Is genomic diversity a useful proxy for census population size? Evidence from a species‐rich community of desert lizards

Species abundance data are critical for testing ecological theory, but obtaining accurate empirical estimates for many taxa is challenging. Proxies for species abundance can help researchers circumvent time and cost constraints that are prohibitive for long‐term sampling. Under simple demographic models, genetic diversity is expected to correlate with census size, such that genome‐wide heterozygosity may provide a surrogate measure of species abundance. We tested whether nucleotide diversity is correlated with long‐term estimates of abundance, occupancy and degree of ecological specialization in a diverse lizard community from arid Australia. Using targeted sequence capture, we obtained estimates of genomic diversity from 30 species of lizards, recovering an average of 5,066 loci covering 3.6 Mb of DNA sequence per individual. We compared measures of individual heterozygosity to a metric of habitat specialization to investigate whether ecological preference exerts a measurable effect on genetic diversity. We find that heterozygosity is significantly correlated with species abundance and occupancy, but not habitat specialization. Demonstrating the power of genomic sampling, the correlation between heterozygosity and abundance/occupancy emerged from considering just one or two individuals per species. However, genetic diversity does no better at predicting abundance than a single day of traditional sampling in this community. We conclude that genetic diversity is a useful proxy for regional‐scale species abundance and occupancy, but a large amount of unexplained variation in heterozygosity suggests additional constraints or a failure of ecological sampling to adequately capture variation in true population size.     

A comparative analysis of alternative approaches to fitting species-abundance models

Aims Fits of species-abundance distributions to empirical data are increasingly used to evaluate models of diversity maintenance and community structure and to infer properties of communities, such as species richness. Two distributions predicted by several models are the Poisson lognormal (PLN) and the negative binomial (NB) distribution; however, at least three different ways to parameterize the PLN have been proposed, which differ in whether unobserved species contribute to the likelihood and in whether the likelihood is conditional upon the total number of individuals in the sample. Each of these has an analogue for the NB. Here, we propose a new formulation of the PLN and NB that includes the number of unobserved species as one of the estimated parameters. We investigate the performance of parameter estimates obtained from this reformulation, as well as the existing alternatives, for drawing inferences about the shape of species abundance distributions and estimation of species richness.Methods We simulate the random sampling of a fixed number of individuals from lognormal and gamma community relative abundance distributions, using a previously developed 'individual-based' bootstrap algorithm. We use a range of sample sizes, community species richness levels and shape parameters for the species abundance distributions that span much of the realistic range for empirical data, generating 1?000 simulated data sets for each parameter combination. We then fit each of the alternative likelihoods to each of the simulated data sets, and we assess the bias, sampling variance and estimation error for each method.Important findings Parameter estimates behave reasonably well for most parameter values, exhibiting modest levels of median error. However, for the NB, median error becomes extremely large as the NB approaches either of two limiting cases. For both the NB and PLN,>90% of the variation in the error in model parameters across parameter sets is explained by three quantities that corresponded to the proportion of species not observed in the sample, the expected number of species observed in the sample and the discrepancy between the true NB or PLN distribution and a Poisson distribution with the same mean. There are relatively few systematic differences between the four alternative likelihoods. In particular, failing to condition the likelihood on the total sample sizes does not appear to systematically increase the bias in parameter estimates. Indeed, overall, the classical likelihood performs slightly better than the alternatives. However, our reparameterized likelihood, for which species richness is a fitted parameter, has important advantages over existing approaches for estimating species richness from fitted species-abundance models.     

A new statistical approach for assessing similarity of species composition with incidence and abundance data

The classic Jaccard and Sørensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence–absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence‐based forms of these indices and extend this approach to formulate new Jaccard‐type or Sørensen‐type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence‐ or abundance‐based sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species‐rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results.     

Separating the effects of number of individuals sampled and competition on species diversity: an experimental and analytic approach

1 Species richness typically increases with the number of individuals sampled, although many ecological processes that influence species richness are also well known to depend on density of individuals. We separated the effects of density on species richness that are due to sampling, from those due to density-dependent ecological processes such as competition or predation, by manipulating the density of an entire community.
2 A seed bank from a community of desert annual plants that occur on semi-stabilized sand dunes in Israel was collected from the field and sown in an experimental garden at a range of densities from 1/16 to eight times the natural density. The species pool observed in the lowest density plots was used as the null community, which was repeatedly sampled to calculate the species richness (and other diversity indices) in higher density plots that would be expected from sampling considerations alone. The significance of deviations of observed diversity from this expected diversity was then evaluated.
3 Both observed and expected number of species increased substantially with the experimental increase in density. However, observed species richness, the Shannon–Wiener diversity index and Simpson's diversity index were often significantly lower than that expected based on sampling considerations. The magnitude of the deviation from expected increased significantly with increasing density for richness and the Shannon–Wiener index. This provides some of the first direct experimental evidence from diverse natural assemblages that increasing competition among all the individuals in a community can lead to competitive exclusion.     

Comparing the precision,accuracy, and efficiency of branch clipping and sweep netting for sampling arthropods in two Jamaican forest types

ABSTRACT Devising methods for sampling arthropods presents many challenges, including understanding possible differences in results obtained by different individuals (precision), investigating differences between estimates and the actual variable of interest (accuracy), and assessing the effort and cost of a given method (efficiency). We assessed the precision, accuracy, and efficiency of sweep netting and branch clipping, two common methods of sampling arthropods, in mangrove and second‐growth scrub forests in Jamaica, West Indies, in 2009. Three individuals used both methods sequentially to sample arthropods in the territories of American Redstarts (Setophaga ruticilla). We found that both branch clipping and sweep netting lacked precision because different individuals produced different estimates of either arthropod abundance (number of individuals per sample) or biomass. In both forests, more arthropods were sampled with sweep netting, in terms of biomass and abundance, and several orders of arthropods were collected that were missed by branch clipping. We also detected the absence of a predictable habitat‐based difference in arthropod biomass with sweep netting, but not with branch clipping. Sweep netting took longer overall (field and processing time combined) and was therefore less efficient. Despite problems with precision and efficiency, our results suggest that sweep netting may be a more accurate method than branch clipping for sampling foliage arthropods in some forest habitats. Our study also reveals the importance of recognizing and controlling for individual bias and of choosing arthropod sampling methods most appropriate to each study species and habitat type.     

Estimating abundance from detection–nondetection data for randomly distributed or aggregated elusive populations

Estimating abundance is important in many ecological studies in order to understand the spatial and temporal dynamics of a population, which can assist in management and conservation. However, direct estimates of abundance can be difficult and expensive to obtain, particularly for wide-ranging, rare or elusive species. An alternative – estimating from detection-nondetection data – is a challenging but alluring concept to ecologists since the cost and effort of a study can be greatly reduced. This paper describes a method for estimating the abundance of randomly distributed or aggregated populations by using binary data where the probability of detection is less than one. The performances of the models were evaluated by computer simulations comprising 1620 cases. The results show that the accuracy of the abundance estimates increases as the sampling rate, efficiency of survey method, and the number of repeated surveys increase, whereas the accuracy declines as individuals become more aggregated. For a randomly distributed population, using a sampling rate of 0.05 in a survey method with a detection probability of 0.5, and repeating surveys three times provides sufficient accuracy of abundance. For an aggregated population, to achieve reasonably accurate abundance estimates the sampling rate should be doubled and each cell should be repeatedly surveyed on 4 to 6 occasions.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Evaluation of subsampling efforts in estimating community indices and community structures

Even though subsampling can minimize the research efforts needed to deal with numerous specimens, the accuracy of bioassessment and community analysis can be affected according to subsampled individuals. In this study, the effects of subsampling on community indices, the parameters of the species abundance distributions (SADs) and ordination based on communities were evaluated using benthic macroinvertebrates from three different stream pollution levels: less, intermediately, and heavily polluted. The values of the community indices and the parameters of the SADs were clearly different according to the differences in the pollution levels, regardless of subsampling efforts. Detrended correspondence analysis was applied to characterize similarities between the subsamples with different individuals based on community indices and the parameters of the SADs. A self-organizing map was used to characterize the community similarities and properties of the subsamples, differentiating the subsamples from their original datasets. Finally, our results revealed that the effects of subsampling were reflected differently in different indices, parameters, and ordinations of communities at different pollution levels, suggesting that subsample size should be determined differently at different pollution levels depending on the objectives of the study. However, if only one subsample size is to be used for simplicity and to reduce the cost of aquatic ecosystem health monitoring and assessment, then we recommend at least 400 individuals as the subsample size. At least 800 individuals should be subsampled for biodiversity studies.     

The implicit assumption of symmetry and the species abundance distribution

Species abundance distributions (SADs) have played a historical role in the development of community ecology. They summarize information about the number and the relative abundance of the species encountered in a sample from a given community. For years ecologists have developed theory to characterize species abundance patterns, and the study of these patterns has received special attention in recent years. In particular, ecologists have developed statistical sampling theories to predict the SAD expected in a sample taken from a region. Here, we emphasize an important limitation of all current sampling theories: they ignore species identity. We present an alternative formulation of statistical sampling theory that incorporates species asymmetries in sampling and dynamics, and relate, in a general way, the community-level SAD to the distribution of population abundances of the species integrating the community. We illustrate the theory on a stochastic community model that can accommodate species asymmetry. Finally, we discuss the potentially important role of species asymmetries in shaping recently observed multi-humped SADs and in comparisons of the relative success of niche and neutral theories at predicting SADs.     

Estimating similarity of communities: a parametric approach to spatio‐temporal analysis of species diversity

Several stochastic models with environmental noise generate spatio‐temporal Gaussian fields of log densities for the species in a community. Combinations of such models for many species often lead to lognormal species abundance distributions. In spatio‐temporal analysis it is often realistic to assume that the same species are expected to occur at different times and/or locations because extinctions are rare events. Spatial and temporal β‐diversity can then be analyzed by studying pairs of communities at different times or locations defined by a bivariate lognormal species abundance model in which a single correlation occurs. This correlation, which is a measure of similarity between two communities, can be estimated from samples even if the sampling intensities vary and are unknown, using the bivariate Poisson lognormal distribution. The estimators are approximately unbiased, although each specific correlation may be rather uncertain when the sampling effort is low with only a small fraction of the species represented in the samples. An important characteristic of this community correlation is that it relates to the classical Jaccard‐ or the Sørensen‐indices of similarity based on the number of species present or absent in two communities. However, these indices calculated from samples of species in a community do not necessarily reflect similarity of the communities because the observed number of species depends strongly on the sampling intensities. Thus, we propose that our community correlation should be considered as an alternative to these indices when comparing similarity of communities. We illustrate the application of the correlation method by computing the similarity between temperate bird communities.     

Optimizing performance of nonparametric species richness estimators under constrained sampling

Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species‐abundance data from a single sample into two equally sized samples, and using an appropriate incidence‐based estimator to estimate richness. To test this method, we assume a lognormal species‐abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulations, and use the expected mean‐squared error as the performance criterion of the estimators. We test this method for Chao, Jackknife, ICE, and ACE estimators. Between abundance‐based estimators with the single sample, and incidence‐based estimators with the split‐in‐two samples, Chao2 performed the best when CV < 0.65, and incidence‐based Jackknife performed the best when CV > 0.65, given that the ratio of sample size to observed species richness is greater than a critical value given by a power function of CV with respect to abundance of the sampled population. The proposed method increases the performance of the estimators substantially and is more effective when more rare species are in an assemblage. We also show that the splitting method works qualitatively similarly well when the SADs are log series, geometric series, and negative binomial. We demonstrate an application of the proposed method by estimating richness of zooplankton communities in samples of ballast water. The proposed splitting method is an alternative to sampling a large number of individuals to increase the accuracy of richness estimations; therefore, it is appropriate for a wide range of resource‐limited sampling scenarios in ecology.     

Interpreting ecological diversity indices applied to terminal restriction fragment length polymorphism data: insights from simulated microbial communities

Ecological diversity indices are frequently applied to molecular profiling methods, such as terminal restriction fragment length polymorphism (T-RFLP), in order to compare diversity among microbial communities. We performed simulations to determine whether diversity indices calculated from T-RFLP profiles could reflect the true diversity of the underlying communities despite potential analytical artifacts. These include multiple taxa generating the same terminal restriction fragment (TRF) and rare TRFs being excluded by a relative abundance (fluorescence) threshold. True community diversity was simulated using the lognormal species abundance distribution. Simulated T-RFLP profiles were generated by assigning each species a TRF size based on an empirical or modeled TRF size distribution. With a typical threshold (1%), the only consistently useful relationship was between Smith and Wilson evenness applied to T-RFLP data (TRF-E(var)) and true Shannon diversity (H'), with correlations between 0.71 and 0.81. TRF-H' and true H' were well correlated in the simulations using the lowest number of species, but this correlation declined substantially in simulations using greater numbers of species, to the point where TRF-H' cannot be considered a useful statistic. The relationships between TRF diversity indices and true indices were sensitive to the relative abundance threshold, with greatly improved correlations observed using a 0.1% threshold, which was investigated for comparative purposes but is not possible to consistently achieve with current technology. In general, the use of diversity indices on T-RFLP data provides inaccurate estimates of true diversity in microbial communities (with the possible exception of TRF-E(var)). We suggest that, where significant differences in T-RFLP diversity indices were found in previous work, these should be reinterpreted as a reflection of differences in community composition rather than a true difference in community diversity.     

Robust estimation of microbial diversity in theory and in practice

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao''s estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics (‘Hill diversities''), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao''s estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity.     

Models and estimators linking individual-based and sample-based rarefaction,extrapolation and comparison of assemblages

Aims In ecology and conservation biology, the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected. Moreover, comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled. For individual-based data, we treat a single, empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models: estimating the expected number of species (and its unconditional variance) in a random sample of (i) a smaller number of individuals (multinomial model) or a smaller area sampled (Poisson model) and (ii) a larger number of individuals or a larger area sampled. For sample-based incidence (presence–absence) data, under a Bernoulli product model, we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units.Methods The first objective is a problem in interpolation that we address with classical rarefaction (multinomial model) and Coleman rarefaction (Poisson model) for individual-based data and with sample-based rarefaction (Bernoulli product model) for incidence frequencies. The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness. Although published methods exist for many of these objectives, we bring them together here with some new estimators under a unified statistical and notational framework. This novel integration of mathematically distinct approaches allowed us to link interpolated (rarefaction) curves and extrapolated curves to plot a unified species accumulation curve for empirical examples. We provide new, unconditional variance estimators for classical, individual-based rarefaction and for Coleman rarefaction, long missing from the toolkit of biodiversity measurement. We illustrate these methods with datasets for tropical beetles, tropical trees and tropical ants.Important findings Surprisingly, for all datasets we examined, the interpolation (rarefaction) curve and the extrapolation curve meet smoothly at the reference sample, yielding a single curve. Moreover, curves representing 95% confidence intervals for interpolated and extrapolated richness estimates also meet smoothly, allowing rigorous statistical comparison of samples not only for rarefaction but also for extrapolated richness values. The confidence intervals widen as the extrapolation moves further beyond the reference sample, but the method gives reasonable results for extrapolations up to about double or triple the original abundance or area of the reference sample. We found that the multinomial and Poisson models produced indistinguishable results, in units of estimated species, for all estimators and datasets. For sample-based abundance data, which allows the comparison of all three models, the Bernoulli product model generally yields lower richness estimates for rarefied data than either the multinomial or the Poisson models because of the ubiquity of non-random spatial distributions in nature.     

湖南八大公山25 ha常绿落叶阔叶混交林动态监测样地群落组成与空间结构

湖南八大公山国家级自然保护区位于武陵山系北缘, 区内分布有大面积的常绿落叶阔叶混交林, 物种多样性丰富, 群落结构复杂。中国科学院武汉植物园按CTFS (Center for Tropical Forest Sciences)建设规范于2010-2011年在保护区内建设了一个25 ha的动态监测样地, 为亚热带山地森林群落多样性长期动态监测提供了理想的平台。本文初步分析了八大公山25 ha样地的群落组成与空间结构。结果表明: 群落内共有木本植物存活个体186,575株, 隶属于53科114属232种; 个体数超过1,000株的有38个物种(贡献87%的个体数), 个体数最多的物种为黄丹木姜子(Litsea elongata); 样地内稀有种(≤ 25株)种数占样地总物种数的44%, 而个体数仅为样地总个体数的0.4%。样地内个体平均胸径为5.41 cm, 其中68.4%的个体DBH ≤ 5 cm, DBH ≥ 20 cm的个体数(7,474株)仅约占总个体数的4%; 个体胸径直方图呈倒“J”形, 表明样地处于良好更新与正常生长状态。样地的种-面积关系图显示物种数随样地面积的增加而同步增加, 其增长速度由迅速增长逐渐趋于稳定, 取样面积10 ha时可以涵盖90%以上的物种; 1 ha小样地个体数平均为7,261.8 ± 974.8 (SD), 物种数平均为128.2 ± 8.2 (SD), Shannon-Wiener指数平均为3.56 ± 0.11 (SD), Pielou均匀度指数变异最小, 平均为1.69 ± 0.06 (SD); 个体数与各多样性指数均无显著相关, 表明在该样地中物种多样性的取样效应不明显, 物种数量增加的原因可能来自于其他因素的控制。     

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.     

Dynamic relationships between body size,species richness,abundance, and energy use in a shallow marine epibenthic faunal community

We study the temporal variation in the empirical relationships among body size (S), species richness (R), and abundance (A) in a shallow marine epibenthic faunal community in Coliumo Bay, Chile. We also extend previous analyses by calculating individual energy use (E) and test whether its bivariate and trivariate relationships with S and R are in agreement with expectations derived from the energetic equivalence rule. Carnivorous and scavenger species representing over 95% of sample abundance and biomass were studied. For each individual, body size (g) was measured and E was estimated following published allometric relationships. Data for each sample were tabulated into exponential body size bins, comparing species‐averaged values with individual‐based estimates which allow species to potentially occupy multiple size classes. For individual‐based data, both the number of individuals and species across body size classes are fit by a Weibull function rather than by a power law scaling. Species richness is also a power law of the number of individuals. Energy use shows a piecewise scaling relationship with body size, with energetic equivalence holding true only for size classes above the modal abundance class. Species‐based data showed either weak linear or no significant patterns, likely due to the decrease in the number of data points across body size classes. Hence, for individual‐based size spectra, the SRA relationship seems to be general despite seasonal forcing and strong disturbances in Coliumo Bay. The unimodal abundance distribution results in a piecewise energy scaling relationship, with small individuals showing a positive scaling and large individuals showing energetic equivalence. Hence, strict energetic equivalence should not be expected for unimodal abundance distributions. On the other hand, while species‐based data do not show unimodal SRA relationships, energy use across body size classes did not show significant trends, supporting energetic equivalence.     

Minimum sample sizes for population genomics: an empirical study from an Amazonian plant species

High‐throughput DNA sequencing facilitates the analysis of large portions of the genome in nonmodel organisms, ensuring high accuracy of population genetic parameters. However, empirical studies evaluating the appropriate sample size for these kinds of studies are still scarce. In this study, we use double‐digest restriction‐associated DNA sequencing (ddRADseq) to recover thousands of single nucleotide polymorphisms (SNPs) for two physically isolated populations of Amphirrhox longifolia (Violaceae), a nonmodel plant species for which no reference genome is available. We used resampling techniques to construct simulated populations with a random subset of individuals and SNPs to determine how many individuals and biallelic markers should be sampled for accurate estimates of intra‐ and interpopulation genetic diversity. We identified 3646 and 4900 polymorphic SNPs for the two populations of A. longifolia, respectively. Our simulations show that, overall, a sample size greater than eight individuals has little impact on estimates of genetic diversity within A. longifolia populations, when 1000 SNPs or higher are used. Our results also show that even at a very small sample size (i.e. two individuals), accurate estimates of F_ST can be obtained with a large number of SNPs (≥1500). These results highlight the potential of high‐throughput genomic sequencing approaches to address questions related to evolutionary biology in nonmodel organisms. Furthermore, our findings also provide insights into the optimization of sampling strategies in the era of population genomics.     

A simulation study of sample size for DNA barcoding

For some groups of organisms, DNA barcoding can provide a useful tool in taxonomy, evolutionary biology, and biodiversity assessment. However, the efficacy of DNA barcoding depends on the degree of sampling per species, because a large enough sample size is needed to provide a reliable estimate of genetic polymorphism and for delimiting species. We used a simulation approach to examine the effects of sample size on four estimators of genetic polymorphism related to DNA barcoding: mismatch distribution, nucleotide diversity, the number of haplotypes, and maximum pairwise distance. Our results showed that mismatch distributions derived from subsamples of ≥20 individuals usually bore a close resemblance to that of the full dataset. Estimates of nucleotide diversity from subsamples of ≥20 individuals tended to be bell‐shaped around that of the full dataset, whereas estimates from smaller subsamples were not. As expected, greater sampling generally led to an increase in the number of haplotypes. We also found that subsamples of ≥20 individuals allowed a good estimate of the maximum pairwise distance of the full dataset, while smaller ones were associated with a high probability of underestimation. Overall, our study confirms the expectation that larger samples are beneficial for the efficacy of DNA barcoding and suggests that a minimum sample size of 20 individuals is needed in practice for each population.     

王朗自然保护区地栖脊椎动物群落结构和生境类型的关系

2006年8—11月,采用围栏陷阱法对四川省王朗自然保护区地栖脊椎动物的群落结构进行了研究,结合森林砍伐、旅游和植被恢复方式等干扰因子所推动的动物栖息生境变化,探讨了动物群落结构和生境类型的关系。监测历时75 d,8个样地共捕获3纲6目17科21种778只地栖脊椎动物,物种组成包括3种两栖类、1种鸟类和17种哺乳类。每百陷阱日的捕获率为32.4%;以普通鼩鼱、高山姬鼠、纹背鼩鼱、高原林蛙等4种为优势种,占全部捕获数的76.9%;其余17种的个体数均较少,物种多度和其分布区域呈显著线性相关(P0.001)。动物群落多样性指数,以干扰相对弱的原生针叶林物种多样性指数和均匀度指数为最高,而优势度指数最低;其次为旅游干扰的原生林灌丛和自然更新的次生林灌丛;人工种植的次生针叶林多样性指数和均匀度指数最低,而优势度指数最高。以动物群落相似性指数进行系统聚类,8个样地的动物群落聚为3类:即干扰相对弱的原生针叶林动物群落、严重干扰的人工针叶林动物群落、部分干扰的原生林灌丛和次生林灌丛动物群落。