古生物学研究中的采样问题及多样性估计方法——以华夏正形贝动物群为例

传统的古生物学采样方法通常很少对同一化石层面展开进行,由此得到的多样性信息往往难以反映化石组合的真实情况。通过观察稀疏化曲线平缓程度来判断化石采样是否充分的经验方法并不严谨。本文在介绍古生物化石采样方法的基础上,提出采样是否充分的本质是采样获得的多样性与所属化石群落潜在多样性之间的接近程度。借鉴现代生物学中的多样性估计方法,以华夏正形贝腕足动物群为例,本研究采用EstimateS软件,定量估计4个化石点的潜在多样性;探讨客观判断采样充分的方法,同时通过曲线拟合法预测达到特定多样性可能需要的样本数量,为二次研究提供重要信息。该多样性估计方法是古生物研究中一个较新的领域,在多样性对比及特定化石群的多样性评估中均有较好应用前景。为得到更全面的多样性信息,文章建议相关研究在使用稀疏化的同时辅以多样性估计方法。     

运用稀疏法分析物种丰富度的海拔梯度分布格局:以样方实测乔木种数据为例

植物物种丰富度随山地海拔梯度的变化格局是生物多样性研究的热点之一.基于种-面积关系的任何模型对群落物种数目所作估计,其精度都依赖于样本的代表性、抽样尺度以及所涉及的分类群.作者以秦岭南坡森林群落样方实测的乔木种数据为例,借鉴群落最小面积(minimum area,MA)的概念及其确定方式,利用稀疏法(rarefacti...     

微生物群落多样性数学表征方法及其在污水处理系统研究中的应用

微生物群落在调节全球气候、人类健康和工业生物技术应用中扮演着重要角色。定量表征微生物群落多样性是认识微生物群落基本特征、动态变化和功能的前提。本文介绍了常用的α多样性指数,包括物种数目、Shannon-Weaver指数、Simpson多样性指数和Hill多样性指数;并介绍了其他多样性评估方法及其原理,包括能够评价样本对微生物群落各物种覆盖程度的稀释性曲线和Good’s coverage指数,以及能估算群落多样性的Chao1、ACE指数和基于物种丰度分布曲线的模型方法;并以最大规模的生物技术应用——污水处理厂为例,介绍了这些方法在认识微生物群落多样性中的应用。现有研究表明:所检测到的城市污水处理厂中微生物群落的物种数目和Shannon-Weaver指数随检测方法解析通量(样本大小)的增加而增大;但现有方法仍无法反映城市污水处理厂微生物群落的真实多样性。基于特定的物种丰度分布曲线对DNA样本数据进行模拟和重建,结果表明对群落物种数目的评估存在较大的不确定性;Shannon-Weaver指数,特别是Simpson多样性指数等受低丰度物种影响较小,可以准确计算,是评价和比较微生物群落分类学多样性的好手段。改进模型拟合方法和加大取样深度能提高微生物群落物种数目的评估精度。此外,认识微生物群落其他方面的多样性如系统发育多样性和功能多样性,也对认识微生物生态特征具有重要意义。     

物种累积曲线及其应用

在生物多样性和群落调查中,物种累积曲线被广泛用于抽样量充分性的判断以及物种丰富度估计。然而该方法在国内却很不常见,致使相关研究无法保证科学的抽样。本文介绍了如何运用EstimateS软件计算物种累积曲线,并通过Excel绘图功能绘制曲线,以期提高生物多样性和群落调查中抽样的科学性。     

生产力、可靠度与物种多样性:微宇宙实验研究

近年来,生物多样性与生态系统功能的关系成为生态领域内一个重大科学问题。有一些实验研究表明,物种多样性的降低会使生态系统的生产力、稳定性等功能受损,然而对这些实验结果的解释却产生了激烈的争论,因为有两种机制-“生态位互补”和“抽样效应”都可能会产生这种结果。本项研究通过微宇宙实验探讨了物种多样性与生态系统生产力及其可靠度的关系。在10种单细胞藻类中随机抽取物种,构建具有不同物种丰富度的水生群落,并使同一物种丰富度水平的群落之间没有物种交叉,然后检测物种丰富度对群落生产力及其可靠度的作用,群落生产力以藻类干重表示,自实验开始后第4周起,每周测定1次,共测5次。结果显示：物种丰富度对群落生产力有正效应,并且这种正效应随时间推移而增强;许多混合群落的生产力超过了该群落内所有物种的单产,即发生了超产现象,在实验初期某些特定物种对一些混合群落生产力有主要贡献,而在实验后期却没有任何多物种群落的生产力受个别物种存在与否的影响,群落生产力的可靠度与物种丰富度之间不存在显著相关。从以上结果可以得知：物种多样性对群落生产力有着逐渐增强的正效应;物种多样性对生产力的正效应是生态位互补和抽样效应共同作用的结果,但随时间推移,抽取效应逐渐减弱,本顶研究支持了关于生态位互补与抽样效应在多样性正效应中共同起作用的认识,并说明了这两种机制的相对重要性随时间推移而发生改变。     

广东鉴江水系底栖硅藻多样性与时空分布特征

为了解广东省鉴江水系底栖硅藻多样性和时空分布特征,对全流域进行了底栖硅藻采样调查。结果表明,从19个采样点4次采样中共检出底栖硅藻10科52属242种,其中舟形藻属(Navicula)、菱形藻属(Nitzschia)和异极藻属(Gomphonema)是优势类群,出现频次和相对丰度较高。硅藻多样性指数(丰富度、真香农多样性指数和真辛普森多样性指数)随河流等级呈现一定的空间分布特征,但它们季节变化不明显。底栖硅藻群落相异性在上游和下游河段较高,从一级到三级河流递减,四级河流又增加。底栖硅藻群落结构空间变化明显,季节变化显著。群落丰富度的稀疏曲线表明,热带河流底栖硅藻群落以400个体计数,不能完整反映底栖硅藻多样性。这些为鉴江水系河流健康监测和水生态保护奠定了基础。     

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。     

景观多样性测度:格局多样性的亲和度分析

介绍景观格局多样性亲和度分析的原理和方法,指出亲和度分析能够测定景观各亚单元的相对位置及镶嵌多样性。镶嵌多样性是综合了亲和度分析信息的一个指标,是对格局多样性的有效测度。镶嵌多样性低意味着景观结构简单,镶嵌多样性高意味着景观结构复杂,亲和度分析可以比较不同景观的多样性和复杂性,可以判断群落与景观整体的关系的远近,还可以判断两个群落的相似性和亲和度差异的显著程度。因而亲和度分析将景观层次的多样性与物     

中国沙拐枣属天然群落特征及其物种多样性研究

根据野外调查资料,应用生物多样性的原理和方法计测了群落的物种丰富度指数(R0、R1)、物种多样性指数(H′、D)、均匀度指数(E)以及优势度指数(C)以分析中国沙拐枣属天然群落的群落学特征。结果表明(1)中国沙拐枣属天然群落组成成分简单,共出现种子植物91种,分属22科62属,科属组成较为分散;生活型组成中1年生植物种类多,数量大,占总数的35.16%;沙拐枣属天然群落的垂直结构明显;(2)多样性分析表明,中国沙拐枣属天然群落物种组成单一,结构简单,其中群落多样性最低分别为塔克拉玛干沙拐枣(Calligonum roborowskii)群落和沙拐枣(C.mongolicum)(若羌)群落。群落多样性最高分别为沙拐枣(C.mongolicum)(采南)群落、小沙拐枣(C.pumilum)群落和艾比湖沙拐枣(C.ebi-nuzicum)群落。不同沙拐枣属植物所形成的群落差异主要表现在物种组成以及物种多样性水平,这种差异表现主要是1年生和多年生草本植物的物种多样性。(3)丰富度指数和物种多样性指数表现出基本一致的变化趋势,而生态优势度指数则表现相反的趋势。物种的丰富度指数和多样性指数与群落的结构以及立地环境条件等有密切的关系,结构复杂的群落较其它群落的多样性指数高,但由于特殊生境所形成的单优群落物种多样性指数则较低。     

生物群落多样性的测度方法 V.生物群落物种数目的估计方法

群落的物种数目,即物种丰富度,是最古老、同时也是最基本的一个多样性概念,从对它的估计中可以得到关于物种灭绝速率方面的信息,这对生物多样性保护是非常重要的。已经提出了很多方法来估计群落中的物种数目,这些方法可以分为两大类,即基于理论抽样的方法和基于数据分析的方法。前者包括经典估计方法和贝叶斯估计方法;后者包括对数正态分布的积分方法、再抽样方法和种－面积曲线的外推方法。发现：（１）有些方法适用于动物群落,如大多数基于理论抽样的方法;有些方法则适用于植物群落,如大多数基于数据分析的方法;（２）这些方法还没有经过全面而系统地比较;（３）还没有一个普遍认为比较好的方法。因此,建议采用野外调查与模拟研究相结合的方法对各种估计方法进行系统地评价。     

A unified mathematical framework for the measurement of richness and evenness within and among multiple communities

Biodiversity can be divided into two aspects: richness (the number of species or other taxa in a community or sample) and evenness (a measure of the distribution of relative abundances of different taxa in a community or sample). Sample richness is typically evaluated using rarefaction, which normalizes for sample size. Evenness is typically summarized in a single value. It is shown here that Hurlbert's probability of interspecific encounter (Δ₁), a commonly used sample-size independent measure of evenness, equals the slope of the steepest part of the rising limb of a rarefaction curve. This means that rarefaction curves provide information on both aspects of diversity. In addition, regional diversity (gamma) can be broken down into the diversity within local communities (alpha) and differences in taxonomic composition among local communities (beta). Beta richness is expressed by the difference between the composite rarefaction curve of all samples in a region with the collector's curve for the same samples. The differences of the initial slopes of these two curves reflect the beta evenness thanks to the relationship between rarefaction and Δ₁. This relationship can be further extended to help interpret species-area curves (SAC's). As previous authors have described, rarefaction provides the null hypothesis of passive sampling for SAC's, which can be interpreted as regional collector's curves. This allows evaluation of richness and evenness at local and regional scales using a single family of well-established, mathematically related techniques.     

A conceptual guide to measuring species diversity

Three metrics of species diversity – species richness, the Shannon index and the Simpson index – are still widely used in ecology, despite decades of valid critiques leveled against them. Developing a robust diversity metric has been challenging because, unlike many variables ecologists measure, the diversity of a community often cannot be estimated in an unbiased way based on a random sample from that community. Over the past decade, ecologists have begun to incorporate two important tools for estimating diversity: coverage and Hill diversity. Coverage is a method for equalizing samples that is, on theoretical grounds, preferable to other commonly used methods such as equal-effort sampling, or rarefying datasets to equal sample size. Hill diversity comprises a spectrum of diversity metrics and is based on three key insights. First, species richness and variants of the Shannon and Simpson indices are all special cases of one general equation. Second, richness, Shannon and Simpson can be expressed on the same scale and in units of species. Third, there is no way to eliminate the effect of relative abundance from estimates of any of these diversity metrics, including species richness. Rather, a researcher must choose the relative sensitivity of the metric towards rare and common species, a concept which we describe as ‘leverage.' In this paper we explain coverage and Hill diversity, provide guidelines for how to use them together to measure species diversity, and demonstrate their use with examples from our own data. We show why researchers will obtain more robust results when they estimate the Hill diversity of equal-coverage samples, rather than using other methods such as equal-effort sampling or traditional sample rarefaction.     

Diversity biases in terrestrial mammalian assemblages and quantifying the differences between museum collections and published accounts: A case study from the Miocene of Nevada

Understanding diversity through time in the fossil record has primarily relied on the raw count of species within a given time interval, or species richness. These estimates are often derived from published fossil data, and standardized for sample size or geographic area. However, most methods that standardize richness by sample size are sensitive to changes in evenness, which introduces a potential problem with relying on published records: published accounts could be more even than the museum collections from which they are drawn. We address this bias in the context of mammalian paleodiversity, comparing published and museum collections of the Hemphillian Thousand Creek fauna to those of the Barstovian Virgin Valley fauna. We rarified specimen data, both number of identified specimens (NISP) and minimum number of individuals (MNI), and presence/absence data to compare published and museum data within and between faunas. Within faunas, published numbers of specimens are more even than museum samples, but the difference for localities in Virgin Valley is not significant. Neither published nor museum numbers of specimens indicate a significant difference between faunas, but the diversity pattern is reversed between the two data sets. Presence/absence rarefactions show no differences between sources; here, published data adequately sample the underlying museum records. Specimen-based evenness is not accurate in the published sample, and therefore we suggest that future studies of diversity in terrestrial mammalian assemblages must assess unpublished collections. Additionally, NISP data for Thousand Creek are more even than MNI data, suggesting that relying solely on NISP for assessing species diversity can also be misleading. Because publication bias alters richness and evenness, diversity estimates using published data must be circumspect about data sources.     

Sample size and sampling error in geometric morphometric studies of size and shape

Geometric morphometric studies are increasingly becoming common in systematics and palaeontology. The samples in such studies are often small, due to the paucity of material available for analysis. However, very few studies have tried to assess the impact of sampling error on analytical results. Here, this issue is addressed empirically using repeated randomized selection experiments to build progressively smaller samples from an original dataset of ∼400 vervet monkey (Cercopithecus aethiops) skulls. Size and shape parameters (including mean size and shape, size and shape variances, angles of allometric trajectories) that are commonly used in geometric morphometric studies, are estimated first in the original sample and then in the random subsamples. Estimates are then compared to give an indication of what is the minimum desirable sample size for each parameter. Mean size, standard deviation of size and variance of shape are found to be fairly accurate even in relatively small samples. In contrast, mean shapes and angles between static allometric trajectories are strongly affected by sampling error. If confirmed in other groups, our findings may have substantial implications for studies of morphological variation in present and fossil species. By performing rarefaction analyses like those presented in our study, morphometricians can be easily provided with important clues on how a simple but crucial factor like sample size can alter results of their studies.     

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Macro‐scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample‐size‐correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species‐rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second‐order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections.     

Statistical challenges of evaluating diversity patterns across environmental gradients in mega‐diverse communities

Ibanez et al. (Journal of Vegetation Science, this issue) applied sample size‐ and coverage‐based rarefaction to analyse the elevational richness pattern in New Caledonian tree communities. We comment on the statistical assumptions behind rarefaction/extrapolation and suggest pooling small plot data to effectively assess/detect the diversity pattern. Broadening the analysis to include abundance‐sensitive diversity measures and phylogenetic information can provide important additional insights.     

Counting Alleles with Rarefaction: Private Alleles and Hierarchical Sampling Designs

The number of alleles (allelic richness) in a population is a fundamental measure of genetic variation, and a useful statistic for identifying populations for conservation. Estimating allelic richness is complicated by the effects of sample size: large samples are expected to have more alleles. Rarefaction solves this problem. This communication extends the rarefaction procedure to count private alleles and to accommodate hierarchical sampling designs.     

Palynological richness and evenness: insights from the taxa accumulation curve

Palynology provides the opportunity to make inferences on changes in diversity of terrestrial vegetation over long time scales. The often coarse taxonomic level achievable in pollen analysis, differences in pollen production and dispersal, and the lack of pollen source boundaries hamper the application of diversity indices to palynology. Palynological richness, the number of pollen types at a constant pollen count, is the most robust and widely used diversity indicator for pollen data. However, this index is also influenced by the abundance distribution of pollen types in sediments. In particular, where the index is calculated by rarefaction analysis, information on taxonomic richness at low abundance may be lost. Here we explore information that can be extracted from the accumulation of taxa over consecutive samples. The log-transformed taxa accumulation curve can be broken up into linear sections with different slope and intersect parameters, describing the accumulation of new taxa within the section. The breaking points may indicate changes in the species pool or in the abundance of high versus low pollen producers. Testing this concept on three pollen diagrams from different landscapes, we find that the break points in the taxa accumulation curves provide convenient zones for identifying changes in richness and evenness. The linear regressions over consecutive samples can be used to inter- and extrapolate to low or extremely high pollen counts, indicating evenness and richness in taxonomic composition within these zones. An evenness indicator, based on the rank-order-abundance is used to assist in the evaluation of the results and the interpretation of the fossil records. Two central European pollen diagrams show major changes in the taxa accumulation curves for the Lateglacial period and the time of human induced land-use changes, while they do not indicate strong changes in the species pool with the onset of the Holocene. In contrast, a central Swedish pollen diagram shows comparatively little change, but high richness during the early Holocene forest establishment. Evenness and palynological richness are related for most periods in the three diagrams, however, sections before forest establishment and after forest clearance show high evenness, which is not necessarily accompanied by high palynological richness, encouraging efforts to separate the two.     

A comparison of rarefaction and bayesian methods for predicting the allelic richness of future samples on the basis of currently available samples

Rarefaction methods have been introduced into population genetics (from ecology) for predicting and comparing the allelic richness of future samples (or sometimes populations) on the basis of currently available samples, possibly of different sizes. Here, we focus our attention on one such problem: Predicting which population is most likely to yield the future sample having the highest allelic richness. (This problem can arise when we want to construct a core collection from a larger germplasm collection.) We use extensive simulations to compare the performance of the Monte Carlo rarefaction (repeated random subsampling) method with a simple Bayesian approach we have developed-which is based on the Ewens sampling distribution. We found that neither this Bayesian method nor the (Monte Carlo) rarefaction method performed uniformly better than the other. We also examine briefly some of the other motivations offered for these methods and try to make sense of them from a Bayesian point of view.     

Diversity and diversification of Eumolpinae (Coleoptera: Chrysomelidae) in New Caledonia

Contemporary taxonomic work on New Caledonian Eumolpinae (Chrysomelidae) has revealed their high species richness in this Western Pacific biodiversity hotspot. To estimate total species richness in this community, we used rapid DNA‐based biodiversity assessment tools, exploring mtDNA diversity and phylogenetic structure in a sample of 840 specimens across the main island. Concordance of morphospecies delimitation with units delimited by phenetic and phylogenetic algorithms revealed some 98–110 species in our sample, twice as many as currently described. Sample‐based rarefaction curves and species estimators using these species counts doubled this figure (up to 210 species), a realistic estimate considering taxonomic coverage, local endemism, and characteristics of sampling design, amongst others. New Caledonia, compared with larger tropical islands, stands out as a hotspot for Eumolpinae biodiversity. Molecular dating using either chrysomelid specific rates or tree calibration using palaeogeographical data dated the root of the ingroup tree (not necessarily a monophyletic radiation) at 38.5 Mya, implying colonizations after the Cretaceous breakage of Gondwana. Our data are compatible with the slowdown in diversification rates through time and are also consistent with recent faunal origins, possibly reflecting niche occupancy after an initial rapid diversification. Environmental factors (e.g. soil characteristics) seemingly played a role in this diversification process. © 2013 The Linnean Society of London