bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sampling, Environmental
Authors: Stehman Stephen V. , Overton W. Scott 
Published: September 2014

AbstractEnvironmental sampling encompasses methods for collecting and analyzing data to address questions in diverse fields such as ecology, environmental engineering, environmental policy, fisheries, forestry, and wildlife science. The goal of sampling is to estimate collective properties or parameters (e.g., means, totals, and proportions) characterizing the attributes or measurements defined on a universe. A universe may consist of discrete objects such as lakes, wetlands, stream segments, forest stands, plant or animal populations, environmental organizations, or recreational users, or a universe may be defined spatially, for example by delineating an area on a map. Environmental sampling objectives typically address questions of status and/or trend. Status refers to current attributes of the universe. For example, how many acidic lakes are there in the northeast United States potentially sensitive to acidification, or how many individuals of a rare plant population exist? Trend focuses on how attributes of a universe are changing over time. Are certain amphibian populations declining in South American forests? Is tree disease increasing following a damaging ice storm? How much agricultural land is being converted to residential and commercial use? These collective properties of status and trend are estimated from a sample, which is a subset of the universe. A sampling strategy consists of a sampling design, which is the protocol for selecting the subset of the sample, and the estimator (formula) of a parameter. Good environmental sampling practice requires a judicious choice of both sampling design and estimator to create a sampling strategy that is practical and yields precise estimates of parameters.
Benchmark Analysis
Authors: Crump Kenneth 
Published: September 2014

Abstract
The benchmark approach is a statistical procedure applied to dose–response data from toxicologic, clinical or epidemiologic studies. It can be used to set health standards or guidelines for exposures to toxic substances. Such guidelines include those promulgated by the United States Environmental Protection Agency (USEPA) called references doses (RfDs), for oral exposures, or reference concentrations (RfCs), for exposures through the ambient air. The USEPA defined the RfD as ‘an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population, including sensitive subgroups, that is likely to be without an appreciable risk of deleterious effects during a lifetime’.
Biomarkers
Authors: Thurston Sally W. 
Published: September 2014

Abstract
Biomarkers are cellular, biochemical, or molecular changes in a biological system. Biomarkers are often broadly classified as markers of exposure (either of internal dose or of the biologically effective dose), markers of preclinical biological effects, markers of susceptibility, or markers of disease. Biomarkers of exposure that are known to be on the causal pathway to cancer have been used to help identify environmental carcinogens. Biomarkers of disease are often associated with a premalignant condition, but recently epigenetic changes such as DNA methylation have been proposed as disease biomarkers. Markers of susceptibility include genetic polymorphisms that affect the probability of proceeding from one disease state to the next. Challenges in using biomarkers in cancer research include the inability to measure biomarker levels in the target tissue of controls, and the possibility that the disease process may alter biomarker levels. Established and well‐validated biomarkers, as well as new biomarkers, hold promise for a better understanding of the cancer process and for identifying individuals at increased cancer risk.
Diet, Measurement Of
Authors: Willett Walter C. 
Published: September 2014

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 Mapping, Hierarchical Models For
Authors: Waller Lance A. 
Published: September 2014

AbstractHierarchical models for disease mapping.Hierarchical models provide a convenient framework to combine information across multiple observational units. Such models are particularly convenient for small area estimation of disease rates to improve statistical precision of estimates associated with small local sample sizes. We review the basic framework of model‐based approaches for combining information across small administrative units through the use of random effects. Varying definitions of the random effects distribution allows one to combine information from all locations, neighboring locations, or a combination of both.
D
ose‐
R
esponse
M
odeling for
C
lustered
D
ata
Authors: Chen James J. 
Published: September 2014

Abstract
Clustered data refer to a set of correlated responses. For example, different animals represent different genetic values inherited from parents that may also be nested within herds or spatial entities. Clustered data also result from study designs, such as repeated observations on the same subject or sampling from correlated subsamples. A great deal of statistical research on dose response modeling of clustered data is devoted to animal reproductive and developmental toxicity studies. In developmental toxicity experiments, chemicals are administered to the dam (female animal), but the observations are made on individual fetuses. The clustering of fetuses within the litters induces the cluster (litter) effect. Modeling the clustered data should account for the correlation among the individual responses within a cluster to draw valid and efficient inferences. Two common approaches for fitting dose response models to clustered data are the likelihood‐based and generalized estimating equations (GEE) (or quasi‐likelihood) approaches.
Fertility Studies
Authors: Dunson David B. 
Published: September 2014

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.
Transmission/Disequilibrium Test
Authors: Seillier‐moiseiwitsch Françoise , Niu Tianhua 
Published: September 2014

Abstract
The transmission/disequilibrium test (TDT) can be utilized as a test of linkage between a marker and a disease locus or as a test of association between these loci. It relies on the marker information from triads consisting of parents and their affected child. Its properties depend on the purpose it is used for. Its validity for stratified populations makes it particularly attractive. Also, it is often more powerful than allele‐sharing methods for complex diseases.
BACI
Design
Authors: Smith Eric P. 
Published: September 2014

Abstract

The purpose of impact assessment is to evaluate whether a stressor has changed the environment, which components are adversely affected, and to estimate the magnitude of the effects. The evaluation of impact involves comparative methods. Early approaches to impact assessment involved the use of computer simulation models to predict the impact. Decisions were then made based on the soundness of those predictions. Although the data used to interpret effects are quite varied, the methods for analysis are often quite similar and involve comparison of impact areas with control areas. When information is available prior to the potential impact, the design is often referred to as a
Before–After Control‐impact
(BACI) design. Several variations on the basic design have been proposed and are discussed in this article.
Capture–Recapture Models, Spatially Explicit
Authors: Gopalaswamy Arjun M. 
Published: September 2014

AbstractAnimal density is a parameter of basic interest to field biologists. But owing to logistical and analytical constraints, estimating animal density has been very challenging. Spatially explicit capture–recapture (SECR) models were developed to address this particular, long‐standing problem. In this article, some of the important “milestones” in the SECR literature are discussed. The basic idea in SECR models is to include information about locations of animal captures into the modeling so that density is estimated directly. The early SECR type of approach involved estimation of density via simulations and inverse prediction. The first direct SECR estimator was likelihood‐based, which was more flexible and allowed for inclusion of covariates. In parallel began the development of Bayesian versions of the SECR models. These allowed for greater flexibility and for extensions which could, in addition, also estimate population vital rates. SECR models have witnessed several applications, ranging from analysis of camera‐trap data on large cats to acoustic signal data from birds. The future of SECR models is expected to go far beyond density estimation. It has great potential to answer some long‐standing questions about how behavioral processes drive population dynamics in wild animals.
Capture–Recapture Sampling Designs
Authors: Schwarz Carl 
Published: September 2014

Abstract
Good study design is necessary to obtain unbiased and efficient results from capture–recapture (CR) studies. We describe the considerations necessary for successful completion of CR studies. Rather than focusing on analytical issues, we discuss the stochastic processes that give rise to the data derived from such studies. We focus on the attributes of studies necessary to ensure that the assumptions of analyses associated with CR studies are met. We hope to expose investigators to data collection issues associated with classic CR designs, and also to point out novel applications of these designs.
Co‐limitation
Authors: Wacker Alexander 
Published: September 2014

Abstract

Understanding the interactions in resource limitations is a crucial challenge for ecological sciences. If organisms are simultaneously limited by more than one nutrient, they face a multiple resource limitation, which is often described by the term
co‐limitation
. Though the concept of co‐limitation has been successfully applied to describe nutrient‐limited growth responses of plants, this concept has been poorly applied to the nutrient‐limited growth of animals. This article provides essential insights into the concept of co‐limitation and its different facets, in particular the differences among modeling equations.
Decision Support Systems, Environmental
Authors: Nathanail C. Paul 
Published: September 2014

AbstractDecisions involving environmental aspects are complex. The underlying environmental processes are tackled by teams that are invariably multidisciplinary in nature and involve sundry specialists: earth, physical, chemical, geochemical, biological, benthic, and ecological sciences. Many decisions, such as water resources allocation, irrigation and agricultural practices, toxic waste management, industrial effluent regulation, acid rain mitigation, and climate change accommodation are interrelated. The solution or mitigation of one problem usually affects another. In addition, the constraints to these solutions are legion: 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 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.
Environmental Forensics
Authors: Johnson Glenn W. 
Published: September 2014

AbstractEnvironmental forensics is the scientific evaluation of environmental data for the purposes of evaluation of the history and sources of environmental contamination. Forensics investigations are often conducted in legal rather than purely academic context. Scientific methods that may be used in such studies are large and diverse: limited only by relevance to the problem at hand, and the degree to which the method has proved reliable and has been accepted within the scientific community. Several common data analysis methods that have seen extensive use in forensic studies are principal components analysis, receptor models, and simple graphical displays such as concentration maps.
E
xposure
A
ssessment
Authors: Brown Kathleen Ward , Levy Jonathan 
Published: September 2014

Abstract
Human exposure to an agent has been defined as an event consisting of contact with a specific concentration of the agent at a boundary between the human and the environment (e.g., lung or skin) for a specific amount of time. Exposure therefore refers to the intensity, frequency, and duration of contact with an agent, which is distinct from the actual amount of the agent absorbed or received by target organs (the dose). This generally entails identification of population(s) exposed as well as the sources of the agent, pathways for the agent to contact the target, exposure routes, and the uncertainties in the assessment. The goal of exposure assessment is often to determine the distribution of exposures to a specific agent across individuals, which provides both the population mean exposure (relevant for quantifying population risks) and the exposure levels for more highly exposed or susceptible individuals.
Extremal Events
Authors: Katz Richard W. 
Published: September 2014

AbstractThe term extremal event is commonly used in relation to environmental occurrences that impact greatly on day‐to‐day life, for example, 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.
Kriging, Asymptotic Theory
Authors: Du Juan 
Published: September 2014

Abstract
The fixed‐domain asymptoic theory regarding the impact of misspecified covariance structure on the optimality of spatial interpolation is reviewed. Conditions in terms of equivalence of measures, similar tail behavior of spectral densities, and the distance of variances after orthogonalization are provided to ensure the pseudo‐BLP to be asymptotically optimal and the assessment of the mean squared error to be comparably accurate.
Ranked Set Sampling
Authors: Patil G.P. , Surucu Baris , Egemen Didem 
Published: September 2014

Abstract
Environmental monitoring and assessment typically requires observational data as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, and individuals, while typical characteristics of interest include biomass, chemical concentrations, or “head counts.” Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is comparatively simple. One can therefore achieve great observational economy if one can identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.
Risk Assessment, Ecological
Authors: Fox David R. , Halbrook Richard S. 
Published: September 2014

Abstract
Ecological Risk Assessment is a process for identifying, estimating, assessing and communicating both the likelihood and consequences of adverse outcomes on the biotic environment. These adverse outcomes can be associated with both natural (eg. earthquakes, floods) and human (eg. chemical pollution, mining, logging) activities or events. The scale and complexity of ecological risk assessments can vary from recommendations based on simple descriptive studies to regulatory frameworks arising from the application of detailed scientific investigations and advanced statistical analyses. While the methods and procedures have evolved constantly over the last 80 years or so, there remain gaps in key understanding of ecosystem processes and parameterisation of models to represent them. This ‘model incertitude’ represents a significant source of uncertainty in the utility of ERA outputs and is the focus of more contemporary methods. This article briefly traces the origins or ERA, highlighting similarities and differences with other variants of risk assessment before discussing common paradigms and structured approaches to the conduct of an ERA.
Sample Support
Authors: Goovaerts Pierre 
Published: September 2014

AbstractSample support generally refers to the length, area, or volume associated with a measurement or observation. The term originates in the field of geostatistics, primarily from mining applications. To appreciate the issues associated with this term, this entry provides (i) a more complete definition of sample support as used in geostatistics; (ii) a description of the problems that arise in working with data of differing supports; (iii) a brief overview of the solutions to change of support problems.
Spacing Trials
Authors: Burkhart Harold E. 
Published: September 2014

Abstract
One of the most important factors that is within the direct control of managers of plant crops is plant spacing. Managers of agronomic, horticultural, and forestry cropping systems must decide on an initial planting density. Economic considerations dictate that one strives for the ‘optimal’ spacing of plants. The optimum spacing will, of course, vary depending on the management objective. Designed spacing experiments provide reliable information on optimal density for given product objectives.
Spatial Covariance
Authors: Sampson Paul D. 
Published: September 2014

AbstractModeling of the spatial dependence structure of environmental processes is fundamental to almost all statistical analyses of data that are sampled spatially. Such analyses address tasks such as spatial estimation (Kriging), monitoring network design (Optimal spatial design), as well as the basic scientific characterization of the second‐order properties of these processes. This article reviews the literature on nonstationary spatial covariance modeling primarily under the headings of smoothing and kernel methods, basis function models, process convolution models, and spatial deformation models. This discussion raises some general issues and comments on new and forthcoming developments.
Species Diversity
Authors: Latimer Andrew M. 
Published: September 2014

Abstract
Species diversity is the number of species present in an area or sample, together with the characteristics of their relative abundance distribution, especially its evenness. Species diversity is only one dimension of biodiversity, but because species are fundamental units of ecology and evolution, and because most diversity data consist of species presence/absence, species diversity is often central to assessing and conserving biodiversity. After noting major conceptual and practical issues with species diversity and species richness, we discuss how best to estimate them from samples both for estimating total species number and for comparing diversity across samples and areas. Despite errors associated with extrapolation, recent research has demonstrated that in many cases, given adequate sampling, both nonparametric and parametric methods can estimate total species richness and effectively standardize across samples. Much research has focused on developing and interpreting compound diversity measures that incorporate patterns of both richness and abundance, some with weightings for additional factors such as rarity, phylogenetic distinctness, and ecological function. For a well‐known set of these indices, including the Shannon and Simpson indices, lognormal mean and variance, log‐series α and related neutral theory parameters, we discuss their consistency in ordering samples and their sensitivity and dependence on sample size. Finally, we touch upon major global patterns of diversity including estimates of the number of species on earth (5 million to 50 million) and large‐scale diversity gradients.
Source Apportionment
Authors: Watson John G. , Chow Judith C. 
Published: September 2014

Abstract
Source apportionment models use variations in air pollutant composition, time, and space to infer source types and quantify their contributions. Marker species are sought in source emissions that minimize collinearity among the pollutant patterns found in many source types. Engine exhaust, biomass burning, geological material, sea salt, and secondary sulfates and nitrates can be separated and quantified with commonly measured elements, ions, and carbon fractions. More detailed organic compounds, isotopic abundances, and particle morphology are usually needed to distinguish source subtypes, such as diesel and gasoline engine exhaust. The data sets are noisy, and uncertainties in source contribution estimates must be assessed. At a minimum, variability in the chemical patterns in emissions and ambient concentrations should be quantified and propagated. Sample sizes should encompass a range of source contributions, which can be accomplished by locating sampling sites and selecting measurement periods where large contributions are expected. A “weight of evidence” approach assesses uncertainties by the application of several source apportionment techniques with reconciliation of discrepancies for the source contribution estimates.
Geographic Information Systems (GIS), Spatial Statistics In
Authors: Cressie Noel , Liu Desheng 
Published: September 2014

AbstractA Geographic Information System (GIS) is a networked collection of hardware and software tools that recognizes georeferencing in the data and features the georeferencing in database management, data analysis, and visualization of results (principally maps) from the analysis. This combination of geography and computer science has been known as geographic information science. Spatial statistics in GIS has grown in importance, however, spatial databases in a GIS can be very large. It may be that, without some form of dimension reduction, only the most simple statistical methodologies will scale up.