bluuebunny/crossref_metadata_2025_split huggingface dataset:
Capture–Recapture Methods
Authors: Gould William R. , Pollock Kenneth H. 
Published: September 2006

Abstract
Capture–recapture methods have a long history of use, but most recently have received greater attention from biologists and statisticians due to their frequent application. The Petersen method, in which a single mark, release and recapture is made, forms the basis for many of the more sophisticated methods that exist today. The basic principle inherent to capture–recapture is that given knowledge of the size of a marked subset of a population, one can estimate demographic information on the larger population of interest. Closed population models do not allow for migration or mortality or natality. Open population models allow for these processes and thus are more intensely parameterized. Capture–recapture models often require specific assumptions that may prove to be invalid. For example, in some models all animals are assumed to have equal capture probabilities on a given capture occasion. Thus, more realistic models with weaker assumptions are being developed by incorporating multiple recapture periods, multiple sources of recoveries, improved sampling designs, and age and spatial structure into the modelling process. In addition, combinations with other survey types and enhanced technology, e.g. radio telemetry, have allowed additional information sources to be included in estimating important demographic parameters.
Catastrophical Risk
Authors: Chichilnisky Graciela 
Published: September 2006

Abstract
This article analyses decision under uncertainty, motivated by problems emerging from global environmental risks. These are typically low‐probability events with major irreversible consequences. For such risks the Von Neumann–Morgenstern (NM) Axioms for decision‐making under uncertainty are not appropriate, since they are insensitive to low‐probability events. The article introduces an alternative set of axioms that require sensitivity to both low and large probability events. Through a representation theorem in functional analysis, the results characterize all the operators whose maximization leads to the fulfillment of these axioms. They involve a convex combination of expected utility and a criterion based on the desire to avoid low‐probability and potentially catastrophical events. It is shown that the new axioms help resolve the Allais paradox. Open questions about risk aversion, games under uncertainty and calculus of variations are discussed.
Categorical Data
Authors: Imrey Peter.B. , Simpson Douglas G. 
Published: September 2006

AbstractCategorical data refers to counts of events or individuals observed through some defined process and often allocated to subgroups, or categories, corresponding to levels of one or more attributes. This article reviews methods for interpreting collections of such counts, when they arise from apparently random environmental processes and may be treated as dependent variables relative to potentially explanatory factors or covariates. After introducing basic terminology including measures of relative frequency and association, we review the Poisson probability distribution. This is followed by the binomial, multinomial and hypergeometric distributions and products thereof, that result from conditioning upon sums of independent Poisson counts. These form the basis for modeling the random variation in observed categorical data. For modeling structural relationships, generalized linear models are first defined, and Poisson regression, logistic regression, and log‐linear models are each considered within that framework. We then summarize several methods for analyzing the correlated counts that occur when observing a categorical dependent variable on the same observational units under several measurement conditions or at multiple observation times, or on multiple observational units within matched sets. These methods include weighted least‐squares functional regression, conditional logistic regression, Cochran–Mantel–Haenszel tests, generalized linear mixed models, and analyses using generalized estimating equations. Finally, we briefly comment on Bayes and empirical Bayes methods, spatial modeling, exact methods, and add inevitably ephemeral comments on the status of software at the turn of the millenium.
Censored Data
Authors: Buhamra Sana S. , Al‐Ibrahim A.H. 
Published: September 2006

Abstract

Data with values below detection are often coded to the detection level or to a specified constant. These values are known as
censored values
and the data are referred to as
left censored data
. If such data were analyzed using traditional methods, the estimates obtained from the analysis would always be contaminated with some unspecified amount of bias. Methods have been developed that allow for the unbiased estimation of the parameters in the presence of censored values.
Center for Statistical Ecology and Environmental Statistics
Authors: Patil G.P. 
Published: September 2006

Abstract
The Center for Statistical Ecology and Environmental Statistics is a unit in the Department of Statistics of the Pennsylvania State University and was initiated under a cooperative agreement between the US National Oceanic and Atmospheric Administration (NOAA) and the Pennsylvania State University as a formal substitute for the earlier Penn State Program in Statistical Ecology and Environmental Statistics in operation since 1969. The Center is the first of its kind in the nation and in the world, and enjoys a national and international reputation. Broad research areas of the Center relate to statistical ecology, environmental statistics, and quantitative risk analysis, with emphasis on mathematical statistics, statistical methodology, and data interpretation and improvement for future use. The adopted approach is to advance statistics for environment, ecology, and environmental health, and to advance environmental and ecological theory and practice using valid statistics.
Change‐in‐Ratio
Authors: Udevitz Mark S. 
Published: September 2006

Abstract
Change‐in‐ratio (CIR) methods are used to estimate parameters for ecological populations subject to differential removals from population subclasses. Subclasses can be defined according to criteria such as sex, age or size of individuals. Removals are generally in the form of closely monitored sport or commercial harvests. Estimation is based on observed changes in subclass proportions caused by the removals.
Chemometrics
Authors: Park Kwang‐Su , Spiegelman Clifford H. 
Published: September 2006

Abstract
A definition of chemometrics by Massart et al. is as follows: ‘Chemometrics is a chemical discipline that uses mathematics, statistics and formal logic (a) to design or select optimal experimental procedures; (b) to provide maximum relevant chemical information by analyzing chemical data; and (c) to obtain knowledge about chemical systems.’ In an application to the industry, point (c) of the definition can be changed as follows: (c′) to obtain knowledge about chemical systems for monitoring or controlling operation of systems. Chemometrics applications are quite broad, often involving both traditional and nontraditional statistical techniques in the chemical sciences. Frequently, the novel aspects of chemometric practice deal with problems of high correlations and very high dimensionality within the data.
Climatology
Authors: Henderson‐Sellers Ann 
Published: September 2006

Abstract
Climatology, once the study of ‘average weather’, now encompasses the atmosphere, hydrosphere, cryosphere, land surface and biosphere. Modern climatology includes not only these components but importantly their interactions involving detailed global observing systems and complex computer‐based numerical models. People's interest in climatology has been, and is likely to continue to be, concerned with social issues of habitability and sustainability. We tend to evaluate climate in personal terms: Is it too hot or too cold? Is the air pleasant to breathe? Is there enough water for drinking and for growing crops? Does it feel comfortable? These characteristics are interdependent; together forming the climate system and posing a larger question: Can this planet continue to sustain life?
Clustering
Authors: Marriott F.H.C. 
Published: September 2006

Abstract
Cluster analysis is concerned with the subdivision of individual objects into natural groups on the basis of collected data relating to these objects. The data may consist of observations on each object, or of perceived similarities or dissimilarities between pairs. As a statistical technique, cluster analysis is a form of ‘unsupervised classification’, as opposed to supervised classification, in which the groups and the group membership of the data are known, and are used to form rules to classify future observations. It is closely related to ordination, including principal component analysis and projection pursuit. These techniques are often used to justify the results of cluster analysis.
Multicollinearity
Authors: Wakefield Jon 
Published: September 2006

Abstract
Multicollinearity is said to occur when at least one column of the design matrix is linearly dependent on the other columns, and can cause problems of both interpretation and computation in regression analysis. The problem manifests itself when a regressor is highly correlated with another regressor, or a linear combination of a number of regressors. This does not guarantee multicollinearity, however, since two vectors may have correlation one, but be orthogonal. Sometimes this phenomenon is referred to as collinearity but, in this article, this term is reserved for the situation in which several points lie on a line.
Community Food Webs
Authors: Deangelis Don 
Published: September 2006

Abstract
Community food webs describe the feeding relationships, or trophic interactions, between species in an ecological community. Food webs can be represented both pictorially and mathematically. Historically, the mathematical study of community food webs developed from two different lines of inquiry. The first line extended the predator–prey models of Lotka and Volterra to describe the dynamics of communities of many species. The second line derived from the study of the flows of energy in ecosystems, beginning with Lindeman. Ecologists studying the dynamics, and researchers focusing on the energy flows of community food webs found it useful to model food webs with sets of differential equations, one equation for each species (or ‘trophospecies’, aggregations of species sharing the same prey and predators) in the system.
Community, Ecology
Authors: Philippi Tom 
Published: September 2006

Abstract
Ecological communities are the set of potentially interacting species co‐occurring at one place. The field of community ecology investigates both the pattern and processes of ecological communities, the structure of communities, and what factors and processes determine that structure. The three main approaches to these questions are comparing naturally occurring variations in communities across both space and time, comparing responses to experimental manipulations, and theoretical modeling. As a field, community ecology partially overlaps with population ecology, landscape ecology, ecosystem ecology and biogeography.
Competing Risk
Authors: Gupta R.C. 
Published: September 2006

AbstractIn studies of risk assessment due to environmental exposures, we are interested in the mortality due to a particular disease when several other diseases are acting on a person's life. The method of analyzing survival data in such a population is called competing risk analysis. We also have a fundamental assumption that each death takes place due to a single cause.When an individual is considered, only his time of death and cause of death are noted. These competing risk data provide insufficient information to determine the joint distribution of the survival times, giving rise to the identifiability problem. In this article we introduce the cause specific hazard functions and discuss their estimation and testing, in the case of independent and dependent models. We also address the identifiability problem and suggest some ways to deal with the competing risk data in the presence of association between the latent survival times
Composite Sampling
Authors: Patil G.P. 
Published: September 2006

Abstract
The high costs of laboratory analytical procedures frequently strain environmental and public health budgets. Whether soil, water or biological tissue is being analyzed, the cost of testing for chemical and pathogenic contaminants can be quite prohibitive. Composite sampling can substantially reduce analytical costs because the number of required analyses is reduced by compositing several samples into one and analyzing the composited sample. By appropriate selection of the composite sample size and retesting of select individual samples, composite sampling may reveal the same information as would otherwise require many more analyses. Many of the limitations of composite sampling have been overcome by recent research, thus providing more widespread potential for the use of composite sampling to reduce costs of environmental and public health assessments, while maintaining and often increasing the precision of sample‐based inference.
Computer‐Intensive Methods
Authors: Fortin Marie‐Josée , Jacquez Geoffrey M. , Shipley Bill 
Published: September 2006

Abstract
With recent advances in the power of personal computers, reference distributions of statistics are now often generated using computer‐intensive methods. Several terms are erroneously used interchangeably when referring to such computer‐intensive statistical methods, including ‘resampling techniques’, ‘Monte Carlo’, ‘permutation’, ‘randomization’ and ‘bootstrap’. These techniques are not interchangeable but are fundamentally different in terms of their statistical mechanics. The generic terms ‘resampling techniques’ and ‘computer‐intensive methods’ refer to all methods in which the observed data are used to generate a reference distribution by means of randomization. This reference distribution is then used to assess the significance of a statistic calculated from the observed (not randomized) data. Significance is evaluated under the assumption that the statistic computed using the observed data is sampled from the reference distribution generated with a randomization technique.
Concentration, Ambient
Authors: Hopke Philip K. 
Published: September 2006

Abstract
The atmosphere consists of both gases and condensed phases. The concentrations of these components are not constant, with the variability being inversely related to reactivity of the individual species. More highly reactive components such as reactive organic compounds have distributions with larger relative standard deviations than low reactivity species. There are variations in concentrations between the northern and southern hemispheres. There are generally higher concentrations of various components in the northern hemisphere than in the southern hemisphere. These differences are the result of higher human populations in the northern hemisphere, as well as the relatively slow mixing that occurs across the equator between the two hemispheres.
Conditional Simulation
Authors: Shafer John M. 
Published: September 2006

Abstract
Conditional simulation, also referred to as stochastic imaging, is a stochastic simulation process whereby equally probable realizations of a spatial random function are created with each realization honoring discrete data values at specific locations or geometric patterns, i.e. ‘conditioned’ on the data. Conditional simulations of a random function satisfy two exactitude constraints, the local exactitude of honoring data values and the global exactitude of reproduction of a specified spatial correlation structure. The random variable may be categorical such as an indicator of the presence or absence of some feature (e.g. rock type), or spatially continuous such as porosity or ore grade. Conditional simulation is often used to address problems associated with the spatial distribution of physical properties relevant to the mining industry and several fields of earth sciences, especially hydrogeology, contaminant hydrology, meteorology, and gravimetry. Through Monte Carlo analysis, for example, conditional simulation can be used to evaluate the uncertainty in numerical model parameter values and the translated uncertainty in model response.
Creel Surveys
Authors: Jones Cynthia 
Published: September 2006

Abstract

The term
creel survey
is applied to sampling surveys that target recreational anglers. The name comes from the woven wooden basket, or creel, that freshwater anglers use to hold captured fish while they continue fishing. Traditionally, the survey is conducted on‐site at access points along the water and the angler is asked about the fish species that have been targeted, the numbers of each species caught and released, and the time spent fishing. These data are used to estimate the total catch and effort for that recreational fishery in order to manage its harvest. Additionally, other measures such as catch per unit effort are used to assess qualities of the fishery that lead to angler satisfaction with his/her recreational experience. Anglers can also be contacted by other means, such as by telephone or mail, and may also be asked other questions, such as those related to economic expenditures.
Critical Loads
Authors: Hirst David 
Published: September 2006

Abstract
In the 1960s and 1970s it became clear that air pollution was causing damage to ecosystems across the world, and in particular acid rain was thought to be damaging forests and water bodies long distances from the pollutant source. International agreements were necessary in order to reduce emissions of these pollutants, and it was therefore necessary to quantify both current damage, and the level of reductions that would reduce damage to acceptable levels. Critical loads (CLs) were therefore defined in the 1979 Geneva Convention on Long‐range Transboundary Air Pollution, as ‘a quantitative estimate of an exposure to one or more pollutants below which significant harmful effects on specified sensitive elements of the environment do not occur according to present knowledge’. The first CLs were defined and calculated for acidity due to sulfur (S) deposition. These were superseded by acidity CLs due to combined nitrogen (N) and S deposition. There are now also CLs for nutrient N, and more recently ‘critical levels’ have been defined for ozone. CLs have now been defined and mapped for all natural and semi‐natural areas in Europe. The data are held at the Co‐ordination Center for Effects at the National Institute of Public Health and the Environment in the Netherlands (RIVM).
Cross‐Scale Morphology
Authors: Allen Craig R. , Holling C.S. 
Published: September 2006

AbstractThe scaling of physical, biological, ecological and social phenomena has become a major focus of efforts to develop simple representations of complex systems. Much of the attention has been on discovering universal scaling laws that emerge from simple physical and geometric processes. But there are regular patterns of departures both from those scaling laws and from continuous distributions of attributes of systems; these departures often demonstrate the development of self‐organized interactions between living systems and physical processes over narrower ranges of scale. Cross‐scale morphology refers to morphological attributes of animals that are influenced by interaction with ecological structures and patterns at different scales.
Cross‐Validation
Authors: Friedl Herwig , Stampfer Erwin 
Published: September 2006

AbstractCross‐validation is a resampling technique that is often used for the assessment of statistical models, as well as selection amongst competing model alternatives. Basically, it is a method to estimate the prediction error of statistical predictor functions. This technique can be very useful in data problems involving minimal distributional assumptions. It has found many applications ranging from linear regression, partial least squares, ridge regression, classification and discrimination, to smoothing and neural networks, in univariate as well as in multivariate settings. Cross‐validation is rooted in the well‐known phenomenon that estimating prediction error on the same data used for model building tends to give downward‐biased estimates. The reason for this is that the parameter estimates are optimized to reflect the peculiarities of the dataset. When new data arrive, the model usually performs worse than expected on the grounds of assessment measurements on the training set. A reliable method to estimate the prediction error is especially necessary for very flexible models, e.g. neural networks and tree‐based classifiers, where overfitting is ubiquitous.
Databases, Health Assessment
Authors: Ross N. Phillip , Geyer Susan 
Published: September 2006

Abstract
Environmental issues – in particular questions about the relationship of environmental pollution to human and ecological health – are becoming more numerous and controversial. The ideal approach to assessing potential health risks from environmental exposure is to conduct well‐designed, prospective, environmental, epidemiological studies. However, prospective epidemiological studies are difficult and expensive to undertake, and even retrospective studies can be prohibitively complex. Reliable environmental data are not easy to find and often they may not have been collected in a manner that makes then easy to use. The purpose of this article is to provide an overview of the major databases that have potential for providing data on environmental pollution as related to measures of human and ecological health.
Decision Support Systems
Authors: Lam David 
Published: September 2006

AbstractEnvironmental 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

Abstract
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.