bluuebunny/crossref_metadata_2025_split huggingface dataset:
Capture‐Recapture Methodology
Authors: Gould William R. , Kendall William L. 
Published: January 2013

Abstract
Capture‐recapture methods were initially developed to estimate human population abundance, but since that time have seen widespread use for fish and wildlife populations to estimate and model various parameters of population, metapopulation, and disease dynamics. Repeated sampling of marked animals provides information for estimating abundance and tracking the fate of individuals in the face of imperfect detection. Mark types have evolved from clipping or tagging to use of noninvasive methods such as photography of natural markings and DNA collection from feces. Survival estimation has been emphasized more recently as have transition probabilities between life history states and/or geographical locations, even where some states are unobservable or uncertain. Sophisticated software has been developed to handle highly parameterized models, including environmental and individual covariates, to conduct model selection, and to employ various estimation approaches such as maximum likelihood and Bayesian approaches. With these user‐friendly tools, complex statistical models for studying population dynamics have been made available to ecologists. The future will include a continuing trend toward integrating data types, both for tagged and untagged individuals, to produce more precise and robust population models.
Catchment Hydrology
Authors: Croke B.F.W. , Blakers R.S. , Kelly (Nee Letcher) R.A. , Jakeman A.J. 
Published: January 2013

Abstract
This article examines the role that catchment hydrology plays in modern society: from understanding the behavior of catchments, to providing information for the management of water resources. This is discussed in the context of three classes of hydrological models (empirical, conceptual, and physics‐based), and considers the problems faced by hydrologists: scale, heterogeneity, ungauged catchments, and the impact of change (both land use and climate) on catchment response. This article concludes with discussion on the future directions of catchment hydrology, and the challenges that need to be met.
Center for Statistical Ecology and Environmental Statistics
Authors: Patil G. P. 
Published: January 2013

Abstract
The Center for Statistical Ecology and Environmental Statistics is a unit in the Department of Statistics of the Pennsylvania State University and was initiated with Professor G.P. Patil as its founding director under a cooperative agreement between the US National Oceanic and Atmospheric Administration (NOAA) and the Pennsylvania State University as a formal substitute for the earlier Penn State Program in Statistical Ecology and Environmental Statistics in operation since 1969. The Center is the first of its kind in the nation and in the world, and enjoys a national and international reputation. Broad research areas of the Center relate to statistical ecology, environmental statistics, and quantitative risk analysis with emphasis on mathematical statistics, statistical methodology, and data interpretation and improvement for future use. The adopted approach is to advance statistics for environment, ecology, and environmental health, and to advance environmental and ecological theory and practice using valid statistics.
Circular Data
Authors: Otieno Bennett Sango , Anderson‐Cook Christine M. 
Published: January 2013

AbstractCircular data are the simplest case of directional data, which commonly occur in environmental studies when measurements are taken from an orientation or cyclical timing application. Circular data have a single response which is not scalar, but angular or directional. Typical orientation observations are measured by a compass and indicate direction, whereas 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 applications 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). These 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.
Climatology
Authors: Szeto Kit K. 
Published: January 2013

Abstract
Climatology is the interdisciplinary study of the earth system that encompasses the atmosphere, hydrosphere, cryosphere, lithosphere, biosphere, and anthrosphere. Modern climatology includes not only these components but also importantly their interactions involving detailed global observing systems and complex computer‐based numerical models. The state of the climate system at any time is determined by the forcings acting upon it and the complex and interlocking internal feedbacks that these forcings prompt. Because of these feedback processes, the climate system does not respond to forcings, including human‐induced ones, in a smooth and gradual way and the response can be rapid and sometimes discontinuous. Climate models, with which we hope to predict future climates, must be able to capture such apparent discontinuities that have been observed in paleoclimatic records. To meet these requirements, modern climate models are composed of multiple interacting modules, which attempts to mimic components of the earth system, so that complicated feedbacks and synergisms in the climate system can be simulated faithfully. Human activities and associated needs, such as food and energy and water resources, are becoming increasingly dependent on, and even an integral part of, the climate system. With increasing evidence of the effects of human activities on the climate system, international bodies were established to coordinate scientific assessments of human influence on climate and to recommend appropriate mitigation and adaptation measures for policy makers.
Coastal Processes
Authors: Prandle David 
Published: January 2013

Abstract
Coastal seas are influenced by dynamical, chemical, biological, and geological processes. The predominant dynamical processes, namely, tides, waves, and density currents are described in this article. Seasonal variations in the latter two components are often important in determining biological and chemical distributions. Computer simulation models are used to link all such processes and predict pathways and concentrations of contaminants.
Community Food Webs
Authors: Deangelis Donald L. 
Published: January 2013

Abstract
Community food webs describe the feeding relationships, or trophic interactions, between the species of an ecological community. Both the structure and dynamics of such webs are the focus of food web research. The topological structures of empirical food webs from many ecosystems have been published on the basis of field studies and they form the foundation for theory concerning the mean number of trophic levels, the mean number of trophic connections versus number of species, and other food web measures, which show consistency across different ecosystems. The dynamics of food webs are influenced by indirect interactions, in which changes in the level of a population in one part of the food web may have indirect effects throughout the web. The mechanisms of these interactions are typically studied microcosm experiments, or sometimes in‐field experiments. The use of mathematical models is also a major approach to understanding the effects of indirect interactions. Both empirical and mathematical studies have revealed important properties of food webs, such as keystone predators and trophic cascades.
Compartmental Analysis
Authors: Becka Michael 
Published: January 2013

Abstract
Ecological, toxicological, and pharmacological research is often concerned with the answer to the question of how a substance is processed within a biological system. The exact knowledge of the corresponding kinetic pattern forms the basis for a useful answer. In order to understand and to characterize kinetic processes, as well as to identify nonlinear kinetics, compartmental analysis yields a useful approach.
Composite Sampling
Authors: Patil G.P. 
Published: January 2013

Abstract
The high costs of laboratory analytical procedures frequently strain environmental and public health budgets. Whether soil, water, or biological tissue is being analyzed, the cost of testing for chemical and pathogenic contaminants can be quite prohibitive. Composite sampling can substantially reduce analytical costs because the number of required analyses is reduced by compositing several samples into one and analyzing the composited sample. By appropriate selection of the composite sample size and retesting of select individual samples, composite sampling may reveal the same information as would otherwise require many more analyses. Many of the limitations of composite sampling have been overcome by recent research, thus providing more widespread potential for the use of composite sampling to reduce costs of environmental and public health assessments, while maintaining and often increasing the precision of sample‐based inference.
Control Theory
Authors: Young Peter C. 
Published: January 2013

Abstract
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.
CorrelatedMultinomialData
Authors: Zhu Yiliang 
Published: January 2013

AbstractCorrelated multinomial data, including correlated binomial, arise from clustered sampling schemes such as grouped sampling or repeated measurements. Correlation induced by clustering can be of interest in its own right, but must be accounted for in statistical analyses to avoid inaccurate or inefficient inference. This article discusses common approaches to the treatment of correlated multinomial data and illustrates dose‐response modeling via generalized estimating equations using an example from developmental toxicity experiment.
Creel Surveys
Authors: Jones Cynthia M. 
Published: January 2013

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: January 2013

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.
Detection Limits
Authors: Walshaw David 
Published: January 2013

Abstract

In analytical chemistry, the
detection limit
is the lowest level of an analyte that can reliably be detected by a well‐defined operating procedure. It is often confused with the
decision limit
, which is a threshold of measurement used to determine whether or not the analyte is present in a particular study. We discuss the history and confusion relating to the definition in a univariate context. We consider the detection limit as a function of the sources of variation within an operating procedure. Estimation of the detection limit assuming constant variance in one‐level designs and calibration designs is described, as well as a brief treatment of the assumption of non‐constant variance. We outline modifications for multiple future detection decisions. Finally we consider approaches to multivariate detection, when decisions are based on more than one quantitative signal.
Disease Mapping
Authors: Waller Lance A. 
Published: January 2013

AbstractMaps of disease reveal patterns of incidence and prevalence. Such patterns often suggest possible associations between disease and potential risk factors mapped over the same area. We briefly review the historical use of maps in the study or disease and classify different types of geographic data available (geostatistical, point, and lattice data). For each data type, we outline the analytic methods available and the types of questions the methods can address.
Disease Registry
Authors: Darnell Ross 
Published: January 2013

Abstract
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.
Dispersion Parameter
Authors: Piegorsch Walter W. 
Published: January 2013

AbstractTo describe the stochastic nature of an outcome variable, environmetricians use probability distributions. A crucial feature of many simple probability models is that structural relationships may be required between the population mean and other components of the model. Many environmental sampling and experimental settings occur, however, where such a mean/variance structure is too restrictive: the data often exhibit variation larger than or, in some instances, smaller than that required under these simpler models. Such occurrences are known collectively as overdispersion or underdispersion, respectively. We quantify the extent of the over/underdispersion by dedicating a model parameter to it in the variance term. The resultingdispersion parametercan be used to identify, study, test, or otherwise account for the excess variation. This entry reviews statistical aspects of modeling and estimation for such dispersion parameters.
Distance Sampling
Authors: Thomas Len , Buckland Stephen T. , Burnham Kenneth P. , Anderson David R. , Laake Jeffrey L. , Borchers David L. , Strindberg Samantha 
Published: January 2013

AbstractDistance sampling is a widely used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods areline‐transect samplingandpoint‐transect sampling. In both cases, observer(s) perform a standardized survey along a series of randomly located lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all objects will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. The key to distance sampling analyses is to use the observed distances to fit adetection functionthat describes how detectability decreases with increasing distance from the transect. The fitted function is used to estimate the average probability of detecting an object; from here, one can readily obtain point and interval estimates for the density and abundance of objects in the survey area. Various extensions to the basic methods allow assumptions to be relaxed, and include methods that integrate capture‐recapture and distance sampling, as well as methods to model spatial variation in density.
Diversity Profiles
Authors: Patil G. P. 
Published: January 2013

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.
DNA
A
dducts
Authors: Thurston Sally W. 
Published: January 2013

Abstract
DNA adducts are DNA‐chemical complexes that can cause DNA mutations which eventually lead to cancer. Adducts are typically formed after exposure to compounds in cigarette smoke, aflatoxins, benzene, or other agents. DNA adducts are considered a biomarker of the biologically effective dose, that is, the amount of the agent or its metabolite that has bound to DNA. Among current smokers, DNA adducts counts in mononuclear blood cells are much larger on average in subjects with lung cancer than in controls. Blood adduct levels are also a reasonable surrogate for adduct levels in the lung. Thus, the number of DNA adducts in blood cells have the potential to be extremely useful for predicting lung cancer risk.
D
ose‐
R
esponse
M
odeling for
C
lustered
D
ata
Authors: Chen James J. 
Published: January 2013

Abstract
Clustered data refer to a set of correlated responses. For example, different animals represent different genetic values inherited from parents that may also be nested within herds or spatial entities. Clustered data also result from study designs, such as repeated observations on the same subject or sampling from correlated subsamples. A great deal of statistical research on dose response modeling of clustered data is devoted to animal reproductive and developmental toxicity studies. In developmental toxicity experiments, chemicals are administered to the dam (female animal), but the observations are made on individual fetuses. The clustering of fetuses within the litters induces the cluster (litter) effect. Modeling the clustered data should account for the correlation among the individual responses within a cluster to draw valid and efficient inferences. Two common approaches for fitting dose response models to clustered data are the likelihood‐based and generalized estimating equations (GEE) (or quasi‐likelihood) approaches.
E
chelon
A
nalysis
Authors: Patil G. P. 
Published: January 2013

Abstract

Quantitative spatial data are important inputs for fueling the engines of many environmental models that determine future implications of current resource use, policies, and interventions. End products of applying such models are often mappings of indexes for level of potential environmental impact, which then become guides to allocation of economic and technical resources for amelioration. Errors in quantitative spatial data will propagate through environmental models and find expression in the resulting impact indexes. However, the consequences of such errors for decision making may well depend upon where the errors occur. There may be relatively little confusion introduced by moderate errors occurring in a vicinity that otherwise has consistently high values of a variable. In contrast, errors compound confusion in areas that are highly variable. Errors can also substantially distort the apparent state of areas that otherwise have consistently low values of a variable. It is therefore desirable to have a systematic means of determining spatial organization in mappings of quantitative variables, both for input variables to environmental models and for indexes of potential impact generated by the models. Modern computer capabilities for visualization of surfaces are helpful in this regard, but their interpretation is subjective.
Echelons
are a method for objectively determining quantitative spatial structure for direct mapping, either with or without computer‐assisted visualization. Thus, they can facilitate analysis of errors associated with environmental models that take quantitative layers as input, or produce quantitative output layers, or both.
E
cological
S
ociety of
A
merica
Authors: Porter John H. 
Published: January 2013

Abstract
The Ecological Society of America (ESA) is a 10 000‐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 operating a public affairs office.