泛函连接网络计算软件及其在生物多样性研究中的应用

针对农田生物多样性分析的需要,研制出泛函连接网络（FLANN）计算软件。该软件由7个Java类和1个HTML文件组成,是一种Internet在线计算工具,可运行于多种操作系统和Web浏览器上,并在各种类型的PC及工作站上使用,可读取多种类型的数据库文件。对水稻田昆虫生物多样性的两组取样调查数据Zmar18和Zapr15,用生物多样性工具软件LUMP和非监督分类－离差平方和聚类法进行统计归纳及分类,分别划分为21个和20个功能群,各包含60个样本。以FLANN计算软件对昆虫生物多样性进行了模式分类分析。结果表明,泛函连接网络的模式分类及预测与实际测查结果吻合良好。泛函连接网络Internet在线计算软件的应用可促进生物多样性数据采集和分析的规范化,有利于数据和信息共享,也为形成高度的生物多样性智能分析系统提供了一种工具。     

田间试验数据的统计分析（下）：显著性检验

从事试验研究工作的基本目的在于了解不同处理或事物间的差异。但是试验的众多差异的性质不同,有一些属于事物本质的真正差异,另一些则属于偶然因素引起的随机误差。例如,已知产量水平截然不同的两个小麦品种种植于同一块麦田中,二者间的产量差异应该主要属于本质差异;同一个品种在两个不同小     

生物分类和多样性保护

生物分类是生物学领域最基础的研究工作,生物多样性是当前生命科学研究热点之一。概述了生物分类思想的历史发展,探讨了基因组多样性研究中的伦理问题。     

生物多样性和生态系统稳定性

生物多样性和生态系统稳定性黄建辉,韩兴国（中国科学院植物研究所,北京１０００４４）１引言自从ＭａｃＡｒｔｈｕｒ［１］和Ｅｌｔｏｎ［２］分别在本世纪五十年代提出了多样性一稳定性理论以后,对多样性一稳定性的关系问题展开了激烈的争论。最初,为了简便起见,有...     

生物多样性评价软件BiodiversityMapping的设计与实现

针对目前国内生物多样性相关软件缺乏的情况,利用可视化开发方法和地理信息系统组件包shape Viewer Objects开发了一个综合性的生物多样性评价软件BiodiversityMapping。该软件运行于Windows98／NT操作系统下,是一个32位Windows应用软件;可以计算8类37种d、D生物多样性测度并能利用地理信息系统技术将结果空间化表达。利用该软件计算了北京东灵山地区3个研究区域(小龙门、梨园岭和东灵山主峰)内11个生境中大步甲属(Carabus)昆虫的Shannon-Wiener多样性指数并制图,然后对这3个地区进行了右尾和排序。结果表明,BiodjversityMapping软件能够准确方便地计算多种生物多样性测度,是生物多样性评价的一个有力工具。     

试论生物多样性的概念

随着人口的迅速增长,人类经济活动的不断加剧,作为人类生存最为重要的基础的生物多样性受到了严重的威胁。“无法再现的基因、物种和生态系统正以人类历史上前所未有的速度消失”。如果不立即采取有效措施,人类将面临着能否继续以其固有的方式生活的挑战。     

生物多样性的起源

生物多样性的起源相对于人的一生来说,自然生态系统似乎是永恒而稳定的。除非被人类的活动或自然界的灾变,如大火、洪水、火山爆发等所干扰,草地、沙漠、森林和冻原似乎都是不变的。然而,纵观一个行星的历史,变化却恰恰是生物多样性产生的根源。本章将阐述已经创造...     

Effect size, confidence interval and statistical significance: a practical guide for biologists

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.     

Focusing on individual plants to understand community scale biodiversity effects: the case of root distribution in grasslands

Spatial resource partitioning between species via differences in rooting depth is one of the main explanations for the positive biodiversity–productivity relationship. However, evidence for the importance of this mechanism is limited. This may be due to the community scale at which these interactions are often investigated. Community measures represent net outcomes of species interactions and may obscure the mechanisms underlying belowground interactions. Here, we assess the performance of ~1700 individual plants and their heterospecific neighbours over three growing seasons in experimental grassland plots containing one, four or sixteen different plant species and tested whether their performance in mixtures compared to monocultures was related to their own rooting depth versus the rooting depth of their heterospecific neighbours. Overall, individuals of deep-rooting species performed better in mixtures and this effect significantly increased when surrounded by more shallow-rooting species. This effect was not apparent for the shallow rooting species. Together, including both deep and shallow rooting species increased mixture performance. Our results show that taking the perspective of the individual rather than the community can elucidate the interactions between species that contribute to positive biodiversity effects, emphasizing the need for studies at different scales to disentangle the myriad interactions that take place in diverse communities.     

The continuing misuse of null hypothesis significance testing in biological anthropology

There is over 60 years of discussion in the statistical literature concerning the misuse and limitations of null hypothesis significance tests (NHST). Based on the prevalence of NHST in biological anthropology research, it appears that the discipline generally is unaware of these concerns. The p values used in NHST usually are interpreted incorrectly. A p value indicates the probability of the data given the null hypothesis. It should not be interpreted as the probability that the null hypothesis is true or as evidence for or against any specific alternative to the null hypothesis. P values are a function of both the sample size and the effect size, and therefore do not indicate whether the effect observed in the study is important, large, or small. P values have poor replicability in repeated experiments. The distribution of p values is continuous and varies from 0 to 1.0. The use of a cut‐off, generally p ≤ 0.05, to separate significant from nonsignificant results, is an arbitrary dichotomization of continuous variation. In 2016, the American Statistical Association issued a statement of principles regarding the misinterpretation of NHST, the first time it has done so regarding a specific statistical procedure in its 180‐year history. Effect sizes and confidence intervals, which can be calculated for any data used to calculate p values, provide more and better information about tested hypotheses than p values and NHST.     

鼎湖山森林群落多样性垂直分布格局的研究

根据鼎湖山10 m×1 200 m的植物固定样带调查,沿海拔梯度对植物群落分布格局及生物多样性特性进行了分析。结果表明,本固定样带有3个森林类型,5个群落类型,构成一演替序列,但在垂直梯度上有交错现象。生物多样性指数为:乔木层,中生性阔叶林>针阔混交林>阳生性阔叶林;草本层,针阔混交林>阔叶林。阳性草本植物为主的草本群大部分生长在阳性树种为主的阔叶林和针阔混交林下,但是林下微环境变化对草本层植物优势度的影响也较大,以耐荫植物为主的草本群在阳性树种为主的阔叶林和针阔混交林也有分布,表明相同群落类型林下小环境的变化。     

凡纳对虾淡化养殖虾池微型浮游生物群落及多样性

2 0 0 2年 8月 15～ 10月 8日 ,对广东省珠海市斗门凡纳对虾淡化养殖虾池进行调查 ,虾池平均养殖面积为 0 .37±0 .11hm2 ,水深 136± 8cm,虾苗放养密度 131± 37× 10 4尾 / hm2 ;调查期间虾池水温 2 4 .3～ 33.6℃ ,p H值 7.1～ 9.6 ,早期养殖盐度 2‰～ 3‰ ,中后期为 0 ,透明度中后期稳定在 2 0 cm左右。鉴定浮游植物 4 8属 96种 ,其中绿藻 4 5种 ,硅藻和裸藻各 9种 ,隐藻 2种 ,甲藻 4种。优势种有 10种 ,多为蓝藻 ,如螺旋藻 (Spirulina sp.)、弯形尖头藻 (Raphidiopsis curvata)、小颤藻(Oscillatoria tenuis)、假鱼腥藻 (Pseudoanabaena sp.)、针状蓝纤维藻 (Dactylococcopsis acicularis)及水华微囊藻 (Microcystisaeruginosa)等 ,优势种的优势度突出。常见种有 30种 ,主要是绿藻、硅藻、隐藻 ,少见种有 5 7种。优势种和常见种多为富营养化水体中的或耐污性的种类。虾池原生动物有 2 9种 ,其中肉足虫 3种 ,纤毛虫 2 6种 ,优势种为毛板壳虫 (Colepshirtus)、卵形前管虫 (Prorodon ovum)和旋急游虫 (Strombidium spiralis) ,主要是以藻类为食的 A类群 ,常见种有膜袋虫 (Cyclidium sp.)、小单环栉毛虫 (Didinium balbianitnanum)、瓶口虫 (L agynophrya acuminate)、圆筒状拟铃壳虫 (Tintinnopsis c