bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reproductive Toxicology
Authors: Williams Paige L. 
Published: September 2014

Abstract
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

AbstractEnvironmental 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.
Space‐Time Stochastic Modelling in Human Exposure
Authors: Christakos George 
Published: September 2014

Abstract
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.
Three‐State Carcinogenicity Model
Authors: Dinse Gregg E. 
Published: September 2014

Abstract

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.
Utility Theory
Authors: French Simon 
Published: September 2014

Abstract
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

Abstract
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

AbstractThis 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

Abstract
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

Abstract
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

Abstract
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

AbstractBioabundance 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

AbstractBiomass 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

AbstractIn 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

Abstract
Change‐in‐ratio (CIR) methods are used to estimate parameters for ecological populations subject to differential removals from population subclasses. Subclasses can be defined according to criteria such as sex, age, or size of individuals. Removals are generally in the form of closely monitored sport or commercial harvests. Estimation is based on observed changes in subclass proportions caused by the removals.
Chemometrics
Authors: Hopke Philip K , Spiegelman Clifford H. , Park KWANG‐Su 
Published: September 2014

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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

AbstractThe 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.
Extinction Rates, Estimation Of
Authors: He Fangliang 
Published: September 2014

AbstractThe 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

Abstract
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

Abstract
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

AbstractThe 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.