南岳森林群落生物多样性研究(Ⅲ)广济寺森林群落植物物种相对多度分布格局

本文选用几何级数分布、分割线段、对数级数分布和对数正态分布等模型研究了南岳广济寺森林群落植物物种相对多度的分布格局。结果表明,对数级分布模型适于拟合南岳广济寺森林群落乔木层和灌木层物种相对多度的分布格局;分割线段中的序列一多度模型仅适合于乔木层;对数正态分布模型仅适合于草本层;几何级数分布模型完全适合于拟合任何层次。此外,α指数值亦显示出本群落接近山地季雨林的多样性水平。     

岷江上游典型退化生态系统鸟类物种多样性的初步研究

在岷江上游典型的退化生态系统（十里乡）、逐渐恢复的生态系统（茂县生态站，1986年开始人工恢复）和原始森林及次生高山草甸生态系统（上卡卡沟）选择样地，对其乌类物种多样性在夏秋两季进行了调查，并计算其多样性指数和均匀度指数。各多样性指数的变化有微小差异，但总体一致。综合夏秋两季，茂县生态站夏季的鸟类丰富度、多度和多样性指数值最高，十里乡秋季的鸟类丰富度、多样性指数值最低，上卡卡沟秋季的均匀度指数最高。各群落的相似性指数较低，表明各群落组成有较大差异，拟合各样地鸟类的物种－多度曲线模型，十里乡鸟类的物种－多度模型与对数级数分布拟合，茂县鸟类的物种－多度模型与对数级数分布相似，上卡卡沟鸟类的物种－多度模型与分割线段模型分布拟合，夏秋季的物种－多度模型无明显差异，仅茂县的物种－多度模型与风何级数分布拟合，并对鸟类对生态恢复的监测效应进行了探讨，认为物种－多度模型是良好的生境变化生物指示因子，图3表4参19。     

华南退化草坡自然恢复中物种多度分布的动态与模拟

华南地区退化草坡自然恢复过程中物种多度格局的动态及其模拟,尚缺乏较为系统的研究.文章探讨是否不同演替阶段群落适合不同的种多度模型,是否存在一个最佳模型以揭示演替过程中群落结构的某些内在数量特征;还要推导多个模型的尺度转换形式.为此,在地处南亚热带的鹤山退化草坡选取处于不同演替阶段的2个典型群落样地,分木本层和草本层调查每个维管植物种的多度;且选择7个具有不同函数形式和广泛代表性的种多度模型,均在倍程(即log2)尺度下拟合数据,运用卡方检验和调整决定系数评估各个模型的适合性.结果表明:(1)7个模型的适合性顺序为:对数柯西＞对数双曲正割＞对数正态＞对数级数＞生态位优先占领＞断棒＞重叠生态位,其中对数柯西适合全部数据,重叠生态位则全部不适合;(2)各模型适合与否和演替阶段无关;(3)左截断对数柯西模型预测的种多度分布显示,随着群落演替,上层(木本层)罕见种比例减少、常见种比例增多,下层(草本层)则相反,这与实际相符.对数柯西分布具有普适性,能最好地反映退化草坡自然恢复中种多度分布的格局与动态.     

南岳森林群落生物多样性研究(Ⅵ)上封寺森林群落植物物种相对多度分布格局

本文应用几何级数分布、分割线段、对数级数分布和对数正态分布等4种模型研究了南岳上封寺森林群落植物物种相对多度的分布格局。结果表明,几何级数分布模型适宜拟合乔木层和草本层,而不适宜于灌木层;分割线段模型适宜于乔木层,其中,多度一频度模型还适于草本层;对数级数分布模型完全适宜于拟合任何层次;对数正态分布模型仅适宜于拟合乔木层。此外,α-指数值介于季雨林-稀树草原之间。     

中国珍稀格氏栲林的数量特征

运用伽玛分布，对数正态分布、韦布（Weibull)分布，正态分布等4种概率分布模型对格氏栲林的乔木层、灌木层、藤本层，草本层的物种-多度关系进行拟合分析，并用多种物种多样性指标测定格氏栲林各层次数量特征。结果表明：格氏栲林群落各层次物种-多度关系均符合伽玛分布，即伽玛分布模型应用于格氏栲林物种-多度分布研究是理想的；格氏栲林物种多样性指标介于中亚热带常绿阔叶林与南亚热带季风常绿阔叶林之间，表5参20。     

北京地区大气颗粒物与元素浓度的概率分布

本文根据1983年从东单、北辛安、怀柔定期观测的结果,对颗粒物及Al、Ca、Cu、Fe、K、Mg、Mn、Pb、S、Ti、Zn的浓度频数分布类型进行检验。结果表明,它们的分布类型在这三个地点有些差异,但是,大多数元素浓度呈对数正态(或偏态)分布。依据分布类型,计算了这三个地点的颗粒物及元素浓度的平均值。对呈正态分布,采用算术平均值。呈对数正态分布,用几何平均值,对数正态分布以外的偏态分布经正态化处理后计算平均值表示。     

南亚热带森林3层次物种多度之广义泊松分布模型

生物群落中物种多度分布(species abundance distribution)呈典型的倒J形,即其中存在许多稀有种、少量常见种.物种多度分布模型研究有助于解决森林生态恢复中的物种配置等实际问题.本研究考察了一种过分散(over-dispersion,或称超分布,即方差大于均值)的离散型分布,即具有λ和α两个参数...     

北京市污水处理厂出水中雌二醇的概率生态风险评价

随着北京市再生水补给河湖规模扩大,污水处理厂出水中雌激素活性物质引起的受纳水体生态风险日益受到关注。以雌二醇为例,利用物种敏感度分布(species sensitivity distribution,SSD)模型和联合概率曲线(joint probability curve,JPC)方法开展北京市污水处理厂出水的概率生态风险评价研究。通过文献调研整理了北京市约430个物种,利用美国环境保护署ECOTOX数据库获取了其中7个物种的雌二醇毒性数据,构建了正态分布、对数正态分布、Logistic分布、对数Logistic分布、Weibull分布、Burr III型分布和Gumbel分布等7个SSD模型,评价了北京市污水处理厂二沉池出水以及"混凝-沉淀-过滤-臭氧"、"超滤-臭氧"和"超滤-活性炭-臭氧"3种深度处理工艺组合出水的生态风险。结果表明,利用北京市7个物种雌二醇毒性数据构建的SSD模型具有合理性,SSD模型选择对生态风险评价结果的影响较大,对数正态分布、对数Logistic分布、Weibull分布和Burr III型均是可接受的SSD模型,其中拟合效果最佳的Burr III型分布模型给出了最保守的风险估计。Burr III型分布模型的模拟结果显示,北京市污水处理厂二沉池出水以及3种深度处理工艺组合出水的总体风险期望值分别为0.070、0.040、0.036和0.026,该结果可以为北京市未来水生态保护目标的设定以及污水处理工艺的升级改造提供决策参考。     

某湿地生态保护区水体中铜的生态风险评价及管理限值研究

铜是生物必需的微量元素,但过量暴露会对生物产生毒害效应。针对我国南方城市某湿地生态保护区水体重金属污染问题,参照《澳大利亚和新西兰淡水和海水水质指南》,应用物种敏感度分布(speeies sensitivity distribution,SSD)方法和联合概率曲线(joint probability curve,JPC)方法评价水体中铜的生态风险评价,在此基础上提出水体中铜浓度的管理限值。根据该湿地生态保护区生物调查历史数据以及其他文献数据,整理了415个本地物种的清单,通过美国环境保护署ECOTOX数据库以及其他文献共获取了13个物种的毒性数据,构建了Weibull分布、对数正态分布、正态分布、对数Logistic分布、Logistic分布、BurrⅢ型分布和Gumbel分布等7个SSD模型。结果表明,利用13个本地物种铜毒性数据构建的SSD模型具有合理性,不同模型计算得到的湿地生态保护区水体中铜的总体风险期望值为0.054~0.121。其中,BurrⅢ型分布模型的拟合效果最好,据此推导得到以保护水生生态系统为目标的铜的高可靠性与中等可靠性触发值分别为2.55μg·L~(-1)和1.41μg·L~(-1)。考虑到管理目标的可达性和现状的生态风险水平,提出该湿地生态保护区水体中铜浓度的管理限值为3μg·L~(-1)。     

应用物种敏感性分布评估三氯卡班对我国淡水环境的生态风险

应用物种敏感性分布(species sensitivity distribution,SSD)方法构建了三氯卡班(triclocarban,TCC)对淡水生物的SSD曲线,计算了TCC对淡水生物的5%急性危害浓度(HC5)。采用对数正态(log-normal)分布模型得到的急性ρ(HC5)=3.85μg·L~(-1),在评估因子(AF)取值为3、急慢性毒性比(ACR)为39.3的基础上,计算了TCC的慢性预测无效应浓度(PNEC)为32.7 ng·L~(-1)。采用商值法对21个有数据报道的典型地表水调查点水体中TCC的生态风险进行评估,结果表明,TCC高风险比例为28.6%,中风险比例为47.6%,低风险比例为23.8%。TCC对我国地表水的影响应该引起关注。     

Disentangling the effects of heterogeneity, stochastic dynamics and sampling in a community of aquatic insects

A dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution is fitted to time series of species data from an assemblage of stoneflies and mayflies (Plecoptera and Ephemeroptera) of an aquatic insect community collected over a period of 15 years. In each year except one, we analyze 5 parallel samples taken at the same time of the season giving information about the over-dispersion in the sampling relative to the Poisson distribution. Results are derived from a correlation analysis, where the correlation in the bivariate normal distribution of log abundance is used as measurement of similarity between communities. The analysis enables decomposition of the variance of the lognormal species abundance distribution into three components due to heterogeneity among species, stochastic dynamics driven by environmental noise, and over-dispersion in sampling, accounting for 62.9, 30.6 and 6.5% of the total variance, respectively. Corrected for sampling the heterogeneity and stochastic components accordingly account for 67.3 and 32.7% of the among species variance in log abundance. By using this method, it is possible to disentangle the effect of heterogeneity and stochastic dynamics by quantifying these components and correctly remove sampling effects on the observed species abundance distribution.     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

A new algorithm for estimating the parameters of the spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using a spatial generalized linear mixed model with location specific latent variables. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. So far, several numerical algorithms to solve the problem of calculating maximum likelihood estimates of this model have been presented. In this paper to estimate the parameters an approximate method is considered and a new algorithm is introduced that is much faster than existing algorithms but just as accurate. This is called the Approximate Expectation Maximization Gradient algorithm. The performance of the proposed algorithm and is illustrated with a simulation study and on a real data set.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Effective Population Size in Red-Cockaded Woodpeckers: Population and Model Differences

Loss of genetic variability in isolated populations is an important issue for conservation biology. Most studies involve only a single population of a given species and a single method of estimating rate of loss. Here we present analyses for three different Red-cockaded Woodpecker ( Picoides borealis ) populations from different geographic regions. We compare two different models for estimating the expected rate of loss of genetic variability, and test their sensitivity to model parameters. We found that the simpler model (Reed et al. 1988) consistently estimated a greater rate of loss of genetic variability from a population than did the Emigh and Pollak (1979) model. The ratio of effective population size (which describes the expected rate of loss of genetic variability) to breeder population size varied widely among Red-cockaded Woodpecker populations due to geographic variation in demography. For this species, estimates of effective size were extremely sensitive to survival parameters, but not to the probability of breeding or reproductive success. Sensitivity was sufficient that error in estimating survival rates in the field could easily mask true population differences in effective size. Our results indicate that accurate and precise demographic data are prerequisites to determining effective population size for this species using genetic models, and that a single estimate of rate of loss of genetic variability is not valid across populations.     

Gap Analysis of Conserved Genetic Resources for Forest Trees

Abstract:  We developed a gap analysis approach to evaluate whether the genetic resources conserved in situ in protected areas are adequate for conifers in western Oregon and Washington (U.S.A.). We developed geographic information system layers that detail the location of protected areas and the distribution and abundance of each tree species (noble fir [ Abies procera Rehd.] and Douglas-fir [ Pseudotsuga menzeisii Mirb.]). Distribution and abundance were inferred from available spatial data showing modeled potential and actual vegetation. We stratified the distribution of each species into units for genetic analysis using seed and breeding zones and ecoregions. Most strata contained at least 5000 reproductive-age individuals in protected areas, indicating that genetic resources were well protected in situ throughout most of the study region. Strict in situ protection was limited, however, for noble fir in the Willapa Hills of southwestern Washington. An in situ genetic resource gap arguably occurred for Douglas-fir in the southern Puget lowlands, but this gap was filled by extensive ex situ genetic resources from the same region. The gap analysis method was an effective tool for evaluating the genetic resources of forest trees across a large region.     

Chilean Blue Whales as a Case Study to Illustrate Methods to Estimate Abundance and Evaluate Conservation Status of Rare Species

Abstract: Often abundance of rare species cannot be estimated with conventional design‐based methods, so we illustrate with a population of blue whales (Balaenoptera musculus) a spatial model‐based method to estimate abundance. We analyzed data from line‐transect surveys of blue whales off the coast of Chile, where the population was hunted to low levels. Field protocols allowed deviation from planned track lines to collect identification photographs and tissue samples for genetic analyses, which resulted in an ad hoc sampling design with increased effort in areas of higher densities. Thus, we used spatial modeling methods to estimate abundance. Spatial models are increasingly being used to analyze data from surveys of marine, aquatic, and terrestrial species, but estimation of uncertainty from such models is often problematic. We developed a new, broadly applicable variance estimator that showed there were likely 303 whales (95% CI 176–625) in the study area. The survey did not span the whales' entire range, so this is a minimum estimate. We estimated current minimum abundance relative to pre‐exploitation abundance (i.e., status) with a population dynamics model that incorporated our minimum abundance estimate, likely population growth rates from a meta‐analysis of rates of increase in large baleen whales, and two alternative assumptions about historic catches. From this model, we estimated that the population was at a minimum of 9.5% (95% CI 4.9–18.0%) of pre‐exploitation levels in 1998 under one catch assumption and 7.2% (CI 3.7–13.7%) of pre‐exploitation levels under the other. Thus, although Chilean blue whales are probably still at a small fraction of pre‐exploitation abundance, even these minimum abundance estimates demonstrate that their status is better than that of Antarctic blue whales, which are still <1% of pre‐exploitation population size. We anticipate our methods will be broadly applicable in aquatic and terrestrial surveys for rarely encountered species, especially when the surveys are intended to maximize encounter rates and estimate abundance.