蚂蚁群落研究方法

综述了蚂蚁群落常见抽样方法和分析方法,总结了陷阱法、样方法、诱饵法、枯落物抽样法、手拣法、杀虫剂击倒法、精细抽样法及样地调查法等抽样方法的优点及不足,分析了不同抽样方法采集蚂蚁的效率,介绍了国际上通用的地表蚂蚁抽样方法ALL草案及蚂蚁功能群研究,比较了物种累积曲线、相对多度、物种丰富度和多样性指数等在蚂蚁群落分析中的运用.指出了当前蚂蚁群落研究中存在研究方法不够规范的问题,提出了运用ALL草案在全国范围内进行蚂蚁群落调查,在高度异质性的生境开展抽样方法比较研究,加强对蚂蚁群落物种累积曲线及相对多度的分析,通过它们来进行科学的物种丰富度估计的建议.     

贡嘎山东坡非飞行小型兽类物种多样性的垂直分布格局

为了解贡嘎山地区物种多样性的垂直分布格局,2010年4—9月利用夹日法对贡嘎山东坡非飞行小型兽类的物种多样性进行了详细调查。调查在海拔1200—4000m之间按400m间隔设置了8个采集样地,累计布夹28800夹次,捕获非飞行小型兽类个体701个,观察记录到松鼠个体25个,共调查记录小兽个体726个,分属于3目6科16属25种。非参数估计的物种丰富度Chao2和Jackknife2指数以及物种累积曲线评估表明本次调查取样充分,能很好地反映该地区非飞行小型兽类物种多样性的垂直分布格局。结果表明:非飞行小型兽类物种多样性的垂直分布格局为单峰模型;物种丰富度和物种多度在中海拔地区最高,在低海拔和高海拔地区较低;相反,物种均匀度在中海拔地区较低,在低海拔和高海拔地区较高;而物种优势度则随着海拔的升高而逐渐增加;Shannon-Wiener、Fisher-α、Margalef三个综合性物种多样性指数均显示物种多样性在中海拔地区最高;与其它多样性指数相比,Simpson指数未能很好地反映出不同海拔段群落物种多样性的垂直分布差异;而与Shannon-Wiener和Simpson指数相比,Fisher-α和Margalef指数对不同物种组成的群落多样性区分较好。同时,基于不同海拔段物种组成的聚类分析结果也表明物种多样性在中海拔地区最高。物种多样性在中海拔地区最高的垂直分布模式提示我们在贡嘎山地区的生物多样性保护和生态管理中应特别重视中海拔地段,因为该地段中居于生态食物链中间位置的小兽物种最丰富,是山地生物多样性保护的关键。此外,规范统一的调查方法将有利于研究数据的整合和减少人为因素带来的误差。     

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。     

生物群落多样性的测度方法

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九落中的物种数目,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）有些方法适用珐动物群落     

内蒙古羊草群落、功能群、物种变化及其与气候的关系

基于1981-1994年内蒙古羊草草原的群落学调查数据和同期气象资料,分析了羊草草原群落、功能群与主要物种变化及其生物量与气候的关系.结果表明,1981-1994年5月羊草群落多样性指数、优势度指数、丰富度指数、群落高度、群落生物量的年际变异显著高于其他月,其中羊草群落生物量的变异性自5-9月依次降低,生物量和多样性不存在显著相关性.羊草群落、功能群、物种的年际变异依次增大;不同功能群中一二年生草本和中生植物的年际波动最大,不同物种间的均衡效应降低了群落的变异性.羊草群落生物量具有气候累积效应,主要受上年10月至当年12月的均温、4-8月降水、上年10月至当年8月累积降水的影响,表明羊草群落生物量变化由不同时段的水热因子协同作用决定.     

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。     

松嫩草地草本植物生物多样性:物种多样性和功能群多样性

生物多样性对生态系统功能有重要的影响。作为生物多样性研究的重要方面,物种多样性和功能群多样性引起了生态学者的广泛关注。目前,有关松嫩草地的生物多样性一直缺乏全面而系统的研究。本研究通过对松嫩草地42种植物群落(涉及427个物种)的调查,全面探究了松嫩草地草本植物的生物多样性。从整体上看,松嫩草地主要以禾本科、菊科、豆科植物为主。松嫩草地优势种地上生物量在群落中占绝对优势,其地上生物量与群落地上生物量之间呈极显著正相关(R2=0.904)。不同群落之间物种多样性指数差异很大,其中最主要群落——羊草群落物种丰富度指数(2.6)最小。同时,松嫩草地不同功能群多样性指数之间差异明显,C4禾草为优势种的群落功能群多样性指数明显高于C3禾草为优势种的群落。松嫩草地功能群多样性与物种多样性之间呈极显著正相关(P0.01),功能群多样性在一定程度上能够指示松嫩草地生态系统的生物多样性。     

伊洛河流域草本植物群落物种多样性

生物多样性沿环境梯度的变化是生物多样性研究的重要内容,环境梯度包含了多种环境因子(海拔高度、水热条件、人类扰动等)的综合。以伊洛河流域草本植物群落为对象,沿河从入黄河口到河源地选取典型样地调查研究伊洛河流域草本植物群落物种多样性及其分布格局。结果表明:物种丰富度和Shannon-Wiener多样性指数沿河均稍呈"S"型曲线变化,不同群落类型中分布格局差别不大,各群落类型中的物种丰富度和多样性均呈现出中游丘陵山地交界区最高,上游河源区次之,下游平原地区最低的趋势;β多样性指数的变化趋势与α多样性较一致,总体上呈现出中游丘陵山地区物种更替速率较快,平原区更替较慢;在流域内上游河源地属于自然植被区,人为干扰较轻,具有较高的物种多样性,物种替代主要受物种的竞争扩散能力和生境条件的制约;在下游平原农业区,人类活动强烈,区域内以人工生态系统为主,物种组成简单,物种替代具有跳跃性的特征,主要受人类活动的制约;在中游从自然生态系统向农业生态系统的过渡区域,人类活动的扰动有一定的强度,导致该区域内自然分布种和伴人种混合生长,具有较高的物种多样性和较快的物种替代速率。总体上伊洛河流域草本植物群落物种多样性分布格局强烈的受到人类活动的影响,物种替代速率较高。     

应用中性理论分析局域群落中的物种多样性及稳定性

如何解释群落中物种的丰富与稀少,并对物种多样性和群落稳定性进行合理的量化评价是群落生态学研究中的一个热点问题。20世纪中期,MacArthur将影响自然群落稳定性的因素归结为物种数量多少以及物种间相互作用的大小,20世纪末Doak等学者提出群落的容纳能力和物种间的维持机制是决定群落稳定性的关键因素。同时对群落结构及物种间维持机制的研究也有了新的突破,Hubbell提出"生物多样性与生物地理学统一的中性理论(Unified Neutral Theory of Biodiversity and Biogeography)"为群落生态学研究提供了新的思路和方法。从群落中性理论的基本假设出发,对Hubbell中性理论中局域群落的物种多度动态模型进行分析,归纳得出群落中性理论中物种多样性与群落稳定性之间的量化关系。封闭的局域群落中,出现物种灭绝或单物种独占的时间与群落大小及物种相对多度成正比,物种多样性程度的增加可延长物种灭绝或独占的时间;开放的局域群落中,物种多度期望值与局域群落大小、物种在集合群落中的物种相对多度成正比,周围群落中物种的灭绝会引起局域群落中相应物种的灭绝,最终导致整个生态群落物种多样性的降低;群落中物种多度的方差与局域群落大小、迁移率、物种在集合群落中的物种相对多度相关,局域群落物种多度的波动幅度随着群落间生态隔离的减弱或物种多样性程度的增加而减小。由此,集合群落物种多样性是影响局域群落物种多样性的重要因素,生态隔离程度的减弱及物种多样性的增加都将增强群落的稳定性。     

达赉湖自然保护区冬春季鸟类生物多样性与生境的关系

2004年4月-5月,利用样带法对达赉湖自然保护区5种主要生境类型中冬春季鸟类生物多样性进行了调查,利用Shannon-Wiener指数和Smith相关性系数分析了这5种生境类型中冬春季鸟类的生物多样性、区系、鸟类的群落组成、群落间的相似性和均匀度。结果表明,古北界鸟类是组成达赉湖鸟类群落的主体(约占冬春季鸟类的86%);芦苇湿地的鸟类多样性接近于芦苇甸的2倍:芦苇湿地鸟类群落的物种多样性最高(Shannon-Wiener指数为1.3001),而芦苇甸中鸟类群落的物种多样性最低(Shannon-Wiener指数为0.6629);芦苇湿地和芦苇甸两鸟类群落组成的相关性指数仅为0.038;从具有共同物种的多少考虑,典型草原和芨芨草原鸟类群落之间的关联较大。     

On the estimation of species richness based on the accumulation of previously unrecorded species

Estimation of species richness of local communities has become an important topic in community ecology and monitoring. Investigators can seldom enumerate all the species present in the area of interest during sampling sessions. If the location of interest is sampled repeatedly within a short time period, the number of new species recorded is typically largest in the initial sample and decreases as sampling proceeds, but new species may be detected if sampling sessions are added. The question is how to estimate the total number of species. The data collected by sampling the area of interest repeatedly can be used to build species accumulation curves: the cumulative number of species recorded as a function of the number of sampling sessions (which we refer to as “species accumulation data”). A classic approach used to compute total species richness is to fit curves to the data on species accumulation with sampling effort. This approach does not rest on direct estimation of the probability of detecting species during sampling sessions and has no underlying basis regarding the sampling process that gave rise to the data. Here we recommend a probabilistic, nonparametric estimator for species richness for use with species accumulation data. We use estimators of population size that were developed for capture‐recapture data, but that can be used to estimate the size of species assemblages using species accumulation data. Models of detection probability account for the underlying sampling process. They permit variation in detection probability among species. We illustrate this approach using data from the North American Breeding Bird Survey (BBS). We describe other situations where species accumulation data are collected under different designs (e.g., over longer periods of time, or over spatial replicates) and that lend themselves to of use capture‐recapture models for estimating the size of the community of interest. We discuss the assumptions and interpretations corresponding to each situation.     

A mélange of curves – further dialogue about species–area relationships

Scheiner (2003) presented a classification of species–area curves into six types based on the pattern of sampling and how the data are combined to form the curves. Gray et al. (2004) contended that five of those types should be termed ‘species‐accumulation curves’, reserving ‘species–area curve’ for those based on island‐type data. Their proposition contradicts 70 years of usage and confounds curves that are area‐explicit with those that are area‐undefined. In exploring these issues, I highlight additional aspects of species–area and species‐accumulation curves, including the assumption of nesting in Type IV (island) curves, how to convert area‐unspecified curves into area curves, and the effects of the grain of the analysis on the properties of the curve. Further exploration, theoretical development, and dialogue are needed before we will understand all the biology that species–area curves summarize.     

Temporal and Spatial Diversity and Distribution of Arboreal Carabidae (Coleoptera) in a Western Amazonian Rain Forest1

Diversity of arboreal carabid beetles was sampled by fumigation in 100 3 × 3 m stations within a 100 × 1000 m terra firme forest plot in Ecuadorian Amazonia. Nine sampling dates from January 1994 to October 1996 yielded 2329 individuals belonging to 318 species of which more than 50 percent were undescribed species. A high percentage of the species sampled were rare; the proportion that occurred once per sampling date (singletons) ranged from 50.0 to 62.5 percent. Estimates of species richness were from 82 to 282 species of arboreal carabids in the study plot on a given sampling date. Most richness values were greater than 173 species. Species accumulation curves attained asymptotes for all but one sampling date, indicating that an adequate level of sampling effort was used to characterize the diversity of carabid fauna. Total accumulation curves based on pooled data failed to reach asymptotes. There was a high turnover in species composition between sampling dates; less than 50 percent of the species between the majority of sampling dates were shared, suggesting that the total species pool may be extremely large. Although species composition changed seasonally, species richness varied little. Spatial autocorrelation analysis revealed that the structure of this species assemblage was significantly patterned at distances below 280 m. Taken together, the large percentage of undescribed species, die failure of the overall species accumulation curves to level off, and the high turnover in species composition indicate that the species diversity of carabid beetles is far higher than previously thought.     

Applications of species accumulation curves in large-scale biological data analysis

The species accumulation curve, or collector’s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non-parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45–63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and k-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.     

Maximizing plant species inventory efficiency by means of remotely sensed spectral distances

Aim Inventorying plant species in an area based on randomly placed quadrats can be quite inefficient. The aim of this paper is to test whether plant species richness can be inventoried more efficiently by means of a spectrally‐based ordering of sites to be sampled. Location The study area was a complex wetland ecosystem, the Lake Montepulciano Nature Reserve, central Italy. This is one of the most important wetland areas of central Italy because of the diverse plant communities and the seasonal avifauna. Methods Field sampling, based on a random stratified sampling design, was performed in June 2002. Plant species composition was recorded within sampling units of 100 m² (plots) and 1 ha (macroplots). A QuickBird multispectral image of the same date was acquired and corrected both geometrically and radiometrically. Species accumulation curves based on spectral information were obtained by ordering sites to be sampled according to a maximum spectral distance criterion (i.e. by ordering sampling units based on the maximum distances among them in a four‐dimensional spectral space derived from the remotely sensed data). Different distance measures based on mean and maximum spectral distances among sampling units were tested. The performance of the species accumulation curve derived by the spectrally‐based ordering of sampling units was tested against a rarefaction curve obtained from the mean of 10,000 accumulation curves based on randomly ordered sampling units. Results The spectrally‐derived curve based on the maximum spectral distance among sampling units showed the most rapid accumulation of species, well above the rarefaction curve, at both the plot and the macroplot scales. Other ordering criteria of sampling units captured less richness over most of the species accumulation curves at both the spatial scales. The accumulation curves based on other measurements of distance were much closer to the random curve and did not show differences with respect to the species rarefaction curve based on random ordering of sampling units. Main conclusions The present investigation demonstrated that spectral‐based ordering of sites to be sampled can lead to the maximization of the efficiency of plant species inventories, an activity usually driven by the ‘botanist's internal algorithm’ (intuition), without any formalized rule to drive field sampling. The proposed approach can reduce costs of plant species inventorying through a more efficient allotment of time and sampling.     

The use of incidence-based species richness estimators,species accumulation curves and similarity measures to appraise ethnobotanical inventories from South Africa

The incorporation of suitable quantitative methods into ethnobotanical studies enhances the value of the research and the interpretation of the results. Prediction of sample species richness and the use of species accumulation functions have been addressed little in applied ethnobotany. In this paper, incidence-based species richness estimators, species accumulation curves and similarity measures are used to compare and predict species richness, evaluate sampling effort and compare the similarity of species inventories for ethnobotanical data sets derived from the trade in traditional medicine in Johannesburg and Mpumalanga, South Africa. EstimateS was used to compute estimators of species richness (e.g. Jackknife), rarefaction curves, species accumulation curves and complimentarity. Results showed that while the Michaelis–Menten Means estimator appeared to be the best estimator because the curve approached a horizontal asymptote, it was not able to accurately predict species richness for one of the data sets when two of its subsamples were individually tested. Instead, the first-order Jackknife estimator best approximated the known richness.     

Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

How many replicates to accurately estimate fish biodiversity using environmental DNA on coral reefs?

Quantifying fish species diversity in rich tropical marine environments remains challenging. Environmental DNA (eDNA) metabarcoding is a promising tool to face this challenge through the filtering, amplification, and sequencing of DNA traces from water samples. However, because eDNA concentration is low in marine environments, the reliability of eDNA to detect species diversity can be limited. Using an eDNA metabarcoding approach to identify fish Molecular Taxonomic Units (MOTUs) with a single 12S marker, we aimed to assess how the number of sampling replicates and filtered water volume affect biodiversity estimates. We used a paired sampling design of 30 L per replicate on 68 reef transects from 8 sites in 3 tropical regions. We quantified local and regional sampling variability by comparing MOTU richness, compositional turnover, and compositional nestedness. We found strong turnover of MOTUs between replicated pairs of samples undertaken in the same location, time, and conditions. Paired samples contained non‐overlapping assemblages rather than subsets of one another. As a result, non‐saturated localized diversity accumulation curves suggest that even 6 replicates (180 L) in the same location can underestimate local diversity (for an area <1 km). However, sampling regional diversity using ~25 replicates in variable locations (often covering 10 s of km) often saturated biodiversity accumulation curves. Our results demonstrate variability of diversity estimates possibly arising from heterogeneous distribution of eDNA in seawater, highly skewed frequencies of eDNA traces per MOTU, in addition to variability in eDNA processing. This high compositional variability has consequences for using eDNA to monitor temporal and spatial biodiversity changes in local assemblages. Avoiding false‐negative detections in future biomonitoring efforts requires increasing replicates or sampled water volume to better inform management of marine biodiversity using eDNA.     

Statistical inference on accumulation curves for inventorying forest diversity: A design-based critical look

Abstract

Statistical inference on accumulation curves is considered from a design-based perspective. Preliminaries on probabilistic sampling of plants and species are given, emphasizing the fundamental role of independent replications of the sampling scheme. The role of rarefaction curves as a tool for making inference on the effectiveness of the sampling procedures to compile accurate species lists is outlined. Design-based and model-based inference are discussed and compared. Some future developments for design-based inference are considered.     

Sampling design and the shape of species–area curves on the regional scale

Species–area relationships (SARs) are one of the fundamental patterns in ecology. However, how the way they were constructed influences resulting SAR shapes has gained astonishingly little attention. We use data of the distribution atlas of Polish butterflies to compare SARs constructed from four different designs: adding up species numbers of independent areas (species accumulation curves using contiguous and non-contiguous areas), using a nested design, and comparing species numbers of independent areas of different sizes. It appeared that the way of constructing SARs influences the outcome. We attribute this influence to the pronounced faunal dissimilarities of more distant areas (spatial species turnover). The nested design resulted in significantly higher slopes and lower intercepts of power function SARs than the other designs. SARs from all four sampling designs showed a pronounced downward curvature on small spatial scales. Only the nested design predicted species densities correctly. The implications of these results for the use of SARs in bioconservation are discussed.