bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Support Systems
Authors: Lam David
Published: September 2006

Environmental problems are complex. They are invariably multidisciplinary in nature and involve many processes: physical, chemical, geochemical, biological, benthic, and ecological. Many of these problems, such as water resources allocation, irrigation, agricultural practices, toxic waste, industrial effluent, acid rain, and climate change are inter‐related with each other. The solution or mitigation of one problem usually affects another. In addition, the constraints to these solutions are many: economic considerations for remedial costs, social values of clean water and air, political and public opinions on biodiversity, and so on. Environmental decision makers and policy advisors need to consider all these complex factors in their decision‐making and policy‐building processes. The purpose of this article is to discuss the necessary tools that an environmental manager may need to support decision‐making processes. With the advent of information technology, this article focuses on the possible use of computer systems to provide the support. Their design, implementation, advantages and disadvantages are discussed and examples of applications and future directions are given. These systems are calledenvironmental decision support systems.
Deforestation
Authors: Holmgren Peter
Published: September 2006

In a linguistic sense it is not difficult to explain the meaning of deforestation – it refers to removal of forests. But the term has been subject to conflicting definitions, incomparable assessments and politically influenced explanations to an extent that few environmental concepts can match. Furthermore, deforestation has been praised or condemned depending on the geographic location and the point in history. At present, deforestation is mostly perceived as very negative – at least by most participants in the international debate.
Dendrochronology and Dendroclimatology
Authors: Cook Edward R.
Published: September 2006

Dendrochronology
is the science of tree‐ring analysis. It derives from the Greek root words
dendron
for tree and
chronos
for time. Thus, dendrochronology uses the history of tree growth, as expressed in its annually produced radial growth rings, to provide information about the past. The realization that trees growing in suitable environments put on one annual ring of growth per year has a very long history going back at least to Theophrastus in ancient Greece. However, it was not until the early twentieth century that the basic principles of dendrochronology were formally developed and exploited. Annual tree‐ring chronologies can also be used to reconstruct past climate. This branch of dendrochronology is called
dendroclimatology
.
Diet, Measurement Of
Authors: Willett Walter C.
Published: September 2006

Comparisons of disease rates among countries and animal studies have strongly suggested that aspects of diet are major determinants of cardiovascular diseases and various cancers. However, the potential for confounding by other lifestyle characteristics, and the desire to identify the critical components of diet, created the need for more detailed studies of diet and disease within populations. In addition to interest in dietary factors as primary exposures, diet may also be important as a modifier or confounder of associations between other environmental factors and disease. For example, constituents of certain vegetables are potent inducers of enzymes involved in the detoxification of carcinogens, and persons with a specific occupational exposure could well have different diets than those not exposed. Thus, the measurement of dietary factors may be desirable even when they are not of direct interest.
Disease Mapping
Authors: Waller Lance A.
Published: September 2006

This article provides a brief review of the types of spatial data analyzed in environmental epidemiology, and related analytic considerations.
Disease Registry
Authors: Brookmeyer Ron
Published: September 2006

A disease registry lists cases of a particular disease that have occurred in a population. The registry may include demographic information about the cases (e.g. age, gender and place of residence), dates of disease diagnosis and perhaps additional information such as dates of death, or exposure and risk factor information. Ideally, the disease registry has complete ascertainment; that is, the registry lists
all
cases that have occurred in a population over a specified time. Disease registries can be used to estimate disease incidence and prevalence rates in the population, monitor environmental patterns and trends in disease incidence, identify demographic subgroups with the greatest burden of disease, estimate future disease incidence and prevalence, and identify individuals with disease who could participate in more detailed case‐control or cohort studies of disease etiology or prognosis.
Dispersion Parameter
Authors: Piegorsch Walter W.
Published: September 2006

An important goal in environmetric analysis is the accurate modeling of environmental processes and effects, using parametric statistical models. To describe the stochastic nature of an outcome variable, environmetricians use probability distributions. For example, with continuous data that vary symmetrically about their population mean, the normal distribution is a common choice. Or, for discrete counts a simple probability model is the Poisson distribution; its counterpart for proportion data is the binomial distribution. A crucial feature of many simple probability models is that there may be structural relationships enforced between the mean and other components of the model. Many environmental sampling and experimental settings occur, however, where such a mean/variance structure is too restrictive: the data often exhibit variation larger than (or, in some instances, smaller than) that required under these simple probability models. Such occurrences are known collectively as
overdispersion
(or
underdispersion
), respectively. We can quantify the extent of the over/underdispersion by dedicating a model parameter to it in the variance term. Such a
dispersion parameter
can be used to identify, study, test, or otherwise account for the excess variation. Technically, a dispersion parameter reports the percentage increase or decrease in variability over the simpler model's variance.
Distance Sampling
Authors: Thomas Len , Buckland Stephen T. , Burnham Kenneth P. , Anderson David R. , Laake Jeffrey L. , Borchers David L. , Strindberg Samantha
Published: September 2006

Distance sampling is a widely‐used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods are
line transects
and
point transects
(also called
variable circular plots
). These have been used successfully in a very diverse array of taxa, including trees, shrubs and herbs, insects, amphibians, reptiles, birds, fish, marine and land mammals. In both cases, the basic idea is the same. The observer(s) perform a standardized survey along a series of lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all the objects that the observers pass will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. Intuitively, one would expect that objects become harder to detect with increasing distance from the line or point, resulting in fewer detections with increasing distance. The key to distance sampling analyses is to fit a
detection function
to the observed distances, and use this fitted function to estimate the proportion of objects missed by the survey. From here one can readily obtain point and interval estimates for the density and abundance of objects in the survey area.
Diversity Profiles
Authors: Patil G.P.
Published: September 2006

Community diversity, diversity measurement, and diversity comparisons have been important in community ecology studies for more than four decades.
Diversity
is a generic term referring to the condition of being different; a useful synonym for diversity is variety. It summarizes for qualitative characteristics (e.g. tree species) what the variance summarizes for quantitative measurements (e.g. tree diameter). Diversity is looked upon as a community property and, accordingly, the trophic, taxonomic or structural levels that comprise the community must be specifically delimited In making diversity comparisons, it is not uncommon to find that diversity has increased according to one index, but decreased according to a second index. This merely reflects the fact that diversity is a complex multidimensional property of a community. To view diversity through the lens of a single index is to project that multidimensional complexity onto a one‐dimensional ordinal scale. In view of the inadequacy of a single index, it is desirable to quantify diversity by means of
diversity profiles
. A diversity profile is a curve depicting the simultaneous values of a large collection of diversity indices. Thus, the profile portrays the views of diversity from many different vantage points simultaneously.
DNA
Adducts
Authors: Thurston Sally W.
Published: September 2006

Many compounds are capable of binding to deoxyribonucleic acid (DNA), either directly or after metabolic activation within the cell. The resulting DNA‐chemical complexes, known as
adducts
, are thought to trigger DNA mutations that initiate the process of cancer formation
Dose Calibration
Authors: Davidian Marie
Published: September 2006

Calibration refers broadly to the determination of a variable
x
on the basis of measurement(s) on another variable
y
. In the dose calibration problem,
x
is the unknown level (concentration, dose) of antigen (enzyme, hormone, protein, compound) in a biological test sample to be determined from
y
. It may be infeasible to determine
x
directly from the specimen, but the specimen may be subjected to an assay procedure that yields an associated measurement
y
. Given an understanding of the relationship between
x
and
y
,
x
may be estimated. Such calibration is commonplace in environmental health studies. Toxicokinetic modeling for estimation of internal doses and validation of mechanistic hypotheses is a popular approach in characterizing the health effects of environmental exposures; underlying such studies is the need for precise calibration of levels of exposure agents and their metabolites from blood or other samples.
Environmental and Ecological Statistics
Authors: Patil G.P.
Published: September 2006

The emphasis of
Environmental and Ecological Statistics
is, as its title declares, on statistics, ecology, and the environment. It acts as a forum for multidisciplinary communication and discussion on statistics, ecology, environment, and society.
Ecological Study Design
Authors: Sheppard Lianne
Published: September 2006

The purposes of ecological studies range from descriptive (e.g. hypothesis‐generating) to inferential. The target of inference may be the group (calledecological inference) or the individual (calledbiological inference). Ecological studies are most appropriate for assessing the effects of interventions on populations, although the inference can be limited relative to an individual‐level study. Their applicability to etiological hypotheses about individuals is much more controversial, due to the risk of committing theecological fallacy. This fallacy is present when characteristics of individuals are wrongly inferred from grouped data. Although these reservations have a sound basis, most ecological study literature does not distinguish between issues inherent to the analysis of opportunistic observational data, and those pertinent to valid biological inference from group‐level analyses.
Economics, Environmental
Authors: Hammitt J.
Published: September 2006

Environmental economics is concerned with the use of environmental resources, especially the overuse that occurs when resources are unregulated and available for the taking. For example, unrestricted access to a fishery can lead to over‐fishing and depletion or even extinction of the fish stocks. Similarly, when waste products from burning fossil fuels are freely released to the air, there will be more air pollution than when access to the disposal capacity of the air requires a obtaining a permit or paying a fee. Conventionally, a distinction is drawn between environmental economics and resource economics. Environmental economics studies pollution and the quality of environmental media. Resource economics studies the quantity and optimal use of renewable and exhaustible resources. The fields share many theoretical models and the boundary between them is permeable. This article focuses on environmental economics. Within environmental economics, two principal topics are studied: identifying the optimal environmental quality and developing institutional mechanisms to prevent excessive degradation. Each of these topics will be addressed in turn.
Ecosystem Element Cycling
Authors: McGuire A. David
Published: September 2006

Ecosystem dynamics are changes in the biological and physical characteristics of ecosystems through time. The biological organisms in an ecosystem are responsible for a number of processes that affect ecosystem dynamics. For example, plants obtain carbon as carbon dioxide from the atmosphere through the process of photosynthesis, herbivores obtain carbon by consuming plants in an ecosystem, carnivores obtain carbon by eating animals in an ecosystem, and decomposers obtain carbon from dead organisms in an ecosystem. Physical processes also affect ecosystem dynamics. For example, the leaching of dissolved organic carbon in water flowing through the soil of an ecosystem is one path through which terrestrial ecosystems may lose carbon to aquatic ecosystems like rivers and lakes. In this article, ecosystem element cycling refers to the cycling of elements such as carbon within an ecosystem, as well as the flow of elements into and out of an ecosystem. This article has several purposes: (i) to provide some basic biological and physical background on processes responsible for ecosystem element cycling; (ii) to briefly describe generalized carbon and nitrogen cycles in terrestrial ecosystems and (iii) to discuss human modification of the global carbon and nitrogen cycles.
Effluent
Authors: Cocke D.
Published: September 2006

Effluent is a waste, usually a liquid, which may be released or discharged to the environment.
Elicitation
Authors: O'Hagan A
Published: September 2006

Elicitation is the process of formulating the beliefs and knowledge of someone about one or more uncertain quantities in the form of a probability distribution for those quantities. Because it represents personal judgment, the probability distribution is interpreted in the framework of Subjective probability.
EM Algorithm
Authors: Jorgensen Murray
Published: September 2006

The Expectation‐maximization (EM) algorithm is an approach to maximum likelihood estimation in complex statistical models that avoids confronting the main optimization problem head‐on but rather seeks to piggyback on known solutions to simpler, but related problems. A typical application lies in fitting finite mixtures of probability distributions to a set of observations: this would often be simply done if we knew how to assign each observation to its correct component. The EM algorithm provides a way to use our knowledge of how to solve the simpler problem to help solve the actual problem that we are faced with. The EM algorithm is best understood through examples, and two of these are developed in some detail in this article alongside the necessary theory and references to a range of applications in the literature.
Encountered Data
Authors: P. Smith Eric
Published: September 2006

Data that are obtained in ecological and environmental data is often not collected via an experimental or sampling design but rather is observed. Examples include fish that are collected via a net, plants that are observed when walking a transect line and birds heard in a wetland survey. Data arising from these types of studies are sometimes referred to as
encountered data
.
Impact, Environmental
Authors: Smith Eric P.
Published: September 2006

An environmental impact is defined as the anthropogenic displacement of ecological structure or function beyond normal limits of variability.
Environmental Information Systems
Authors: Tochtermann Klaus
Published: September 2006

Environmental information systems are concerned with the management of data about the soil, the water, the air and the species around us. Environmental information systems serve different purposes for different target groups. To develop efficient environmental protection strategies government administrations need easy and fast access to environmental information that are available and aggregated in environmental information systems. In companies, decision makers analyze the environmental performance of their organization using environmental information systems. These analyses help to measure the adherence to the standards set forth in the companies' statements on environmental principles and illuminate weaknesses and opportunities, and may lead to new environmental goals in a company. The public provision of environmental information is crucial to increase the environmental awareness among the public and, thus, to contribute to a better understanding of environmental problems. Both increased awareness and a better understanding are important factors in inducing a change in environmental behavior and thus helping to protect the environment worldwide in the long term. The article addresses public environmental information systems from different perspectives. This includes the evolution of environmental information systems, the components these systems are based on, the way they manage data and metadata in general and spatial data in particular. The article closes with an outlook on future trends and a brief conclusion. The choice to focus primarily on public environmental information systems was taken because public environmental agencies are disclosing internal information about their systems, which is an indispensable prerequisite to profoundly understand how environmental information systems function.
Global Environment Monitoring System, Water
Authors: Fraser Andrew S.
Published: September 2006

The Global Environment Monitoring System (GEMS) grew out of the 1972 Stockholm Conference on the Environment. At that world conference, governments requested the establishment of a global monitoring program that could determine the status and trends of key environmental issues. The GEMS/Water Program was initiated in 1976 by the United Nations Environment Program (UNEP) and the World Health Organization (WHO), with the assistance of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the World Meteorological Organization (WMO). The program had the twin objectives of improving water quality monitoring and assessment capabilities in participating countries, and determining the status and trends of regional and global water quality.
Restoration, Environmental
Authors: Ryti Randall T.
Published: September 2006

Environmental restoration, as used here, is the process of identification and remediation of unacceptable impacts to human health and the environment from hazardous waste sites.
EnvironmentalStats for
S
‐
PLUS
Authors: Millard Steven P.
Published: September 2006

EnvironmentalStats for S‐Plus
is a comprehensive software package for environmental statistics. It is an add‐on module to the statistical software package
S‐Plus
, and provides a set of powerful, yet simple to use, pull‐down menus and functions for performing graphical and statistical analyses of environmental data.
Environmetrics, Overview
Authors: El‐shaarawi Abdel , Hunter J. Stuart
Published: September 2006

Environmetrics is a new scientific discipline that focuses on the development and use of quantitative methods to understand and solve environmental problems. This article gives a brief overview of its roots, aims, problems and methodologies.
E
nvironmetrics

, The Journal of the
I
nternational
E
nvironmetric
S
ociety
Authors: El‐Shaarawi Abdel
Published: September 2006

Environmetrics
is the official journal of The International Environmetrics Society (TIES). It is a multidisciplinary journal that publishes papers on the development and application of quantitative methods in environmental sciences.
Exceedance Over Threshold
Authors: Drees Holger
Published: September 2006

We discuss how to fit threshold models to the exceedances over high thresholds. In a univariate setting, often the crucial step is to estimate the extreme value index, since the accuracy achieved there essentially determines the estimation error for extreme quantiles or exceedance probabilities. In this context, the choice of the sample fraction used for estimation is particularly important. Besides classical models with independent and identically distributed observations, it is also discussed how to cope with serial dependence, trends and seasonality.

When fitting a multivariate threshold model, one has to estimate whole functions that describe the tail dependence structure. Here mainly semiparametric and nonparametric estimators for the stable tail dependence function, the exponent measure and the angular measure are considered. In addition, we briefly study the problem of testing for asymptotic independence of the marginals. Finally, we touch on software specially designed for tail analysis.
Extra Risk
Authors: West R. Webster , Piegorsch Walter W.
Published: September 2006

To properly quantify the toxic effect of a toxin, the
background risk
of no exposure to the toxin,
R
(0), must first be specified. The excess risk incurred by exposure to a dose,
d
, of the toxin is then viewed as some modification of
R
(
d
) that adjusts for
R
(0).
Extrapolation
Authors: Hertzberg Richard C.
Published: September 2006

Quantitative extrapolation refers to a process where inferences about data‐poor scenarios are developed from studies of data‐rich scenarios. For dose–response relationships in environmental risk assessment, several different kinds of extrapolation are used in order to accommodate the variety of available information and risk assessment goals. Examples of such extrapolation include estimating human health risk at low doses from rodent toxicology data at high doses, or estimating ecosystem response at a given ambient pollution concentration from laboratory studies of individual animal or plant species at similar concentrations. The examples discussed in this article are for human health risk assessment but most of the principles apply to ecological risk assessment as well. These examples include low‐dose extrapolation of cancer response (extending a fitted function below the range of the data), simple scaling methods (e.g. allometric interspecies extrapolation), biomathematical models (e.g. physiologically‐based pharmacokinetic models), and quantitative formulas for estimating mixture response (e.g. modifying dose addition to account for synergism).
Extremal Events
Authors: Ledford Anthony W.
Published: September 2006

The term extremal event is commonly used in relation to environmental occurrences that impact greatly on day‐to‐day life, e.g. storms and floods. Although it is clear that such events are extremal events, it is not clear more generally what an extremal event is, or what an extremal event is not for that matter. We discuss the role of extremal events in extreme value theory and in several application areas so that we may identify any common underlying themes.
Extreme Value Analysis
Authors: Nadarajah Saralees
Published: September 2006

Extreme value theory concerns the behavior of the extremes of a process or processes. The fundamentals of this probability theory have been known since about the beginning of the twentieth century, but the relevant statistical methods for modeling extreme values emerged in the literature only in the past two decades. In fact, since 1980 the literature has seen a flood of applications of statistical extreme values, covering a wide range of areas. The application areas include: environmental sciences, including climate, engineering and hydrology, performance assessment as in sports or policing, astronomy, finance, chemometrics, mortality studies, and outlier detection. Further references to specific applications are noted throughout the rest of this entry.

The aim of this article is to review some fundamentals of extreme value theory and relevant statistical methods. The emphasis will be on the latter and the applications it has attracted in the literature so far. The entry is in two parts. The first part considers univariate extremes and the remainder is for multivariate extremes. Each part begins with a discussion of fundamental theoretical results. This is then followed by a discussion of relevant statistical models, inference and simulation.
Generalized Extreme Value Distribution
Authors: Beirlant Jan , Matthys Gunther
Published: September 2006

Historically, flood frequency analysis constitutes the prime important example in environmental sciences where annual maxima based inference on return levels is the ultimate research goal. Meanwhile other ecological variables such as temperatures, air pollution concentrations or the thickness of the ozone layer have also become intensive research topics for extreme value analysis in view of possible negative influences or evolutions.

For these purposes the (generalized) extreme value (GEV) distribution is the basic distributional tool, in contrast to classical statistical inference which focuses on central measures of a distribution and where the normal distribution is the reference. we review the GEV model, its mathematical motivation, and the statistical estimation of the parameters under the assumption of pure random maximal information. Our final aim in this account is a review of the accurate estimation of return levels. The scope of adequate modelling of maxima is extended through the extremal index, which allows for (short‐range) dependence and clusters in the data.

Throughout the text a practical example illustrates the application of the GEV method.
Factor Analysis
Authors: Graffelman Jan
Published: September 2006

Factor analysis is a multivariate method for data reduction. Its aim is to describe the covariance relationships between a large set of variables in terms of a few new variables, called
latent variables
or
factors
. Factor analysis originated in psychometrics, where scientists like Spearman tried to ascribe the correlations between different mental capabilities to a subjacent, unobservable factor called intelligence. Factor analysis has experienced a large development since then, and has found applications in many branches of science. The method is most popular in the behavioral and social sciences, but also counts with applications in environmental statistics. Applications of factor analysis in environmental studies range from studies that work with interview data on attitudes towards environmental issues to studies that apply factor analysis to directly measured environmental variables in pollution or impact studies.
Familial Aggregation
Authors: Seillier‐moiseiwitsch Françoise
Published: September 2006

By familial aggregation is meant the occurrence of a disorder at a higher frequency in the relatives of affected individuals than in the general population. This aggregation may be due to genetic, cultural or environmental factors. Assessing whether diseases cluster in families, and searching for the explanation when they do so, are the main questions genetic epidemiologists address.
Fertility Studies
Authors: Dunson David B.
Published: September 2006

In recent years there has been increasing concern that human exposure to environmental agents may disrupt the endocrine system and alter reproduction. For example, some studies have observed secular declines in semen quality over the past 50 years, and several widely used chemicals have been shown to adversely effect human fertility. This article briefly reviews statistical methods for assessing environmental effects on human fertility, with particular emphasis on methods for analysis of data from time to pregnancy (TTP) studies.
F
ieller's Theorem
Authors: Buonaccorsi John P.
Published: September 2006

There are numerous environmental problems where the main quantity of interest is a ratio of two parameters, say ρ = θ
1
/θ
2
. Fieller's theorem is one technique for finding a confidence set for the ratio ρ.
Forecasting, Environmental
Authors: Young Peter C. , Pedregal Diego J.
Published: September 2006

The term ‘environmental forecasting’ is open to a range of interpretations, from ‘black‐box’ time series analysis and forecasting, based on relatively simple stochastic methods; to ‘mechanistic’ prediction based on ‘physically meaningful’, and normally quite complex, deterministic simulation models. This article, however, concentrates on stochastic forecasting methods based on models that range from purely black‐box time series characterizations of data to data‐based mechanistic (DBM) models that are physically meaningful but, at the same time, fairly simple in their structure and signal topology. There are numerous statistical approaches to forecasting, from simple, regression‐based methods to optimal statistical procedures formulated in stochastic state–space terms. Since it would be impossible to review all these methods here, this article first reviews briefly those that are felt to have most significance in theoretical and practical terms within the specific context of environmental forecasting. It then discusses in more detail a unified approach to forecasting based on a generic unobserved component (UC) model, formulated in stochastic, state–space terms.
Forest Carbon Cycling
Authors: Vose Jim
Published: September 2006

The terrestrial carbon (C) cycle comprises three components: C inputs, C outputs, and C pools. Worldwide, terrestrial ecosystems contain approximately 2000 Pg C, most of which is in belowground pools (∼1500 Pg C). By contrast, the atmosphere contains ∼750 Pg C. The nearly three‐fold greater C in terrestrial ecosystems relative to the atmosphere means that terrestrial C cycling processes potentially have a major influence on atmospheric
carbon dioxide
(CO
2
) at global scales, and hence on changes in atmospheric CO
2
and climate.
Forest Growth and Yield Modeling
Authors: Vanclay Jerome K.
Published: September 2006

Forest growth models attempt to quantify the growth of a forest, and are commonly used for two principal purposes: to predict the future status of a forest and the nature of any harvests from that forest; and to help consider alternative cultivation practices. Models may also find other uses, such as in education, communicating information, etc. Depending on the purpose of the model, modelers may choose to emphasize physiological detail or statistical efficiency, but generally seek both biological and statistical accuracy.
Forest Health Monitoring
Authors: Olsen Anthony R.
Published: September 2006

Forest health monitoring (FHM) focuses on the health of the forest ecosystem. Recognition of the importance of forest health and, especially, the role of monitoring, increased with the acceptance of the Santiago Declaration. The declaration was accompanied by a set of criteria and indicators for the conservation and sustainable management of temperate and boreal forests. The criteria focus on conservation of biological diversity, maintenance of productive capacity, maintenance of forest ecosystem health, conservation of soil and water resources within forests, maintenance of forest contribution to global carbon cycles, and enhancement of long‐term multiple socio‐economic benefits to meet the needs of societies. The declaration calls for quantitative measurements of indicators associated with the criteria. The periodic and systematic measurement of the indicators requires nations to have FHM programs.
Forestry
Authors: Gregoire Timothy G.
Published: September 2006

A broad definition of forestry recognizes that it is a combination of biological, quantitative, managerial, and social sciences applied to the management and conservation of forest resources.
FractalDimensions
Authors: Li Bai‐Lian
Published: September 2006

The term ‘fractal’ (from the Latinfractus, meaning ‘broken’), introduced by Benoit Mandelbrot about 25 years ago, is used to characterize spatial and/or temporal phenomena that are continuous but not differentiable. Geometrically, a fractal is a rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced‐size copy of the whole. Fractal structures do not have a single length scale, while fractal processes (e.g. time series) cannot be characterized by a single time scale. Fractal theory offers methods for describing the inherent irregularity of natural objects. In fractal analysis, the Euclidean concept of ‘length’ is viewed as a process. This process is characterized by a constant parameterDknown as the fractal (or fractional) dimension. There are many fractal dimensions introduced in mathematical and physical literature. Fractal dimensions can be positive, negative, complex, fuzzy, multifractal, etc. The two most commonly used are the Hausdorf dimension and capacity.
Generalized Additive Models
Authors: Grego John M.
Published: September 2006

A Generalized Additive Model (GAM) is an extension of a generalized linear model (GLM) in which the expected value of a mean response, ⋼ = E(
Y
), is linked to a set of additive nonparametric functions in the independent variables
X
1
, hellip;,

X
p

.
Generalized Estimating Equations
Authors: Pickles Andrew
Published: September 2006

Interdependence is a characteristic feature of many environmental processes. However, the complexities for statistical analysis introduced by such interdependence have hampered the progress of environmental science. Why this is so can be understood by considering the workhorse of standard statistical analysis, thegeneralized linear model(GLM). A typical GLM divides variation in some measure into a systematic part and a random part. Typically substantive theory guides the construction of the model for the systematic part. For the random part, although some guidance may be obtained from knowledge of the distributional consequences of a simple and plausiblestochastic processthat might be responsible for this variation, and from experience gained with other comparable data, we rarely have complete confidence in our choice of distribution. In the case of spatial, longitudinal and multivariate outcomes the choice is among multivariate distributions since we need to describe the association across outcomes as well as within outcome variability. This becomes especially difficult for problems where the multivariate normal distribution is not a suitable choice for the random part of the model, since the available alternative distributions lack flexibility. Under these circumstances it is often helpful to use generalized estimating equations (GEEs). The GEE approach, while making weaker distributional assumptions than that required for a fully parametric linear model, maintains the properties of consistency and asymptotic normality of parameter estimates.
Inverted Wishart Distribution, Generalized
Authors: Brown Philip J.
Published: September 2006

The generalized inverted Wishart (GIW)
distribution
is a flexible extension of the inverted Wishart (IW) distribution which overcomes the major deficiency of that distribution whilst retaining much of the ease of computation inherent in the natural conjugate roots of the inverted Wishart distribution in Bayesian analysis for normal sampling. In offering a more flexible prior distribution for the covariance matrix of a multivariate normal distribution it has general applicability. It also offers a conjugate Bayesian analysis for problems of missing data where the missing data has a monotone structure. It has been especially useful in simultaneous modeling of the spatial distribution of ozone where monitoring stations come on‐stream in sequence, using multivariate regression analysis for interpolation and prediction.
Generalized Linear Models
Authors: Jørgensen Bent
Published: September 2006

The class ofgeneralized linear modelswas introduced in 1972 by Nelder and Wedderburn as a general framework for handling a range of common statistical models for normal and non‐normal data, such as multiple linear regression, analysis of variance (ANOVA), logistic regression, Poisson regression and log‐linear models. Ideas from generalized linear models are now pervasive in much of applied statistics, and are very useful in environmetrics, where we frequently meet non‐normal data, in the form of counts or skewed frequency distributions.
Generalized
P
Values
Authors: Ananda Malwane M.A. , Weerahandi Samaradasa
Published: September 2006

In many statistical problems involving nuisance parameters, conventional statistical methods do not provide exact solutions. For example, when comparing treatments in a one‐way or two‐way analysis of variance (ANOVA) model without the assumption of equal error variances, classical statistical methods do not provide exact solutions. As a result, practitioners often resort to approximate methods or asymptotic methods, which may perform poorly, specifically with small sample sizes. These kinds of situations arise in many environmental statistical applications. In particular, when dealing with small samples, as a result of using weak tests, experimenters often fail to detect significant differences between treatments. The generalized
P
value approach is a recently developed method based on exact probability statements rather than on asymptotic approximations. The generalized
P
value approach is an extension of the classical
P
value approach.
Generalized Regression Estimators
Authors: Fuller Wayne A. , Park Mingue
Published: September 2006

Regression estimators are used in survey sampling when population information is available for some variables observed in the survey. This information, sometimes called auxiliary information, often comes from official sources such as a national census. In a survey of land use, the total surface area and the area in permanent water bodies may be available from national data sources. Regression estimation is used to improve efficiency and to reduce bias due to nonresponse or incomplete sample coverage. The form of the regression estimator depends upon the design of the survey and on the nature of the available information.
Genetic Algorithms
Authors: Sheehan N.A.
Published: September 2006

Forest inventory is an accounting of trees and their related characteristics of interest over a well‐defined land area. It may be compared to census methods for human populations. For example, one of the goals of the periodic decennial census of the US is to enumerate its human population and to retrieve demographic variables such as age, sex and race. This is accomplished by a comprehensive survey of all households in the country. Similarly, forest inventories seek to enumerate the population of trees within a forest and ascertain other information, such as their volume, value, growth and species composition. For all but the smallest tracts of land, complete enumeration of individuals is usually infeasible and survey sampling techniques are required. Unlike human or animal populations, trees are sessile organisms and there is no immigration or emigration to consider. However, tree populations vary widely in their species composition, age, size, site requirements, potential value, longevity and growth–all factors that may influence the design of a forest inventory. In addition, the tree population of interest may exist over a wide variety of topography, making access potentially costly and sometimes even dangerous. Such variety often dictates sampling protocols that may be optimal only for some portion of the population in question. Alternatively, it may call for varying sampling intensity or strategy (the combination of the sampling design and associated estimators) between different regions within the population.
Global Circulation
Authors: North Gerald R. , Shen Samuel
Published: September 2006

The shape of the earth is very nearly spherical with radius approximately 6400 km. The depth of the oceans averages about 4 km and about two‐thirds of the mass of the atmosphere lies within 10 km of sea level. This means that the atmosphere/ocean or the geophysical fluid system is only a very thin film of material moving and often swirling about the surface. The geophysical fluids are differentially heated by the sun, the tropical regions receiving much more solar heating than the polar latitudes. This differential heating, which takes place mostly through heating the earth's surface, sets up air currents, which overturn the fluid and redistribute the heating toward the poles. The earth also rotates about an axis with a fixed orientation in space. This rotation causes apparent forces on the geophysical fluid elements, deflecting them and leading to the formation of circulation patterns. The movements of the fluid media covering the planet form the complex of motions we call the global circulation, which is the main subject of this article.
Global Environmental Change
Authors: Zwiers Francis W.
Published: September 2006

Human activity is altering the composition of Earth's atmosphere through the addition of greenhouse gases and particulates. Anthropogenic changes in the properties of the atmosphere can be thought of as external forcing factors on the climate system. There is a fair degree of confidence in results on greenhouse gas forcing and the climate's response to that forcing. However, knowledge of the forcing and response due to aerosols remains highly uncertain. There are also natural external forcing factors that influence climate, such as changes in orbital geometry and changes in solar irradiance and volcanic activity. The climate system, even when not perturbed by external factors, produces substantial amounts of natural variability. Thus detection and attribution of the effects of external forcing is a statistical signal‐in‐noise problem. The
detection
part of this problem is the process of demonstrating that an observed change is not likely to have been entirely the result of natural internal variability. The
attribution
aspects of the problem are more difficult because it is not possible to conduct controlled experiments with the climate system. The practical approach that has been taken in the climate research community involves statistical analysis and the assessment of multiple lines of evidence to (a) demonstrate that observed changes are consistent with forcing of the climate by a combination of anthropogenic and natural external factors, and (b) demonstrate that the changes are inconsistent with alternative, physically plausible explanations.
Global Warming
Authors: North Gerald R. , Shen Samuel S. , Basist Alan S.
Published: September 2006

Global warming refers to the increase of the air temperature near the Earth’s surface, but the term ‘global warming’ does not indicate the causes and magnitude of the increase, nor does it imply continuity of the warming into the future. Global warming can have a significant impact on life on Earth (rise of sea level, more frequent severe weather, redistribution of food systems, extinction of plants and animals, generation and propagation of diseases, increase of crop yield), however the significance has never been statistically tested.
Graphical Displays
Authors: Carr Dan
Published: September 2006

The purpose of environmental visualization is to facilitate scientific and public understanding of environmental status, trends, and processes. Such understanding is incremental owing to: (i) the complexity of the environment; (ii) the difficulty of parsimoniously conceptualizing this complexity; (iii) the logistic and political impediments to collecting adequate, representative data; (iv) and the limits of human perception and cognition for understanding multivariate summaries. This article can only hint at the range of challenges faced in attempts to characterize the environment and communicate using graphical summaries. It can only touch on the background knowledge that helps experienced readers to assess and interpret environmental graphics. However, the background is important since environmental visualization goes far beyond routine production and superficial interpretation of environmental graphics. After a brief introduction, the focus of this article is on graphical design principles and graphical templates for representing environmental summaries.
Habitat Sampling
Authors: Gould William R.
Published: September 2006

Habitat represents the sum total of food, cover, water, and space that a species needs for survival. Measurement of an animal's habitat is as important as measuring the animal itself. Habitat evaluation has become increasingly important in developing strategies for the management and conservation of species and ecosystems as demand for natural resources increases with human population growth. Two decades ago, a subcommittee of the US National Research Council was charged with examining the effects of agricultural trends on fish and wildlife habitat. Among their conclusions, the committee emphasized the need for greater assessment of the effects of agricultural and forestry practices on fish and wildlife habitats. A comprehensive discussion of the wide array of habitat sampling techniques required by each situation is not possible here; this article focuses on the most basic types of vegetation measurements for terrestrial animals: frequency of occurrence, density, cover, and biomass.
Healthy Worker Effect
Authors: Eisen Ellen A. , Robins James M.
Published: September 2006

Epidemiologic studies of occupationally, environmentally exposed cohorts are useful in identifying and quantifying environmental risks because workers are generally exposed to higher levels of toxic materials than the general population. Although higher exposures make it easier to detect and characterize modest elevations in risk, such studies are plagued by a particularly pervasive form of selection bias, referred to as the healthy worker effect (HWE). The HWE is reflected in better health status of workers relative to the general population. This bias arises because workers were healthy enough to be hired initially and those who remain at work stayed healthy enough to maintain employment, whereas general populations include persons unfit for work because of impaired health.
Hierarchical Models for Disease Mapping
Authors: Waller Lance A.
Published: September 2006

The analysis of disease rates or counts from small areas often involves a trade‐off between statistical stability of estimates and geographic precision. Detection of locally elevated risk or rates requires geographically small units to distinguish local risk/rates from area‐wide values. On the other hand, smaller regions result in rate estimates based on smaller population sizes. For a rare disease, small population sizes result in particularly unstable rate estimates. The statistical literature contains various methods of combining information or ‘borrowing strength’ between regions to achieve local rate stabilization without losing geographic resolution. The most common approaches involve hierarchical models with random effects (intercepts) for each region.