bluuebunny/crossref_metadata_2025 huggingface dataset:
Catch Statistics
Authors: Sampson D.B.
Published: September 2006

Fisheries science uses a wide variety of quantitative methods ranging from simple data tabulations to complex statistical models with hundreds of estimated parameters. Unlike the other sciences, however, in which experiments are often used successfully to evaluate competing hypotheses, many issues in fisheries are not amenable to controlled experiments because the fish are long‐lived and the fish populations are widely dispersed and suffer highly variable environmental conditions. Because fishers tend to operate in areas of high fish density where they can obtain favorable catch rates, in general the characteristics of the harvested fish will not be representative of the fish population at large. Further, different fish species often are not comparable in value and larger individuals of a given species are normally preferred to smaller ones. Hence, many of the quantitative methods in fisheries science are related to two broad problems: collecting data on the characteristics of the harvested fish and the magnitude of the harvest, and relating the harvest to the dynamics of the fish population in its entirety.
Catchment Hydrology
Authors: Letcher R.A. , Jakeman A.J.
Published: September 2006

The Ad Hoc Panel on Hydrology of the US Federal Council for Science and Technology recommended that hydrology be defined as ‘the science that treats of the waters of the Earth, their occurrence, circulation and distribution, their chemical and physical properties and their reaction with their environment, including their relation to living things’. More specifically catchment hydrology is the scientific study of that part of the hydrological cycle that relates to the concept of a catchment. This excludes the study of oceans or detailed atmospheric processes. A catchment can be defined as the area of land that drains to a point and may be subdivided into contributing subcatchments (or watersheds) by considering relative elevations and subsequently flow directions in different areas.
Center for Global Environmental Research
Authors: El‐Shaarawi Abdel
Published: September 2006

The Center for Global Environmental Research (CGER) was established to contribute to the solution of pressing environmental problems and has three major activities: integration of environmental research through enhanced communication and international cooperation; support to global environmental research; and long‐term and continuous monitoring relevant to global environmental issues.
Change, Detecting
Authors: Esterby S.R.
Published: September 2006

Fundamental to the assessment of the effect of human activities on the environment is the detection of changes in environmental systems. It may be of interest to detect changes occurring over time, over space, or over both time and space. Although closely linked to the determination of status and the estimation of the size and components of change, detection is the primary consideration here. Methods for detecting change include: (1) graphical methods, exploratory and more formal; (2) tests of hypotheses, where the hypothesis characterizes the original status; and (3) diagnostic tests that detect departures from a specified model. Most methods considered in this article are for changes over a single dimension and will often have been developed for data collected over time. However, such methods will usually be applicable to data collected in one dimension over space. Datasets for which more than one dimension needs to be taken into account generally require more modeling and thus are less often investigated from the detection point of view.
Circular Data
Authors: Anderson‐Cook Christine M. , Otieno Bennett Sango
Published: September 2006

Directional data commonly occur in environmental studies when measurements are taken from an orientation or cyclical timing application. Circular data is the simplest case of this category of data, where the single response is not scalar, but angular or directional. Typical orientation observations are measured by a compass and indicate direction, while cyclical timing applications are measured by a clock or calendar. The key characteristic that differentiates circular data from data measured on a linear scale is its wrap‐around nature, with no maximum or minimum. Specific environmental endpoints that use directional measures include wind direction, plant or animal migration (orientation), as well as timing of meteorological or life cycle events during the day or in the calendar year (cyclical timing). Environmental applications include long‐axis orientations of fieldspar laths, cross‐bed azimuths of paleocurrents, and orientations of pebbles or rock cores. Also, the repeating nature of time increments, such as days, months or years, is captured by the wrapping‐around of the circle. Examples can include time of the daily maximum temperature, or occurrence of hurricanes during the year.
Clean Water Act,US
Authors: Piegorsch Walter W.
Published: September 2006

The US Clean Water Act was intended to direct environmental attention/protection to surface waters in the US and specifically to ‘… restore and maintain the chemical, physical and biological integrity of the Nation's waters.’ In some cases, the Act also has had the effect of guiding environmental management as an effective, efficient, and equitable land/water resource‐use planning tool.
Coarse Woody Debris
Authors: Stahl Göran , Dolloff C. Andrew
Published: September 2006

Coarse woody debris (CWD), synonymous with large woody debris, is a term used to describe wood from dead trees. Although an exact definition is not available, many researchers include all pieces of wood that are at least 10 cm in diameter and 1m in length. CWD can consist of any coarse part of a dead tree, such as trunks or parts of trunks, branches, stumps, and root wads. A distinction is often made between standing dead trees, snags, and dead trees on the ground, logs. Dead roots in the soil are generally not considered as CWD although there is no principal reason why they should not be. Challenges to the measurement and quantification of CWD range from sampling issues to multivariate statistics for studying species occurrences and communities on dead wood. A brief overview of some specific environmetrical challenges is given in this article.
Species Competition
Authors: Young Linda J.
Published: September 2006

Interspecific competition arises when the interaction of two species has an adverse effect on both species. Competition may occur due to (a) exploitation (individuals from different species share the same limited resource(s)) or (b) interference (individuals from different species fight or exhibit other directly damaging behavior). Examples include two bird species seeking the same supply of insects, and tree seedlings of differing species sharing sunlight, water, and nutrients in the forest understory. Interest in competition dates at least to the time of Charles Darwin. Early studies tended to focus on discovering examples of one species replacing another. Competition studies have expanded to include investigations of the effect of environmental conditions on the competitive ability of one species relative to another, the role of competition in determining the distribution and abundance of organisms, and the conditions needed for species to coexist.
Compositional Data
Authors: Billheimer Dean
Published: September 2006

Compositional data are vectors of proportions describing the relative contributions of each ofDcategories to the whole. This structure is an essential feature of data in many disciplines: in geology as oxide composition of rocks, in ecology as the relative abundance of plants or animals, in air pollution monitoring as the relative concentrations of different chemical species (among others). Statistical approaches for modeling compositional data exhibiting temporal or spatial correlation structure are described in this article. Because methods for compositions are not widely included in statistics education, a brief introduction is given to data characteristics and analysis techniques for independent observations. The focus is observations with three or more categories (D ≥ 3). With only two categories, compositional data are (essentially) univariate, and can be addressed by means of standard statistical techniques (e.g. logit transform). Since the compositional structure is present in a wide variety of problems, many of the methods summarized in this article have been developed outside the environmental context. The approach is to present methods in a general form, and note when there is special development for an environmental application.
Computer Modeling
Authors: Richter Otto , Seppelt Ralf , Söndgerath Dagmar
Published: September 2006

The starting point of computer modeling is the design of aconceptual model. The conceptual model aggregates our knowledge of the system and implies a thorough (albeit subjective) selection of components and processes judged essential for the processes under study in a given spatiotemporal context. A conceptual model is graphically presented in form of a compartment system. Compartments are defined with respect to morphology and to physical, chemical and biological states. A computer model implies the translation of a conceptual model into a code, i.e. an algorithm.
Constrained Optimization
Authors: Stein Alfred
Published: September 2006

Constrained optimization aims to find an optimum of a function in the presence of constraints. Constrained optimization generally yields a lower optimum on the optimization function as compared with unconstrained optimization, but it honors the presence of constraints. A simple example concerns maximization of the production of a farm subject to constraints on the amount of fertilizers (say
n
kg per week) and man hours (
h
hours per day). Another example is minimization of leaching towards the environment, subject to constraints on soil parameters, groundwater table and other environmental variables.
Control Theory
Authors: Young Peter C.
Published: September 2006

Control theory is concerned with the mathematical characterization and analysis of deterministic and stochastic dynamic systems for the purpose of identification, estimation and control. It emphasizes the importance of feedback processes, both as an integral aspect of all dynamic systems and as a means of artificially controlling or optimizing the behavior of such systems. In relation to environmetrics, the main importance of control theory relates to the impact that it has had on the analysis of environmental time series and the modeling of environmental systems. In addition, however, its powerful ability to improve our understanding of how dynamic systems behave in feedback terms can have a profound effect on how environmetric models are interpreted in physically meaningful terms.
Correlated Multinomial Data
Authors: Zhu Yiliang
Published: September 2006

Correlated multinomial data, of which correlated binomial data are a special case, often arise from studies with a clustered sampling plan. Grouped sampling and repeated measurements are common mechanisms of clustered sampling. Correlation induced by clustering must be accounted for in statistical analyses. Ignoring the correlation may lead to inaccurate or inefficient inference because of incorrect characterization of variation in the data. Sometimes, characterization of the correlation structure itself can be of interest, particularly in longitudinal studies.
Data Mining
Authors: Hand David
Published: September 2006

Data mining is a new discipline that has sprung up at the confluence of several other disciplines, stimulated chiefly by the growth of large databases. The basic motivating stimulus behind data mining is that these large databases contain information that is of value to the database owners, but this information is concealed within the mass of uninteresting data and has to be discovered. That is, one is seeking surprising, novel or unexpected information, and the aim is to extract this information. This means that the subject is closely allied to exploratory data analysis. However, issues arising from the sizes of the databases, as well as ideas and tools imported from other areas, mean that there is more to data mining than merely exploratory data analysis.
Developmental Toxicity Study
Authors: Regan Meredith M.
Published: September 2006

Development toxicology investigates the impacts on human development of exposure to potentially toxic substances or conditions. Controlled development toxicity studies in laboratory animals play an important role in the toxicological assessment of foods, drugs and chemicals. In contrast to carcinogenicity studies, methods to extrapolate safe exposure levels from animals to humans are not well developed. The mechanisms of most development toxicants are unknown, and statistical methods for carcinogenicity do not directly apply since the outcomes and the experimental design differ. Manifestations of development toxicity include death, malformations, growth alterations, and functional impairment. Improving methods of quantitative assessment of adverse development effects is a current research focus.
Discriminant Analysis
Authors: Seheult Allan
Published: September 2006

Suppose that two ‘populations’ (classes or groups) ∏
1
and ∏
2
of ‘individuals’ can be mainly distinguished by a ‘feature vector’
y
 = (
y
1
, …,
y
p
) of
p
measurements. In discriminant analysis, the problem is to devise a rule which allocates (classifies) an individual with a given feature vector to one of the two populations, assuming it comes from one of them.
Disease Clustering
Authors: Elliott Paul
Published: September 2006

In recent years there has been heightened awareness about the possible effects of environmental pollution on health, reflecting an increasing public expectation of a clean and healthy environment. The public and media alike have focused on the possible risks to individuals and their communities from local sources of industrial pollution. These concerns have to some extent been fueled by scientific uncertainties about the health effects of low levels of pollution. All these issues tend to come to the fore when disease excesses (
clusters
) in the local community are reported or suspected. This article reviews some of the issues affecting the analysis and interpretation of clusters of disease, including examples where evidence of local disease excess is derived from a disease mapping exercise or a point source investigation.
Dispersion in Confined Spaces
Authors: Phillips M.
Published: September 2006

The release of a gas or some other form of airborne substance into a confined space can present some form of hazard. Some gases are, for example, inherently toxic while others may result in hazardous conditions because they displace the oxygen necessary for normal respiration. In either case, an appreciation of the processes that result in the dispersion of the substance around the space is likely to be essential to a full understanding of the hazard present. Gases or airborne particles can be dispersed in confined spaces by a variety of physical processes. Which process or processes dominate in any particular situation depends on many factors. The most significant factors are normally the nature of the dispersing substance, the prevailing conditions in the space in which it disperses, and the geometry of the space. Since conditions such as air movements and ventilation rates are difficult to estimate or measure precisely and comprehensively, estimates made can rarely be relied on to provide highly precise data. Nevertheless, the techniques described in this article do provide valuable means of analyzing and quantifying the likely consequences of the release of gaseous or airborne substances in a confined space, albeit in broad terms rather than precise ones.
Dose‐Response Model
Authors: Gaylor David
Published: September 2006

A measurement,
Y
, of an effect can be represented by
Y
 = E(
Y
) + error. E(
Y
) is the expected (true) value of an effect and error is the deviation of the measurement from its true value due to inherent measurement error and/or experimental variation. A dose–response model is a mathematical function
f
that relates the expected value, E(
Y
), of some measurable effect,
Y
, to a dose,
d
, of a substance E(
Y
) = 
f
(
d
).
Dose–Response Modeling for Clustered Data
Authors: Chen James J.
Published: September 2006

Clustered data refer to a set of correlated responses. Clustered data arise naturally when the experimental units are subject to random variations, biologically or environmentally. 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.
Dynamic Programming
Authors: Whitaker David
Published: September 2006

Dynamic programming is a technique whose development is mainly due to Bellman. The term ‘dynamic programming’ originally arose because the technique was closely associated with computer programming where the solution to a problem was often obtained using the computer. A better view of the technique is to think of it as a method for deriving solutions tomultistage decision processes, which often, because of their computational intensive nature, rely on the use of the computer for solution. Dynamic programming is applicable to a wide variety of situations where the problem can be structured into stages and it is clear that the return from a procedure optimizing over each individual stage of the problem does not give the overall optimum. For example, when the stages are represented by months, a sacrifice in the return from a decision in January may enhance returns in future months so that the overall return at the end of the year is maximized. These multistage problems occur naturally in a wide variety of practical situations in economics, environmental science, science and engineering, ecology, and even sports.
Earth Observation Programme (
EOP
)
Authors: El‐Shaarawi Abdel
Published: September 2006

This article looks at the European program for observing the earth using satellite remote sensing techniques.
Ecological Society of America
Authors: Porter John H.
Published: September 2006

The Ecological Society of America (ESA) is a 7600‐member scientific society that focuses on improving communication among ecological scientists and between ecologists and society in general. Membership in the society is open to anyone with an interest in the advancement of ecology. The society has as its goals to: (i) promote ecological science by improving communication among ecologists; (ii) raise the public's level of awareness of the importance of ecological science; (iii) increase the resources available for the conduct of ecological science; and (iv) ensure the appropriate use of ecological science in environmental decision making by enhancing communication between the ecological community and policy‐makers. The society achieves these goals through annual meetings, the publication of scientific journals and special publications, supporting regional chapters and topic‐based sections and a operating a public‐affairs office.
Ecological Statistics
Authors: Smith Eric P.
Published: September 2006

Statistical applications in ecology are numerous and encompass many of the classical and modern methods, as well as some novel methodology. Because of the breadth of the topic, this article focuses on several important methods that are outside the realm of standard statistical texts with application to environmental problems. Some of the methods that are used in statistical ecology are described, along with references to additional material.
Time Series, Ecological
Authors: Solow A.R.
Published: September 2006

The most common situation in which time series data are the central object of ecological analysis is in population dynamics/population modeling. Let

X
t

be the size or density of a population in period
t
. A general deterministic model of the dynamics of this population is the first‐order nonlinear difference equation:

X
t

 = 

X
t−

1
F
(

X
t

−1
) where
F
is a non‐negative function.
Ecosystem Monitoring
Authors: Kimmins Hamish
Published: September 2006

The world's population has increased 6 times over the past 200 years. It has doubled over the past 40 years. Air and water pollution, climate change, loss of species, soil erosion and salinization, desertification, loss of forest, and a range of other undesirable environmental alterations have accompanied this unprecedented increase in human numbers. While the rate of population growth is declining, human pressure on the environment will continue to increase as human standards of living and per capita consumption increase, and it is clear that a fundamental change must occur in the relationship between humans and the global ecosystem if we are to avoid highly undesirable environmental change. Development of a sustainable relationship between humans and the environment requires an understanding of, and respect for, both human needs and desires, and the structure and functioning of the world's ecosystems. Policies and resource management practices must be developed that sustain desired ecosystem conditions and functions. To ensure that these policies and practices are effective, the condition of the world's ecosystems must be monitored.
Edge Effect
Authors: Stevens Jr Don L.
Published: September 2006

A common objective in sampling a population of biological, ecological, or environmental concern is an inference about the mean or total of some attribute per unit area. In many instances, population attributes are measured in the field by compositing information collected on plots of non‐negligible extent. For example, a forest inventory may count the number of trees and measure their basal area on circular plots. Fish population size might be estimated by counting the number of fish in selected stream reaches in a basin. Estimates of total biomass in a region could be obtained by collecting all biomass in randomly located quadrats. Unfortunately, it is well documented that this general method can result in a discrepancy, often callededge‐effect bias, between the expected value of the estimator and the true value of the total. This discrepancy results from lack of coherence between the measured density and the true density stemming from injudicious handling of plots that are near to or actually straddle the domain boundary. Unless such plots are treated appropriately, population elements near the boundary are weighted incorrectly in computing the overall total. Because edges often have properties that are distinctly different from interior properties, giving an incorrect weight to the edges can result in substantial bias. This article gives an explicit definition of edge‐effect bias, provides a sufficient condition for its absence, and discusses techniques to counter it.
Electromagnetic Fields
Authors: Savitz David A.
Published: September 2006

Concerns with possible health effects of low‐level exposure to extremely low‐frequency electric and magnetic fields have been prominent for over 20 years. The hypothesis of particular interest is that prolonged exposure, over periods of months to years, to relatively elevated power‐frequency (50 or 60 Hz) magnetic fields in the environment increases the risk of leukemia and brain cancer. Beyond the substantial public interest and policy relevance to the question of whether this ubiquitous exposure poses a threat to human health, the topic serves as a prototype of a low‐level, ambiguous environmental health concern. Like the concerns with persistent organochlorines such as dichlorodiphenyltrichloroethane (DDT), polychlorinated biphenyls (PCBs), or dioxin, or with drinking water disinfection by‐products, human health effects are at the margins of being detectable if present at all. Furthermore, as with many ubiquitous exposures such as indoor Radon, it is difficult to clearly identify exposure gradients: a few individuals may be highly exposed but most of the population is in a range in which discrimination of exposure levels is a challenge. Finally, because the exposure is so widespread, even a modest influence on disease risk is of public health interest. That is, public health concerns push back the boundaries of what is scientifically feasible, creating tension between the knowledge that is desired for setting policy on regulatory standards, and the often rather blunt instruments of environmental epidemiology. In the face of this pressure, statistical methods for maximizing the sensitivity and precision of risk estimates are paramount.
Emission Inventory
Authors: Fujita Eric M.
Published: September 2006

An emission inventory is a comprehensive listing of air pollutant emissions within a specific geographic location and time interval. Emission inventories provide the technical foundation for air quality management plans designed to improve or maintain ambient air quality. Air pollution control agencies use emission inventories to identify potential control measures and sources that are subject to controls, to determine control program effectiveness and to predict future air quality through air quality simulation models.
Empirical Bayes Methods
Authors: Louis Thomas A.
Published: September 2006

Statistical design, analysis, and reporting are fundamental to environmental research and assessment; to environmetrics. The variety and complexity of applications rapidly grows with a commensurate growth in statistical design and analysis. Environmental applications include all aspects of design, the analysis of spatially structured information (exposure, health outcomes, and demographics), nonlinear dose–response assays, interspecies extrapolation, placement of air or water monitoring stations, remote sensing, and gene–environment interactions. Analytic challenges include stabilizing estimates via statistical modeling and otherwise ‘borrowing information’, integrating information that is misaligned in space and time, dealing with missing data, accounting for measurement error in input information (e.g. exposures), missing data, causal modeling, addressing nonstandard goals and ensuring that statistical evaluations account for the dominant stochastic and modeling uncertainties. In addressing these and other challenges and goals, Bayes and empirical Bayes (EB) designs and analyses have proven valid, efficient, and informative. The beauty of the Bayesian approach is its ability to structure complicated assessments, guide development of appropriate statistical models and inferences, and produce summaries that properly account for uncertainties. Properly structured, these methods can produce objectively valid analyses, commonly more effective than traditional methods. When available, prior information can be integrated into the design and analysis. Computing innovations enable implementation of complex, relevant models and applications burgeon. This article outlines the Bayesian approach, traces the history of the EB approximation, gives a small sampling of environmetric applications and speculates on future developments.
Environmental Protection Agency,
US
Authors: Olsen Anthony R.
Published: September 2006

This article gives an overview of the U.S. Environmental Protection Agency for use in an Encyclopedia of Environmental Statistics. A perspective is given on the history of the Agency's formation, its mission, its legal authority, and its organizational structure. Selected activities of the Agency are given to provide a limited view of how the agency addresses environmental issues and cases in which environmental statistics have been used.
Sampling, Environmental
Authors: Stehman Stephen V. , Overton W. Scott
Published: September 2006

Environmental sampling is the science of 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 describe collective properties of populations defined as a universe. These collective properties are estimated using information from a subset of the universe called the sample. The universe may consist of discrete objects such as lakes, wetlands, stream segments, forest stands, plant or animal populations, environmental organizations, or recreational users, or it 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 values of population attributes. How many acidic lakes are there in the northeast US potentially sensitive to acidification and what are their characteristics? How many individuals of a rare plant population exist, and what is the geographical distribution of this population? What is the status of a nation’s wetlands? Trend focuses on how populations 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?
Environmental Tobacco Smoke (
ETS
)
Authors: Tweedie R.L.
Published: September 2006

Since the early 1980s, there has been an ongoing debate about the health effects of exposure to environmental tobacco smoke (ETS), also called passive smoke or second‐hand smoke. Prior to this period, most reviews had not found ETS to be associated with increased health risk. Indeed, in 1979 the US Surgeon General found no real indication that ETS was associated with either lung cancer or heart disease. In 1981, however, two studies were announced which identified an elevated relative risk of lung cancer in women with spouses who smoked. This has been followed by some 40 further studies, the results of which differ. A number show a significantly elevated risk, the great majority are rather equivocal, and a few are actually negative.
Epidemic Models
Authors: Renshaw Eric
Published: September 2006

The importance of studying disease outbreak and spread cannot be overstated. In fourteenth century Europe the Black Death killed 25 million people out of a population of 100 million; the Aztecs lost half their population of 3.5 million from smallpox; whilst around 20 million people died in the world pandemic of influenza in 1919. Today the overriding epidemic concern is the spread of human immunodeficiency virus (HIV) acquired immune deficiency syndrome (AIDS), to the considerable detriment of the vast numbers of people suffering from less mediaconscious diseases such as malaria, schistosomiasis, filariasis, and hookworm disease. Parallel problems in marine and agriculturally based environments take a similar toll on plant, animal, and fish populations. Human attempts to control such epidemiological disasters can themselves lead to further problems e.g. the improper use of pesticides and management strategies. Increasing understanding of the underlying processes involved is therefore one of the major environmental problems of our age, and the best way forward is through the careful use of mathematical modeling.
Escape Trajectory, Ecological
Authors: Domenici P.
Published: September 2006

Escape responses consist of sudden accelerations as reactions to threatening stimuli and are present in most animals as a means of avoiding predation. Escape responses are currently receiving increased attention as valuable models for animal behavior, ecology, and neurobiology. The path along which an animal moves during an escape response (its escape trajectory) has attracted the interest of ecologists and behaviorists in relation to its functional role in predator–prey interactions. Escape trajectories (ETs) as considered in this article refer to the initial prey response to a predator’s attack. Such a trajectory should be measured at the end of the main rotational motion present during the escape response, which usually corresponds to a specific kinematic stage of the animal’s locomotion. Beyond this stage, prey may continue escaping along a zigzag path, especially if predators follow up their attack with a chase.
Exposure Assessment
Authors: Levy Jonathan
Published: September 2006

Human exposure to a contaminant has been defined as an event consisting of contact with a specific concentration of the contaminant 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 a chemical, which is distinct from the actual amount of chemical absorbed or received by target organs (the dose). Exposure assessment is the process of quantifying and interpreting the pathway from the emission of a chemical to human exposure, which can have applications within risk assessment, environmental epidemiology, or pollution control development. The goal of exposure assessment is often to determine the distribution of exposures to a specific pollutant 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.
Fisheries
Authors: Myers Ransom A.
Published: September 2006

Fisheries biologists use statistical models to transform disparate data, such as catch, research surveys estimates, and the catch rates of fishermen into estimates of abundance, management regulations, and risk analysis. The result is an integrated assessment of a fish stock – a model of population dynamics that can be used to evaluate management decision. The core statistical problem in fisheries is the estimation of parameters of such a model. Such a statistical model combines data from many sources, using a variety of likelihoods, each representing a different piece of the data.
Forest Fire Models
Authors: Preisler Haiganoush K. , Weise David R.
Published: September 2006

Forest fire models pertain to at least one of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects and economics models). A model can be as simple as a formula calculating a fire danger index, or as complex as a numerical model that requires hours of computer time to arrive at an output.
Frequency Curves
Authors: Hosking J.R.M.
Published: September 2006

The fundamental property of a random quantityXis the frequency with which its possible values occur. A frequency curve is the graphical representation of this distribution of frequencies.
Fuzzy Numbers and Nonprecise Data
Authors: Viertl Reinhard
Published: September 2006

Real data obtained from measurements of continuous quantities are not precise numbers or vectors but more or less nonprecise. This uncertainty is different from measurement errors and has to be described formally in order to obtain realistic results of data analysis. Real‐life examples are poisons in different environmental media at a fixed time. These are typically not a precise multiple of the scale unit for the measurements. Such data are called
nonprecise
. In the past, this kind of uncertainty was neglected in statistical analysis. The reason behind this was the philosophical idea of the existence of a ‘true’ value which is identified with a real number times the measurement unit; but this is not realistic. The formal description of such nonprecise measurements can be given using so‐called
fuzzy numbers.
Gaussian Process
Authors: Davis Richard A.
Published: September 2006

The class of Gaussian processes is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space, or time and space. The popularity of such processes stems primarily from two essential properties. First, a Gaussian process is completely determined by its mean and covariance functions. This property facilitates model fitting as only the first‐ and second‐order moments of the process require specification. Second, solving the prediction problem is relatively straightforward. The best predictor of a Gaussian process at an unobserved location is a linear function of the
observed
values and, in many cases, these functions can be computed rather quickly using recursive formulas.
Gene–Environment Interaction
Authors: Niu Tianhua
Published: September 2006

Gene–environment interaction refers to the fact that individuals with different genotypes have different responses to the same environmental factor. It represents the major component of genetic epidemiology, an emerging discipline that aims at elucidating the role of genetic factors as well as their interaction with environmental factors in the occurrence of disease in populations. There are two broad categories of approaches in examining gene–environment interactions: conventional and nonconventional study designs. Conventional study designs include mainly case–control and cohort studies in which gene–environment interaction can be assessed by incorporating an interaction term into the statistical model. Quite a few publications have addressed the issue of power and sample size calculations using such approaches. However, exposure misclassification can lead to bias in the estimation of an interaction effect, and an increased sample size may be needed in applying these strategies. Therefore, nonconventional study designs, which include case‐only studies, twin studies, transmission/disequilibrium tests (TDT), and affected relative pair studies, have become more and more popular. This article primarily focuses on nonconventional approaches.
Generalized Linear Mixed Models
Authors: McCulloch Charles E.
Published: September 2006

Generalized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.
Geographic Information Systems (GISs), Spatial Statistics in
Authors: Cressie Noel
Published: September 2006

GIS is a networked collection of hardware and software tools that recognize georeferencing in the data and feature the georeferencing in database management, data analysis, and visualization of results (principally maps) from the analysis. This combination of geography and computer science has recently become known as geographic information science. Since data locations are usually an important part of any environmental study, the use of GIS in environmental sciences is growing rapidly.Spatial analysishas become the accepted term within the GIS community for any data analysis that involves the georeferential aspects of the data. This entry is concerned with spatial statistical methods, which make up quite a small fraction of the many GIS functionalities that are spatial in nature.
Gibbs Sampling
Authors: Storvik Geir
Published: September 2006

Stochastic simulation is an important part of statistical computing as well as a much‐used numerical tool for calculating integrals in other fields (Monte Carlo integration). For low‐dimensional problems inversion/transformation or rejection sampling can be applied for simulating from a specific distribution. When working with real problems, simulation from high‐dimensional distributions often becomes necessary. Gibbs sampler is an algorithm for simulation in high‐dimensional spaces.
G
lobal
E
nvironmental
E
pidemiology
N
etwork (
WHO
)
Authors: El‐Shaarawi Abdel
Published: September 2006

GEENET aims to increase the national capacity of developing countries to secure environmental health by strengthening education, training and applied research in environmental epidemiology.
Hansen–Hurwitz Estimator
Authors: Christman Mary C.
Published: September 2006

The first description of the Hansen–Hurwitz estimator was in the context of cluster sampling. Hansen and Hurwitz noted that when clusters are not all the same size, efficiencies might be gained by sampling the clusters using probabilities proportional to size, rather than simple random sampling.
Hazardous Agents
Authors: Urfer Wolfgang
Published: September 2006

Evaluation of hazards associated with exposure to chemicals, understanding of relationships between dose and adverse effect, extrapolation of effects from high experimental doses to low doses associated with actual exposures, and extrapolation from effects observed in animals to effects expected in humans are the main issues of statistical research in risk assessment of hazardous agents. Statistical aspects of inhalation toxicology, proof of hazard, genetic toxicology and the role of oncogenes in carcinogenesis are discussed. Finally, the need for more statistical research to identify hazardous agents by DNA and protein sequence analysis is outlined.
Hierarchical Bayes Methods
Authors: Ghosh Malay
Published: September 2006

Dose–response studies of environmental toxins are designed to determine the detrimental effects associated with exposure to the toxin, over a particular range of exposures. To properly quantify the toxic effect of a toxin, thebackground riskof no exposure to the toxin, R(0), must first be specified. The additional risk incurred by exposure to a dose,d, of the toxin is then called the added risk (AR) at this dose and is given by AR(d) = R(d) –R(0).
Hierarchical Model
Authors: Carlin Bradley P.
Published: September 2006

Ahierarchical modelrefers to a statistical model specified in stages, often with the purpose of borrowing strength across the various experimental units.
Hierarchy Theory, Ecological
Authors: Allen Timothy F.H.
Published: September 2006

Hierarchy theory is a dialect of general systems theory, and as such is particularly concerned with the role of the observer in choosing criteria for prescribing or describing levels. In simple ecosystems there is an accepted set of values. In complex systems, the very structure changes as new levels emerge and the system invites the observer to assess the situation at any of a large number of levels. In the face of complexity, the myth of the objective observer fails because structure can melt into process, meaning can disappear, and qualitative distinctions change into quantifiable matters of degree. These fundamental changes come about as the complex system matures, decays, or reorganizes, or as the observer shifts her level of analysis. Even when the change has a material basis, the observer is still responsible for deciding difference. How much difference must there be before the system is estimated to be unstable or a new structure is recognized? Thus hierarchy theory focuses on the observer's decisions, which makes it particularly adept at dealing with complex systems.
Hormesis
Authors: Calabrese Edward J.
Published: September 2006

Hormesis is a term used in toxicology and pharmacology to describe dose–response relationships in which there is a stimulatory response at low and an inhibitory response at higher doses.
Hydrological Extremes
Authors: Burn Donald H.
Published: September 2006

An accurate estimation of the probability of occurrence of an extreme hydrological event is often required for the effective development and management of water resources. Examples include characterizing extreme rainfall event magnitudes to predict urban runoff, determining river flow magnitudes to design a culvert or bridge opening at a road crossing, or determining the likely severity of a drought event during the design life of a water supply system. In each of these examples, an understanding of the hydrological response of a watershed is essential in order to obtain a reliable estimate of the relationship between extreme hydrological event magnitudes and the probability of these events occurring.
Incomplete Block Designs
Authors: Federer Walter T. , Nguyen Nam‐ky
Published: September 2006

The subject of statistics deals with variability and how to deal with it. In the planning and conduct of an environmental or ecological investigation, the items used to control variability are (a) refinement of experimental technique, (b) selection of homogeneous material and/or environments, (c) grouping (blocking, stratifying) material into homogeneous subgroups (blocks, strata), and (d) measurement of related variables and use of covariance. There are many ways of blocking (arranging) the experimental units (EUs) in a comparative experiment withvtreatments. If the sample of EUs is from a homogeneous population, then no blocking is required and a completely randomized experiment design (ED) of thevtreatments randomly allotted to thervEUs is used. The replicate number (sample size) for each treatment isrunless unequal replication is desired. If homogeneous blocks of sizevare available to accommodate allvtreatments, a randomized complete block ED (allvtreatments in each block, not necessarily an equal number of times) is used. In many situations, the block size,k, is less thanvand an incomplete block ED (not all treatments in each of the blocks) is used.
Influence Diagrams
Authors: Jensen Finn Verner
Published: September 2006

Influence diagrams are graphical models for structuring decision scenarios, particularly scenarios consisting of a predefined sequence of actions and observations. Influence diagrams were originally introduced as a compact representation of symmetric
decision trees
but they may also be thought of as extensions of
Bayesian networks
.
Intercropping, Statistical Issues In
Authors: Federer Walter T.
Published: September 2006

Intercropping is defined as the simultaneous or sequential growing of more than one crop on the same area of land. It may involve intermixing the plants of the crops, growing them in alternate rows, or growing them sequentially through time. When dealing with a cropping system, it is necessary to study the response for each component of the mixture (when available) as well as for the combined data for the mixture. This will involve statistical procedures not found in standard statistics textbooks. A popular statistical procedure for analyzing results from experiments involving several intercropping treatments is a relative yield ratio or land equivalent ratio (LER). The yields of the individual crops in a mixture need to be available in order to compute a LER. An LER is defined as the sum of the ratios of the yield of the
i
th crop in a mixture to its yield as a sole crop.
Landscape Ecology
Authors: Urban D.L.
Published: September 2006

Landscape ecology is concerned with the generation and dynamics of pattern in ecosystems, and the implications of pattern for population, community, and ecosystem‐level processes. The discipline has laid a special claim to the reciprocal interaction between pattern as a consequence of ecological processes and also as a constraint on these processes.