bluuebunny/crossref_metadata_2025 huggingface dataset:
Disease Registers: Basic
Authors: Darnell Ross
Published: September 2014

A disease registry defines a process that manages registers, which comprise cases of a particular disease that have occurred in a population. The registry may include demographic information about the cases (e.g., age, gender, and place of residence), dates of disease diagnosis, and perhaps additional information such as dates of death, or exposure and risk factor information. Registers are multipurpose being used to improve patient care, reviewing of patients with chronic illness, public health reporting, surveillance of diseases within a population, planning and provision of health care, and research. There is a tension between the increasing demands for personal autonomy and the need for society to recover as much information as possible to learn how best to control disease. There is a real need for maintaining confidentiality of individual health data and for informed consent before these data are used for research.
Dose Calibration
Authors: Davidian Marie
Published: September 2014

Calibration involves determination of an unknown level (dose, concentration) of an antigen, which may be an enzyme, hormone, protein, chemical compound, or other substances in a test sample based on observation of a (usually) quantitative measurement derived from an assay technique, such as an optical density (enzyme‐linked immunoassay) or gamma count (radioimmunoassay). This procedure is commonly used in environmental health studies where levels of allergens, exposure agents, and other substances are ascertained and related to health outcomes. The statistical framework underlying calibration, including development of the so‐called standard curve from which unknown levels may be calibrated, assessment of the precision with which calibration is performed, and evaluation of assay quality and consistency, is discussed. Copyright © 2015 John Wiley & Sons, Ltd.
Ecotoxicology
Authors: Newman Michael C. , Evans David A.
Published: September 2014

Ecotoxicology is the science of contaminants in the biosphere and their effects on constituents of the biosphere. Associated environmetric issues range widely and include those describing contaminant bioaccumulation, trophic transfer, and effects to ecological entities, for example, individuals, populations, communities, and ecosystems.
Extrapolation, Risk‐Analytic
Authors: Hertzberg Richard C.
Published: September 2014

Quantitative extrapolation refers to a process where inferences about data‐poor scenarios are developed from studies of data‐rich scenarios. For dose–response relationships in environmental risk assessment, several different kinds of extrapolation are used in order to accommodate the variety of available information and risk assessment goals. Examples of such extrapolation include estimating human health risk at low doses from rodent toxicology data at high doses, or estimating ecosystem response at a given ambient pollution concentration from laboratory studies of individual animal or plant species at similar concentrations. The examples discussed in this article are for human health risk assessment but most of the principles apply to ecological risk assessment as well. These examples include low‐dose extrapolation of cancer response (extending a fitted function below the range of the data), simple scaling methods (e.g. allometric interspecies extrapolation), biomathematical models (e.g. physiologically‐based pharmacokinetic models), and quantitative formulas for estimating mixture response (e.g. modifying dose addition to account for synergism).
Familial Aggregation
Authors: Seillier‐moiseiwitsch Françoise
Published: September 2014

By familial aggregation is meant the occurrence of a disorder at a higher frequency in the relatives of affected individuals than in the general population. This aggregation may be due to genetic, cultural or environmental factors. Assessing whether diseases cluster in families, and searching for the explanation when they do so, are the main questions genetic epidemiologists address.
Heritability
Authors: Fry James D.
Published: September 2014

The heritability of a trait is the proportion of variation in a population that can be attributed to genetic differences among individuals. Although usually applied to continuously varying traits, such as milk yield in dairy cows or blood pressure in humans, the concept of heritability can also be applied to discontinuous traits that have complex patterns of inheritance, such as the presence or the absence of medical conditions such as diabetes. The heritability can be estimated by measuring the degree of resemblance, quantified by the covariance, between relatives of various types, although special care must be taken when estimating heritability in humans.
Hormesis
Authors: Calabrese Edward J.
Published: September 2014

This article provides a general description of the hormesis dose–response model. Hormetic dose–responses have become reported more frequently in the literature with advances in measuring chemicals at progressively lower doses and with the extensive use of
in vitro
methods, which permits an evaluation of more doses/concentrations that has been typically used in
in vivo
studies. It is expected that the hormesis concept will play a progressively more significant role in the hazard assessment process and in drug discovery and development in forthcoming years.
Interlaboratory Studies
Authors: Vangel Mark G.
Published: September 2014

Interlaboratory studies (sometimes referred to as
collaborative studies
, or
round robins
) are investigations in which measurements are made on one or several materials at several laboratories. These studies are typically conducted for one of three reasons: (i) to evaluate a measurement method; (ii) to evaluate a reference material; or (iii) to evaluate the proficiency of the laboratories themselves.
Morbidity and Mortality
Authors: Danaei Goodarz , Hernán Miguel A.
Published: September 2014

The frequency of overall or cause‐specific morbidity and mortality is used in national and global statistics to characterize the health status of a population. Summary measures of population health combine the frequency of morbidity and mortality into one measure of burden of disease using disability weights or quality of life measures.
Occupational Mortality
Authors: Symons Michael J.
Published: September 2014

In studies of mortality of industrial workers, statistical options are limited when: (1) only a few deaths are observed; (2) exposure information at the workplace is nonexistent; (3) personal concomitant exposure data are not available. With none, some, or all of these limitations, the standardized mortality ratio (SMR) is the most frequently used statistic for occupational and environmental cohorts. The SMR is the total number of observed events,O, divided by the total number expected events,E, for those under study. The SMR, as a one‐dimensional summary statistic, is both attractive and limited in scope. It is widely used in mortality studies of industrial populations, serving to screen for excess causes of death and to test for an excess of a specific cause of mortality. A variety of study designs – commonly, proportional mortality, cohort mortality, and case‐control studies – are utilized in the further investigation of hypotheses generated or for more confirmatory tests of results from previous analyses.
Outcome‐Dependent Selection Models
Authors: Ding Jieli , Lu Tsui‐shan Eva , Zhou Haibo
Published: September 2014

An outcome‐dependent selection (ODS) is a retrospective sampling scheme where one observes the environmental exposure/covariates with a probability that depends on the observed outcome values. By allowing the selection probability of each individual to depend on the outcome variable, ODS design can reduce the cost of the study and improve the efficiency. The case‐control study and case‐cohort study in epidemiology are well‐known examples of ODS schemes. We review recent advances in research on ODS designs with a continuous response. This includes inferences on more sophisticated designs with survival data, clustered/longitudinal data, two‐stage ODS design, and outcome and auxiliary‐dependent (OADS) schemes.
Personal Exposure Monitoring
Authors: Liu L.‐j. Sally
Published: September 2014

Exposure to a pollutant occurs when an individual encounters the specific pollutant. The science of exposure assessment is to assess how much, how long, and how frequently an individual is exposed to the compounds of interest. Accurate and reliable exposure‐related information is essential for informed decisions about protecting and promoting public health.
Quantitative Structure‐Activity Relationships (
QSAR
)
Authors: Williams Paige L.
Published: September 2014

Quantitative structure activity relationships (QSARs) have historically been used in the design and evaluation of new pharmaceutical products. With the rapid increase in modern computing power, the use of QSAR methods in the field of drug development has seen an explosive range of applications in the late 1990s. QSAR approaches have also been used in environmental risk assessment to predict the carcinogenicity or genotoxicity of toxic agents, and thus reduce the costs of conducting long‐term animal studies. The application of QSAR methods has traditionally encompassed two different approaches: the extrathermodynamic approach proposed by Hansch and Fujita, and the ‘substructural’ or ‘connectivity’ approach of Free and Wilson.
Repeated Measures
Authors: Kenward Michael G.
Published: September 2014

A repeated measures study is distinguished by the collection of a sequence of measurements from each unit over a period of time. Repeated measures can be collected in both experimental and observational settings; examples of the latter are often referred to as longitudinal studies. The sequence of measurements from one unit is sometimes called the unit's
profile
. These profiles form a collection of short time series, and one should expect the measurements from the same unit to be mutually correlated.
Reproductive Epidemiology
Authors: Cho Sung‐Il , Wang Xiaobin
Published: September 2014

A widely accepted definition of epidemiology is ‘the study of the distribution and determinants of health‐related states or events in specified populations, and the application of this study to control of health problems’. Reproductive epidemiology focuses on the events in various stages of the human reproductive process, including sperm production, menstrual cycling, ovulation, sexual interaction, conception, fetal growth, and growth and development of the infant.
Reproductive Toxicology
Authors: Williams Paige L.
Published: September 2014

Reproductive toxicology is the study of adverse effects of environmental agents on the male and female reproductive systems, including reduced fertility, menstrual disorders, spontaneous abortion, developmental defects including low birth weight, and genetic diseases affecting the reproductive system and offspring. In particular, there is concern regarding the high rate in humans of pregnancy, congenital malformations, and serious developmental defects possibly due to environmental factors. Also of specific interest is an apparent increase in male sexual effects such as reduced sperm production and testicular cancer. Public health concern regarding these reproductive issues has been heightened by an increase in the number of women in the workforce during their childbearing years, and by the large increase in numbers of couples seeking treatment for infertility. While the cause of many of these adverse reproductive outcomes is unknown, at least some proportion can be attributed to known environmental agents.
Respiratory Epidemiology
Authors: Coull Brent A. , Schwartz Joel
Published: September 2014

Environmental health scientists now recognize respiratory illness as a potential health effect in both children and adults. Respiratory effects may include asthma, wheezing, altered pulmonary function in the form of respiratory flow or volume, dyspnea (shortness of breath), and bronchitic or chronic bronchitic symptoms. Risk factors having documented associations with respiratory illness or disease include maternal smoking, environmental tobacco smoke, respirable particulate matter, ozone, nitrogen dioxide, and acid aerosols. Other studies have focused on possible protective effects of diet against lung disease in humans. This article focuses on some statistical challenges often encountered in the analysis of respiratory data.
Sick Building Syndrome
Authors: Zhang Heping
Published: September 2014

Sick building syndrome (SBS) is loosely defined as an increase in the frequency of a constellation of acute nonspecific symptoms characteristically affecting multiple occupants of a building.
Smoothing in Environmental Epidemiology
Authors: Ryan Louise
Published: September 2014

Smoothing is a branch of regression analysis that allows for a predictor to impact the response as an arbitrary smooth function. It is also known as nonparametric regression. The purpose of this article is to alleviate the restrictiveness of parametric functional relationships, such as that imposed by linearity.
Space‐Time Stochastic Modelling in Human Exposure
Authors: Christakos George
Published: September 2014

Perceptions regarding the meaning of the term stochastic modeling (SM) are not as uniform as one might think. This should not come as a surprise, in view of the multidisciplinary nature of environmental health science. Depending on the application considered, one may refer to stochastic models as environmental, ecologic, genetic, epidemiologic, medical, etc. A common factor in all these cases is that SM is concerned with the mathematically rigorous and scientifically meaningful representation, explanation, and prediction of natural phenomena (physical, biological, etc.) in uncertain environments. Uncertainty may be due to measurement errors, heterogeneous databases, erratic biologic fluctuations in space/time, insufficient knowledge, etc. Within such a framework, SM seeks to provide a realistic environmental situation with spatio‐temporal continuity and internal consistency.
Systematics, Numerical Methods
Authors: Giribet Gonzalo
Published: September 2014

An introduction to biological systematics is provided with description of basic principles in systematics and tree topologies. Tree search strategies for methods based on optimality criteria are discussed, and common modern software is briefly introduced. The article is complemented with a list of general recommended readings.
Three‐State Carcinogenicity Model
Authors: Dinse Gregg E.
Published: September 2014

A common stochastic model for animal carcinogenicity data has three states: N (alive with no tumor), T (alive with tumor), and D (dead from natural causes). The intensity rates for N → T, T → D, and N → D transitions at time
t
are denoted by λ(
t
), α(
t
), and β(
t
), respectively, which correspond to the hazard rates for times from birth to tumor onset, from tumor onset to natural death, and from birth to natural death without a tumor. Carcinogenesis refers to the development of new tumors; so analyses of carcinogenicity data should focus on the tumor incidence rate, λ(
t
). Three factors complicate tumor incidence analyses: all onset times are censored if tumors are occult; tumor lethality makes censoring informative; and differential mortality exacerbates biases arising from inadequate survival or lethality adjustment. The most appropriate analysis of tumor incidence data depends on what information is available and what assumptions are reasonable. This article describes a variety of scenarios and, in each case, outlines approaches for comparing tumor incidence rates across treatment groups.
Trend Tests for Binary Data
Authors: Corcoran Chris
Published: September 2014

In the context of biomedical studies (particularly in fields such as environmental toxicology or epidemiology), trend tests are widely used to detect a dose–response relationship between ordered levels of some exposure and the probability of a binary outcome of interest.
Frontier Model
Authors: Ley Eduardo
Published: September 2014

Frontier models in econometrics are used to construct reference technologies to make inferences about production activities. A similar frontier specification is proposed to make inferences in the context of landscape ecology–when studying the tolerance of wild animals to human settlements.
Utility Theory
Authors: French Simon
Published: September 2014

Utility theory has a long history, tracing its origins back to Bernoulli in the early eighteenth century; but only in the last half century have the assumptions behind the use of ‘utilities’ been understood. At its simplest, utility provides a model of the preferences of a decision‐maker (DM) between alternatives. The complexity arises when it is asked what aspects of the DM's preferences are modeled and how the model is to be used in an analysis. For instance, it is possible to model strength of preference and risk attitude either separately or simultaneously. Moreover, is the model to be used descriptively, reporting peoples preferences ‘as they are’, or is it to used prescriptively, guiding the evolution of judgment in a complex evaluation process? Thus a very brief guide to different types of utility model is given in this article, followed by the use of such models in analyses. First a single DM is considered; later the use of utility theory within group and societal contexts is discussed.
Abundance: Population Size and Density Estimation
Authors: Udevitz Mark S. , Gould William R.
Published: September 2014

Estimates of population size (total number of individuals) or density (number of individuals per unit area) are some of the most basic requirements for wildlife research and management. This article provides a brief overview of approaches for wildlife population estimation. These include habitat‐based approaches such as quadrat, line intercept, distance, and repeated count methods, as well as animal‐based approaches such as capture–recapture and harvest methods. In contrast to methods that only index changes in these parameters, estimates of size or density must account for the probability that an individual will be included in the sample used for estimation. The essential differences in the approaches are in how the samples are obtained and how the sampling probabilities are estimated.
Acid Rain Modeling
Authors: Holland David M. , Sirois Alain
Published: September 2014

This article provides an overview of existing statistical methodologies for the estimation of site‐specific and regional trends in wet deposition. The interaction of atmospheric processes and emissions tend to produce wet deposition data patterns that show large spatial and temporal variability. We conclude that a number of approaches are useful for trend estimation, but no one method is appropriate for all purposes.
Action Levels, Regulatory
Authors: Ee Jeffrey
Published: September 2014

Environmental data are often collected to assess whether an action level or regulatory standard has been exceeded. If a statistician is involved in the decision‐making process, it is often at the latter stages when the data have already been collected and a comparison is needed; however, at times a statistician may not be involved. In either case, a major problem in making the comparisons is a lack of understanding of basic assumptions and principles that lie behind the important comparisons. The US Environment Protection Agency (EPA) has advanced for years the ‘Data Quality Objective’ (DQO) process to define better basic issues in the decision‐making process and to aid in communicating acceptable costs and errors in the decision‐making process between stakeholders. Although the DQO process is performance‐based and provides real opportunities for cost savings, it can be misinterpreted. A major problem occurs when the basic principles that underlie the setting of the action‐level or regulatory standard and the representativeness of the sample data for comparison purposes are not applied concurrently.
Aerial Survey Data
Authors: Ver Hoef Jay M.
Published: September 2014

Aircraft are important platforms for sampling and monitoring the environment. Aerial surveys often use photography to estimate some environmental feature over large geographic areas. In this article I use wildlife surveys from Alaska as examples, which are broadly classified based on visibility of the response variable. Census data is covered first, where all objects are visible to be counted with complete survey coverage. Sampling is used when all objects are visible but complete survey coverage is not possible. When objects are only partially visible, distance sampling and capture‐recapture methods are popular. Even when items are only partially visible, it may still be possible to estimate trend and habitat relationships. Occupancy models can be used for less expensive, rapid surveys, or when objects are very difficult to see but evidence of presence is possible. Advancements in technology such as digital cameras, GPS systems, etc., have made aircraft more valuable and cost effective for surveys, and this trend will likely continue.
Animal Movement Models
Authors: Schwarz Carl
Published: September 2014

Where do animals move to, how many animals move, which routes do they choose, and how many animals survive the migration or dispersal are key questions for ecologists. Information on movement and migration often comes from tagging studies. This article presents a short review of the models typically used in tagging studies. New models are being developed to deal with continuous, complete data from satellite and archival tags and state‐space models for this type of data are presented.
Bioabundance
Authors: Ickstadt Katja
Published: September 2014

Bioabundance is a quantity that describes the number of individuals of a species in an area of interest, either directly or through other measurements such as biomass or percentage of cover. It is the underlying quantity of interest in a number of ecological investigations, such as monitoring population sizes and studying the spatial or space–time pattern of a single plant or animal species in a region or plot. It is also the key ingredient for analyzing biodiversity, the relative abundance of several or all species in an ecological community.
Biomass
Authors: Parresol Bernard R.
Published: September 2014

Biomass is the mass or the weight of living tissue provided by a given area or volume of the earth's surface. It is commonly subdivided into five trophic levels forming the basic food chain. In terrestrial ecosystems, the biomass distribution resembles a pyramid, where mass progressively decreases from producer to top predator. Some aquatic systems may have a dipyramid‐shaped biomass distribution, that is, narrow at the bottom and top, and wide in the middle. Plant biomass recycles atmospheric carbon. If we replant harvested biomass, there is no net gain in atmospheric carbon to add to the greenhouse effect. Large‐scale biomass burning, that is, deliberately set fires, is a significant source of carbon dioxide and particles in our atmosphere. Bioenergy is a fuel energy that can be derived directly or indirectly from biological sources. Biomass energy from wood, crop residues (known asbagasse), and dung remains the primary source of energy in developing regions. Satellites scan the normalized difference vegetation index over terrestrial habitats, and scan sea‐surface chlorophyll levels over oceans to estimate global net primary production, which averages about 104.9 billion tonnes C per year. Local biomass resources are determined from broad‐based inventories and statistical predictions.
Biomonitoring
Authors: Conquest Loveday L.
Published: September 2014

In its ecological sense, biomonitoring refers to the recording of observations of animal or plant populations, often over space and time. Owing to the possible presence of spatially and temporally correlated observations, the “what, where, when, and how” of biomonitoring studies must be carefully considered, beginning with the choice of appropriate response variables to measure. There are many available methods to analyze data from biomonitoring field studies or experiments. The standard toolbox of statistical techniques, particularly in the area of regression analysis, now includes generalized linear models and generalized additive models. These allow the incorporation of non‐Gaussian (non‐normal) error terms, and can describe nonlinear relationships using nonparametric smoothing functions. Analysis of a single dataset may include liberal use of graphics, handling of data with heterogeneous variances and below detection limits, and dealing with irregular coverage through space and time. A multipronged approach to analysis of complex datasets that often arise from biomonitoring studies is not unusual.
Change‐in‐Ratio
Authors: Udevitz Mark S.
Published: September 2014

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: Hopke Philip K , Spiegelman Clifford H. , Park KWANG‐Su
Published: September 2014

Chemometrics is a chemical discipline that uses mathematics, statistics, and formal logic (i) to design or select optimal experimental procedures; (ii) to provide maximum relevant chemical information by analyzing chemical data; and (iii) to obtain knowledge about chemical systems. In applications in industry, (iii) can be changed as follows: to obtain knowledge about chemical systems for monitoring or controlling operation of systems. This article focuses on areas of chemometrics that are most relevant to environmetrics.
Conditional Entropy Profiles
Authors: Patil G.P.
Published: September 2014

When a landscape is represented by multiple landcover types, the challenge is to define a measurement of landscape fragmentation that can be applied to any defined geographic area. Such a measurement would ideally allow quantitative determination for when a landscape pattern has changed significantly, either within the same geographic extent over time, or among different locations within a similar ecoregion. Of interest is ecosystem identification, such as may be delineated by watershed boundaries close to a critical point of into a different, possibly degraded, ecosystem where the landscape matrix has become developed land. Such a measurement of landscape fragmentation can then be a primary component of an ecosystem risk assessment.
Creel Surveys
Authors: Jones Cynthia M.
Published: September 2014

Angler (or creel) surveys are used to measure the impact of recreational fishing on fisheries resources. Survey‐contact methods include mail, telephone, internet, face‐to‐face, access‐point, roving, and aerial. Choice of survey method depends on the type of information needed and the available budget. Most frequently, a combination of survey‐contact methods is used and estimation of quantities such as catch and fishing effort will depend on the survey design. When accurate catch data are necessary, surveys that are conducted on site (access‐point, roving) are preferred because catch can be observed and counted by the survey agent. In contrast, off‐site surveys (mail, telephone, and internet) rely on self‐reported data that require the angler to remember the fishing trip catch and effort accurately. Sampling frames for off‐site surveys include angler‐license lists, telephone directories, or other data containing angler contact information. Sampling frames for on‐site surveys include lists of access sites, geographic maps of water bodies, and calendars. The choice of survey method and design will depend on availability of the appropriate sampling frame, the need for accuracy in catch or effort estimation, timeliness, and cost.
Cross‐Scale Morphology
Authors: Garmestani Ahjond S. , Allen Craig R. , Holling C.S.
Published: September 2014

The scaling of physical, biological, ecological and social phenomena is 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. However, there are regular patterns of departures both from those scaling laws and from continuous distributions of attributes of systems. Those departures often demonstrate the development of self‐organized interactions between living systems and physical processes over narrower ranges of scale.
Data Monitoring
Authors: Rodríguez José E.
Published: September 2014

The monitoring air, water, soil, or biota is the practice of sampling systematically these environmental elements. Thus, in the Environmental Sciences, the data monitoring denotes the data obtained by sampling systematically the environment. The primary purposes of data monitoring are, among others, the detection of levels of air pollutants, the detection of trends, and the elaboration of spatial and temporal summaries. Additional purposes include the estimation of the indexes of air quality or the support of a database for the application of environmental policies and regulations.
Diversity Profiles
Authors: Patil G. P.
Published: September 2014

Community diversity, diversity measurement, and diversity comparisons have been important in community ecology studies for more than four decades.
Diversity
is a generic term referring to the condition of being different; a useful synonym for diversity is variety. It summarizes for qualitative characteristics (e.g., tree species) what the variance summarizes for quantitative measurements (e.g., tree diameter). Diversity is looked upon as a community property and, accordingly, the trophic, taxonomic or structural levels that comprise the community must be specifically delimited in making diversity comparisons, it is not uncommon to find that diversity has increased according to one index, but decreased according to a second index. This merely reflects the fact that diversity is a complex multidimensional property of a community. To view diversity through the lens of a single index is to project that multidimensional complexity onto a one‐dimensional ordinal scale. In view of the inadequacy of a single index, it is desirable to quantify diversity by means of
diversity profiles
. A diversity profile is a curve depicting the simultaneous values of a large collection of diversity indices. Thus, the profile portrays the views of diversity from many different vantage points simultaneously. Today in the age of indicators, however, multiple indicators of multidimensional diversity may replace the traditional diversity indices.
Ecological Statistics
Authors: Smith Eric P.
Published: September 2014

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.
Ecosystem Monitoring
Authors: Mcclintock Brett
Published: September 2014

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.
Encountered Data
Authors: Smith Eric P.
Published: September 2014

Data that are obtained in ecological and environmental data is often not collected via an experimental or sampling design but rather is observed. Examples include fish that are collected via a net, plants that are observed when walking a transect line and birds heard in a wetland survey. Data arising from these types of studies are sometimes referred to as
encountered data
.
Environmetrics
Authors: El‐shaarawi Abdel , Hunter J. Stuart
Published: September 2014

Environmetrics is a new scientific discipline that focuses on the development and use of quantitative methods to understand and solve environmental problems. This article gives a brief overview of its roots, aims, problems and methodologies.
Extinction Rates, Estimation Of
Authors: He Fangliang
Published: September 2014

The currently available estimation methods for extinction rates vary widely in data requirement, reported rates, and their accuracy. The methods include: (i) the taxonomic survivorship curve for estimating background extinction rates; (ii) the species–area relationship for estimating extinction caused by habitat loss (this is by far the most widely used method for estimating future extinction rates due to habitat loss but has recently been shown to be technically incorrect); (iii) methods based on the change in threat status of species on the IUCN Red Lists; and (iv) population viability analysis designed to quantify extinction probability of local species populations. Problems common to these methods include the associated uncertainties in their estimates and lack of reliable data to verify the estimated extinctions.
Fisheries Stock Assessment
Authors: Jiao Yan
Published: September 2014

Natural resource modeling of fisheries is called stock assessment and is crucial for proper fisheries management. This paper reviews the commonly used models, the data needed, and the parameter estimation methods for these models in fisheries stock assessment. Fisheries management strategy evaluation and decision analysis, which are based on the models developed and the estimated parameters, are discussed. Newly developed models and modeling techniques are also reviewed briefly.
Food Web Modeling
Authors: Chiu Grace S.
Published: September 2014

Many features of a food web are semi‐ to fully quantitative and are subject to uncertainty. Various techniques have been developed to describe these features. Others have been developed to model food web dynamics for purposes that include prediction. Yet, few approaches facilitate systematic, unified conclusions to be readily drawn about the food web's characteristics. Even fewer address the uncertainty that is inherent in the behavior of organisms within the web and in the surrounding environment. Recently, statistical modeling techniques have been adapted to food web research; they allow the investigator to simultaneously address multiple questions about the food web of interest under a unified quantitative framework, while fully exploiting the important role that uncertainty plays in food web ecology. This article reviews some conventional methods for drawing insights into food webs, and discusses recent statistical developments in food web research. Some details of the latter on network flows are provided.
Forecasting, Environmental
Authors: Young Peter C. , Pedregal Diego J.
Published: September 2014

The term ‘environmental forecasting’ is open to a range of interpretations, from ‘black‐box’ time series analysis and forecasting, based on relatively simple stochastic methods; to ‘mechanistic’ prediction based on ‘physically meaningful’, and normally quite complex, deterministic simulation models. This article, however, concentrates on stochastic forecasting methods based on models that range from purely black‐box time series characterizations of data to data‐based mechanistic (DBM) models that are physically meaningful but, at the same time, fairly simple in their structure and signal topology. There are numerous statistical approaches to forecasting, from simple, regression‐based methods to optimal statistical procedures formulated in stochastic state–space terms. Since it would be impossible to review all these methods here, this article first reviews briefly those that are felt to have most significance in theoretical and practical terms within the specific context of environmental forecasting. It then discusses in more detail a unified approach to forecasting based on a generic unobserved component (UC) model, formulated in stochastic, state–space terms.
Forest Growth and Yield Modeling
Authors: Vanclay Jerome K.
Published: September 2014

Forest growth models attempt to quantify the growth of a forest, and are commonly used for two principal purposes: to predict the future status of a forest and the nature of any harvests from that forest; and to help consider alternative cultivation practices. Models may also find other uses, such as in education, communicating information, etc. Depending on the purpose of the model, modelers may choose to emphasize physiological detail or statistical efficiency, but generally seek both biological and statistical accuracy.
Forest Inventory
Authors: Scott Charles T. , Gove Jeffrey H.
Published: September 2014

Forest inventory is an accounting of trees and their related characteristics of interest over a well‐defined land area. It may be compared to census methods for human populations. For example, one of the goals of the periodic decennial census of the US is to enumerate its human population and to retrieve demographic variables such as age, sex and race. This is accomplished by a comprehensive survey of all households in the country. Similarly, forest inventories seek to enumerate the population of trees within a forest and ascertain other information, such as their volume, value, growth and species composition. For all but the smallest tracts of land, complete enumeration of individuals is usually infeasible and survey sampling techniques are required. Unlike human or animal populations, trees are sessile organisms and there is no immigration or emigration to consider. However, tree populations vary widely in their species composition, age, size, site requirements, potential value, longevity and growth – all factors that may influence the design of a forest inventory. In addition, the tree population of interest may exist over a wide variety of topography, making access potentially costly and sometimes even dangerous. Such variety often dictates sampling protocols that may be optimal only for some portion of the population in question. Alternatively, it may call for varying sampling intensity or strategy (the combination of the sampling design and associated estimators) between different regions within the population.
Geostatistics, Model‐Based
Authors: Zhu Jun , Thurman Andrew L.
Published: September 2014

Classical geostatistics involves statistical inference for random fields with a continuous spatial index based on geostatistical data, but the methods tend to be ad hoc. Alternatively, likelihood‐based and Bayesian methods provide more efficient estimation of model parameters and more rigorous quantification of uncertainty in prediction of responses at unsampled locations.
Habitat Survey and Sampling Designs
Authors: Gould William R.
Published: September 2014

Habitat measurements will always be an important component of ecological studies. Traditionally, researchers have used methods applied to relatively small areas using either plot‐based or plotless methods. Variables such as percent cover, density, and biomass are commonly used together as a means of quantifying habitat in a particular area. The efficacy of habitat measurements depends on observer effects, vegetation form, and spatial pattern, and the scale at which measurements are made. In all cases, choice of variables and measurement techniques directly impact accuracy of results and must be carefully considered prior to data collection. Regardless of technique, random sampling strategies such as simple or stratified designs are essential for valid statistical inference. Recent advances in satellite and radar technology have resulted in a myriad of additional methods for collecting and analyzing habitat data. Such technology has also altered the scale and types of questions being asked. However, these technological advances are not without error and still require careful use and in many cases, ground‐truthing.
Informatics, Environmental
Authors: Tochtermann Klaus
Published: September 2014

Environmental informatics is the science that deals with the development and application of methods and tools for environmental information processing. The main objectives of environmental informatics are to make the best possible use of information technologies: (a) to contribute to an effective and efficient protection of the environment; (b) to provide the public with information on the state of the environment and, thus, to increase environmental awareness; (c) to support environmental planning; and (d) to forecast future states of the environment.
Intercropping, Statistical Issues In
Authors: Federer Walter T.
Published: September 2014

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.
Law and Environmental Statistics
Authors: Esterby Sylvia R.
Published: September 2014

Many environmental regulations and standards are based on statistical data and inferences drawn from them. Often the statistical analysis used by the US Environmental Protection Agency (EPA) or other governmental body, in developing an exposure or effluent limit or a monitoring program, is challenged by the industry or public interest group that is most affected by its decision. Then, the courts need to examine the record on which the EPA or other agency relied in setting the standard. Because there are several statutes regulating the environment in the United States as well as in Europe, in assessing the adequacy of the underlying scientific support of a regulation or protocol, one needs to be aware of the relevant statute. This article discusses several important statistical issues that have arisen in actual cases and briefly describe some other topics that arise in this area.
Model‐Assisted Sampling
Authors: Ver Hoef Jay M.
Published: September 2014

Model‐assisted sampling is concerned with making design‐based inference using model‐based estimators to improve precision.
Modeling, Environmental
Authors: Solow Andrew R.
Published: September 2014

In discussing environmental models, it is useful to distinguish between process‐based or mechanistic models and phenomenological or empirical models. Although the distinction is not always clear, mechanistic models are based on a scientific theory about the underlying environmental processes, whereas empirical models are based on observation alone. This article focuses on statistical analysis under mechanistic environmental models. This is not to imply that empirical environmental models are less interesting or less useful. Rather, it is because empirical environmental models are, from a statistical perspective, no different from empirical models in other fields. While the methods of statistical analysis under mechanistic environmental models are certainly not unique, such models tend to share certain characteristics that determine in part the kind of statistical issues that arise, and even the kind of statistical methods used to address these issues.
Multivariate Covariogram Models
Authors: Zhang Hao
Published: September 2014

When multiple variables are observed at various spatial locations, it may greatly improve spatial prediction if spatial cross correlation is employed. A multivariate covariogram is a matrix‐valued function that consists of marginal and cross covariograms. This article provides some classes of multivariate covariogram models.
Multivariate Kriging
Authors: Wackernagel Hans
Published: September 2014

Kriging is a special type of optimal linear prediction applied to random functions, with the particular requirement that the covariance structure of the random function should be known. Kriging may be extended to several variables by specifying a corresponding system of direct and cross covariance functions to be used in cokriging. Alternatively, secondary variables known at all sample and prediction locations can be included into (co‐)kriging as external drift.
Nearest Neighbor Methods: Overview with Examples
Authors: Dixon Philip M.
Published: September 2014

This article discusses nearest neighbor (NN) methods for the analysis of spatial point patterns and the analysis of field experiments. The distance from a point to its NN, or quantities derived from that distance, can be used to test whether the observed locations are a realization of a specific spatial point process. Edge effects, caused by recording data only within a bounded study area, can be important. When locations are marked (e.g.,by the species of plant), NN methods provide information on the relationship between the marks. NN methods for field experiments can be used to model local spatial variability.
Ornithological Data
Authors: T. Morgan Byron J. , Viallefont Anne
Published: September 2014

This article introduces the different types of ornithological data, obtained either at the population level (count data, occupancy data) or at the individual level (capture–mark–recapture data, radiotracking data, and geolocation data), as well as references to the modeling/analysis techniques appropriate for each type of data. It also gives a list of organizations and web sites through which such data can be made available, as well as an extensive list of references to up‐to‐date articles and books.