放系统,在受到外界干扰的条件下极易被外来植物所入侵并受到严重影响。以上海青浦区为例,通过对河流滨岸带生态系统入侵植物分布及现状调查,以及对两种入侵植物：白花三叶草（Trifolium repens）和加拿大一枝黄花（Solidago canadensis）群落的物种多样性及其土壤特性的实证研究,探讨外来植物入侵对河流滨岸带生态系统造成的影响。结果表明：目前该地区河流滨岸带外来入侵植物共有14科24种,其科、种数分别占到该生态系统所有植物的48．3％与26．1％。入侵植物白花三叶草与加拿大一枝黄花在春、秋两季均显著降低了河流滨岸带植物群落的物种多样性。此外,两种人侵植物也对当地河流滨岸及土壤特性造成了不同程度的影响,其中对土壤含水量、pH值的影响最为显著。该研究填补了平原河网地区河流滨岸带生态系统外来植物入侵研究的空白,有助于今后开展对该生态系统的评价、恢复及管理工作。     

珠三角工业城市污染物入河量空间特征分析

以东莞市第一次全国污染源普查成果为数据基础,调查估算2010年东莞市33个镇街和25个水功能区的COD、氨氮入河量,并应用GIS技术及相关分析法分析污染物入河量空间分布特征及其影响因素。研究发现：①东莞市各镇街及水功能区的污染物入河量呈明显不均匀分布,人口、GDP是影响污染物入河量空间分布的主要因素。②河网区、非河网区镇街的工业污染物入河量与GDP均呈较好的正相关关系,但非河网区单位污染物入河量GDP产出拟合曲线斜率是河网区的31倍,显示河网区镇街每增长单位污染物入河量所产生的GDP远远小于非河网区,河网区镇街往往付出巨大的污染代价而经济增长却非常有限。     

Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America

Centrality metrics evaluate paths between all possible pairwise combinations of sites on a landscape to rank the contribution of each site to facilitating ecological flows across the network of sites. Computational advances now allow application of centrality metrics to landscapes represented as continuous gradients of habitat quality. This avoids the binary classification of landscapes into patch and matrix required by patch-based graph analyses of connectivity. It also avoids the focus on delineating paths between individual pairs of core areas characteristic of most corridor- or linkage-mapping methods of connectivity analysis. Conservation of regional habitat connectivity has the potential to facilitate recovery of the gray wolf (Canis lupus), a species currently recolonizing portions of its historic range in the western United States. We applied 3 contrasting linkage-mapping methods (shortest path, current flow, and minimum-cost-maximum-flow) to spatial data representing wolf habitat to analyze connectivity between wolf populations in central Idaho and Yellowstone National Park (Wyoming). We then applied 3 analogous betweenness centrality metrics to analyze connectivity of wolf habitat throughout the northwestern United States and southwestern Canada to determine where it might be possible to facilitate range expansion and interpopulation dispersal. We developed software to facilitate application of centrality metrics. Shortest-path betweenness centrality identified a minimal network of linkages analogous to those identified by least-cost-path corridor mapping. Current flow and minimum-cost-maximum-flow betweenness centrality identified diffuse networks that included alternative linkages, which will allow greater flexibility in planning. Minimum-cost-maximum-flow betweenness centrality, by integrating both land cost and habitat capacity, allows connectivity to be considered within planning processes that seek to maximize species protection at minimum cost. Centrality analysis is relevant to conservation and landscape genetics at a range of spatial extents, but it may be most broadly applicable within single- and multispecies planning efforts to conserve regional habitat connectivity.     

Optimal locations of monitoring stations in water distribution systems under multiple demand patterns: a flaw of demand coverage method and modification

A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring stations is calculated by accumulating their demand coverage under each demand pattern, and the impact of temporal distribution between different time periods or demand patterns is ignored. This could lead to miscalculation of the optimal locations of the monitoring stations. To overcome this flaw, this paper presents a Demand Coverage Index (DCI) based method. The optimization considers extended period unsteady hydraulics due to the change of nodal demands with time. The method is cast in a genetic algorithm framework for integration with Environmental Protection Agency Net (EPANET) and is demonstrated through example applications. Results show that the set of optimal locations of monitoring stations obtained using the DCI method can represent the water quality of water distribution systems under multiple demand patterns better than the one obtained using previous methods.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

基于BP神经网络的垃圾焚烧过程故障诊断

应用BP神经网络方法,建立了垃圾焚烧过程故障诊断的模型.该方法采用梯度下降法训练网络权值,利用最优停止法对网络进行了优化,避免了过拟合现象.提高了BP神经网络的训练速度和泛化能力.并以实际焚烧过程工况数据进行性能检验,检验结果表明了该BP网络具有较高的诊断精度.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

时延神经网络对宝兴树蛙异质种群鸣叫的聚类分析

对瓦屋山宝兴树蛙3个不同海拔分布(A:山下;B:山腰;C:山上)的异质种群鸣叫的时频参数进行分析,发现三地鸣叫各参数互相重叠,无明显界线.常规统计分析不能使三地的鸣叫聚类为各自的组.使用时延神经网络对三地宝兴树蛙鸣叫的时频参数进行有指导聚类,发现三种群鸣叫有地区性差异,并表现出由个体交流引起的山腰种群群体特征的不显著.推测差异可能是因为长期地理隔离所导致.基于神经网络的分析结果,对各种群间的个体交流情况进行了初步估计.图6表1参20     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。