生物多样性和均匀度显著性的随机化检验及计算软件

多样性指数和均匀度以其简单易用而被广泛应用于群落生物学和生物多样性等研究中,然而由于缺乏合适的统计检验方法等原因,其分析的可信性往往较低,因而限制了其应用。鉴于生物多样性研究中广泛应用主观和直接的比较不,有必要建立和使用较为严格的多样性统计检验。本研究建立和应用了如下随机化检验方法：单群落多样性指数和均匀度的显著性检验,单群落多样性指数和均匀度的置认区间,群落间多群样和均匀度的差异显著性检验。随机化方法已被成功地应用于群落生态学研究,其原理是：随机排序某一向量中的元素,或随机交换两向量中的对应元素。计算该随机化数据的多样性和均匀度,重复该过程多次,统计和计算显著性检验的p值,由向量中的对应元素。计算该随机化数据的多样性和均匀度,重复该过程多次,统计和显著性检验的p值。由此可确定多样性和差异的统计显著性。同时,研制了相应的Internet计算软件BiodiverisytTest。该软件由7个Java类和1个HTML文件组成,可运行于多种操作系统和网络浏览器上,可读取多种类型的ODBC数据库文件如Access,Excel,FoxPro,Dbase等。该软件中包括Shannon-Wiener多样性指数,Simpson多样性指数,McIntosh多样性指数,Berger-Parker多样性指数,Hrlbert多样性指数以及Brillouin多样性指数。基于Shannon-Wiener多样性指数和Berger-Parker多样性指数,用BiodiversityTest软件对水稻田节肢动物群落多样性（15个地点,17个功能群,125个节肢动物种）进行了比较和分析。结果显示,两组结果可较好地反映水稻节肢动物群落多样性的差异显著性,这些检验方法可有效地反映多样性指数和均匀度的变化。与水稻田节肢动物群落间多样性的直接比较法相比,该随机化检验方法获得更客观的结果。本算法与软件有助于改进生物多样性研究中使用的某些不甚严格的分析方法,为随机化检验方法在生物多样性研究中的进一步应用提供了一种可用的工具。     

西南亚热带典型天然常绿阔叶林的空间结构特征

以西南亚热带分布较广泛的两种典型的天然常绿阔叶林——四川大头茶林和栲树林为研究对象,根据树种组成,采用混交度、大小比数和角尺度3个空间结构参数,对比分析2种林分的空间结构特征。结果显示:(1)四川大头茶林和栲树林群落的乔木层分别有8和9个树种,2种林分内的优势种的密度及每公顷断面积优势明显。(2)2种林分的树种混交程度较高,林分平均混交度分别为0.61和0.73,混交程度均在中等以上;栲树林群落的树种隔离程度优于四川大头茶林;林分内的平均大小比数分别为0.47和0.45,多数林木处于中庸状态,林分内不同径级的林木在各组成的空间结构单元内分布比较均匀。(3)四川大头茶林的平均角尺度为0.523,属于聚集分布;栲树林的平均角尺度为0.517,属于随机分布,且呈现轻微的聚集分布。研究表明,两种林分的混交度差异显著,而大小比数和角尺度差异不显著,栲树林较四川大头茶林的群落结构稳定。     

暖温带森林土壤微生物量、群落结构和活性对植物地下碳源的响应

该研究以典型的亚热带—温带过渡区森林为对象,采用野外过程监测和控制试验相结合的方法,利用磷脂脂肪酸和土壤胞外酶活性分别表征土壤微生物群落结构和活性,并结合微环境因子,重点探究土壤微生物生物量、群落结构和活性对植物地下碳输入的响应特征。结果表明：在观测周期内,处理均能显著降低三组年龄段林分的土壤微生物量碳,其变化幅度在－8.72％~－5.72％之间,其中在80年的林分中降幅最大,而在160年的林分中降幅最小;微生物量氮的变化规律与相应的微生物量碳的变化规律相似,但与对照相比其差异性均未达到显著性水平;另外,经壕沟处理2~4个月后,所有林分的土壤微生物量碳和氮与对照相比出现增加的现象。处理均能对三组年龄段林分的土壤微生物群落结构产生不同程度的影响,其中40年林分的土壤微生物群落对处理的响应程度要高于另外两个年龄段的林分;与对照相比,壕沟处理样方的腐生真菌的相对丰富度均下降明显,其中在40年和80年林分中的下降幅度达到显著水平,而细菌、放线菌和丛枝菌根真菌均无明显变化;壕沟处理样方的水解酶(β-葡萄糖苷酶和N-乙酰-葡萄糖苷酶)活性均显著下降,而氧化酶(酚氧化酶和过氧化物酶)活性的变化相对较小,除80年的林分外,其余林分均不显著。此外,处理均不能显著影响土壤的含水量和温度。该研究结果为初步阐明全球气候变化背景下森林土壤微生物结构及其功能的变化特征以及更加精确预测未来森林土壤碳的变化趋势提供了科学依据。     

基于空间结构调查的林分密度估计

利用林分空间结构抽样调查技术,采用测量抽样点到其第k株最近相邻木的距离(距离法)进行密度估计,分别选取k=4和k=6两种距离调查方法进行分析,并对Prodan、Persson、Thompson 3种不同密度估计方法的预估能力进行检验.结果表明：不同预估方法的预估能力受林木水平分布格局影响.Prodan法在均匀分布的林分中有较强的预估能力,随着分布格局聚集性增加会产生越来越大的偏差;Persson计算法在均匀及随机分布的林分中产生正偏差,但随着分布格局聚集性增加产生的相对误差接近0,预估能力增强;Thompson计算法对随机或接近随机分布的林分有较强的预估能力,而在均匀分布和聚集分布的格局中分别产生正偏差和负偏差.抽样点为49个时,选择6株木与4株木预估能力无显著差异,因此,密度估计可与选取4株相邻木的空间结构参数调查整合在一起.     

多样化松林中昆虫群落多样性特征

马尾松和湿地松是我国南方的2种主要松树。通过对6种不同林分结构下的马尾松林和湿地松林内昆虫群落调查与多样性指数分析,表明2种松树内的昆虫种类和数量无显著差异,混交林中的昆虫群落的种类和数量比纯林多,尤其以捕食天敌类群的种类和数量更为明显。整个昆虫群落和植食类群多样性指数以湿地松林内较大,而天敌(捕食类群和寄生类群)多样性指数则以马尾松林较高。从不同林分结构下昆虫多样性的比较来看,混交林内昆虫群落多样性指数波动较小,明显地高于纯林。但不同林分结构下昆虫多样性随水平分布和垂直分层格局而变化,松树北面和东面各样地之间的昆虫群落多样性指数差异显著,而南、西面之间差异较小;树冠层各样地之间的差异达极显著水平,而枯枝落叶层和树干层之间差异不显著。由此,还进一步讨论了混交林中昆虫群落稳定性问题。     

基于DNA条形码的物种丰富度估计: 以宿迁地区鳞翅目蛾类为例

为了探究基于DNA条形码方法量化物种多样性指标的可行性, 本研究以江苏省宿迁地区蛾类群落为例, 基于DNA条形码方法估计群落物种丰富度并绘制等级多度分布曲线(rank-abundance curves), 同时与基于传统形态学的对应指标进行比较。结果表明: (1)基于DNA条形码的物种丰富度估计与基于形态的物种丰富度估计之间没有显著差异; (2)基于形态和DNA条形码的等级多度分布曲线趋势一致, 通过K-S检测发现二者之间没有显著性差异(P > 0.05)。结果显示, 基于DNA条形码的物种丰富度估计能够在一定程度上补充基于形态学的方法, 可以尝试将其应用于蛾类群落生态学调查研究中。     

广西地区汉族人群TNF-α基因启动子-238G/A多态性分布的研究

目的了解TNF-α基因启动子-238G/A多态性在广西地区汉族人群中的分布特点.方法应用聚合酶链反应-限制性片段长度多态性法(PCR-RFLP)检测170例正常汉族人TNF-α基因型并与其它种族的分布进行比较.结果 TNF-α基因型以GG型最为多见(95.9%),这种基因多态性分布在男女间均无显著差异(P>0.05).与其它种族比较,发现不同种族间TNF-α基因型分布及等位基因频率均存在显著差异(P<0.05).结论在广西地区汉族人群中存在TNF-α基因多态性,但在不同种族间分布有明显的差异,这种差异可能是导致一些疾病在不同种族间的发生、转归和预后不同的遗传因素之一.     

六盘山南坡不同密度华北落叶松水源林生长过程比较

以六盘山南侧的华北落叶松水源涵养林为研究对象,利用标准木树干解析法,研究了21年生低、中、高3种密度(1200、1500和2000 株·hm^-2)华北落叶松人工林的生长过程和直径结构.结果表明：华北落叶松3种密度林分在10年生前各项生长指标差异不显著;10年生后的林木直径、单株材积和林分蓄积生长过程明显不同;21年生时,低密度林分的生长状况明显优于中、高密度林分,但树高生长受密度影响不显著;3种密度林分直径分布的偏度系数(S_k)差异较大,高密度林分的S_k（0.338)大于中密度(0.072)和低密度林分(0.015).前者直径分布偏离正态分布,呈现顶峰偏左的现象;后者的直径分布接近正态分布,密度结构较合理;中密度林分直径分布的峰度系数(K,1.691)大于高密度(1.532)和低密度林分(0.665).说明中密度林分的林木分化程度比高、低密度林分小;林龄为21年的华北落叶松人工林的合理保留密度应为1200 株·hm^-2.     

樟子松沙地人工林直径分布模拟

利用反 Weibull函数模拟了章古台沙地樟子松人工林的直径分布规律 .结果表明 ,反 Weibull分布函数模拟樟子松人工林的直径分布效果好 ,具有精度高、适应性强等特点 .分别建立了反 Weibull分布函数 3个参数与林分年龄及密度之间的关系方程以及直径分布与 3个参数的回归方程 ,这些方程可用来预估林分产量、出材量以及评价经营效果     

华东地区黑果蝇自然群体同工酶遗传多态的研究

我们用标准垂直板聚丙烯酰胺凝胶电泳和水平板琼脂糖凝胶电泳技术检测了黑果蝇(D.virilis)在合肥、芜湖、九江、南昌、福州、泉州和常州7个自然群体中Est-α、Est-β、Amy、Acph和α-Gpdh 5个座位的遗传变异,发现Est-α、Est-β和Amy 3个座位是高度多态的,Acph、α-Gpdh两个座位则是单态的。根据这5个座位等位基因的频率,我们计算了群体间的遗传距离。综合何朝珍报道的宁波、杭州、南京和洪泽4个群体的结果和我们的结果,我们作出系统树并发现泉州、福州两群体和其他群体在基因频率的分布和遗传距离方面有显著差异;分析显示这种差异与群体间地理隔离有关。     

A Comparative Statistical Analysis of Genetic Distances. II–Confidence Intervals and Tests of Comparison

We present methods for constructing the confidence intervals of the estimations of absolute genetic distance and Nei's minimum and standard genetic distances. Two different estimators of the standard distance are considered: jack-knife and corrected. A Monte-Carlo simulation analysis permitted to choose the best adapted confidence interval type for each distance estimator and for each size of the samples of loci and of gametes, taking into account the magnitude of the estimation value. Simulated true confidence levels showed that the best results are obtained using abolute genetic distance. We also give rules for testing the significance of the difference of two distance estimations. Monte-Carlo simulations were performed in order to obtain the distributions of the difference of two distance estimations under the null hypothesis and consequently the true type I errors of tests. Test powers, obtained by simulation under the alternative hypothesis, also showed the superiority of absolute genetic distance.     

A Comparative Statistical Analysis of Genetic Distances. I - Estimation of Distances and of their Variances: Distribution of the Estimators

We present a Monte-Carlo simulation analysis of the statistical properties of absolute genetic distance and of Nei's minimum and standard genetic distances. The estimation of distances (bias) and of their variances is analysed as well as the distributions of distance and variance estimators, taking into account both gamete and locus samplings. Both of Nei's statistics are non-linear when distances are small and consequently the distributions of their estimators are extremely asymmetrical. It is difficult to find theoretical laws that fit such asymmetrical distributions. Absolute genetic distance is linear and its distributions are better fit by a normal distribution. When distances are medium or large, minimum distance and absolute distance distributions are close to a normal distribution, but those of the standard distance can never be considered as normal. For large distances the jack-knife estimator of the standard distance variance is bad; another standard distance estimator is suggested. Absolute distance, which has the best mathematical properties, is particularly interesting for small distances if the gamete sample size is large, even when the number of loci is small. When both distance and gamete sample size are small, this statistic is biased.     

On measures of association among genetic variables

Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative 'distances' between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart.     

A Test Procedure of Equivalence in Ordinal Data with Matched‐Pairs

There are many epidemiologic studies or clinical trials, in which we may wish to establish an equivalence rather than to detect a difference between the distributions of responses. In this paper, we develop test procedures to detect equivalence with respect to the tail marginal distributions and the marginal proportions when the underlying data are on an ordinal scale with matched pairs. We include a numerical example concerning the unaided distance vision of two eyes over 7477 women to illustrate the practical usefulness of the proposed procedure. Finally, we include a brief discussion on the relation between the test procedures developed here and an asymptotic interval estimator proposed elsewhere for the simple difference in dichotomous data with matched‐pairs.     

Performance of individual vs. group sampling for inferring dispersal under isolation‐by‐distance

Models of isolation‐by‐distance formalize the effects of genetic drift and gene flow in a spatial context where gene dispersal is spatially limited. These models have been used to show that, at an appropriate spatial scale, dispersal parameters can be inferred from the regression of genetic differentiation against geographic distance between sampling locations. This approach is compelling because it is relatively simple and robust and has rather low sampling requirements. In continuous populations, dispersal can be inferred from isolation‐by‐distance patterns using either individuals or groups as sampling units. Intrigued by empirical findings where individual samples seemed to provide more power, we used simulations to compare the performances of the two methods in a range of situations with different dispersal distributions. We found that sampling individuals provide more power in a range of dispersal conditions that is narrow but fits many realistic situations. These situations were characterized not only by the general steepness of isolation‐by‐distance but also by the intrinsic shape of the dispersal kernel. The performances of the two approaches are otherwise similar, suggesting that the choice of a sampling unit is globally less important than other settings such as a study's spatial scale.     

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups

ABSTRACT: BACKGROUND: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. RESULTS: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. CONCLUSION: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.     

Phylogeography of the Northern short-tailed shrew,Blarina brevicauda (Insectivora: Soricidae): past fragmentation and postglacial recolonization

Blarina brevicauda is distributed across the northeastern region of North America, in areas previously covered by Pleistocene glaciers. Previous molecular systematic study of the species in the genus Blarina suggested the presence of two distinct eastern and western phylogroups within B. brevicauda, in agreement with traditionally recognized semi-species. To expand the previous work, a collection of 76 individuals from 14 localities collected throughout the range of B. brevicauda was used to assess the mitochondrial (mt) cytochrome b genealogy for this species. Minimum evolution, maximum parsimony, analysis of molecular variance and nested clade analysis each supported the same conclusions of two well-differentiated and monophyletic east-west groups, separated by the Mississippi River. Denser sampling in areas immediately East of the Mississippi basin revealed further subdivision within the eastern phylogroup into an East-Central and an Appalachian clade. The western phylogroup differed from the eastern phylogroup by 2.5% mean absolute DNA sequence difference. About 65% of the genetic variance among samples was explained by the east-west subdivision alone. High haplotype diversities, low nucleotide diversities and unimodal mismatch distributions within subclades suggest recent expansion or diversification within each group. No phylogeographic structure was found within the western phylogroup, but genetic structure because of restricted gene flow and isolation by distance was inferred for the eastern group. The present distribution of B. brevicauda is best explained by past fragmentation and range expansion events during and following the Pleistocene glacial cycles.     

MigClim: Predicting plant distribution and dispersal in a changing climate

Aim Many studies have forecasted the possible impact of climate change on plant distributions using models based on ecological niche theory, but most of them have ignored dispersal‐limitations, assuming dispersal to be either unlimited or null. Depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under‐ or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of ‘potentially suitable’ and ‘potentially colonizable’ habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed Mig Clim, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. Mig Clim implements various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long‐distance dispersal. Location Western Swiss Alps. Methods Using our Mig Clim model, several simulations were run for two virtual species by varying dispersal distance and other parameters. Each simulation covered the 100‐year period 2001–2100 and three different IPCC‐based temperature warming scenarios were considered. Results of dispersal‐limited projections were compared with unlimited and no‐dispersal projections. Results Our simulations indicate that: (1) using realistic parameter values, the future potential distributions generated using Mig Clim can differ significantly (up to more than 95% difference in colonized surface) from those that ignore dispersal; (2) this divergence increases under more extreme climate warming scenarios and over longer time periods; and (3) the uncertainty associated with the warming scenario can be as large as the one related to dispersal parameters. Main conclusions Accounting for dispersal, even roughly, can importantly reduce uncertainty in projections of species distribution under climate change scenarios.     

Estimation of average heterozygosity and genetic distance from a small number of individuals

The magnitudes of the systematic biases involved in sample heterozygosity and sample genetic distances are evaluated, and formulae for obtaining unbiased estimates of average heterozygosity and genetic distance are developed. It is also shown that the number of individuals to be used for estimating average heterozygosity can be very small if a large number of loci are studied and the average heterozygosity is low. The number of individuals to be used for estimating genetic distance can also be very small if the genetic distance is large and the average heterozygosity of the two species compared is low.     

Phylogenetic inference under the pure drift model

When pairwise genetic distances are used for phylogenetic reconstruction, it is usually assumed that the genetic distance between two taxa contains information about the time after the two taxa diverged. As a result, upon an appropriate transformation if necessary, the distance usually can be fitted to a linear model such that it is expressed as the sum of lengths of all branches that connect the two taxa in a given phylogeny. This kind of distance is referred to as "additive distance." For a phylogenetic tree exclusively driven by random genetic drift, genetic distances related to coancestry coefficients (theta XY) between any two taxa are more suitable. However, these distances are fundamentally different from the additive distance in that coancestry does not contain any information about the time after two taxa split from a common ancestral population; instead, it reflects the time before the two taxa diverged. In other words, the magnitude of theta XY provides information about how long the two taxa share the same evolutionary pathways. The fundamental difference between the two kinds of distances has led to a different algorithm of evaluating phylogenetic trees when theta XY and related distance measures are used. Here we present the new algorithm using the ordinary- least-squares approach but fitting to a different linear model. This treatment allows genetic variation within a taxon to be included in the model. Monte Carlo simulation for a rooted phylogeny of four taxa has verified the efficacy and consistency of the new method. Application of the method to human population was demonstrated.