中国两性生殖卤虫形态特征的数值分类研究

对中国两性生殖卤虫１２个品系的１０个品系形态特征进行了聚类分析和主成分分析，并与Ａ．ｕｒｍｉａｎａ进行了比较研究．研究结果表明，Ａ．ｕｒｍｉａｎａ和中国两性生殖卤虫各品系明显可分为２个类群，证明中国各两性生殖卤虫品系均属于Ａ．ｓｉｎｉｃａ．而Ａ．ｓｉｎｉｃａ不属于Ａ．ｕｒｍｉａｎａ．在中国两性生殖卤虫各品系中ＮＬ、ＨＪＱ、ＳＢ可分为另一类群，显示可能存在种下分类阶元．本文所选取的形态特征是稳定的，可做为卤虫品系形态特征数值分类的重要依据．     

基于多维特征优选支持向量机算法的城市土地利用变化遥感监测

随着遥感影像时、空、谱、辐分辨率和数据处理能力的提升,综合多维影像特征已成为提高土地利用分类精度的关键.目前并非所有特征均有助于分类,且传统分类仍拘泥于单一特征,因此,急需有效的特征优化选择方法.基于光谱指数、穗帽变换、最小噪声分离、高斯滤波、灰度共生矩阵等变换提取了Landsat TM/ETM+/OLI影像的31维特...     

多位点酶电泳在黄芪根瘤菌分类中的应用

报导了分析根瘤菌各种多位点酶的电泳缓冲系统和染色方法，并对黄芪根瘤菌进行了分类研究，结果表明：采用多位点酶电泳图谱进行聚类可得到与数值分类基本一致的结果，证实这是一种行之有效、简便快速的分类方法．     

风险分析：优化环境管理工作的有力手段

识别和评价危险是有效风险管理的量重要阶段．最佳方法论或技术的选择取决于各种因素，例如问题类型、研究范围、所寻求的结果，可获取的信息，风险概率和可供利用的资源选定“最佳方法”的先决条件是了解所有这些要素方法论的选择将以按所涉及的活动分类的问题类型为基础：制造、技术装置、产品推出等为了更好地分析较大的系统必须分成若干子系统．目的和所寻求的结果也是主要因素．例如：进行风险分类，以确定研究部门、优先任务和资源分配；进行风险分析，以更多地了解它们，从而识别对该工艺可以做或必须做的改变；进行专利技术转让，以使知识得到充分利用；按风险和成本进行技术对比评价；收集论据，以便能辩论法律问题或防止滥用．     

湖南省生态经济分区及其发展研究

生态经济分区旨在协调区域经济发展与生态环境保护及自然资源利用的关系，是宏观管理区域社会经济持续发展的一种新模式、文章根据省级生态建设与发展需要，从生态经济系统原理出发，选择了34个适合湖南省生态经济系统分类的特征指标，以行政县为分类的基本单元，采用模糊聚类分析方法对湖南省进行了不重叠、内在的聚合分类。研究结果将湖南省分为6个生态经济区和24个生态经济亚区，与实际相符。在此基础上，对各生态经济区的生态经济特点、发展方向和环境保护策略进行了具体分析，为宏观指导湖南省生态经济建设与可持续发展提供科学依据。     

文本聚类算法的分析与比较

聚类是一种重要的文本信息处理方法,文章介绍了常用的文本聚类算法,从这些算法的适用范围、初始参数的影响、终止条件以及对噪声的敏感性等方面对它们进行了分析与比较.对文本聚类算法的应用有一定的指导意义.     

关于自然遗产的概念及其几个相关问题的探讨

讨论了自然遗产的概念,通过对自然遗产概念的讨论进一步分析了自然遗产的特征并对自然遗产进行了分类,最后对自然遗产与文化遗产、自然遗产与地质遗迹进行了辨析     

干旱地区几种豆科植物根瘤菌的数值分类和SDS-PAGE全细胞蛋白电泳分析

从新疆、内蒙干旱地区苜蓿属、草木樨属、锦鸡儿属植物的根瘤中分离出５４株根瘤菌，对其中４８株根瘤菌和２２株参比菌一起进行了数值分类研究，在８５％的相似性水平上，未知菌分为３个不同于已知种的新群．另加入６株分离自锦鸡儿属植物的根瘤菌，共７６株菌进行了全细胞蛋白ＳＤＳＰＡＧＥ分析，在８０％的相似性水平上，已知菌相应成群，未知菌除ＸＪ９６３４２在６８％的相似性水平上和群５、６、７、８、Ｒ．ｇａｌｅｇａ聚群外，其余未知菌则分布在４、５、６、７、８、９、１０七个群中．群４中所有菌株属于数值分类的群１；群６中有３株菌属于数值分类的群３；群９中有８株菌为数值分类群２的菌株．反映了两种方法在分类上得到的结果比较近似．     

基于景观指数聚类的粤北典型区景观生态区划

通过选取不同层次的景观指数,对粤北典型区域的景观生态子区进行分区研究,探讨了景观指数在景观生态区划方面的应用,为景观生态区划提供了一种新的思路和方法。采用2009年广东省南雄市DOM数字正射影像为数据源,应用ENVI 4.3和ARCGIS 9.3对遥感影像进行解译和分类,把研究区景观类型分为耕地、林地、疏林地、城镇建设用地、水体、未利用地6大类;根据镇级行政界线把研究区分为18个景观生态子区;选取不同层次的景观指数,应用Fragstats 3.3计算研究区的景观指数;以经过筛选后的景观指数为变量因子,18个景观生态子区为聚类对象,在SPSS 17.0中选择Q型聚类欧式距离平方法进行聚类分析,在聚类图谱中选择水平距离7.5为标准线,对景观子区进行分类,把18个景观生态子区分为4个区域。聚类结果显示,各分区内子区的景观结构特征高度一致。把景观指数应用到景观生态区划,通过聚类分析,分区结果能够较好的反映景观生态结构的显著特征与空间分异状况。     

高温堆肥中微生物的生长特征及动力学建模

对高温好氧堆肥中微生物的生长特征和动力学进行了研究，发现微生物的生长受温度影响很大，随温度变化呈波动性变化．同时，基于Logistic模型和Malthus模型，对微生物生长进行分析，得到了描述堆肥过程中的微生物生长动力学模型和模型参数．用实验数据与模型计算值进行验证比较，模型计算与实验结果拟合良好，模型正确地反映了高温好氧堆肥中微生物的生长过程及其动力学机制．图3表1参12     

An Evaluation of Threatened Species Categorization Systems Used on the American Continent

Abstract:  Endangered species lists are important tools in conservation. It is essential that these lists be prepared using categorization systems that objectively assess species extinction risk. To determine which threatened species categorization system is the most appropriate and the virtues and limitations of systems used on the American continent, we evaluated 25 categorization systems from 20 countries. These systems included examples of international lists, most national systems used on the American continent, and some systems independently proposed by academics. We based our assessment on 15 characteristics that categorization systems should have, in terms of categories, criteria, and other relevant issues, in order to evaluate species conservation status objectively. Of all evaluated systems, the current World Conservation Union system is the most suitable for assessing species extinction risk. Most categorization systems, but particularly national systems, have serious deficiencies and need to be improved substantially. We recommend governments use three types of lists: (1) threatened species lists constructed following a sound categorization system, (2) lists of species of conservation priority, and (3) lists that serve as normative tools (e.g., Convention on International Trade in Endangered Species of Wild Fauna and Flora). Additionally, the information used to categorize species should be explicit and available to the public. To make the most of threatened species lists in conservation, it is imperative that all countries use the same categorization system.     

Text analysis tools for identification of emerging topics and research gaps in conservation science

Keeping track of conceptual and methodological developments is a critical skill for research scientists, but this task is increasingly difficult due to the high rate of academic publication. As a crisis discipline, conservation science is particularly in need of tools that facilitate rapid yet insightful synthesis. We show how a common text‐mining method (latent Dirichlet allocation, or topic modeling) and statistical tests familiar to ecologists (cluster analysis, regression, and network analysis) can be used to investigate trends and identify potential research gaps in the scientific literature. We tested these methods on the literature on ecological surrogates and indicators. Analysis of topic popularity within this corpus showed a strong emphasis on monitoring and management of fragmented ecosystems, while analysis of research gaps suggested a greater role for genetic surrogates and indicators. Our results show that automated text analysis methods need to be used with care, but can provide information that is complementary to that given by systematic reviews and meta‐analyses, increasing scientists’ capacity for research synthesis.     

Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.     

Model averaging,missing data and multiple imputation: a case study for behavioural ecology

Model averaging, specifically information theoretic approaches based on Akaike’s information criterion (IT-AIC approaches), has had a major influence on statistical practices in the field of ecology and evolution. However, a neglected issue is that in common with most other model fitting approaches, IT-AIC methods are sensitive to the presence of missing observations. The commonest way of handling missing data is the complete-case analysis (the complete deletion from the dataset of cases containing any missing values). It is well-known that this results in reduced estimation precision (or reduced statistical power), biased parameter estimates; however, the implications for model selection have not been explored. Here we employ an example from behavioural ecology to illustrate how missing data can affect the conclusions drawn from model selection or based on hypothesis testing. We show how missing observations can be recovered to give accurate estimates for IT-related indices (e.g. AIC and Akaike weight) as well as parameters (and their standard errors) by utilizing ‘multiple imputation’. We use this paper to illustrate key concepts from missing data theory and as a basis for discussing available methods for handling missing data. The example is intended to serve as a practically oriented case study for behavioural ecologists deciding on how to handle missing data in their own datasets and also as a first attempt to consider the problems of conducting model selection and averaging in the presence of missing observations.     

城市环保信息专网及视频会议系统的设计--以天津市为例

以天津市为例,简要介绍了城市环保信息专网的承载网络和组网方案,并从需求分析、设计原则、操作平台、设备选型以及通讯方式等方面对视频会议系统的设计做了说明。     

多媒体数据库的知识发现

介绍了多媒体知识挖掘的基本结构,说明了不同类型多媒体数据挖掘的基本方法和技术,包括文本数据、图形图像音频数据、以及因特网上数据.     

A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.     

Strength of evidence for density dependence in abundance time series of 1198 species

Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently, with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.     

Geostatistical model averaging based on conditional information criteria

Variable selection in geostatistical regression is an important problem, but has not been well studied in the literature. In this paper, we focus on spatial prediction and consider a class of conditional information criteria indexed by a penalty parameter. Instead of applying a fixed criterion, which leads to an unstable predictor in the sense that it is discontinuous with respect to the response variables due to that a small change in the response may cause a different model to be selected, we further stabilize the predictor by local model averaging, resulting in a predictor that is not only continuous but also differentiable even after plugging-in estimated model parameters. Then Stein’s unbiased risk estimate is applied to select the penalty parameter, leading to a data-dependent penalty that is adaptive to the underlying model. Some numerical experiments show superiority of the proposed model averaging method over some commonly used variable selection methods. In addition, the proposed method is applied to a mercury data set for lakes in Maine.