Bewertung heterogener Altlasten durch Probenahme, statistische und geostatistische Modellierung

A slack heap in Saxony serves as an example for the model procedure of evaluation heterogeneous waste sites via sampling as well as statistical and geostatistical data analysis. This paper demonstrates the usefulness of the known regulations for sampling and of the tried and proven geostatistical methods developed for assessing homogeneous wastes and the concornitantly contaminated soil. Emphasized here are the contents of iron and sulfur. Considered in addition are the concentrations of arsenic and the heat loss. To establish a conclusion, the variograms of the original data are used to evaluate the waste site in different directions and at determined depths. With the aid of a spot interpolation, further data can be calculated for additional sites which have not been sampled and can be used consequently for displaying the spatial variability. Finally, the statistical data analysis is applied using the standard methods of random sampling as an alternative to the geostatistical modeling. In this manner, the consequences for the sampling strategy can be demonstrated.     

Inference about the ratio of means from Negative Binomial paired count data

We derive some statistical properties of the distribution of two Negative Binomial random variables conditional on their total. This type of model can be appropriate for paired count data with Poisson over-dispersion such that the variance is a quadratic function of the mean. This statistical model is appropriate in many ecological applications including comparative fishing studies of two vessels and or gears. The parameter of interest is the ratio of pair means. We show that the conditional means and variances are different from the more commonly used Binomial model with variance adjusted for over-dispersion, or the Beta-Binomial model. The conditional Negative Binomial model is complicated because it does not eliminate nuisance parameters like in the Poisson case. Maximum likelihood estimation with the unconditional Negative Binomial model can result in biased estimates of the over-dispersion parameter and poor confidence intervals for the ratio of means when there are many nuisance parameters. We propose three approaches to deal with nuisance parameters in the conditional Negative Binomial model. We also study a random effects Binomial model for this type of data, and we develop an adjustment to the full-sample Negative Binomial profile likelihood to reduce the bias caused by nuisance parameters. We use simulations with these methods to examine bias, precision, and accuracy of estimators and confidence intervals. We conclude that the maximum likelihood method based on the full-sample Negative Binomial adjusted profile likelihood produces the best statistical inferences for the ratio of means when paired counts have Negative Binomial distributions. However, when there is uncertainty about the type of Poisson over-dispersion then a Binomial random effects model is a good choice.     

Continuous plankton records: Persistence in time-series of annual means of abundance of zooplankton

Time-series of annual means of abundance of zooplankton of the north-east Atlantic Ocean and the North Sea, for the period 1948 to 1977, show considerable associations between successive years. The seasonal dynamics of the stocks appear to be consistent with at least a proportion of this being due to inherent persistence from year-to-year. Experiments with a simple model suggest that the observed properties of the time-series cannot be reproduced as a response to simple random forcing. The extent of trends and long wavelength variations can be simulated by introducing fairly extensive persistence into the perturbations, but this underestimates the extent of shorter wavelength variability in the observed time-series. The effect of persistence is to increase the proportion of trend and long wavelength variability in time-series of annual means, but stocks can respond to short wavelength perturbations provided these have a clearly defined frequency.     

Sampling variability and estimates of density dependence: a composite-likelihood approach

It is well known that sampling variability, if not properly taken into account, affects various ecologically important analyses. Statistical inference for stochastic population dynamics models is difficult when, in addition to the process error, there is also sampling error. The standard maximum-likelihood approach suffers from large computational burden. In this paper, I discuss an application of the composite-likelihood method for estimation of the parameters of the Gompertz model in the presence of sampling variability. The main advantage of the method of composite likelihood is that it reduces the computational burden substantially with little loss of statistical efficiency. Missing observations are a common problem with many ecological time series. The method of composite likelihood can accommodate missing observations in a straightforward fashion. Environmental conditions also affect the parameters of stochastic population dynamics models. This method is shown to handle such nonstationary population dynamics processes as well. Many ecological time series are short, and statistical inferences based on such short time series tend to be less precise. However, spatial replications of short time series provide an opportunity to increase the effective sample size. Application of likelihood-based methods for spatial time-series data for population dynamics models is computationally prohibitive. The method of composite likelihood is shown to have significantly less computational burden, making it possible to analyze large spatial time-series data. After discussing the methodology in general terms, I illustrate its use by analyzing a time series of counts of American Redstart (Setophaga ruticilla) from the Breeding Bird Survey data, San Joaquin kit fox (Vulpes macrotis mutica) population abundance data, and spatial time series of Bull trout (Salvelinus confluentus) redds count data.     

Clustering of the abundance of West Nile virus vector mosquitoes in Peel Region,Ontario, Canada

Understanding the spatial–temporal distribution of vector mosquitoes is essential in designing an efficient mosquito control strategy to reduce the risk of the mosquito-borne disease. In this paper, we apply a non-parametric clustering method, CLUES, to the surveillance data of West Nile virus vector mosquitoes collected by light traps in Peel Region, Ontario, during the mosquito seasons in 2004–2010. In order to obtain robust and reliable results, a statistical smoothing procedure LOWESS is applied to the original time series data. It was found that the mosquito trap sites can be clustered into three groups. The weather impact on the mosquito abundance of each clustered group are similar, while the interannual variability and the highest abundance and peak time in each mosquito season are different. The impact of weather factors on this clustering is investigated.     

砷的吸附解吸及其与土壤性质的关系

研究四川六大类紫色土对五价砷的吸附解吸及其数学模拟，并采用灰色系统的关联度方法，力图使吸附解吸参数与土壤性质联系起来。结果表明；Ｆｒｅｕｎｄｌｉｃｈ方程、Ｌａｎｇｍｕｉｒ方程和Ｔｅｍｋｉｎ方程对五价砷的吸附解吸的模拟，均达到了极显著水平，其中尤以Ｆｒｅｕｎｄｌｉｃｈ方程和Ｌａｎｇｍｕｉｒ－（３）方程最佳；根据二式求出的Ｘｍ、Ｑｍ主要受土壤ｐＨ、粘粒含量、ＣａＣＯ３、有效磷、Ｃａ２＋、Ｍｇ２＋饱和度的影响。     

Methods for estimating peak physiological performance and correlating performance measures

Estimates of animal performance often use the maximum of a small number of laboratory trials, a method which has several statistical disadvantages. Sample maxima always underestimate the true maximum performance, and the degree of the bias depends on sample size. Here, we suggest an alternative approach that involves estimating a specific performance quantile (e.g., the 0.90 quantile). We use the information on within-individual variation in performance to obtain a sampling distribution for the residual performance measures; we use this distribution to estimate a desired performance quantile for each individual. We illustrate our approach using simulations and with data on sprint speed in lizards. The quantile method has several advantages over the sample maximum: it reduces or eliminates bias, it uses all of the data from each individual, and its accuracy is independent of sample size. Additionally, we address the estimation of correlations between two different performance measures, such as sample maxima, quantiles, or means. In particular, because of sampling variability, we propose that the correlation of sample means does a better job estimating the correlation of population maxima than the estimator which is the correlation of sample maxima.     

Introducing effects of temperature and CO2 elevation on tree growth into a statistical growth and yield model

Impacts of elevated temperature and CO₂ on tree growth were introduced into a statistical growth and yield model for Finnish conditions based on corresponding predictions obtained from a physiological growth model. This one-way link between models was made by means of species-specific transfer functions describing the increase in stem volume growth of trees as a function of elevated temperature and CO₂, stand density and the tree's competition status in a stand of Scots pine (Pinus sylvestris), silver birch (Betula pendula) and Norway spruce (Picea abies). This method allows the inner dynamics of the statistical model to be followed when the impacts of temperature and CO₂ elevation on tree growth are introduced into the calculation of volume growth and further allocated between diameter and height growth. In this way compatibility with previous predictions of tree growth by means of statistical models and related model systems under current climatic conditions could be retained.The performance of the statistical model with species-specific transfer functions was evaluated by comparing its predictions with corresponding predictions given by a physiological model under conditions of elevated temperature and CO₂. These calculations revealed that the growth response of individual trees to elevated temperature and CO₂ can be introduced into the statistical model from a physiological growth model with an outcome that results in fairly satisfactory growth responses at the stand level as well.     

Net primary production of Chinese croplands from 1950 to 1999.

Considerable efforts have been made to assess the contribution of forest and grassland ecosystems to the global carbon budget, while less attention has been paid to agriculture. Net primary production (NPP) of Chinese croplands and driving factors are seldom taken into account in the regional carbon budget. We studied crop NPP by analyzing the documented crop yields from 1950 to 1999 on a provincial scale. Total NPP, including estimates of the aboveground and belowground components, was calculated from harvested yield data by (1) conversion from economic yield of the crop to aboveground mass using the ratio of aboveground residue production to the economic yield, (2) estimation of belowground mass as a function of aboveground mass, and (3) conversion from total dry mass to carbon mass. This approach was applied to 13 crops, representing 86.8% of the total harvested acreage of crops in China. Our results indicated that NPP in Chinese croplands increased markedly during this period. Averaging for each decade, the amount of NPP was 146 +/- 32, 159 +/- 34, 260 +/- 55, 394 +/- 85, and 513 +/- 111 Tg C/yr (mean +/- SD) in the 1950s, 1960s, 1970s, 1980s, and 1990s, respectively. This increase may be attributed to synthetic fertilizer application. A further investigation indicated that the climate parameters of temperature and precipitation determined the spatial variability in NPP. Spatiotemporal variability in NPP can be well described by the consumption of synthetic fertilizer and by climate parameters. In addition, the total amount of residue C and root C retained by the soils was estimated to be 618 Tg, with a range from 300 to 1040 Tg over the 50 years.     

Teil I: Stoffstromanalysen landwirtschaftlicher Produktionsverfahren

When discussing strategies for realising an environmental friendly agricultural production, there are methods for quantifying environmental effects which do not originate in agricultural contexts. For instance, the methodology of Life Cycle Assessment is mainly developed from analyses of industrial production lines and products. Substance flow analyses in agriculture contain a complete analysis of agriculturally caused substance and energy flows, to which certain environmental effects are assigned, and also an assessment of these effects. For the provision of production means, balancing agricultural production procedures includes the extraction of energy sources and mineral resources from their reservoirs. The growth phase of the crops regarded (cereals, sugar beet, rape) is described depending on the nutrients nitrogen, phophor, potassium and calcium. The nitrogen flux within the system plant-soil-atmosphere is recorded referring to its temporal dynamic. In the framework of this study, selected environmental effects of agricultural production procedures of winter wheat are calculated regarding different conditions of locations and are supplemented by declining scenario simulations. Essential features of the structure of the substance flow net, which is implemented for the calculation and which can be used completely or as for single modules for further studies, are explained. Basing on selected categories of effectiveness, ecological optimisation potentials of varying agricultural provision of production means and procedures are estimated within the whole context of provision. Thereby, the effects of a reduced employment of fertilisers, of a change as for the kind of fertiliser and of the region of origin of the N-fertiliser, as well as variations concerning mechanical work in the fields are outlined for the cultivation of winter wheat.     

土壤空间变异性研究评述

在研究方法上，土壤空间变异性研究经历了由定性描述到传统统计学、再到地统计学的不断改进过程，而研究内容也由对单一特性的研究逐渐转向多特性的研究。土壤空间变异性研究虽取得了明显的进展，但也存在地统计学方法在实际应用中主观性太强，土壤综合特性的空间变异性研究、时空结合的变异性研究以及与数学模型有机结合研究不够深入等问题。文章在分析国内外研究现状的基础上，提出了亟待深入研究的若干问题，对这些问题的研究有助于“数字土壤”、“精准农业”和生态环境建设的实施。     

Quantifying the importance of local niche-based and stochastic processes to tropical tree community assembly

Although niche-based and stochastic processes, including dispersal limitation and demographic stochasticity, can each contribute to community assembly, it is difficult to quantify the relative importance of each process in natural vegetation. Here, we extend Shipley's maxent model (Community Assembly by Trait Selection, CATS) for the prediction of relative abundances to incorporate both trait-based filtering and dispersal limitation from the larger landscape and develop a statistical decomposition of the proportions of the total information content of relative abundances in local communities that are attributable to trait-based filtering, dispersal limitation, and demographic stochasticity. We apply the method to tree communities in a mature, species-rich, tropical forest in French Guiana at 1-, 0.25- and 0.04-ha scales. Trait data consisted of species' means of 17 functional traits measured over both the entire meta-community and separately in each of nine 1-ha plots. Trait means calculated separately for each site always gave better predictions. There was clear evidence of trait-based filtering at all spatial scales. Trait-based filtering was the most important process at the 1-ha scale (34%), whereas demographic stochasticity was the most important at smaller scales (37-53%). Dispersal limitation from the meta-community was less important and approximately constant across scales (-9%), and there was also an unresolved association between site-specific traits and meta-community relative abundances. Our method allows one to quantify the relative importance of local niche-based and meta-community processes and demographic stochasticity during community assembly across spatial and temporal scales.     

Non-photosynthetic oxygen production and non-respiratory oxygen uptake in the dark: a theory of oxygen dynamics in plankton communities

Plankton respiration is commonly measured in terms of oxygen uptake, usually employing the Winkler method, much less commonly the polarographic method. Both methods produce results that can be misinterpreted when H₂O₂ production and decomposition are ignored. This paper: (1) presents experimental evidence of significant H₂O₂ involvement during plankton incubation in dark bottles, (2) explains how results differ between the Winkler and polarographic methods in the presence of H₂O₂, (3) discusses how this difference is clouded by side issues of variability inherent in the Winkler technique and the use of different bottle sizes, and (4) shows that unexpected and variable results of light-/dark-bottle incubations can all be explained by a theory of H₂O₂ production and decomposition. During an initial period in the dark, when plankton respiration has been poisoned by mercuric chloride or chloroform, O₂ increase can be measured with a polarographic oxy‐gen sensor (POS). The trend in O₂ changes is linear for several days when only respiration is occurring, but curvilinear when there is concurrent O₂ production. O₂ production in the dark and H₂O₂ decomposition are one and the same process. Measurement of oxygen by Winkler analysis and POS produce different results when H₂O₂ is present because the former method measures oxidizing equivalent while a POS measures O₂ pressure. A real difference in results between the two methods is prima facie evidence that H₂O₂ is involved. The synthesis of this new empirical evidence with diverse knowledge from various fields shows that the common practice of estimating gross community primary production from oxygen changes in light and dark bottles is based on untenable assumptions. Received: 15 April 1997 / Accepted: 17 June 1997     

Uncertainty characterization for emergy values

While statistical estimation of uncertainty has not typically accompanied published emergy values, as with any other quantitative model, uncertainty is embedded in these values, and lack of uncertainty characterization makes their accuracy not only opaque, it also prevents the use of emergy values in statistical tests of hypotheses. This paper first attempts to describe sources of uncertainty in unit emergy values (UEVs) and presents a framework for estimating this uncertainty with analytical and stochastic models, with model choices dependent upon on how the UEV is calculated and what kind of uncertainties are quantified. The analytical model can incorporate a broader spectrum of uncertainty types than the stochastic model, including model and scenario uncertainty, which may be significant in emergy models, but is only appropriate for the most basic of emergy calculations. Although less comprehensive in its incorporation of uncertainty, the proposed stochastic method is suitable for all types of UEVs. The distributions of unit emergy values approximate the lognormal distribution with variations depending on the types of uncertainty quantified as well as the way the UEVs are calculated. While both methods of estimating uncertainty in UEVs have their limitations in their presented stage of development, this paper provides methods for incorporating uncertainty into emergy, and demonstrates how this can be depicted and propagated so that it can be used in future emergy analyses and permit emergy to be more readily incorporated into other methods of environmental assessment, such as LCA.     

Estimating a distance dependent contagion function using point sample data

Natural events and human activities cause changes in landscape structure. Landscape metrics are used as a useful tool to study landscape trends and ecological processes related to the landscape structure. These metrics are commonly calculated on wall-to-wall raster data from remote sensing. A recent trend is to use sample data to estimate landscape metrics. In this study, point sampling was used to estimate a vector-based and distance dependent contagion metric. The metric is an extension of the established contagion. The statistical properties, for both unconditional and conditional contagions, were assessed by a point (point pairs) sampling experiment in maps from the National Inventory of landscapes in Sweden. Random and systematic sampling designs were tested for nine point distances and five sample sizes and for two classification systems. The systematic design showed slightly smaller root mean square error (RMSE) and bias than the random design. Both true and estimated values were calculated using computer programs in FORTRAN, which was specifically written for the purpose of the study. For a given sample size, RMSE and bias increased with increasing point distance. The estimator of unconditional contagion had acceptable RMSE and bias for moderate sample sizes, but in the conditional case the bias (and thus the RMSE) was unacceptably large. The main reason for this is that small classes (by area) affect both the true value of the contagion and are often missing in the sample. The method proposed can be adopted in gradient-based model of landscape structure where no distinct border is assumed between polygons. The method can also be applied in field-based inventories.     

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

Statistical hypothesis testing formulations for U.S. environmental regulatory standards for ozone

Environmental regulatory standards are intended to protect human health and environmental welfare. Current standards are based on scientific and policy considerations but appear to lack rigorous statistical foundations and may have unintended regulatory consequences. We examine current and proposed U.S. environmental regulatory standards for ozone from the standpoint of their formulation and performance within a statistical hypothesis testing framework. We illustrate that the standards can be regarded as representing constraints on a percentile of the ozone distribution, where the percentile involved depends on the defined length of ozone season and the constraint is stricter in regions with greater variability. A hypothesis testing framework allows consideration of error rates (probability of false declaration of violation and compliance) and we show that the existing statistics on which the standards are based can be improved upon in terms of bias and variance. Our analyses also raise issues relating to network design and the possibilities of defining a regionally based standard that acknowledges and accounts for spatial and temporal variability in the ozone distribution.     

Estimating zooplankton biomass through image analysis

Zooplankton biomass can be reliably estimated by a non-destructive method based on the statistical relationship between biovolume and the contents of carbon and nitrogen. Integrated zooplankton biovolume and the corresponding organic C and N contents were analysed in paired fresh and fixed (hexamine-buffered formalin) samples. Sample biovolume was estimated through the integration of individual volumes obtained by semi-automatic image analysis on fixed zooplankton samples. The corresponding C and N contents of paired fresh and preserved zooplankton were analysed with a Carlo-Erba C-H-N Analyser. The method allows simultaneous measurement of integrated and individual numbers and biomass (biovolume, C and N) spectrum. It is non-destructive and allows the minimization of errors and variability attributable to differences in the handling processes of samples and to the presence of particles other than zooplankton, like phytoplankton or detritus particulate matter.     

基因间隔序列(ITS)在细菌分类鉴定和种群分析中的应用

Use of 16S -23S intergenic transcribed spacer (ITS) variability, as a relatively new method, is becoming an important supplement to the molecular methods based on 16S rRNA for which has a fairly constant size and is not divergent enough to give good separation in close relationships. This paper summarizes the structures and characteristics of ITS regions that are extremely variable in copy number, length and sequence per genome. The ITS region can be amplified easily taking advantage of conserved nucleotide stretches at the 5′of the 16S and 3′of the 23S gene, and the amplicon can contain different amounts of the 16S rDNA by choosing primers at different conserved areas within this gene. These primers are listed and discussed for perfecting the methodology of ITS. Furthermore, some recent progresses on the taxonomy, identification and community analysis of bacteria by means of ITS in epidemiology, ecology and artificial environment are reviewed, as well, the virtues and limitations of that method are discussed. Fig 2, Tab 1, Ref 51     

Dynamics and physiology of saxitoxin production by the dinoflagellatesAlexandrium spp.

Toxin content (fmol cell^–1) and a suite of elemental and macromolecular variables were measured in batch cultures of the dinoflagellatesAlexandrium fundyense, A. tamarense andAlexandrium sp. from the southern New England region, USA. A different perspective was provided by semicontinuous cultures which revealed sustained, steady-state physiological adaptations by cells to N and P limitation. Two types of variability were investigated. In batch culture, changes in nutrient availability with time caused growth stage variability in toxin content, which often peaked in mid-exponential growth. A second type of variability that could be superimposed on growth stage differences is best exemplified by the high toxin content of cells grown at suboptimal temperatures. Calculations of the net rate of toxin production (R _tox ; fmol cell^–1 d^–1) for these different culture treatments and modes made it possible to separate the dynamics of toxin production from cell division. Over a wide range of growth rates, cells produced toxin at rates approximating those needed to replace losses to daughter cells during division. The exception to this direct proportionality was with P limitation, which was associated with a dramatic increase in the rate of toxin production as cells stopped dividing due to nutrient limitation in batch culture. Growth stage variability in batch culture thus reflects small imbalances (generally within a factor of two) between the specific rates of toxin production and cell division. N limitation and CO₂ depletion both affect pathways involved in toxin synthesis before those needed for cell division; P limitation does the opposite. The patterns of toxin accumulation were the same as for major cellular metabolites or elemental pools. The highest rates of toxin production appear to result from an increased availability of arginine (Arg) within the cell, due to either a lack of competition for this amino acid from pathways involved in cell division or to increased de novo synthesis. There were no significant changes in toxin content with either acclimated growth at elevated salinity, or with short term increases or decreases of salinity. These results demonstrate that toxin production is a complex process which, under some conditions, is closely coupled to growth rate; under other conditions, these processes are completely uncoupled. Explanations for the observed variability probably relate to pool sizes of important metabolites and to the differential response of key biochemical reactions to these pool sizes and to environmental conditions.