bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kriging for Functional Data
Authors: Giraldo Ramón , Mateu Jorge 
Published: September 2014

Abstract
Spatial functional data analysis is a relatively new topic in statistics. We have functional data when a random variable takes its values in a functional space and is observed at some sampling points. In this article, we focus on the case of functional data presenting spatial dependence, and present an overview of the existing methodology to make spatial predictions when the data values are functions. Kriging and its several related versions are widely known and used for spatial data. However, when the spatial data are functions, a bridge between functional data analysis and geostatistics has to be built. Ordinary kriging as well as cokriging and multivariate spatial prediction adapted to the case where the observations at each sampling location consist of samples of random functions are reviewed and compared.
Landscape Pattern Metrics
Authors: McGarigal Kevin 
Published: September 2014

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.
Monitoring, Biological
Authors: Smith Eric P. 
Published: September 2014

AbstractIn the environmental context, biological monitoring refers to the gathering of information on biological organisms to assess the environmental impact or the biological health of the environment. In a strict sense, the term applies to a continuing collection of data used to establish whether explicitly stated quality control conditions are being met. Other terms that are related include biological surveys, assessment, and surveillance. Biological surveys are commonly carried out by national agencies to monitor birds, whales, fish, and other organisms. Volunteer‐based surveys are an integral part of biomonitoring.
Natural Resources Modeling
Authors: Rathbun Stephen L. 
Published: September 2014

Abstract
Environmental resources are biotic, including fish and game populations, timber, and grasslands, or abiotic, including water, air, and soil. Interest may focus on either the quantity of available resources (e.g. volume of available timber, or size of fish populations), or their quality (e.g. the concentration of contaminants in air, water, or soil samples). Environmental resource models can lead to a better understanding of environmental processes, and assist managers in making decisions regarding sustainable levels of resource exploitation, remediation of impaired habitats, and the maintenance of biodiversity. This article describes univariate and multivariate methods for predicting the data at unsampled sites from observations at sampled sites. Univariate models are used when the values of the explanatory variables are available at all sites for which predictions are desired. A multivariate approach is required when the explanatory variables are measured only at a sample of sites. For both approaches, models for the spatial correlation structure of the data are considered, including both parametric and nonparametric forms. Methods for model fitting and spatial prediction are described.
Official Statistics, Environmental
Authors: Ross N. Phillip , Kollander Mel 
Published: September 2014

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.
Population Dynamics
Authors: J. Quinn Terrance 
Published: September 2014

Abstract
Population 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.
P
opulation
V
iability
A
nalysis
Authors: McCarthy Michael A. , Possingham Hugh P. 
Published: September 2014

Abstract
Population viability analysis (PVA) assesses risks of population decline of species, and how those risks can be managed. Typically, stochastic population models are used to characterize the deterministic and stochastic components that govern changes in population size. Sources of stochasticity include demographic stochasticity due to the chance birth and death of individuals, stochasticity in the environment that is common to all individuals, chance variation in genetic composition, and spatial structure. These factors can interact to create extinction vortices that drive population decline. Predicted risks of population decline from PVA models are usually uncertain due to imprecise parameter estimates, imperfect knowledge about factors that influence dynamics, and uncertainty about future conditions. Despite these sources of uncertainty, changes in risks due to management can be predicted more reliably. Using these changes in risk, PVA can help determine efficient management strategies for reducing high contemporary rates of extinction.
Predictive Scores
Authors: Gneiting Tilmann 
Published: September 2014

AbstractPredictive scores assess the quality of forecasts, by assigning a numerical score based on the forecast and on the event or value that materializes. Probabilistic forecasts take the form of a predictive probability distribution. Their evaluation calls for the use of proper scoring rules, such as the logarithmic score or the continuous ranked probability score. Effective point forecasting requires a directive in the form of a target functional, say a quantile or the mean of the predictive distribution. It is then critical for the scoring function to be decision theoretically consistent, so that the expected score is minimized when following the directive. For example, the squared error is consistent when the point forecast is desired to be a mean, and the absolute error is consistent when we seek a median.
Satellite Data
Authors: Shao Guofan 
Published: September 2014

AbstractEarth observation satellites collect data relevant to a wide range of land resource topics and there are a variety of satellite data that are designed for different purposes. Satellite data are collected, transmitted, stored, and analyzed in digital forms. Satellite data have a broad range of spatial, spectral, radiometric, and temporal characteristics. Commonly used terms are spatial, spectral, radiometric, and temporal resolutions. A general indication of advancement of satellite data is that their resolutions are getting higher over time. Satellite can be used alone, to derive new data, and integrated with other geospatial data sources. While most of commercial satellite data can be purchased from data providers, many governmental satellite data can be downloaded without charge from US government websites.
Screening Effect, Geostatistical
Authors: Du Juan 
Published: September 2014

Abstract
This article reviews various applications and some theory of the screening effect, which describes the phenomenon where a spatial interpolant primarily depends on nearby observations in kriging. Justified by the screening effect, the nearest‐neighbor approach has been widely used in practice, mainly to reduce computational burden. However, caution needs to be exercised in choosing model and observation locations, since there may be no screening effect in some cases.
Seemingly Unrelated Regressions
Authors: Bartels R. 
Published: September 2014

Abstract
Seemingly unrelated regression (SUR) is a widely used modeling approach introduced by Zellner for situations where several linear regression relationships are being investigated at the same time. Examples of the use of SUR in environmetrics include Boisvert et al., who considered separate equations for the effect of water contamination on both the purchase price and rental value of farmland, and Xu, who estimated separate equations for the impact of environmental legislation on the output of each of a number of industrial sectors. The key feature in these cases is that each of the linear equations, on its own, satisfies the classical conditions underpinning the linear regression model, and hence the ordinary least squares (OLS) estimator can be used to estimate each equation individually. However, if the error terms of the different equations are correlated across the equations, then joint estimation of the equations may be able to exploit this cross equation correlation to obtain more efficient estimates.
Soil Surveys
Authors: Brus Dick J. , Lark Richard Murray 
Published: September 2014

Abstract

The aim of a soil survey is to predict soil conditions at unvisited sites. In traditional soil survey, the use of statistics is principally for
post hoc
evaluation of the quality of the choropleth map in terms of purity and within‐ and between‐map unit variance of soil properties. Probability sampling designs have been proposed for this. Pedometrical soil surveys treat all phases of the soil survey explicitly as a statistical problem. This had led to the adoption and development of, amongst other techniques, optimized sampling for mapping purposes, automated fuzzy classification of soil profiles, and geostatistical interpolation. Typically covariates related to the soil‐forming factors and derived from remotely sensed images or digital elevation models are integrated into the geostatistical prediction.
Spatial Analysis in Ecology
Authors: Fortin Marie‐josée , Dale Mark R.T. , Ver Hoef Jay M. 
Published: September 2014

Abstract

The first step in understanding ecological processes is to identify patterns. Ecological data are usually characterized by spatial structures due to spatial autocorrelation.
Spatial autocorrelation
refers to the pattern in which observations from nearby locations are more likely to have similar magnitude than by chance alone. The magnitude, intensity, as well as extent of spatial autocorrelation can be quantified using spatial statistics. Most ecological data exhibit some degree of spatial autocorrelation, depending on the scale at which the data were recorded and then analyzed. Ecological phenomena are also characterized by the multiple ecological processes that act upon them; these processes often operate at more than one spatial scale. Ecological data are a composite of several spatial scales: trends at macroscales; patches, gradients and patterns at meso‐ and local scales; and random patterns at local and microscales. The different processes and patterns at different scales are not necessarily linear or additive, and this contributes to the degree of spatial dependence in the data.
Species Competition
Authors: Young Linda J. 
Published: September 2014

Abstract
Competition occurs when the interaction of two organisms has an adverse effect on both. The primary models for competition are based on the Lotka‐Volterra equations. Replacement series experiments are a standard approach to evaluating the competitive relationships of two or more species. Measures of niche overlap, such as similarity indices, are often used as initial indicators of competition although they are recognized not to be true measures of competition. The probability of a species being present may be related to environmental variables and the presence of other species using logistic regression.
Species Interaction Networks
Authors: Chiu Grace S. 
Published: September 2014

Abstract
To gain insight into the food web structure through analysis of empirical food webs, it is necessary to disentangle the complex interdependence among the species interactions that form the food web. This article reviews some basics for making sense of species trophic (feeding) interactions and any associated nonconsumption information. It then provides some details of a more novel statistical‐model‐based social network analysis approach. The inference for food web structure resulting from the single rigorous quantitative analysis incorporates trophic data and any available nontrophic data, and accounts for uncertainty inherent in the data. The inference can reveal trophic patterns simultaneously from three different perspectives of feeding behavior: trophic activity, predator's preference, and so‐called prey's preference.
Standards, Environmental
Authors: Esterby Sylvia 
Published: September 2014

Abstract
This article describes the past, present, and future techniques by which environmental standards are set. Concern regarding the lack of acknowledgment of uncertainty and variability in many standards, and thus in reported results on compliance, has lead to an emphasis on the role of statistical methodology in environmental standard setting. Research into the feasibility, advantages, and disadvantages of such methodology is ongoing, but much remains to be done. Encompassing within its line of investigation areas, such as toxicology, environmental sampling methodology, and spatiotemporal modeling, and affecting the fundamental procedures of industry, traffic management, medical and environmental agencies, to name just a few of many, this area of the rapidly growing field of environmetrics may prove to be one of the most far‐reaching and important statistical applications in the coming years.
Surveillance, Environmental
Authors: Järpe Eric 
Published: September 2014

Abstract
In many environmental random processes it is relevant to consider a sudden shift in a parameter of the probability distribution of the random variables. This parameter may be a mean level of the process or the natural variation of the variables. Suddenly the parameter can shift to some other, commonly higher, value or to another, commonly increasing, function in time and/or space. To prevent or mitigate the fatal consequences such shifts could imply, it is often of utter importance to detect the change as accurately and quickly as possible. Therefore, stopping rules which are functions of the random process data observed are evaluated, on‐line or retrospectively, in order to indicate a systematic change. Different shift processes are mentioned. An example with data from the Swedish Radiation Safety Authority is given.
Switching Process
Authors: Shumway Robert H. 
Published: September 2014

Abstract

Often, there is a need to develop models that describe environmental processes that change regimes at unknown points in time or space. Dynamic regression or state–space models that are switching from one regime to the other according to an evolving stationary discrete state Markov chain have great potential for modeling changing environmental phenomena. Processes that involve these kinds of models are often referred to as
switching processes
or hidden Markov processes.
Uncertainty Analysis
Authors: Smith Eric P. 
Published: September 2014

Abstract
Mathematical, statistical, and other models are often developed to aid researchers and management in making decisions about environmental problems and the associated risk. Uncertainty analysis is associated with a set of methods to aid in understanding the limitations in the decision process and predictions from these models. Uncertainty is typically associated with deterministic (model, parameter) and stochastic (variation) components. Several methods for estimating the uncertainty such as Monte Carlo methods, frequentist and Bayesian analysis, and sensitivity analysis are described. Common problems in uncertainty such as safety and extrapolation factors, decision making under uncertainty, and estimation of uncertainty are discussed.
Weather Prediction
Authors: Lin Charles 
Published: September 2014

Abstract
At the heart of the weather forecast procedure is the numerical weather prediction model. The governing equations are the equations for the three spatial components of momentum, temperature, humidity and mass. The model is made from a spatial discretization of the governing equations on the entire globe using numerical methods (e.g. finite difference and spectral methods). The finite model grid size means that parameterizations of physical processes such as mixing and convective adjustment are necessary. Such models typically have millions of degrees of freedom, and frequently require supercomputers for their efficient solution. The equations are integrated forward in time, again using numerical methods, starting from some initial state. Global forecasts of meteorological quantities such as surface temperature and precipitation are made for up to about two weeks.
Flow Over Complex Terrain, Numerical Modeling of
Authors: Lundquist Katherine A. , Chow Fotini Katopodes 
Published: September 2014

AbstractThis article provides a general description of numerical modeling over complex terrain, including both diagnostic and prognostic approaches and their associated advantages and limitations. Diagnostic models solve simplified versions of the equations of motion and are widely used in the engineering community for simulations of flow over complex terrain. These models produce a description of the steady‐state flow, while requiring few inputs and being computationally efficient. Diagnostic models do not predict time evolution of flow, and are often only appropriate for terrain with small slopes. Prognostic models solve the full equations of motion and are used for both engineering and numerical weather prediction applications. Details of the implementation of prognostic models vary widely, but they are capable of producing time‐varying descriptions of the flow and handling very complex terrain with no limitations on terrain slope. These models can require detailed three‐dimensional inputs and large computational resources. Additional simplifications can reduce these requirements, but generally at the expense of functionality, such as producing a steady‐state solution or the inability to handle arbitrarily complex terrain.
Uncertainty Quantification, Physical Models
Authors: He Xian , Zhang Hao 
Published: September 2014

Abstract
A physical model refers to a deterministic mathematical model that is built partially based upon physical or biological principles. Because it is deterministic, it is vital to quantify the uncertainty of its output. The ensemble is a common method for a model whose output can be simulated efficiently. For those models that are more computationally demanding to simulate, statistical emulation and stochastic differential models are two useful approaches. This article briefly reviews these methods.
More nonlocality with less entanglement in a tripartite atom‐optomechanical system
Authors: Zhang Jing , Zhang Tiancai , Xuereb André , Vitali David , Li Jie 
Published: September 2014

Quantum effects in hybrid atomic optomechanics in a system comprising a cloud of atoms and a mobile mirror mediated by a single‐mode cavity are studied. Tripartite nonlocality is observed in the atom‐light‐mirror system, as demonstrated by the violation of the Mermin‐Klyshko (MK) inequality. It has been shown [C. Genes, et al., PRA 77, 050307 (R) (2008)] that tripartite entanglement is optimized when the cavity is resonant with the anti‐Stokes sideband of the driving laser and the atomic frequency matches the Stokes one. However, we show that this is not the case for the nonlocality. The MK function achieves minima when the atoms are resonant with both the Stokes and anti‐Stokes sidebands, and unexpectedly, we find violation of the MK inequality only in a parameter region where entanglement is far from being maximum. A negative relation exists between nonlocality and entanglement with consideration of the possibility of bipartite nonlocality in the violation of the MK inequality. We also study the non‐classicality of the mirror by post‐selected measurements, e.g. Geiger‐like detection, on the cavity and/or the atoms. We show that with feasible parameters Geiger‐like detection on the atoms can effectively induce mechanical non‐classicality.