bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dendrochronology and Dendroclimatology
Authors: Cook Edward R. 
Published: September 2006

Abstract

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

Abstract
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 Registry
Authors: Brookmeyer Ron 
Published: September 2006

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Dose Calibration
Authors: Davidian Marie 
Published: September 2006

Abstract

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.
Ecological Study Design
Authors: Sheppard Lianne 
Published: September 2006

AbstractThe 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

Abstract
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

Abstract
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.
EM Algorithm
Authors: Jorgensen Murray 
Published: September 2006

AbstractThe 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.
Environmental Information Systems
Authors: Tochtermann Klaus 
Published: September 2006

AbstractEnvironmental 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

Abstract
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.
EnvironmentalStats for
S
‐
PLUS
Authors: Millard Steven P. 
Published: September 2006

Abstract

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.
Exceedance Over Threshold
Authors: Drees Holger 
Published: September 2006

AbstractWe 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

Abstract

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

Abstract
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

AbstractThe 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

AbstractExtreme 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

Abstract
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

Abstract

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

Abstract
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

AbstractIn 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.