bluuebunny/crossref_metadata_2025 huggingface dataset:
Wildlife ecology
Authors: Bowyer R. Terry , Stewart Kelley M. , Monteith Kevin L. , Long Ryan A.
Published: January 2013

Studies in wildlife ecology often involve species that are hunted, trapped, or are of additional economic or aesthetic importance to humans, including species that are rare, threatened, or endangered. The focus of the discipline is on the population rather than the individual; consequently, population ecology is central to wildlife conservation and management. Wildlife ecology has its philosophical and scientific beginnings in the early works of Aldo Leopold. Modern wildlife ecology often applies ecological theory to management and conservation of animals, but also includes theoretical studies on species that are considered wildlife. Wildlife ecology has become increasingly quantitative over time, and often employs capture‐recapture methods to estimate population size, survival, and other life‐history characteristics. Quantitative methods used to study wildlife include, but are not limited to, estimates of abundance, population viability analysis, home‐range estimation, movements, migration, spatial distributions, and landscape metrics. Radio telemetry is widely used to conduct research on wildlife. Habitat selection, stable isotope analysis, genetics, and behavioral sampling also play important roles in studies of wildlife.
World Climate Programme
Authors: El‐shaarawi Abdel H.
Published: January 2013

World Climate Program was established after the holding of the First World Climate Conference in Geneva in 1979. It consists of four program components: Data and Monitoring; Applications and Services; Impact Assessment and Response Strategies; and Research. These will provide an overall picture for understanding the climate system.
Microbial Regulations
Authors: Wade Timothy J. , Dufour Alfred P. , Soller Jeffrey A. , Wymer Larry
Published: January 2013

Until relatively recently, exposure to fecally contaminated water caused massive morbidity and mortality due to diseases such as typhoid and cholera. While waterborne disease remains a major threat to public health in the developing world, the treatment and disinfection of public water and wastewater supplies have vastly reduced the transmission of these and other waterborne infectious diseases. The discovery and application of fecal indicator bacteria as a simple marker of feces resulted in simple methods to identify contaminated water sources and has been used worldwide. Despite these and other recent improvements in sanitation and hygiene, challenges remain. Even in developed countries, outbreaks of waterborne disease occur regularly affecting thousands each year. Outbreak surveillance, epidemiology studies, and quantitative microbial risk assessment can be applied to identify, prioritize, and quantify risks. Events such as the reemergence of cholera in Haiti emphasize the need for continued vigilance to maintain the microbial safety of water supplies.
The Canadian Environmental Sustainability Indicators: On Population‐Weighted Ground‐Level Ozone
Authors: Rastan Soheil , St. Lawrence Joe , Herod Dennis
Published: January 2013

This article extends the trend analysis and builds upon the Canadian Environmental Sustainability Indicators (ground‐level ozone indicator). It presents two additional population‐weighted ground‐level ozone concentration trends from 1990 to 2005. During this period, the estimated increase in population‐weighted concentration levels, based on the lower end of the annual concentration data, was statistically more significant than the estimated increase based on the middle range of the annual concentration data. However, no increase or decrease in population‐weighted concentration levels was detected in the upper end of the data. The results of this hypothesis‐generating study indicate that as far as the national estimate of population‐weighted eight‐hour maximum ground‐level ozone concentration is concerned, the good days are getting worse and the bad days remain the same.
Carbon Capture and Storage, Regulatory Framework
Authors: Torvanger Asbjørn
Published: January 2013

Carbon capture and storage (CCS) has potential to become a major option to mitigate emissions of greenhouse gases globally, but this requires a well‐functioning regulation system at both national and international levels. Owing to the long‐term perspective needed for geological storage of carbon dioxide and uncertainties with regard to characteristics and integrity of storage sites, storage is more challenging to regulate than capture of carbon dioxide from exhaust gases and transport of the compressed gas to a storage site. This article gives an overview of the status of CCS regulation at the international level and in some key countries, and discusses the main issues CCS regulation is facing as well as possible solutions.
E
nvironmental
R
egulation in the
E
uropean
U
nion
Authors: FassÒ Alessandro , Arduino Giorgio
Published: January 2013

This article briefly reviews the European legislation on the environment with particular focus on air quality. It considers the historical development starting from the Treaty establishing the European Community and ending in the IPR Decision on Clean Air Directive. It also addresses some issues on data availability.
Extremes: Spatial Parametric Statistical Modeling
Authors: Davison Anthony C.
Published: January 2013

Statistics of spatial extremes is developing very rapidly, owing to the demands of applications in the environmental sciences and the insurance and risk industries. This article sketches the main ideas, based on classical extreme value statistics. The two main threads of work are the fitting of max‐stable models to spatial extremes, and the use of latent variable models, the latter typically in a Bayesian context.
Copula Modeling for Extremes
Authors: Genest Christian , Nešlehová Johanna
Published: January 2013

This article introduces extreme‐value copulas, reviews models from this class, and describes their main properties. The associated rank‐based estimation procedures and model validation techniques are also presented.
Flood Risk Management, Quantitative Methods
Authors: van Gelder P. H. A. J. M
Published: January 2013

In this article, quantitative methods for flood risk management are discussed. Reliability analysis of flood defense systems and the probabilistic flood risk management approach are outlined. The application of quantitative methods offers the designer a way to unify the design of engineering structures, processes, and management systems. For this reason, there is a growing interest in the use of these methods in the design and safety assessment of flood defenses. This article describes the background of probabilistic analyses, uncertainties, and systems analysis for flood risk management.
Multivariate Extremes
Authors: Bortot Paola , Gaetan Carlo
Published: January 2013

This paper reviews the main probabilistic results on multivariate extremes. Historically, this branch of probability focused on the limiting distribution of the componentwise maximum of independent and identically distributed random vectors. A number of equivalent asymptotic characterizations are available and are summarized. Inferential issues arising from the statistical application of classical results are discussed. They motivate the development of alternative models for multivariate extremes, based on multivariate threshold exceedances, which have received increasing attention in the literature; these will also be reviewed.
Wildfire risk analysis
Authors: Rego Francisco C. , Colaço Maria Da Conceição A.
Published: January 2013

Wildfires are natural hazards that have both biological components (vegetation as fuel) and meteorological components (air temperature and humidity, wind). It can be defined as “any unplanned and uncontrolled vegetation fire which, regardless of the ignition source, may require suppression response or other actions according to agency policy.”
Probabilistic Geotechnical Hydraulics
Authors: van Gelder P.H.A.J.M.
Published: January 2013

In the field of hydraulic and geotechnical engineering and especially in the overlapping field, geotechnical hydraulic engineering, there are a large number of issues, which are probably solvable with a classical engineering approach, but which can be brought to a better financial and social optimum with a probabilistic approach. The reason for this is that in hydraulic engineering, the size and probability of exceedance of loading variables are only partially known and that in geotechnical engineering the strength variables of soils are also only partially known. The soil behavior depends strongly on its location and also on time because of the pore water pressures, for example, the softening of dikes at high water levels and the strength dissipation at excavations (building pits). In this short article, important research issues within geotechnical hydraulic engineering are discussed, followed by the modeling of failure mechanisms and random fields.
Thresholds, Ecological
Authors: Toms Judith D.
Published: January 2013

Ecological thresholds occur when the relationship between the response and explanatory variables is nonlinear, changing abruptly at one or more values. Such thresholds may be continuous or discontinuous, and different methods for estimating the location and number of thresholds have been developed for each type. For continuous thresholds, methods based on piecewise regression are preferred. Discontinuous thresholds can be transformed using the cumulative sum, resulting in transformed data with continuous thresholds, and piecewise regression techniques are then used for inference. Alternatively, Bayesian change‐point analysis can be used for inference. Special methods have been developed for detecting thresholds in spatial data.
Genetically Modified Organisms
Authors: Perry Joe N.
Published: January 2013

Genetically modified organisms (GMOs) are those in which the genetic material (the DNA) has been altered in a way that does not occur naturally. In all countries, the release of GMOs into the environment is regulated and a risk assessment is required. GMOs have many medical applications, but this article focuses on agricultural and ecological uses. The principle of the comparative approach is explained and various statistical issues arising from it are discussed. These include the need for an equivalence or noninferiority test in addition to the traditional difference test; the need for power analysis; consumer and producer risk, false positives and false negatives; multiple testing and sample surveys. The importance of meta‐analyses, mathematical modeling, and uncertainty analysis is stressed. The issue of estimation for events that are deemed risks but which have never been observed is considered. Two case studies provide further background reading. The article finishes with some challenges for estimating the environmental impact of future GMOs.
Causal Assessment
Authors: Norton Susan B. , Suter Glenn W.
Published: January 2013

Causal assessments bring together information to reach conclusions about how biological effects happened or can be produced. In environmental studies, a causal assessment may be prompted by observations of undesirable biological effects, concerns over a source or stressor, or questions about the efficacy of management actions. Observational studies are often the principal source of evidence available for causal assessments of environmental issues, but are problematic because causes are not manipulated, replicated, or randomly applied. Pragmatic strategies for interpreting observational study results for causal assessment include breaking up and analyzing causal pathways by mechanistic segment; comparing evidence for alternative causal explanations; using study design or statistical techniques to isolate the effects of individual variables; modeling the effects of multiple variables together; and supplementing observational results with evidence of sufficiency, exposure, and symptoms. Weight‐of‐evidence approaches are well suited to causal assessments because they can provide a common framework for synthesizing disparate types of information while retaining the evidence supporting or refuting conclusions.
Farm Odor
Authors: Smith Eric P.
Published: January 2013

Farm odor is becoming an increasingly important environmental problem due to the increase and specialization of farms and proximity to housing. Two common methods for odor measurement, olfactometry and gas chromatography, are described. Odor regulation and reduction methods are discussed.
Fisheries Stock Assessment
Authors: Jiao Yan
Published: January 2013

Natural resource modeling of fisheries is called stock assessment and is crucial for proper fisheries management. This paper reviews the commonly used models, the data needed, and the parameter estimation methods for these models in fisheries stock assessment. Fisheries management strategy evaluation and decision analysis, which are based on the models developed and the estimated parameters, are discussed. Newly developed models and modeling techniques are also reviewed briefly.
Geostatistical Methods in Water Resources
Authors: Fasbender Dominique
Published: January 2013

This article intends to present, through various examples of applications, how geostatistical methods have contributed to a better understanding of water resources around the world. Each section is dedicated to a particular water resource and regroups the different applications in which geostatistical methods have been applied. These sections are not meant to be exhaustive but illustrative. A final section presenting a list of useful software and toolboxes has also been added.
Multivariate Analysis of Hydrological Variables
Authors: Chebana Fateh
Published: January 2013

Hydrological events are described through a number of dependent features. To be correctly treated, the latter should be considered jointly in a multivariate framework. The aims of the multivariate hydrological frequency analysis are given and the justifications of adopting the multivariate setting in hydrology are discussed. The main steps of the analysis are described. Some extensions and perspectives are presented.
Floods and Changing Climate: Seasonal Forecasts and Reconstruction
Authors: Sankarasubramanian A. , Lall Upmanu , Rajagopalan Balaji
Published: January 2013

It is widely acknowledged that both climate and land use changes modify flood frequency, thereby challenging the traditional assumption that the underlying stochastic process is stationary in time, and that the annual maximum flood corresponds to an independent identically distributed (iid) process. In this article, we employ a semiparametric approach to estimate flood quantiles conditional on selected “climate indices” that carry the signal of structured low‐frequency climate variation, and influence the atmospheric mechanisms that enhance or retard local precipitation and flood potential. The semiparametric approach that maximizes the local likelihood of the observed annual maximum peak in the climatic predictor state space is applied to estimate conditional flood quantiles for the Blacksmith Fork River near Hyrum (BFH), Utah. The estimated conditional flood quantiles correlate well with the observed annual maximum peaks, thus offering prospects for reconstructing past flood series as well as for short‐term forecasting.
Drought
Authors: Wilhite Donald A. , Hayes Michael J. , Svoboda Mark D.
Published: January 2013

Drought is a naturally occurring event that is associated with virtually all climatic regions. There is no universal definition of drought since its characteristics and impacts vary by region and economic sector. Given its slow onset and other characteristics, including its spatial dimensions and duration, impacts are difficult to assess and have been, historically, poorly documented. These impacts are strongly influenced by a society's exposure to the hazard and the vulnerability of that society to the hazard. This vulnerability is continually changing in response to increasing population, land use changes, technology, government policies, and many other factors. Therefore, each drought event is superimposed on a society with differing vulnerabilities than existed when the previous drought event occurred. Drought impacts are increasing worldwide, both as a result of these changing vulnerabilities and, perhaps, because of an increase in the frequency, severity, and duration of drought events in association with a changing climate. To lessen societal vulnerability, it is imperative for nations to move away from crisis management approach to drought management and toward a more proactive, risk‐based approach. Emphasis must be given to the development of improved drought monitoring and early warning systems and the delivery of this information to decision makers at all levels. It is also essential that vulnerability assessments be conducted in order to determine who and what is at risk and why. A final step is the identification and implementation of appropriate mitigation measures or actions that will reduce future impacts on economic sectors and population groups.
Kriging for Functional Data
Authors: Giraldo Ramón , Mateu Jorge
Published: January 2013

Spatial functional data analysis is a relatively new topic in statistics. We have functional data when a random variable takes its values in a functional space and is observed at some sampling points. In this article, we focus on the case of functional data presenting spatial dependence, and present an overview of the existing methodology to make spatial predictions when the data values are functions. Kriging and its several related versions are widely known and used for spatial data. However, when the spatial data are functions, a bridge between functional data analysis and geostatistics has to be built. Ordinary kriging as well as cokriging and multivariate spatial prediction adapted to the case where the observations at each sampling location consist of samples of random functions are reviewed and compared.
Machine Learning
Authors: Vishwanathan S. V. N.
Published: January 2013

With the advent of modern technology, vast amounts of data are being collected in many fields, particularly in environmental and biological sciences. Machine learning, which is concerned with the design and development of algorithms that allow computers to extract useful patterns and trends automatically in order to make predictions, is a scientific discipline at the interface between statistics and computer. In this article, we focus on one particular learning paradigm, namely,
regularized risk minimization
, and show that it embodies some traditional statistical methods such as LASSO, linear regression, and kriging.
Geostatistics, Model‐Based
Authors: Zhu Jun , Thurman Andrew L.
Published: January 2013

Classical geostatistics involves statistical inference for random fields with a continuous spatial index based on geostatistical data, but the methods tend to be ad hoc. Alternatively, likelihood‐based and Bayesian methods provide more efficient estimation of model parameters and more rigorous quantification of uncertainty in prediction of responses at unsampled locations.
Satellite Data
Authors: Shao Guofan
Published: January 2013

Earth observation satellites collect data relevant to a wide range of land resource topics and there are a variety of satellite data that are designed for different purposes. Satellite data are collected, transmitted, stored, and analyzed in digital forms. Satellite data have a broad range of spatial, spectral, radiometric, and temporal characteristics. Commonly used terms are spatial, spectral, radiometric, and temporal resolutions. A general indication of advancement of satellite data is that their resolutions are getting higher over time. Satellite can be used alone, to derive new data, and integrated with other geospatial data sources. While most of commercial satellite data can be purchased from data providers, many governmental satellite data can be downloaded without charge from US government websites.
Screening Effect
Authors: Du Juan
Published: January 2013

This article reviews various applications and some theory of the screening effect, which describes the phenomenon where a spatial interpolant primarily depends on nearby observations in kriging. Justified by the screening effect, the nearest‐neighbor approach has been widely used in practice, mainly to reduce computational burden. However, caution needs to be exercised in choosing model and observation locations, since there may be no screening effect in some cases.
Sensor Network
Authors: Zhu Zhengyuan
Published: January 2013

This article provides an introduction to sensor networks. A brief history of the development of sensor networks is given, followed by some examples of environmental sensor networks. Issues in sensor network design and data collection are discussed, along with pertinent references
E
nsemble
Authors: Li Bo
Published: January 2013

This article introduces the concept of ensemble, mainly in the context of environmental and climate studies. It summaries several different types of ensembles and discusses why in general the ensemble is a popular and useful tool for problems with a large degree of freedom. Finally, it illustrates this concept through an example of past temperature reconstruction.
Spatial Scan Statistics
Authors: Zhang Tonglin
Published: January 2013

This article reviews the spatial scan statistic, including its definition, adjustment for overdispersion, bootstrap methods for
p
‐value computation, and selected software for implementation.
Singular Value Decomposition and High Dimensional Data
Authors: Allen Genevera I. , Perry Patrick O.
Published: January 2013

The singular value decomposition and related principal components analysis are foundational multivariate methods used for dimension reduction and exploratory analysis of high‐dimensional data. Studying this decomposition using random matrix theory, however, reveals troubling behavior when the number of variables is larger than the number of samples. This article surveys random matrix theory results related to the singular value decomposition and high‐dimensional data as well as recent developments such as sparse principal components analysis. Software on the random matrix theory distributions related to the singular value decomposition can be found elsewhere; several software packages implementing methods for sparse principal components analysis are also available.
Uncertainty and Computer Models
Authors: Sain Stephan R.
Published: January 2013

With the explosion of computing power in recent years, computer models have become important tools for scientists to study the behavior of different phenomena as well as for prediction and decision making. However, there are many sources of uncertainty in computer models that can have significant impact on what is learned from such models. The field of uncertainty quantification, which combines elements from statistics, applied mathematics, and computational science, is emerging to provide tools to better understand, quantify, and mitigate the many uncertainties in computer models, and this article covers some of the key issues and problems facing researchers in this area.
Fate and Transport Modeling
Authors: Raun Loren
Published: January 2013

The use of environmental contaminant fate and transport models stems from the need to predict a concentration of contaminant at a spatial location separate from the source at a specified temporal point. The models simulate a simplification of the real‐world scenario. A prediction of this sort is used to support decisions in a wide range of hazardous waste contaminant source scenarios, from small and localized to major industrial. The level of complexity of the model chosen is contingent upon the configuration of the source, the temporal question, the end use needs, and the availability of field data. The theoretical basis for the most common models, the methods used to solve the equations, and the general use of the models to predict the fate and transport of contaminants in groundwater are reviewed. Further, the assumptions, varying levels of model complexity, and sources of uncertainty are discussed.
Analysis of Variance, Multivariate (
MANOVA
)
Authors: Bilodeau Martin
Published: January 2013

A designed experiment is set up in which the system studied is under the control of an investigator. The individuals, the treatments, and the variables measured are decided by the investigator. In a clinical trial, patients satisfying some eligibility criteria are assigned at random, either to a new treatment or to a placebo. Patients are followed for some time and a few responses are recorded for each patient. In an agricultural experiment, a field is divided into plots of shape and size determined by the investigator. The plots are assigned at random one among a few treatments which could be, for example, different fertilizers. The variables measured on each plot could be the yields of some crops. The general objective of a designed experiment is to assess the effects of different treatments on the responses. These effects are evaluated by statistical estimates and confidence intervals of the magnitude of the differences between treatments. The estimates should avoid biases and the random errors should be minimized as much as possible. The statistical methodology used to analyze such designed experiments in which there are several responses is termed
MANOVA
, an acronym for multivariate analysis of variance.
Common Principal Components
Authors: Verdebout Thomas
Published: January 2013

The Common Principal Components model is a multivariate model in which the covariance matrices
Σ

i

,
i
 = 1,…,
m
of
m
 > 1 populations are assumed to have identical eigenvectors. Gaussian parametric inference methods are provided for common principal components. We present those Gaussian parametric tools and discuss several attempts to build robust testing methods for the null hypothesis of Common Principal Components.
Copulas and Copula Models
Authors: Genest Christian , Nešlehová Johanna
Published: January 2013

This entry introduces the notion of copula, reviews classical copula models, and describes their main properties. The entry also presents rank‐based estimation procedures and goodness‐of‐fit tests for copula modeling.
Depth
Authors: Serfling Robert
Published: January 2013

Depth functions represent a recently emerging powerful methodology in nonparametric multivariate inference. They provide multivariate notions of order statistics and generate quantile contours, outlyingness functions, and sign and rank functions. There are wide possibilities for constructing such functions, but these narrow considerably when criteria such as affine equivariance or invariance, computational ease, asymptotic behavior, and robustness are applied. We review basic definitions, leading examples, key properties, and selected applications.
Factor Analysis, Dynamic
Authors: Lippi Marco
Published: January 2013

We shortly reviewed small‐ and medium‐sized standard and dynamic factor analysis, and then concentrate on recent studies on high‐dimensional dynamic factor models. The latter allow the construction of consistent estimators of the factor model, which are based on principal components either in the time or in the frequency domain. Though mainly developed within Time Series Econometrics, high‐dimensional factor models are quite appealing for applications to environmental datasets.
Hotelling'sT2Tests (Robust Versions Of)
Authors: Hallin Marc , Van Aelst Stefan
Published: January 2013

Hotelling'sT2tests are the classical testing procedure for one‐ and two‐sample location problems under Gaussian assumptions. Although asymptotically valid under non‐Gaussian distributions with finite second‐order moments, they are highly nonrobust. Robust and rank‐based alternatives, resisting violations of the finite second‐order moment assumption, are described.
LAD
Estimation with Applications in Time Series Analysis
Authors: Davis Richard A. , Wu Rongning
Published: January 2013

In this article, we are concerned with least absolute deviation (LAD) estimation applied to linear regression and linear time series models. LAD estimation is widely used in statistical modeling and analysis. Compared to classical least squares (LS), LAD is a robust estimation procedure and is typically more efficient when handling heavy‐tailed data. We provide a selective overview on its developments in linear regression and time series models. This overview includes a description of a basic method for establishing the asymptotic properties of the LAD estimator for finite variance autoregressive moving average (ARMA) models. Efficiency of LAD estimation relative to LS estimation as well as computational issues are also discussed.
Random Fields, Nonparametric Methods
Authors: Tjøstheim Dag
Published: January 2013

An overview is given of nonparametric methods in random fields including estimation of the density, the autocovariance, the specrum, and nonlinear additive models. The difference between expanding domain asymptotics and infill asymptotics is highlighted. Throughout the article similarities and differences relative to time series modeling are pinpointed.
Runs Tests
Authors: Ley Christophe , Paindaveine Davy
Published: January 2013

Given a sequence of two or more types of symbols, a run is defined as a succession of – one or more – identical symbols that are followed and preceded by a different symbol (or by no symbol at all). Runs tests are based on the length of the longest run or on the total number of runs. They figure among the oldest nonparametric procedures, and can be used in various setups. We review the different definitions of runs, their development over the last 100 years, describe the domains of application of runs tests, and discuss their extension to the multivariate case.
Principal Components
Authors: Hallin Marc , Hörmann Siegfried
Published: January 2013

Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observedk‐dimensional variables in such a way that the new variables are vectors ofkmutually orthogonal (uncorrelated) components – theprincipal components– ranked by decreasing variances. In case the original variables were strongly interrelated, the few first principal components, typically, will account for most of the variation in the original data. Restricting to those few components then allows for a reduction in the dimension of the dataset that retains most of the variability of the original one. Karl. Pearson is generally credited for introducing the method in 1901. The same method was rediscovered in 1933 and popularized by Hotelling; ever since, it has been an essential part of daily statistical practice, basically in all domains of application. From a mathematical point of view, the method is directly rooted in the linear algebra of positive semidefinite matrices and their spectral factorization.
Exponential Families
Authors: Hallin Marc
Published: January 2013

Exponential families of distributions are parametric dominated families in which the logarithm of probability densities take a simple bilinear form (bilinear in the parameter and a statistic). As a consequence of that special form, sampling models in those families admit a finite‐dimensional sufficient statistic irrespective of the sample size, and optimal solutions exist for a number of statistical inference problems: uniformly minimum risk unbiased estimation, uniformly most powerful one‐parameter one‐sided tests, and so on. Most traditional families of distributions–binomial, multinomial, Poisson, negative binomial, normal, gamma, chi‐square, beta, Dirichlet, Wishart, and many others–constitute exponential families. Note, however, that the uniform, logistic, Cauchy, or Student (for given degrees of freedom) location‐scale families are not exponential; the double‐exponential or Laplace family is exponential for scale only, at fixed location.
Multinomial Distribution, Inferences for
Authors: Hallin Marc
Published: January 2013

This article briefly summarizes, for the purpose of easy reference, the definition and main properties of multinomial distributions.
Microbial Bioremediation of Metals and Radionuclides
Authors: Gadd Geoffrey M.
Published: January 2013

Microorganisms possess a variety of mechanisms that effect changes in metal speciation and mobility. As well as being components of natural elemental cycles for metals, these processes may be exploited in environmental biotechnology and almost all metal‐microbe interactions have been examined for removal, recovery, or detoxification of metal, organometal, metalloid, or metal radionuclide pollutants. Metal mobilization can be achieved by, for example, protonation, chelation, chemical and redox transformations, while immobilization can occur by precipitation or biomineralization, or by sorption, uptake, and intracellular sequestration. Redox reactions can mobilize or immobilize metals depending on the metal species involved. Solubilization may enable metal removal from solids such as soils, sediments, dumps, and industrial and mining wastes. Metal immobilization results in insoluble forms of low bioavailability. Biotechnological applications of microbial systems in bioremediation may provide alternatives or adjuncts to conventional treatment methods for solids, and contaminated effluents and wastewaters. While much research is laboratory‐based, there are many developments to pilot/demonstration scale, with several
in situ
and
ex situ
large‐scale processes in successful operation. Metal removal/transformation processes are also intrinsic components of traditional means of water/sewage treatment as well as reed bed, lagoon, and wetlands technologies.
Waste‐to‐Energy Technologies
Authors: KlemeŠ JiŘí Jaromír , Varbanov Petar Sabev
Published: January 2013

The features and properties of waste as a fuel for converting waste to energy (WTE) are analyzed considering various types of waste. Nonthermal treatments are also covered – for example, anaerobic digestion and microbial fuel cells. Benefits from best available and best applicable techniques (BREFs) developed with the support from the European Union are discussed.
Flow Over Complex Terrain, Numerical Modeling Of
Authors: Lundquist Katherine A. , Katopodes Chow Fotini
Published: January 2013

This article provides a general description of numerical modeling over complex terrain, including both diagnostic and prognostic approaches and their associated advantages and limitations. Diagnostic models solve simplified versions of the equations of motion and are widely used in the engineering community for simulations of flow over complex terrain. These models produce a description of the steady‐state flow, while requiring few inputs and being computationally efficient. Diagnostic models do not predict time evolution of flow, and are often only appropriate for terrain with small slopes. Prognostic models solve the full equations of motion and are used for both engineering and numerical weather prediction applications. Details of the implementation of prognostic models vary widely, but they are capable of producing time‐varying descriptions of the flow and handling very complex terrain with no limitations on terrain slope. These models can require detailed three‐dimensional inputs and large computational resources. Additional simplifications can reduce these requirements, but generally at the expense of functionality, such as producing a steady‐state solution or the inability to handle arbitrarily complex terrain.
Climate Change and North American Great Plains' Drought
Authors: Sauchyn David J. , Bonsal Barrie R.
Published: January 2013

Since human activities and ecosystem health are dependent on adequate, reliable water supplies, droughts pose a serious threat to society and the environment. Over much of the Northern Hemisphere high latitudes, droughts are a recurrent feature of the natural climate as evidenced from direct measurements during the instrumental period, and as inferred from paleo‐reconstructions dating back several centuries. However, concern has been expressed regarding climate‐change impacts on future drought frequency, duration, and severity over various regions of the world and in particular, continental interior regions at high latitudes. This article synthesizes relevant scientific research regarding drought in the North American Great Plains, and the Canadian Prairies in particular. First, we review existing knowledge regarding the large‐scale atmospheric causes of North American drought. This is followed by a synopsis of past trends and variability of drought occurrence in the instrumental and paleo record. We then summarize research on the future drought in a changing climate. This article concludes with the identification of major research gaps that will aid in the ability to understand and predict future changes to Great Plains' droughts.
Atmospheric Boundary Layer
Authors: Sorbjan Zbigniew
Published: January 2013

The chapter reviews basic characteristics of the atmospheric boundary layer. It briefly describes atmospheric turbulence and the vertical structure of the boundary layer during the day and at night.
Nonparametric Curve Estimator
Authors: Tarter Michael E.
Published: January 2013

The roles that nonparametric curve estimators can play are illustrated in studies of polluting chemicals that have built up in the environment and of crop forecasting techniques. The multivariate normal model and model‐free curve estimator representations are compared. Equations and approaches are described that enable researchers to input prior information that divides variates into two categories, variates of primary importance and variates that are nuisances in the sense that their detained distributional structure is relevant solely insofar as its effects on variates of primary importance. It is shown that conditional density estimators can be computed given user‐selected values of nuisance variates, not computed as a second step that follows a first step, which requires the estimation of a high‐dimensional joint distribution. It is argued that this approach resembles parametric methodology that has long been fruitfully employed in the field of experimental design.
Nanoparticles: Environmental Effects
Authors: Pettitt Michala E. , Römer Isabella , Lead Jamie R.
Published: January 2013

Manufactured nanoparticles (NPs) possess novel physicochemical properties which may have unforeseen environmental effects. Accurately describing the ecotoxicity of NPs requires a more complete understanding of their properties and behavior
in vitro
. The metric chosen as the descriptor of dose can profoundly influence the interpretation of the dose‐response curve and derived ecotoxicity endpoints. Accurately determining the actual dose (or exposure) received by a test organism during ecotoxicity assays requires appropriate characterization of particles
in situ
in exposure media. Characterization should be conducted before and after exposure, as a minimum. Silver nanoparticles (AgNPs) are the most common nanomaterial currently in commercial production and are predicted to have pervasive release to aquatic environments. Mechanisms of action for AgNPs are discussed and cytotoxic, genotoxic and whole‐organism endpoints are reviewed.
Climate Change Scenarios for Impacts Assessment
Authors: Zwiers Francis W. , Bürger Gerd
Published: January 2013

The study of the impacts of potential future climate change and evaluation of actions that might be used to reduce those impacts requires the development of plausible scenarios of future climate change and variability. Projections of future climate change are produced by climate modeling centers using physically based climate system models that are driven with scenarios of future greenhouse gas emissions. While climate models have been increasing in complexity and resolution, the climate projections that they produce generally still require postprocessing to correct biases and to further refine resolution so that the output can be used to inform local and regional impacts assessments, and to drive impacts models, such as crop models or fine‐scale surface hydrology models. This article briefly describes several of the “statistical downscaling” techniques that are used for this postprocessing.
Species Distribution, Monitoring Changes in
Authors: Yoccoz Nigel G.
Published: January 2013

Changes in species distributions constitute strong evidence that global change is affecting ecosystems and biodiversity. Expansion of invasive species and retraction of endangered species are examples of environmental issues that need to be understood and addressed. Monitoring range shifts require that we can accurately estimate the state(s) of the system – that is, the present and past distributions – its rate of change, and assess the underlying causes. Existing monitoring programs differ in terms of data quality and coverage of the environmental space, and very few have considered different sources of errors as well as alternative explanations of observed changes. Modeling species distributions is needed to derive alternative predictions of future changes. The design of monitoring programs should assess the assumptions
and
the predictions of these models.
J
oint
M
odels
Authors: Thompson Darby J. S.
Published: January 2013

Joint modeling encompasses strategies to simultaneously model several outcomes of interest. There are three principal strategies; classical joint modeling, conditional models, and conditional independence models. Likely the most pervasive area of joint modeling is in the modeling of longitudinal and time‐to‐event data; in particular, accounting for drop‐out in longitudinal data or incorporating error‐prone, sporadically measured, longitudinal outcomes in models for event times. Conditional independence is a popular strategy, which assumes the outcomes of interest are noisy, independent measures of some underlying latent process; it is this process that induces their correlation providing a tractable assumption in many practical settings.
Extinction Rates, Estimation Of
Authors: He Fangliang
Published: January 2013

The currently available estimation methods for extinction rates vary widely in data requirement, reported rates, and their accuracy. The methods include: (i) the taxonomic survivorship curve for estimating background extinction rates; (ii) the species–area relationship for estimating extinction caused by habitat loss (this is by far the most widely used method for estimating future extinction rates due to habitat loss but has recently been shown to be technically incorrect); (iii) methods based on the change in threat status of species on the IUCN Red Lists; and (iv) population viability analysis designed to quantify extinction probability of local species populations. Problems common to these methods include the associated uncertainties in their estimates and lack of reliable data to verify the estimated extinctions.
Life‐Stage Analysis
Authors: Young Linda J.
Published: January 2013

For animals with well‐defined life stages, life stage analysis may be used to estimate properties of the stages, such as the total number of individuals that enter the stage, the mean time spent in a life stage, the probability of surviving each life stage, the mean time to entering each life stage, or the unit time survival rate. In life tables, population survival and mortality are displayed in a systematic manner and may be either age specific or time specific. The Kiritani–Nakasuji–Manly method is perhaps the most widely used method for estimating time unit and stage survival rates and stage duration. Key stages may be defined to be those life stages exhibiting the most variation or those having the greatest effect on the variation in the numbers entering the final stage. Given the structure of a continuously reproducing population in one time period, matrix population models are used to estimate the numbers in each life stage during the next time period or at some more distant time.
Natural Disturbance Processes
Authors: Delong Craig , Burton Philip , Geertsema Marten
Published: January 2013

Natural disturbance processes have a major influence on landscape and stand‐level patterns of vegetation age, size of vegetation patches, and horizontal and vertical structures of vegetation communities. A selective reorganization of the ecological community occurs after disturbances, resulting from a pulse of resources to which the new community responds. Through these influences, natural disturbance affects habitat availability and processes such as site productivity, hydrology, and animal movement. Natural disturbance processes as diverse as fire, biotic agents (such as grazing mammals, forest insects or fungal pathogens), volcanic eruptions, landslides, and flooding all affect an ecosystem in different ways and lead to unique vegetation patterns. Attempting to approximate natural disturbance processes with human‐caused disturbance is possible at some levels but remains fundamentally different, necessitating the retention of natural areas where natural disturbance can continue to play its important role in maintaining healthy ecosystems.
PopulationViabilityAnalysis
Authors: McCarthy Michael A. , Possingham Hugh P.
Published: January 2013

Population viability analysis (PVA) assesses risks of population decline of species, and how those risks can be managed. Typically, stochastic population models are used to characterize the deterministic and stochastic components that govern changes in population size. Sources of stochasticity include demographic stochasticity due to the chance birth and death of individuals, stochasticity in the environment that is common to all individuals, chance variation in genetic composition, and spatial structure. These factors can interact to create extinction vortices that drive population decline. Predicted risks of population decline from PVA models are usually uncertain due to imprecise parameter estimates, imperfect knowledge about factors that influence dynamics, and uncertainty about future conditions. Despite these sources of uncertainty, changes in risks due to management can be predicted more reliably. Using these changes in risk, PVA can help determine efficient management strategies for reducing high contemporary rates of extinction.
Animal Movement Models
Authors: Schwarz Carl
Published: January 2013

Where do animals move to, how many animals move, which routes do they choose, and how many animals survive the migration or dispersal are key questions for ecologists. Information on movement and migration often comes from tagging studies. This article presents a short review of the models typically used in tagging studies. New models are being developed to deal with continuous, complete data from satellite and archival tags and state‐space models for this type of data are presented.
Food Web Modeling
Authors: Chiu Grace S.
Published: January 2013

Many features of a food web are semi‐ to fully quantitative and are subject to uncertainty. Various techniques have been developed to describe these features. Others have been developed to model food web dynamics for purposes that include prediction. Yet, few approaches facilitate systematic, unified conclusions to be readily drawn about the food web's characteristics. Even fewer address the uncertainty that is inherent in the behavior of organisms within the web and in the surrounding environment. Recently, statistical modeling techniques have been adapted to food web research; they allow the investigator to simultaneously address multiple questions about the food web of interest under a unified quantitative framework, while fully exploiting the important role that uncertainty plays in food web ecology. This article reviews some conventional methods for drawing insights into food webs, and discusses recent statistical developments in food web research. Some details of the latter on network flows are provided.