都昌县多宝砂山物种多样性研究及保护初探

对鄱阳湖都昌县多宝砂山地区土地风沙化过程中物种多样性的变化特征进行了分析,依据植物种在各类型风沙化土地的出现频率与不同风沙化阶段各生活型植物种的组成,探讨了风沙化过程中植物种的绝灭与定居特点.物种多样性指数分析表明:风沙化过程是物种多样性衰减的过程,风沙化首先导致特有种的绝灭,其次为稀有种.从植物的生活型来看,多年生草本与乔木类植物受风沙化影响最大,而灌木类植物可存活于风沙化过程的各个阶段,在风沙化过程中植物种的绝灭速率大于定居速率.最后,针对造成多宝砂山生物多样性衰减的原因,提出了治理对策,即在控制人类行为干扰的同时,要加强对固定沙丘的管理与保护.      

Ecological effects of crude oil residues on the functional diversity of soil microorganisms in three weed rhizospheres

Ecological effects of crude oil residues on weed rhizospheres are still vague. The quantitative and diversity changes and metabolic responses of soil-bacterial communities in common dandelion （Taraxacum officinale）, jerusalem artichoke （Silphiurn perfoliatum L.） and evening primrose （A colypha australis L.） rhizospheric soils were thus examined using the method of carbon source utilization. The results indicated that there were various toxic effects of crude oil residues on the growth and reproduction of soil bacteria, but the weed rhizospheres could mitigate the toxic effects. Total heterotrophic counting colony-forming units （CFUs） in the rhizospheric soils were significantly higher than those in the non-rhizospheric soils. The culturable soil-bacterial CFUs in the jerusalem artichoke （S. perfoliatum） rhizosphere polluted with 0.50 kg/pot of crude oil residues were almost twice as much as those with 0.25 kg/pot and without the addition of crude oil residues. The addition of crude oil residues increased the difference in substrate evenness, substrate richness, and substrate diversity between non-rhizospheric and rhizospheric soils of T. officinale and A. australis, but there was no significant （p〉0.05） difference in the Shannon＇s diversity index between non-rhizospheric and rhizospheric soils of S. perfoliatum. The rhizospheric response of weed species to crude oil residues suggested that S. perfoliatum may be a potential weed species for the effective plant-microorganism bioremediation of contaminated soils by crude oil residues.     

基于遗传算法的生命线工程网络抗震优化设计

进行网络抗震可靠性分析的目的,不仅在于定量评价生命线工程网络系统的抗震性能,更重要的是利用这种分析工具指导网络抗震性能的优化设计。以无向边权网络系统为分析对象,分别以管网造价和系统抗震可靠度作为优化目标和约束条件,建立网络系统拓扑优化模型,利用递推分解算法作为网络系统抗震可靠性分析工具,并引入系统单元的灵敏度分析,采用遗传算法求解网络系统的拓扑优化问题,从而发展了一类工程网络抗震优化设计方法。实例分析结果表明,网络系统拓扑结构与系统可靠度之间有显著的正相关关系。     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失,提高滑坡预测的准确性,通过搭建灾害模拟平台获得滑坡的实验数据,在获得多组模拟实验数据后,分析各变量的特性。首先,通过层次分析(Analytic Hierarchy Process,AHP)算法,对滑坡进行危险度划分;然后,通过支持向量机(Support Vector Machine,SVM)建立模型,遗传算法(Genetic Algorithm,GA)再优化SVM参数,提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据,通过GA与SVM结合建立的模型精度较好,实验预测结果与实际结果较为吻合,与单一SVM相比,精度更高,结果更好,更加适用于多变量的复杂非线性滑坡预警。     

Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.     

The Influence of Linear Elements on Plant Species Diversity of Mediterranean Rural Landscapes: Assessment of Different Indices and Statistical Approaches

This paper mainly aims to study the linear element influence on the estimation of vascular plant species diversity in five Mediterranean landscapes modeled as land cover patch mosaics. These landscapes have several core habitats and a different set of linear elements -habitat edges or ecotones, roads or railways, rivers, streams and hedgerows on farm land- whose plant composition were examined. Secondly, it aims to check plant diversity estimation in Mediterranean landscapes using parametric and non-parametric procedures, with two indices: Species richness and Shannon index.Land cover types and landscape linear elements were identified from aerial photographs. Their spatial information was processed using GIS techniques. Field plots were selected using a stratified sampling design according to relieve and tree density of each habitat type. A 50×20 m² multi-scale sampling plot was designed for the core habitats and across the main landscape linear elements. Richness and diversity of plant species were estimated by comparing the observed field data to ICE (Incidence-based Coverage Estimator) and ACE (Abundance-based Coverage Estimator) non-parametric estimators.The species density, percentage of unique species, and alpha diversity per plot were significantly higher (p < 0.05) in linear elements than in core habitats. ICE estimate of number of species was 32% higher than of ACE estimate, which did not differ significantly from the observed values. Accumulated species richness in core habitats together with linear elements, were significantly higher than those recorded only in the core habitats in all the landscapes. Conversely, Shannon diversity index did not show significant differences.     

Selecting Species for Marine Assessment of Radionuclides Around Amchitka: Planning for Diverse Goals and Interests

Considerable attention has been devoted to selecting bioindicator species as part of monitoring programs for exposure and effects from contaminants in the environment. Yet the rationale for selection of bioindicators is often literature-based, rather than developed with a firm site-specific base of data on contaminant levels in a diverse range of organisms at different trophic levels in the same ecosystem. We suggest that this latter step is an important phase in the environmental assessment process that is often missing. In this paper we address the problem of how to select a wide range of species representing different trophic levels that serve as a basis for selecting a few species suitable as bioindicators. We illustrate this with our assessment of radionuclides on Amchitka Island, Alaska. We propose a multi-stage process for arriving at the list of available species that includes review of literature, review by experts experienced in the area, review by interested and affected parties, selection of trophic levels or groups for analysis, arraying of possible species, and selection of species within each trophic level group for sample collection. We first had to identify all likely species, then narrow our focus to those we could collect and analyze. In all cases, review includes suggestions for possible target species with justifications. While this method increases the up-front costs of developing bioindicators for an ecosystem, it has the advantage of providing information for selection of species that will be most informative in the long run, including those that are the best bioaccumulators, thus providing the earliest warning of any potential environmental consequences. Further, the recognition that a range of stakeholder's needs and interests should be included increases the utility for public-policy makers, and the potential for continued usage to establish long-term trends.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Diversity of antibiotic resistance genes and encoding ribosomal protection proteins gene in livestock waste polluted environment

The rapid development and increase of antibiotic resistance are global phenomena resulting from the extensive use of antibiotics in human clinics and animal feeding operations. Antibiotics can promote the occurrence of antibiotic resistance genes (ARGs), which can be transferred horizontally to humans and animals through water and the food chain. In this study, the presence and abundance of ARGs in livestock waste was monitored by quantitative PCR. A diverse set of bacteria and tetracycline resistance genes encoding ribosomal protection proteins (RPPs) from three livestock farms and a river were analyzed through denaturing gradient gel electrophoresis (DGGE). The abundance of sul(I) was 10³ to 10⁵ orders of magnitude higher than that of sul(II). Among 11 tet-ARGs, the most abundant was tet(O). The results regarding bacterial diversity indicated that the presence of antibiotics might have an evident impact on bacterial diversity at every site, particularly at the investigated swine producer. The effect of livestock waste on the bacterial diversity of soil was stronger than that of water. Furthermore, a sequencing analysis showed that tet(M) exhibited two genotypes, while the other RPPs-encoding genes exhibited at least three genotypes. This study showed that various ARGs and RPPs-encoding genes are particularly widespread among livestock.     

Metabolic diversity of the heterotrophic microorganisms and potential link to pollution of the Rouge River

The heterotrophic microbial communities of the Rouge River were tracked using Biolog Ecoplates to understand the metabolic diversity at different temporal and spatial scales, and potential link to river pollution. Site less impacted by anthrophogenic sources (site 1), showed markedly lower metabolic diversity. The only substrates that were utilized in the water samples were carbohydrates. Sites more impacted by anthrophogenic sources (sites 8 and 9) showed higher metabolic diversity. Higher functional diversity was linked to the physico-chemical and biological properties of the water samples (i.e. higher concentrations of DO, DOC, chlorophyll, and bacterial density). Biolog analysis was found to be useful in differentiating metabolic diversity between microbial communities; in determining factors that most influence the separation of communities; and in identifying which substrates were most utilized by the communities. It can also be used as an effective ecological indicator of changes in river function attributable to urbanization and pollution.