bluuebunny/crossref_metadata_2025_split huggingface dataset:
Landscape Pattern Metrics
Authors: McGarigal Kevin 
Published: January 2013

AbstractReal landscapes contain complex spatial patterns in the distribution of resources that vary over time; quantifying these patterns and their dynamics is the purview of landscape pattern analysis. Landscape pattern analysis involves quantifying the spatial heterogeneity of point patterns, linear networks, continuous surface patterns, and categorical patch mosaics, and sometimes custom hybridizations of these basic models, although the focus in landscape ecology and of this article has been on patch mosaics. In this context, landscape metrics are algorithms that quantify specific spatial characteristics of patches, classes of patches, or entire landscape mosaics, or of the patch mosaic in the neighborhood of each focal cell. These metrics fall into two categories: those that quantify the composition of the map without reference to spatial attributes (i.e., the variety and abundance of patch types), and those that quantify the spatial configuration of the map (i.e., the spatial character and arrangement, position, or orientation of patches within the class or landscape). Metrics are further categorized as structural metrics that measure physical structure of the mosaic without explicit reference to a particular phenomenon, or functional metrics that require additional parameterization and measure the structure of the mosaic as it relates to a specific organism or ecological process. Not surprisingly, landscape metrics are highly sensitive to landscape definition with respect to the thematic content and resolution, spatial grain and extent, and the landscape boundary. The current use and interpretation of landscape metrics is constrained by the lack of a proper theoretical understanding of metric behavior across a wide range of landscape structure gradients, the lack of a proper spatial and/or temporal reference framework for ecologically interpreting the computed value of each metric, and the challenges of choosing a parsimonious suite of metrics for a particular application given the plethora of existing metrics. Ultimately, the definition of the landscape, the establishment of a meaningful reference framework, and the choice of metrics should be based on some hypothesis about the observed landscape pattern and what processes or constraints might be responsible for that pattern. Fortunately, programs like FRAGSTATS make the calculation of landscape metrics practical and accessible to all landscape ecologists.
Longitudinal Studies
Authors: Dean C. B. , Hu X. J. 
Published: January 2013

AbstractA longitudinal study observes its subjects repeatedly over time. The subjects can be individuals or pre existing groups of interest, such as species and regions, and the time refers to the metameter for the occasions of observation. Longitudinal data allow one to separate cohort and time effects. This article summarizes the special features of longitudinal studies, and overviews statistical study design and data analysis in longitudinal settings.
Long‐Term Environmental Monitoring
Authors: Van Der Maarel Eddy 
Published: January 2013

Abstract
Monitoring (from the Latin monere = to warn) is the act of following or controlling a process through a monitor and being warned if the process deviates from expectation. Environmental monitoring is the controlling of an environmental process, or ‘the intermittent surveillance to ascertain the extent of compliance with a predetermined standard or degree of deviation from an expected norm’. This means in practice a series of carefully planned, repeated observations of parameters indicating changes in a process we do not want to change. Monitoring ‘provides the essential data on how systems are changing and how fast’.
Measurement Error
Authors: Stefanski L.A. 
Published: January 2013

Abstract
Recognition that measurement error in the independent (predictor) variable of data used for regression modeling necessitates special methods of model fitting dates back to the late 1800s. In the ensuing 100 years attention was devoted almost entirely to measurement error in linear regression models, at which time advances in computing opened the door to the nonlinear statistical modeling generally, and to the study of measurement error in nonlinear regression modeling in particular. This article contains an introduction to the problem of measurement error in regression using linear regression measurement error models to illustrate key ideas. It includes a discussion of the types of measurement error commonly studied (classical and Berkson, differential and nondifferential) and how those types arise in practice. A summary of the consequences of ignoring measurement error when analyzing data measured with error is presented, with detailed results given for simple and multiple linear measurement error models. Pointers to the literature on both linear and nonlinear measurement error models are provided.
Meta‐Analysis
Authors: Weerasinghe Swarna 
Published: January 2013

Abstract
Meta‐analysis is an analytical method of synthesizing, systematically combining, and integrating results across different studies with similar research focuses. While limiting the analytical methods applicable to environmental studies, this article presents a step‐by‐step approach to meta‐analysis. Brief descriptions presented in each section of this article cover the initial steps of meta‐analysis such as formulation of a research question, development of a literature search strategy, and assessment of quality of the published literature by reviewing possible biases. Next steps provide essentials of data extraction, methods of meta‐data analysis as well as interpretation of results. An attempt is made to provide hypothetical examples to illustrate each meta‐analytical step. The goal of this article is to provide an overview of the fundamentals of meta‐analysis of environmental studies.
Meteorological Extremes
Authors: Lombardo Franklin T. , Pintar Adam L. , Possolo Antonio , Simiu Emil , Yeo Donghun 
Published: January 2013

Abstract
This article reviews basic principles of the statistical analysis of large extreme values (EVs), the role of simulations in the development of relevant meteorological data sets, the interaction between the EV modeling process and its application to engineering, and specific issues pertaining to wind, snow, ice, storm surge, and ocean waves. Classical approaches and the peak over threshold approach (POT) to the estimation of univariate extreme value distributions are briefly described. The distributions include: the Gumbel (Type I), Fréchet (Type II), reverse Weibull (Type III) extreme value distributions. One version of the POT approach relevant for meteorological extremes describes the joint observation of the time and magnitude of the threshold exceedance as a two‐dimensional nonhomogeneous Poisson process. It is noted that the dependence on direction of the extreme meteorological quantities of interest plays an important role in the estimation of extreme effects of these quantities on engineering structures. Also noted are the relative paucity of data on freezing rain and associated glaze ice accretion, and difficulties encountered in the estimation of tornado wind speeds. The article is concluded with some brief comments on multivariate extremes.
Mixed Outcomes
Authors: Molenberghs Geert , Geys Helena 
Published: January 2013

Abstract
Measurements of both continuous and discrete outcomes are encountered in many statistical problems. A common example is a developmental toxicity study, which typically involves exposing timepregnant animals (rats, mice) to an environmental agent during major organogenesis. The dams are sacrificed prior to normal delivery, at which time the uterus is removed and thoroughly examined. Among viable fetuses, the incidence of any malformation (binary data) and reductions in fetal weight (continuous data) are typically of primary concern, since both have been found to be sensitive indicators of a toxic effect. This motivates the formulation of a joint distribution with mixed continuous and discrete outcomes.
Mixing Distribution
Authors: Leroux Brian 
Published: January 2013

AbstractA random variableYis said to have a mixture distribution if its probability density (or mass) function can be written as an average over a parametric family of distributions. An example is the Poisson‐gamma distribution, in which conditional on a gamma‐distributed variableM, the variableYhas a Poisson distribution with meanM. The probability distribution assigned to the conditioning parameter (e.g., gamma) is called the mixing distribution. Mixture distributions are particularly useful in environmental and other sciences for modeling overdispersion in discrete data. An extension called the hidden Markov model is available to also account for autocorrelation in discrete data.
Multivariate Kriging
Authors: Wackernagel Hans 
Published: January 2013

Abstract
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.
National Resources Inventory (
NRI
),
US
Authors: Nusser Sarah M. 
Published: January 2013

Abstract
The US National Resources Inventory (NRI) is a longitudinal survey conducted by the US Department of Agriculture's Natural Resources Conservation Service in cooperation with the Iowa State University Center for Survey Statistics and Methodology. The NRI is designed to periodically assess conditions and trends for soil, water, and related natural resources on nonfederal lands of the United States, with major releases in years ending in two and seven. The survey has evolved over a period of several decades, with the goal of providing information to support agricultural and environmental policy development and program implementation. The current design involves annual remotely sensed data collection on a large area sample, with smaller onsite surveys to collect more intensive data on specific natural resources.
Nelder Plots
Authors: Barry Ronald P. 
Published: January 2013

Abstract
A Nelder plot, also called a Nelder Wheel or Nelder Fan, is a systematic planting design in which plants or trees are planted at the intersection of circular arcs and linear spokes. In general, Nelder plots allow many different planting densities to be examined in a single plot. This is frequently more efficient than planting a different plot for each planting density. Nelder plots can be constructed that allow the effect of different planting geometries to be examined in a single plot. This article discusses the construction of Nelder plots and the analysis of data derived from Nelder plots.
Neurotoxicology, Environmental
Authors: Catalano Paul J. 
Published: January 2013

Abstract
Neurotoxicity testing is an important area of noncancer toxicity evaluation. This article provides a description of the functional observational battery (FOB), a frequently employed neurobehavioral assessment, and discusses some of the statistical and data analytical issues surrounding its analysis. Emphasis is given to describing the multivariate nature of the FOB data.
Occupancy Problem
Authors: Mcdonald Trent L. 
Published: January 2013

AbstractThe “occupancy problem” has also been called the “sequential occupancy problem” and the “coupon‐collector's problem”. Although there exist many variations, one popular statement of the problem asks, “What is the probability that a set ofnindependent multinomial trials yield at least one member ofyclasses?” The answer to this question has direct application in many sampling situations, particularly when sampling for members of a population with certain characteristics (such as species members, members with defects, etc.). An answer to this question, under general probabilities of class membership, is given in this article through the occupancy probability mass function and cumulative mass function. This article also mentions the corresponding waiting‐time question that asks how many multinomial trials need to be conducted (i.e. how large shouldnbe) in order to have a certain chance of obtaining at least one member ofyclasses.
Official Statistics, Environmental
Authors: Ross N. Phillip , Kollander Mel 
Published: January 2013

Abstract

The generation of
official
statistics on the environment is an evolving activity as nations begin to understand better the impact of anthropogenic environmental pollution on the state of the world's environment. Countries are experiencing increasing pressure to compete globally and to provide for the health and welfare of their human populations. These countries are realizing that proper measures of the environment are necessary for their ability to compete in the global market place and participate as members in geographic trading blocs. This pressure will have a positive effect on the production of
official
environmental statistics worldwide. In this article, we provide a brief overview of
official
statistics activities of a number of countries and international organizations that provide
official
statistics on the environment. Emphasis is on North American and European
official
statistics activities. We provide references to several web sites that provide broader information on environmental statistics for other areas of the world.
Ornithological Data
Authors: Morgan Byron J. T. , Viallefont Anne 
Published: January 2013

AbstractThis 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.
Outcome‐Dependent Selection Models
Authors: Ding Jieli , Eva Lu Tsui‐shan , Zhou Haibo 
Published: January 2013

Abstract
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.
Outliers
Authors: Van Aelst Stefan 
Published: January 2013

Abstract
Datasets frequently contain outliers which are observations that strongly deviate from the pattern in the majority of the data. Detecting outliers and measuring their influence on an estimation procedure is not trivial. In one or two dimensions graphical inspection of the data may be helpful, but in higher dimensions more reliable methods are needed. We discuss robust estimation to obtain estimates that can resist the influence of outliers and related outlier diagnostics to identify the outliers in a dataset.
Ozone
Authors: Cocchi Daniela , Trivisano Carlo 
Published: January 2013

Abstract
After illustrating the differences between tropospheric and stratospheric ozone, ozone production from photochemical reactions is described. Remarks follow on potential damages on human health and vegetation due to ozone toxicity and the need of its control via air quality standards and guidelines. Emphasis is given to monitoring, data collection and statistical analysis of ozone concentrations: statistical models are becoming important for ozone analysis and can be associated to deterministic chemistry transport and meteorological models. They are able to summarize large amounts of data collected in time and space, to account for confounders, like meteorological variables, and to evaluate uncertainties due to measurement errors. Global, regional and local scales are different contexts for analysis. Bayesian methods are very flexible tools for solving non standard problems, like missing values, change of support and misalignment.
A variety of modern statistical methods useful for the analysis of ozone concentrations is reviewed: model fitting and extreme value analysis, spatio and spatio‐temporal models, methods for long‐term trends estimation, models for the analysis of the relationship between ozone concentrations and human health.
Point Processes, Spatial‐Temporal
Authors: Schoenberg Frederic Paik , Brillinger David R. , Guttorp Peter 
Published: January 2013

AbstractA spatial–temporal point process (also called space–time or spatio‐temporal point process) is a random collection of points, where each point represents the time and location of an event. Examples of events include incidence of disease, sightings or births of a species, or the occurrences of fires, earthquakes, lightning strikes, tsunamis, or volcanic eruptions. Typically the spatial locations are recorded in three spatial coordinates, e.g. longitude, latitude, and height or depth, though sometimes only one or two spatial coordinates are available or of interest.
Population Dynamics
Authors: Quinn Terrance J. 
Published: January 2013

AbstractPopulation dynamics is the study of change in an animal or fish population. Studies range from field work to collect biological data to complex mathematical and statistical models. Contemporary models attempt to achieve biological realism by containing components representing key factors affecting the population and uncertainty in those processes by adding stochastic effects. Estimation of animal abundance is an important subject and includes mark‐recapture, line‐transect, and several other methods. The simplest useful population model is the logistic, in which a population has sigmoidal growth to the carrying capacity. More complex models may include age and/or length structure, natural and harvesting mortality, recruitment of new individuals into the population, individual growth, reproductive parameters, species interactions, spatial structure, and movement. A common feature of realistic models that have an equilibrium carrying capacity is that there is density‐dependence in some population process that causes a reduction in the rate of increase as the population gets large. For example, many studies have found density‐dependence in early life survival. An important use of population dynamics models is stock assessment of fisheries. Scientists collect fisheries and biological data to estimate population parameters in assessment models that are then used to develop a sustainable harvest strategy.
Radon
Authors: Ginevan Michael E. , Watkins Deborah K. 
Published: January 2013

Abstract
This article discusses radon, the second leading cause of lung cancer after cigarette smoking. Statistical issues discussed include estimation of the form of the dose‐response function relating lung cancer risk to radon exposure, the radiation dose that one might receive from a given level of radon exposure, and appropriate strategies for reduction of radon related risk.
Random Fields
Authors: Ensor Katherine Bennett 
Published: January 2013

AbstractRandom fields provide a framework to address both spatial and spatial‐temporal random structure and have served as a key modeling tool since the 1950s. Adler's text in 1981 on the geometry of random fields served as a foundational piece [1] and currently has well over 1500 citations at the time of this article. This article is adapted from Adler's original submission [2] with updated references.
Ranked set sampling
Authors: Patil G.P. , Surucu Baris , Egemen Didem 
Published: January 2013

Abstract
Environmental monitoring and assessment typically requires observational data as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, and individuals, while typical characteristics of interest include biomass, chemical concentrations, or “head counts.” Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is comparatively simple. One can therefore achieve great observational economy if one can identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.
Receptor and Hybrid Modeling Tools
Authors: Vedantham Ram , Norris Gary , Duvall Rachelle 
Published: January 2013

AbstractPractical and economical plans to reduce air pollution require models that relate the sources of airborne pollution to concentrations at sampling sites. In receptor models, samples of airborne pollution are subjected to extensive chemical analysis. Contributing sources leave chemical fingerprints in the sample. The amount of pollution coming from each source can be estimated if the chemical fingerprints of the sources are known. Thus, receptor models do not require any knowledge of emission rates or meteorological observations that are very important to atmospheric dispersion models. However, receptor models do rely on the observed concentrations of chemical species in the atmosphere. These species must be conserved during transport between the source and the receptor. Chemical elements are good candidates for receptor modeling since these are often immutable under ordinary conditions and relatively easily measured.
Ripley'sKFunction
Authors: Dixon Philip M. 
Published: January 2013

AbstractRipley's K function summarizes spatial point process data. It can be used to describe a set of locations, test hypotheses about patterns, and estimate parameters in a spatial point process model. For a stationary point process,K(t) is the expected number of additional points within distancetof a focal point divided by the intensity of the process. A univariate version is used for one set of locations and a multivariate version is used when points can be labeled by a small number of groups. This article reviews the properties of Ripley's K function and two related functions, then illustrates the computation and interpretation using data on the locations of trees in a swamp hardwood forest.