湖泊营养物基准的制定方法研究进展

系统论述了国外湖泊营养物基准制定程序和技术方法,重点分析了国外营养物基准指标的选取和确定湖泊营养参照状态的方法,并分析了其适用性和可行性.湖泊营养物基准指标的选取要综合考虑富营养化控制关键因子和不同分区湖泊区域差异性,所选指标要具有相对稳定、因地制宜、早期预警等特点.湖泊参照状态的确定主要包括调查数据和历史数据的统计分析、模型预测和推断、古湖沼学重建等方法.统计学方法充分利用历史及现状的实测水质和生物数据,保证制定的基准适用于大多数湖泊,对于历史监测资料缺乏或污染严重的湖泊,该方法确定参照状态较困难;模型预测和推断方法用于受人类影响较严重的湖泊参照状态确定,但也需要大量数据进行校准和验证;古湖沼学重建法利用湖泊沉积重建湖泊自然营养本底和定量恢复历史时期湖泊营养状态演化序列,进而推导湖泊参照状态,需要复杂的数据分析和专家判断,同时也难以适用于沉积物受扰动较大的浅水湖泊.我国的大部分湖泊普遍受人类影响较大,历史数据稀缺,不同分区湖泊的差异性显著,针对国外营养物基准制定方法优缺点,需要加以优化改进,建立我国完善的湖泊营养物基准制定方法体系,指导我国湖泊营养物基准的制定.     

湖泊营养物参照状态建立方法研究

建立生态分区内各类型湖泊营养物的参照状态是营养物基准制定过程中最为核心的内容之一。在系统分析和评价国外确定湖泊营养物参照状态的若干种方法,包括参照湖泊法、湖泊群体分布法、三分法、回归分析等几种统计学方法以及模型推断和古湖沼学重建方法后,文章根据总磷（total phosphorus,TP）、总氮（total nitrogen,TN）、叶绿素a（chlorophyll a,Chl-a）和塞氏透明度（Secci depth）等四项指标的历史监测数据,应用若干统计学方法建立了巢湖的营养物基准参照状态。通过互相之间的比较分析以及长江中下游湖区古湖沼学重建数据的验证,推荐采用湖泊群体分布法5％点位对应的值作为巢湖营养物基准的参照状态。因此,巢湖营养物参照状态阈值范围为总磷0．023～0．27mg·L^-1,总氮O．62～0．63mg·L^-1,叶绿素aO．65～0．67mg·m^-1,塞氏透明度0．65-0．72m。     

河流营养物参照状态的建立方法研究进展

建立营养物参照状态是河流水污染控制的关键问题之一。系统论述了参照状态的各种内涵,重点分析了国外建立河流营养物参照状态的各种方法及优缺点,并分析了其在中国的适用性。最后,提出了河流营养物参照状态的进一步研究的重点。参照状态根据允许人类活动影响的程度可有多种含义:最小干扰状态、历史状态、最少干扰状态和最佳可达成状态。其中,最少干扰状态和最佳可达成状态在现实管理中具有一定程度的可操作性。参照河流百分比法是建立营养物参照状态的首选方法,但中国水环境污染形势严峻,参照点变得越来越少。当参照点不存在时,一般河流百分比法是参照河流百分比法的替代方法,然而一般河流百分比法和参照河流百分比法的匹配性关系并不完全保持一致。由于栖息地退化等因素对生物完整性的影响程度可能比营养物浓度更大,生物响应法在实际应用中是非常困难的。流域模型法虽然有众多优点,但是数据要求较高,并且常用流域模型的机理与中国有较大差距,在中国应用的结果具有较大的不确定性。综合考虑中国水环境污染现状和数据要求,以多元线性回归模型为代表的简易模型方法在中国应有最大的适用性。然而,环境因素与营养物质间的关系往往都是非线性的,今后应着重研究建立河流营养物参照状态的多元非线性回归模型方法。此外,还应加强季风河流营养物参照状态的季节性差异与年际差异研究,并在全国层面上根据自然因素划分适当规模的生态区,分区确定各生态区的河流营养物参照状态。     

中国湖泊富营养化影响因素研究——基于中国22个湖泊实证分析

基于1：25万河流水系和数字高程（DEM）数据制备了选取湖泊的流域边界,据其集成了流域社会经济数据,并据中国科学院资源环境数据中心提供的1990、1995、2000、2005年4个时段土地利用数据形成了流域反映人类活动强度的表征指标,选取了全国湖泊富营养化区域差异性调查课题获取的案例湖泊的水质数据,集成了中国湖泊富营养化影响因素计量分析的面板数据集,研究了1990-2005年间中国湖泊营养状态的发展趋势与差异特征,探讨了湖泊富营养化的自然气候与社会经济影响机制,构建了中国湖泊富营养化影响因素分析的计量模型,厘定了1990-2005年间影响湖泊营养状态变化的重要因素,测度了其对湖泊富营养化的影响方向和作用强度。基于面板数据的计量分析结果表明,近15年人类活动强度对湖泊营养状态呈显著的正影响,其表征指标人均GDP与建设用地所占流域面积比均具有较强的显著性,而在人类扰动较小的情况下,湖泊的营养状态与湖泊补给系数成正比,与湖泊蓄容量成反比,补给系数较小且其他因素也相同时,水体营养状态应与流域土壤本底值相关;以林、草地的面积比重所测度的流域自然生态系统发育程度与湖泊营养状态呈显著负相关关系,表明林地与草地生态系统对流域的氮、磷营养物的人湖量具有一定的削减作用,论文研究结果将为流域管理措施方案制订提供科技支撑。     

利用概率单位法估算EC50的研究（Estimation of Probit Methods on EC50）

以两组典型毒性数据为例,综合比较了5种常见概率单位模型计算程序在EC50估算上的差异,并在此基础上比较了3种不同置信区间的估算方法.在对照组受抑制率为零时,5种方法的参数估算结果一致;当对照组受抑制率不为零时,将对照组数据纳入整个计算过程的方法可以给出可靠的参数估算结果.3种不同置信区间估算方法的比较表明:在毒性数据分布对称性良好的情况下,基于Delta函数与Fieller定理的方法给出的结果相近,但后者可以在更广泛的情况下给出置信区间;相比而言,基于bootstrap重抽样的非参数方法获得的置信区间较窄,表现出更好的灵敏度.     

京沪高铁天津地区地表沉降空间插值方法研究

京沪高速铁路天津、沧州地区存在不同程度的地表沉降。以京沪高速铁路天津地区沿线的水准观测数据为研究对象,采用反距离加权法、径向基函数法与泛克里格插值法对研究对象进行了空间插值分析,通过精度评定与结果检验发现泛克里格方法能够较好地反映地表趋势面,可以作为研究该区域地表沉降的一种方法。     

南京主要湖泊表层沉积物中重金属污染潜在生态风险评价

为掌握南京城市湖泊表层沉积物中重金属的污染概况及其潜在生态风险,选取玄武湖、月牙湖、紫霞湖、琵琶湖和前湖5个主要湖泊为研究对象,应用电感耦合等离子体原子发射光谱法( ICP - AES)分析表层沉积物中Cd、Cr、Cu、Ni、Pb和Zn含量,并采用地累积指数法和潜在生态风险指数法对重金属污染风险和潜在生态危害进行评价.结果表明,湖泊表层沉积物中Cd、Cr、Cu、Ni、Pb和Zn含量分别为0.8～5.1、10.3 ～67.6、6.2 ～70.5、未检出～53.2、12.9 ～ 55.9和40.6 ～456.3 mg·kg -1.地累积指数法评价结果显示,6种重金属元素累积程度由高到低依次为Cd、Zn、Pb、Cu、Ni和Cr,其中Cd处于偏中等累积到重累积水平.潜在生态风险指数法评价结果显示,表层沉积物重金属污染最严重的是月牙湖,其次是前湖和紫霞湖,这3个湖泊都处于中等潜在生态风险状态,琵琶湖和玄武湖处于低潜在生态风险状态.     

太湖站湖泊生态系统动态监测数据库的建立与管理

太湖站是研究湖泊生态系统的一个生太研究网络站,它观测 和记录了长期大量的第一手资料。由于缺乏数据的有效管理,数据长期以来没得到充分整理和应用。根据中国生态研究网络（CERN）关于建立统一的动态监测数据库的要求,太湖站建立了湖泊生态系统动态监数据库,以便有效地管理数据。本文主要就动态监测数据库的建立和数据的管理做一介绍。     

物种敏感度分布(SSD)方法在农药环境风险评估中的应用

物种敏感度分布法(Species Sensitive Distribution,SSD)能够依据不同物种对农药等胁迫因素的敏感度服从一定的累积概率分布,以统计形式将多种非靶标物种的实验室数据进行汇总分析,获得物种对农药的敏感度差异,作为效应评估指标应用于农药风险评估。本文综述了SSD概念、方法原理、国内外研究和应用进展,并以毒死蜱为例进行了数据分析。由此建议继续关注国际上对SSD及更复杂模型模拟研究进展,以便应用于我国农药环境风险评估,推动我国农药管理的持续发展。     

江苏省不同营养状况湖泊底栖动物群落结构与多样性比较

为了解江苏省湖泊底栖动物群落结构和多样性并研究其对水环境质量变化的响应,于2012年春秋两季对江苏省16个湖泊51个采样点湖泊底栖动物群落结构与多样性以及湖泊综合营养状态指数进行调查,分析水质指标与底栖动物指数间Pearson相关关系。结果表明,江苏省16个湖泊营养状态指数范围为35.5~66.4,其中约81.2%的湖泊处于轻度-中度富营养状态,表明水质从好到中度污染;湖泊底栖动物优势种为寡毛类的霍甫水丝蚓(Limnodrilus hoffmeisteri),优势度为13.0%;香浓多样性指数(Shannon-Wiener diversity index)范围为0.00~2.20,级别范围由极贫乏到较丰富状态,表明水质污染程度从重度到轻度污染。从全省尺度看,水质评价结果与生物学(香浓多样性指数、Pielou均匀度指数和Biotic Index(BI)指数)评价结果存在一定差异。与历史数据相比,江苏省湖泊底栖动物清洁敏感物种减少甚至消失,总体群落结构趋于小型化。16个湖泊水体总氮和总磷与底栖动物密度呈显著负相关,而综合营养状态指数与底栖动物指数(香浓多样性指数、Pielou均匀度指数和BI指数)间关系不显著。上述研究结果表明综合营养状态指数无法完全客观反映湖泊水生态健康状况,因此需要综合水质、水文、水生生物、生境状况等因素发展新的评价指标体系。     

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.     

基于CVM的南昌城市河湖生态服务功能价值评估

条件价值评估法(CVM)是当前可用于确定环境物品非市场的和非使用价值的有效方法.在分析南昌市城市河湖生态环境问题的基础上,采用条件价值评估法,共回收194份单边界二分式CVM有效问卷,研究了南昌城市河湖生态系统服务改善的支付意愿及其经济价值.研究表明:1)南昌市城区河湖生态系统服务改善的平均支付意愿约为105.83元/...     

Animal abundance estimation in independent observer line transect surveys

The theory of conventional line transect surveys is based on an essential assumption that 100% detection of animals right on the transect lines can be achieved. When this assumption fails, independent observer line transect surveys are used. This paper proposes a general approach, based on a conditional likelihood, which can be carried out either parametrically or nonparametrically, to estimate the abundance of non-clustered biological populations using data collected from independent observer line transect surveys. A nonparametric estimator is specifically proposed which combines the conditional likelihood and the kernel smoothing method. It has the advantage that it allows the data themselves to dictate the form of the detection function, free of any subjective choice. The bias and the variance of the nonparametric estimator are given. Its asymptotic normality is established which enables construction of confidence intervals. A simulation study shows that the proposed estimator has good empirical performance, and the confidence intervals have good coverage accuracy.     

Bootstrap confidence intervals for adaptive cluster sampling design based on Horvitz–Thompson type estimators

Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) introduced several bootstrap methods under adaptive cluster sampling using a Horvitz–Thompson type estimator. Using a simulation study, they showed that their proposed methods provide confidence intervals with highly understated coverage rates. In this article, we first show that their bootstrap methods provide biased bootstrap estimates. We then define two bootstrap methods, based on the method of Gross (Proceeding of the survey research methods section. American Statistical Association, Alexandria, VA, pp 181–184, 1980) and Bootstrap With Replacement, that provide unbiased bootstrap estimates of the population mean with bootstrap variances matching the corresponding unbiased variance estimator. Using a simulation study, we show that the bootstrap confidence intervals based on our proposed methods have better performance than those based on available bootstrap methods, in the sense of having coverage proportion closer to the nominal coverage level. We also compare the proposed intervals to empirical likelihood based intervals in small samples.     

An estimating function approach to the inference of catch-effort models

A class of catch-effort models, which allows for heterogeneous removal probabilities, is proposed for closed populations. The model includes three types of removal probabilities: multiplicative, Poisson and logistic. The usual removal and generalized removal models then become special cases. The equivalence of the proposed model and a special type of capture-recapture model is discussed. A unified estimating function approach is used to estimate the initial population size. For the homogeneous model, the resulting population size estimator based on optimal estimating functions is asymptotically equivalent to the maximum likelihood estimator. One advantage for our approach is that it can be extended to handle the heterogeneous populations in which the maximum likelihood estimators do not exist. The bootstrap method is applied to construct variance estimators and confidence intervals. We illustrate the method by two real data examples. Results of a simulation study investigating the performance of the proposed estimation procedure are presented.     

Long-term fire frequency not linked to prehistoric occupations in northern Swedish boreal forest

Knowledge of past fire regimes is crucial for understanding the changes in fire frequency that are likely to occur during the coming decades as a result of global warming and land-use change. This is a key issue for the sustainable management of forest biodiversity because fire regimes may be controlled by vegetation, human activities, and/or climate. The present paper aims to reconstruct the pattern of fire frequency over the Holocene at three sites located in the same region in the northern Swedish boreal forest. The fire regime is reconstructed from sedimentary charcoal analysis of small lakes or ponds. This method allows fire events to be characterized, after detrending the charcoal influx series, and allows estimation of the time elapsed between fires. The long-term fire regime, in terms of fire-free intervals, can thus be elucidated. At the three sites, the mean fire-free intervals through the Holocene were long and of similar magnitude (approximately 320 years). This similarity suggests that the ecological processes controlling fire ignition and spread were the same. At the three sites, the intervals were shorter before 8600 cal yr BP (calibrated years before present), between 7500 and 4500 cal yr BP, and after 2500 cal yr BP. Geomorphological and vegetation factors cannot explain the observed change, because the three sites are located in the same large ecological region characterized by Pinus sylvestris-Ericaceae mesic forests, established on morainic deposits at the same elevation. Archaeological chronologies also do not match the fire chronologies. A climatic interpretation is therefore the most likely explanation of the long-term regional pattern of fire. Although recent human activities between the 18th and the 20th centuries have clearly affected the fire regime, the dominant factor controlling it for 10000 years in northern Sweden has probably been climatic.     

Experimental and environmental factors affect spurious detection of ecological thresholds

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (tau) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.     

Confidence limits for secondary production estimates: Application of the bootstrap to the increment summation method

In field studies, somatic production of animals is often calculated by means of the increment summation method, which is based on consecutive samples from the population. The main disadvantage of this method is the lack of any measurement of variability, therefore the statistical significance of the calculated production value is uncertain. This paper shows that in many cases a nonparametric statistical approach called the “bootstrap” can be used to overcome this problem. By means of this procedure, natural variability of production and production to biomass ratios can be assessed by 95% confidence intervals, standard deviation or related parameters from a sample of limited size.     

Seasonal variation in the occurrence of ischemic stroke: A meta-analysis

Stroke was demonstrated to correlate with seasonal variation. However, the relevant studies were incongruous. To better understand the rules of seasonal impact on ischemic stroke (IS) patients, we performed this meta-analysis. We systematically searched relevant observational studies in Pubmed, Web of science and Embase from January 1, 1980, to November 1, 2017, in English. Patients included in this study were adults who suffered from IS. Stata version 12.0 software was used to pool useful data and calculate incidence rate ratios (IRRs) and their 95% confidence intervals (CIs). We also performed heterogeneity and sensitivity analyses and evaluated publication bias. Thirty-three observational studies involving 234,196 participants were incorporated into the meta-analysis. Summer and December were regarded as reference, respectively. The IRRs were calculated showing: IRR_Winter 1.05 (95% CI 1.04–1.07), IRR_Autumn 1.03 (95% CI 1.02–1.04), IRR_Spring 1.02 (95% CI 1.01–1.03). No obvious difference existed among 12 months. Stratified analyses on Köppen classification were also conducted. Between-study heterogeneity was discovered; however, predefined stratified analyses and meta-regression could not reduce this heterogeneity. Our meta-analysis has revealed very little seasonal variation in the overall study. Both cold and hot months may be high risky for IS after stratified by Köppen Climate Classification. Thus, a rationale to environmental setting of risky patient management could be provided. More studies with specific assessments are warranted for further comprehensive investigation.