bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting, Environmental
Authors: Young Peter C. , Pedregal Diego J. 
Published: September 2006

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.
Forest Carbon Cycling
Authors: Vose Jim 
Published: September 2006

Abstract

The terrestrial carbon (C) cycle comprises three components: C inputs, C outputs, and C pools. Worldwide, terrestrial ecosystems contain approximately 2000 Pg C, most of which is in belowground pools (∼1500 Pg C). By contrast, the atmosphere contains ∼750 Pg C. The nearly three‐fold greater C in terrestrial ecosystems relative to the atmosphere means that terrestrial C cycling processes potentially have a major influence on atmospheric
carbon dioxide
(CO
2
) at global scales, and hence on changes in atmospheric CO
2
and climate.
Forest Growth and Yield Modeling
Authors: Vanclay Jerome K. 
Published: September 2006

Abstract
Forest growth models attempt to quantify the growth of a forest, and are commonly used for two principal purposes: to predict the future status of a forest and the nature of any harvests from that forest; and to help consider alternative cultivation practices. Models may also find other uses, such as in education, communicating information, etc. Depending on the purpose of the model, modelers may choose to emphasize physiological detail or statistical efficiency, but generally seek both biological and statistical accuracy.
Forest Health Monitoring
Authors: Olsen Anthony R. 
Published: September 2006

AbstractForest health monitoring (FHM) focuses on the health of the forest ecosystem. Recognition of the importance of forest health and, especially, the role of monitoring, increased with the acceptance of the Santiago Declaration. The declaration was accompanied by a set of criteria and indicators for the conservation and sustainable management of temperate and boreal forests. The criteria focus on conservation of biological diversity, maintenance of productive capacity, maintenance of forest ecosystem health, conservation of soil and water resources within forests, maintenance of forest contribution to global carbon cycles, and enhancement of long‐term multiple socio‐economic benefits to meet the needs of societies. The declaration calls for quantitative measurements of indicators associated with the criteria. The periodic and systematic measurement of the indicators requires nations to have FHM programs.
FractalDimensions
Authors: Li Bai‐Lian 
Published: September 2006

AbstractThe term ‘fractal’ (from the Latinfractus, meaning ‘broken’), introduced by Benoit Mandelbrot about 25 years ago, is used to characterize spatial and/or temporal phenomena that are continuous but not differentiable. Geometrically, a fractal is a rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced‐size copy of the whole. Fractal structures do not have a single length scale, while fractal processes (e.g. time series) cannot be characterized by a single time scale. Fractal theory offers methods for describing the inherent irregularity of natural objects. In fractal analysis, the Euclidean concept of ‘length’ is viewed as a process. This process is characterized by a constant parameterDknown as the fractal (or fractional) dimension. There are many fractal dimensions introduced in mathematical and physical literature. Fractal dimensions can be positive, negative, complex, fuzzy, multifractal, etc. The two most commonly used are the Hausdorf dimension and capacity.
Generalized Additive Models
Authors: Grego John M. 
Published: September 2006

Abstract

A Generalized Additive Model (GAM) is an extension of a generalized linear model (GLM) in which the expected value of a mean response, ⋼ = E(
Y
), is linked to a set of additive nonparametric functions in the independent variables
X
1
, hellip;,

X
p

.
Generalized Estimating Equations
Authors: Pickles Andrew 
Published: September 2006

AbstractInterdependence is a characteristic feature of many environmental processes. However, the complexities for statistical analysis introduced by such interdependence have hampered the progress of environmental science. Why this is so can be understood by considering the workhorse of standard statistical analysis, thegeneralized linear model(GLM). A typical GLM divides variation in some measure into a systematic part and a random part. Typically substantive theory guides the construction of the model for the systematic part. For the random part, although some guidance may be obtained from knowledge of the distributional consequences of a simple and plausiblestochastic processthat might be responsible for this variation, and from experience gained with other comparable data, we rarely have complete confidence in our choice of distribution. In the case of spatial, longitudinal and multivariate outcomes the choice is among multivariate distributions since we need to describe the association across outcomes as well as within outcome variability. This becomes especially difficult for problems where the multivariate normal distribution is not a suitable choice for the random part of the model, since the available alternative distributions lack flexibility. Under these circumstances it is often helpful to use generalized estimating equations (GEEs). The GEE approach, while making weaker distributional assumptions than that required for a fully parametric linear model, maintains the properties of consistency and asymptotic normality of parameter estimates.
Inverted Wishart Distribution, Generalized
Authors: Brown Philip J. 
Published: September 2006

Abstract

The generalized inverted Wishart (GIW)
distribution
is a flexible extension of the inverted Wishart (IW) distribution which overcomes the major deficiency of that distribution whilst retaining much of the ease of computation inherent in the natural conjugate roots of the inverted Wishart distribution in Bayesian analysis for normal sampling. In offering a more flexible prior distribution for the covariance matrix of a multivariate normal distribution it has general applicability. It also offers a conjugate Bayesian analysis for problems of missing data where the missing data has a monotone structure. It has been especially useful in simultaneous modeling of the spatial distribution of ozone where monitoring stations come on‐stream in sequence, using multivariate regression analysis for interpolation and prediction.
Generalized Linear Models
Authors: Jørgensen Bent 
Published: September 2006

AbstractThe class ofgeneralized linear modelswas introduced in 1972 by Nelder and Wedderburn as a general framework for handling a range of common statistical models for normal and non‐normal data, such as multiple linear regression, analysis of variance (ANOVA), logistic regression, Poisson regression and log‐linear models. Ideas from generalized linear models are now pervasive in much of applied statistics, and are very useful in environmetrics, where we frequently meet non‐normal data, in the form of counts or skewed frequency distributions.
Generalized
P
Values
Authors: Ananda Malwane M.A. , Weerahandi Samaradasa 
Published: September 2006

Abstract

In many statistical problems involving nuisance parameters, conventional statistical methods do not provide exact solutions. For example, when comparing treatments in a one‐way or two‐way analysis of variance (ANOVA) model without the assumption of equal error variances, classical statistical methods do not provide exact solutions. As a result, practitioners often resort to approximate methods or asymptotic methods, which may perform poorly, specifically with small sample sizes. These kinds of situations arise in many environmental statistical applications. In particular, when dealing with small samples, as a result of using weak tests, experimenters often fail to detect significant differences between treatments. The generalized
P
value approach is a recently developed method based on exact probability statements rather than on asymptotic approximations. The generalized
P
value approach is an extension of the classical
P
value approach.
Generalized Regression Estimators
Authors: Fuller Wayne A. , Park Mingue 
Published: September 2006

Abstract
Regression estimators are used in survey sampling when population information is available for some variables observed in the survey. This information, sometimes called auxiliary information, often comes from official sources such as a national census. In a survey of land use, the total surface area and the area in permanent water bodies may be available from national data sources. Regression estimation is used to improve efficiency and to reduce bias due to nonresponse or incomplete sample coverage. The form of the regression estimator depends upon the design of the survey and on the nature of the available information.
Genetic Algorithms
Authors: Sheehan N.A. 
Published: September 2006

AbstractForest inventory is an accounting of trees and their related characteristics of interest over a well‐defined land area. It may be compared to census methods for human populations. For example, one of the goals of the periodic decennial census of the US is to enumerate its human population and to retrieve demographic variables such as age, sex and race. This is accomplished by a comprehensive survey of all households in the country. Similarly, forest inventories seek to enumerate the population of trees within a forest and ascertain other information, such as their volume, value, growth and species composition. For all but the smallest tracts of land, complete enumeration of individuals is usually infeasible and survey sampling techniques are required. Unlike human or animal populations, trees are sessile organisms and there is no immigration or emigration to consider. However, tree populations vary widely in their species composition, age, size, site requirements, potential value, longevity and growth–all factors that may influence the design of a forest inventory. In addition, the tree population of interest may exist over a wide variety of topography, making access potentially costly and sometimes even dangerous. Such variety often dictates sampling protocols that may be optimal only for some portion of the population in question. Alternatively, it may call for varying sampling intensity or strategy (the combination of the sampling design and associated estimators) between different regions within the population.
Global Circulation
Authors: North Gerald R. , Shen Samuel 
Published: September 2006

Abstract
The shape of the earth is very nearly spherical with radius approximately 6400 km. The depth of the oceans averages about 4 km and about two‐thirds of the mass of the atmosphere lies within 10 km of sea level. This means that the atmosphere/ocean or the geophysical fluid system is only a very thin film of material moving and often swirling about the surface. The geophysical fluids are differentially heated by the sun, the tropical regions receiving much more solar heating than the polar latitudes. This differential heating, which takes place mostly through heating the earth's surface, sets up air currents, which overturn the fluid and redistribute the heating toward the poles. The earth also rotates about an axis with a fixed orientation in space. This rotation causes apparent forces on the geophysical fluid elements, deflecting them and leading to the formation of circulation patterns. The movements of the fluid media covering the planet form the complex of motions we call the global circulation, which is the main subject of this article.
Global Environmental Change
Authors: Zwiers Francis W. 
Published: September 2006

Abstract

Human activity is altering the composition of Earth's atmosphere through the addition of greenhouse gases and particulates. Anthropogenic changes in the properties of the atmosphere can be thought of as external forcing factors on the climate system. There is a fair degree of confidence in results on greenhouse gas forcing and the climate's response to that forcing. However, knowledge of the forcing and response due to aerosols remains highly uncertain. There are also natural external forcing factors that influence climate, such as changes in orbital geometry and changes in solar irradiance and volcanic activity. The climate system, even when not perturbed by external factors, produces substantial amounts of natural variability. Thus detection and attribution of the effects of external forcing is a statistical signal‐in‐noise problem. The
detection
part of this problem is the process of demonstrating that an observed change is not likely to have been entirely the result of natural internal variability. The
attribution
aspects of the problem are more difficult because it is not possible to conduct controlled experiments with the climate system. The practical approach that has been taken in the climate research community involves statistical analysis and the assessment of multiple lines of evidence to (a) demonstrate that observed changes are consistent with forcing of the climate by a combination of anthropogenic and natural external factors, and (b) demonstrate that the changes are inconsistent with alternative, physically plausible explanations.
Global Warming
Authors: North Gerald R. , Shen Samuel S. , Basist Alan S. 
Published: September 2006

Abstract
Global warming refers to the increase of the air temperature near the Earth’s surface, but the term ‘global warming’ does not indicate the causes and magnitude of the increase, nor does it imply continuity of the warming into the future. Global warming can have a significant impact on life on Earth (rise of sea level, more frequent severe weather, redistribution of food systems, extinction of plants and animals, generation and propagation of diseases, increase of crop yield), however the significance has never been statistically tested.
Graphical Displays
Authors: Carr Dan 
Published: September 2006

Abstract
The purpose of environmental visualization is to facilitate scientific and public understanding of environmental status, trends, and processes. Such understanding is incremental owing to: (i) the complexity of the environment; (ii) the difficulty of parsimoniously conceptualizing this complexity; (iii) the logistic and political impediments to collecting adequate, representative data; (iv) and the limits of human perception and cognition for understanding multivariate summaries. This article can only hint at the range of challenges faced in attempts to characterize the environment and communicate using graphical summaries. It can only touch on the background knowledge that helps experienced readers to assess and interpret environmental graphics. However, the background is important since environmental visualization goes far beyond routine production and superficial interpretation of environmental graphics. After a brief introduction, the focus of this article is on graphical design principles and graphical templates for representing environmental summaries.
Habitat Sampling
Authors: Gould William R. 
Published: September 2006

AbstractHabitat represents the sum total of food, cover, water, and space that a species needs for survival. Measurement of an animal's habitat is as important as measuring the animal itself. Habitat evaluation has become increasingly important in developing strategies for the management and conservation of species and ecosystems as demand for natural resources increases with human population growth. Two decades ago, a subcommittee of the US National Research Council was charged with examining the effects of agricultural trends on fish and wildlife habitat. Among their conclusions, the committee emphasized the need for greater assessment of the effects of agricultural and forestry practices on fish and wildlife habitats. A comprehensive discussion of the wide array of habitat sampling techniques required by each situation is not possible here; this article focuses on the most basic types of vegetation measurements for terrestrial animals: frequency of occurrence, density, cover, and biomass.
Healthy Worker Effect
Authors: Eisen Ellen A. , Robins James M. 
Published: September 2006

Abstract
Epidemiologic studies of occupationally, environmentally exposed cohorts are useful in identifying and quantifying environmental risks because workers are generally exposed to higher levels of toxic materials than the general population. Although higher exposures make it easier to detect and characterize modest elevations in risk, such studies are plagued by a particularly pervasive form of selection bias, referred to as the healthy worker effect (HWE). The HWE is reflected in better health status of workers relative to the general population. This bias arises because workers were healthy enough to be hired initially and those who remain at work stayed healthy enough to maintain employment, whereas general populations include persons unfit for work because of impaired health.
Hierarchical Models for Disease Mapping
Authors: Waller Lance A. 
Published: September 2006

AbstractThe analysis of disease rates or counts from small areas often involves a trade‐off between statistical stability of estimates and geographic precision. Detection of locally elevated risk or rates requires geographically small units to distinguish local risk/rates from area‐wide values. On the other hand, smaller regions result in rate estimates based on smaller population sizes. For a rare disease, small population sizes result in particularly unstable rate estimates. The statistical literature contains various methods of combining information or ‘borrowing strength’ between regions to achieve local rate stabilization without losing geographic resolution. The most common approaches involve hierarchical models with random effects (intercepts) for each region.
Historical Controls
Authors: Ryan Louise 
Published: September 2006

Abstract
Because development toxicity, carcinogenicity, and other toxicological studies are typically conducted in genetically homogeneous animal strains, the large historical database that builds up over time can often prove useful in interpreting results from current experiments. For instance, if a certain defect has only rarely appeared among control animals, then its appearance among even a few exposed animals may indicate strong cause for concern even though statistical tests may not yield a significant dose effect for that current study. Of course, the use of historical control information requires caution. It is important that the current and historical studies are similar with respect to study design, animal species, quality assurance and review procedures, as well as diagnostic and nomenclature conventions.
Image Analysis
Authors: Glasbey C.A. , Horgan G.W. 
Published: September 2006

Abstract

The term
image analysis
means the use of computers to interpret digital versions of images. Such methods are of increasing relevance in all sciences, including environmetrics. Image analysis is a broad, interdisciplinary field with many challenging problems to which statistical methods are applicable A digital image usually consists of a rectangular array of pixels, each of which measures the brightness of a part of the original scene, where ‘brightness’ may denote reflected light but could equally represent any other variate that has been measured on a 2‐dimensional grid. Methods of analysis range from low‐level processing of individual pixels or small neighborhoods, such as the use of filters to enhance images, to segmentation, the spatial clustering of pixels into segments that represent regions or objects, or to high‐level processing, where the information in whole images is summarized.
Impact Assessment
Authors: Stewart‐Oaten Allan 
Published: September 2006

Abstract
A common monitoring problem is: Describe the effect of an environmental alteration on the abundance of a given species at the ‘Impact’ site. Harbors, breakwaters, developments, sewage outfalls, oil platforms, coastal power plants and recreational access – and removals or redesigns of these – are examples in the marine environment, and plants, invertebrates and fish are typical populations of concern. The Impact site is defined naturally in some cases, as a bay or estuary, but arbitrarily in others, as a region surrounding the alteration. Before permitting such alterations, decision makers review predicted effects. These are often subjective, widely varying, and wrong. Monitoring and assessment can aid later decisions: to close the alteration down, modify its design or operation, require mitigation or compensation, allow expansion, or collect further data. By exposing error, it helps keep predictors honest; by adding information, it improves future decisions.
Informatics, Environmental
Authors: Tochtermann Klaus 
Published: September 2006

Abstract
Environmental informatics is the science that deals with the development and application of methods and tools for environmental information processing. The main objectives of environmental informatics are to make the best possible use of information technologies: (a) to contribute to an effective and efficient protection of the environment; (b) to provide the public with information on the state of the environment and, thus, to increase environmental awareness; (c) to support environmental planning; and (d) to forecast future states of the environment.
Inhalation Toxicology
Authors: Sen Pranab K. 
Published: September 2006

AbstractInhalation toxicology relates to the composite assemblage of ambient toxins with respect to their prevalence, uptake sites and intake process by inhalation, mode of biological, molecular and carcinogenic reactions, and consequential health hazards. From a statistical perspective, the major task is to arouse our awareness of potential and asserted toxic substances, to gather scientific evidence of their impact on human life and on our ecosystem as a whole, and to appraise (statistically as well as epidemiologically) the risk due to the aggregation of such toxic and carcinogenic (overt or not) effects of our own bioenvironment. This risk assessment has emerged as an interdisciplinary one, where toxicology, molecular biology, pharmacology, epidemiology, and environmental health sciences all have major shares; nevertheless, statistics plays a key role in this crucial assessment task.
Inverse Prediction
Authors: Brown Philip J. 
Published: September 2006

AbstractInverse prediction concerns predicting one set of measurements from another set when the direction of causation and possibly error operates in the opposite direction. An example is that of calibration in which it is desired to use a cheap and quick but error‐prone measurementY, to predict the true amount of a constituentx, which in itself can be measured accurately in laboratory conditions but at much greater effort or cost. After taking data on samples using both measurements (the training or learning data) it is desired to use the cheap measurement in future.