An approach to uncertainty estimation in determining polycyclic aromatic hydrocarbons in fuel-contaminated soils

An estimation of uncertainty contribution in the entire set of the analytical data of polycyclic aromatic hydrocarbons (PAHs) in soil during a full year is presented. The analyses were performed as part of a project devoted to the assessment of the natural attenuation of selected PAHs in agricultural soil contaminated by a fuel spill. In particular, we investigated the effect of natural attenuation on the PAH dissipation rate in plots of two types of scenarios in agricultural soils. One of them was previously affected by an accidental fuel spill 10 years ago, and the other one was non-affected. The major components for estimating uncertainty contributions have been evaluated based on intermediate precision from different levels of PAH mass fraction, sample locations, and long term data set. An analysis of variance (ANOVA) test was applied to the mass fractions of clean or blank soil sub-samples versus contaminated soil sub-samples for grouping comparable data. Variables of influence in this study were site, month, and lysimeter of sampling (referring to a stainless steel box, with side lengths of 50 cm). Study sites or the spatial arrangement of lysimeter showed a clear effect in sub-sample results with the lowest mass fraction levels for heavier PAH, while monthly sampling was the variable of most significant influence for the more strongly contaminated sub-sample results.

Finally, the results of the PAH mass fractions in contaminated soil sub-samples indicated a monthly variation for which lighter, volatile PAHs rapidly decreased while the less volatile did not change.     

A new approach to spatial entropy measures

Entropy is widely employed in many applied sciences to measure the heterogeneity of observations. Recently, many attempts have been made to build entropy measures for spatial data, in order to capture the influence of space over the variable outcomes. The main limit of these developments is that all indices are computed conditional on a single distance and do not cover the whole spatial configuration of the phenomenon under study. Moreover, most of them do not satisfy the desirable additivity property between local and global spatial measures. This work reviews some recent developments, based on univariate distributions, and compares them to a new approach which considers the properties of entropy measures linked to bivariate distributions. This perspective introduces substantial innovations. Firstly, Shannon’s entropy may be decomposed into two terms: spatial mutual information, accounting for the role of space in determining the variable outcome, and spatial global residual entropy, summarizing the remaining heterogeneity carried by the variable itself. Secondly, these terms both satisfy the additivity property, being sums of partial entropies measuring what happens at different distance classes. The proposed indices are used for measuring the spatial entropy of a marked point pattern on rainforest tree species. The new entropy measures are shown to be more informative and to answer a wider set of questions than the current proposals of the literature.     

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. We used data from an 8‐year study in Savé Valley Conservancy, Zimbabwe, to show how geographic profiling—a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology—can be adapted for use in investigations of wildlife crime. The data set contained information on over 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. We used a subset of data for which the illegal hunters’ identities were known. Our model identified the illegal hunters’ home villages based on the spatial locations of the hunting incidences (e.g., snares). Identification of the villages was improved by manipulating the probability surface inside the conservancy to reflect the fact that although the illegal hunters mostly live outside the conservancy, the majority of hunting occurs inside the conservancy (in criminology terms, commuter crime). These results combined with rigorous simulations showed for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns, for example, where landscape heterogeneity means some parts of the study area are less likely to be used (e.g., aquatic areas for terrestrial animals) or where landscape permeability differs (e.g., forest bats tend not to fly over open areas). More broadly, these results show how geographic profiling can be used to target antipoaching interventions more effectively and more efficiently and to develop management strategies and conservation plans in a range of conservation scenarios.     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.

An integrated approach to mangrove dynamics and management

The main objective of the MADAM project (Mangrove Dynamics and Management) is to generate the scientific basis enabling the sustainable stewardship of the resources of the Caeté mangrove estuary in Northeast Brazil in the sense of integrated coastal (zone) management. To achieve this, it is necessary to acquire in-depth knowledge of natural processes as well as of the relevant institutional, cultural, economic, social and political dynamics. Causal linkages within the ecosystem, as well as between ecosystem, economy and society, are analysed and explained via dynamic and trophic modelling. Scenario construction is intended to forecast the effects of acute or chronic interference on utilized resources, and to answer wider, management-related questions (e.g. restoration of destroyed areas, utilization potential for aquaculture). This paper describes the project strategy as developed and modified in the context of research results from the initial 2-yr project phase. It is argued that a continuous discussion process is essential to assess the validity of the strategies formulated at the beginning of a medium-time project, particularly if the project is of interdisciplinary nature.     

Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values

In many environmental and ecological studies, it is of interest to model compositional data. One approach is to consider positive random vectors that are subject to a unit-sum constraint. In landscape ecological studies, it is common that compositional data are also sampled in space with some elements of the composition absent at certain sampling sites. In this paper, we first propose a practical spatial multivariate ordered probit model for multivariate ordinal data, where the response variables can be viewed as the discretized non-negative compositions without the unit-sum constraint. We then propose a novel two-stage spatial mixture Dirichlet regression model. The first stage models the spatial dependence and the presence of exact zero values, and the second stage models all the non-zero compositional data. A maximum composite likelihood approach is developed for parameter estimation and inference in both the spatial multivariate ordered probit model and the two-stage spatial mixture Dirichlet regression model. The standard errors of the parameter estimates are computed by an estimate of the Godambe information matrix. A simulation study is conducted to evaluate the performance of the proposed models and methods. A land cover data example in landscape ecology further illustrates that accounting for spatial dependence can improve the accuracy in the prediction of presence/absence of different land covers as well as the magnitude of land cover compositions.     

Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

A state-space model approach to optimum spatial sampling design based on entropy

We consider the spatial sampling design problem for a random field X. This random field is in general assumed not to be directly observable, but sample information from a related variable Y is available. Our purpose in this paper is to present a state-space model approach to network design based on Shannon's definition of entropy, and describe its main points with regard to some of the most common practical problems in spatial sampling design. For applications, an adaptation of Ko et al.'s (1995) algorithm for maximum entropy sampling in this context is provided. We illustrate the methodology using piezometric data from the Velez aquifer (Malaga, Spain). © Rapid Science 1998     

An improved formal approach to demographic loop analysis

Loop analysis is introduced to demographic analysis as a tool to compare relative contributions of different life-history types to population growth rate. In 1998, G. M. Wardle brought in basic concepts of the graph theory to demographic loop analysis and proposed a methodology to determine the loops from any life-cycle graph based on these concepts. However, the mathematics behind Wardle's methodology cannot readily be used by most population ecologists. A new methodology that is also based on graph theory concepts but both makes ecological sense in its application and is simpler to implement is proposed. Three rules of thumb serve as the basis of the proposed methodology that brings a more systematic approach to loop selection: it identifies only those loops that are ecologically meaningful (i.e., loops that are forward-flowing and with positive elasticity values). Thus, it produces a loop set that is more amenable to answer questions on comparison of different lifehistory types. It is tested on several life-cycle graphs from the literature. Three of these are presented: Vouacapoua americana, Dipsacus sylvestris, and Alcyonium sp. In each case, the methodology successfully produced a loop set that makes sense in terms of the ecology of the species. The methodology is also implemented as a couple of open-source computer codes. It is hoped that the proposed methodology will lead to wider use of loop analysis in demographic population studies.     

An application of Bayesian variable selection to spatial concurrent linear models

Spatial concurrent linear models, in which the model coefficients are spatial processes varying at a local level, are flexible and useful tools for analyzing spatial data. One approach places stationary Gaussian process priors on the spatial processes, but in applications the data may display strong nonstationary patterns. In this article, we propose a Bayesian variable selection approach based on wavelet tools to address this problem. The proposed approach does not involve any stationarity assumptions on the priors, and instead we impose a mixture prior directly on each wavelet coefficient. We introduce an option to control the priors such that high resolution coefficients are more likely to be zero. Computationally efficient MCMC procedures are provided to address posterior sampling, and uncertainty in the estimation is assessed through posterior means and standard deviations. Examples based on simulated data demonstrate the estimation accuracy and advantages of the proposed method. We also illustrate the performance of the proposed method for real data obtained through remote sensing.     

Theory and estimation of the household production function for wildlife recreation

The household production function is an intuitively appealing way to model man's interaction with nature. This paper models the interaction between the household's behavior and publicly provided inputs into wildlife recreation. The paper shows how to compute benefits, assuming that the household production function is known. The household production function approach collapses to the simple travel cost approach when households are unable to substitute their own inputs for publicly provided inputs. In addition, the paper demonstrates the conditions under which the parameters of cost and preference functions can be identified. The conditions for identification are quite restrictive when several choices are endogenous.     

A fractal approach for detecting spatial hierarchy and structure on mussel beds

Within beds of blue mussel (Mytilus edulis L.), individuals are aggregated into small patches, which in turn are incorporated into bigger patches, revealing a complex hierarchy of spatial structure. The present study was done to find the different scales of variation in the distribution of mussel biomass, and to describe the spatial heterogeneity on these scales. The three approaches compared for this purpose were fractal analysis, spatial autocorrelation and hierarchical (or nested) analysis of variances (ANOVA). The complexity (i.e. patchiness) of mussel aggregations was described with fractal dimension, calculated with the semivariogram method. Three intertidal mussel beds were studied on the west coast of Sweden. The distribution of wet biomass was studied along transects up to 128 m. The average biomasses of blue mussels on the three mussel beds were 1825낚, 179ᆩ and 576ᇖ g per0.1 m², respectively, and the fractal dimensions of the mussel distribution were 1.726ǂ.010, 1.842ǂ.014 and 1.939ǂ.029 on transects 1-3, respectively. Distributions of mussels revealed multiscaling behaviour. The fractal dimension significantly changed twice on different scales on the first bed (thus showing three scaling regions), the second and third beds revealed two and three scaling regions, respectively. High fractal dimension was followed by significant spatial autocorrelation on smaller scales. The fractal analysis detects the multiple scaling regions of spatial variance even when the spatial structure may not be distinguished significantly by conventional statistical inference. The study shows that the fractal analysis, the spatial autocorrelation analysis and the hierarchical ANOVA give complementary information about the spatial variability in mussel populations.     

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.     

An approach to evaluation of sustainability for Guangzhou's urban ecosystem

Guangzhou has ambitions to build itself into a world class metropolis by 2010. Sustainable development is the only way to achieve this magnificent goal. Based on the ecological perspective of sustainable development and the principles of ecosystem integrity, this paper develops an approach for evaluation of sustainable development in Guangzhou between 1986 and 1995. A hierarchical evaluation system of four tiers of sustainability indicators was established. Using the method of fuzzy multistage synthetic evaluation, sustainability development level index, QIx, was calculated for the indicators at the B, C, D, and E tiers. Development stages were identified based on these index values. The coordination degree among the economic, social, and natural subsystems was also computed. Further, an overall sustainability index for each year was computed by combining the development level index and the coordination degree. It was found that the urban ecosystem in Guangzhou had generally become more sustainable, in spite of fluctuations in coordination degree. The development level index of the economic subsystem has surpassed that of social and natural subsystems since 1995. Appropriate measures must be taken to ensure coordinated development among the subsystems for the purpose of sustainable development.     

茶树上游猎型蜘蛛功能团对景观低碳管理模式的反应

目前温室效应加剧、生物多样性减少以及农田环境污染加重等问题已成为关注的焦点。由于蜘蛛是环境变化的指示标志和农业生中的重要捕食性天敌。通过选择茶树上游猎型蜘蛛为研究对象,调查分析景观低碳管理模式茶园（JG）与习惯管理模式茶园（XG）对茶树上游猎型蜘蛛组成、结构以及群落数量特征的影响。结果表明：JG茶园茶树上游猎型蜘蛛种类较多,有8个科,18个种;XG茶园有6个科,13个种;优势种和丰盛种中,JG茶园个体数为375头、195头,比XG茶园分别提高1.16、0.33倍。JG与XG两类茶园的蜘蛛种群数量特征在年龄结构、性别比例方面存在显著差异（P＜0.001）;JG茶园雌蛛个体数为157头、雄蛛个体数为320头,是XG茶园的1.12和1.32倍;JG茶园成蛛与幼蛛个体数是XG茶园的1.25和0.87倍,成蛛比幼蛛高出46.32%,而XG茶园成蛛比幼蛛高出21.82%;JG茶园蜘蛛种群数量特征发生动态显著高于XG茶园（P＜0.05）。JG茶园蜘蛛多样性指数和丰富度指数比XG茶园增加16.46%和12.14%。主成分分析表明,第一、二主成分累计贡献率达50.47%, JG茶园位置明显接近光盔蛛科（Liocranidae）、圆颚蛛科（Corinnidae）、跳蛛科（Salticidae）和管巢蛛科（Clubionidae）等蜘蛛群落数量分布较多的方向偏移,而XG茶园则向盗蛛科（Pisauridae）和猫蛛科（Oxyopidae）等蜘蛛群落数量分布较少方向靠近。由此可知,茶园实行低碳管理,实行复合种植,有利于减少CO2气体排放,保护茶树上游猎型蜘蛛种类多样性,提高对害虫的自然控制能力,为茶园固碳增汇,茶叶产值提高打下了基础。     

农业技术的综合评价指标初探

为了对农业技术进行推广前景预测或推广后的效益评估，建议采用生物学的合理性、技术的可行性、经济的有利性、生态学的持续性和社会的可接受性作为综合评价的原则．并提出了相应的定量比评价指标体系。     

农业生态经济系统生态效益综合评价方法探讨

根据系统思想和生态学原理，探讨了农业生态经济系统的生态效益和功能的定量评价方法，并给出了相应的计算规则．同时还阐述了这一方法在广西壮族自治区来宾县的实际应用。     

Aluminium and Alzheimer's disease: An epidemiological approach

Epidemiological methods have an important role in the investigation of the postulated connection between exposure to aluminium and the development of Alzheimer's disease. We have examined the usefulness of existing data on prevalence and mortality as a resource for studying variations in the rate of the disease with time and geography. Unfortunately, methodological differences between prevalence surveys and errors and biases in mortality data are large. No reliable conclusions can be drawn from these data about geographical differences in rates of dementia in England and Wales nor about time trends in the disease.Aluminium salts are widely used in the UK for the treatment of drinking water. Residual aluminium concentrations vary more than ten fold between different parts of the country. We have estimated diagnostic rates of pre-senile Alzheimer's disease in seven geographical areas and examined the correlation between rates of Alzheimer's disease and water aluminium concentration.     

HOMINIDS: An agent-based spatial simulation model to evaluate behavioral patterns of early Pleistocene hominids

The HOMINIDS ABM is a new Agent Based Model that simulates the actions of two species of proto-human agents defined by a few, simple parameters. These proto-human agents attempt to subsist by foraging and nesting on dynamic, spatially explicit landscapes. The landscapes are described with a number of parameters based on empirical field data collected in habitats in East Africa. The results of three separate scenarios with 1 year model runs repeated 30 times each, for a total of 90 simulations, are presented and discussed to illustrate both the capacity and flexibility of our ABM modeling environment. The simulations show that the model food preferences and anatomy ascribed to Australopithecus boisei resulted in different expressions of foraging behaviors and subsistence strategies in two distinct ecological settings, and that adding tubers to the diet significantly increases the chances of the hominid agents meeting their daily caloric requirements year-round. In addition, this paper provides links to the open-source implementation code, along with the user documentation, design document, java API, and all datasets required to replicate the simulations.