Suitable for graduate courses and a reference for appropriate statistical approaches to specific environmental problems, this title begins by describing the important role statistics play in environmental science. It then focuses on how to collect data, highlighting the importance of sampling and experimental design in conducting rigorous science.
Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
Biometry for Forestry and Environmental Data with Examples in R focuses on statistical methods that are widely applicable in forestry and environmental sciences, but it also includes material that is of wider interest.
This book provides an introduction to the theoretical development and practical methodology of the so-called science-based spatiotemporal statistics. The book capitalizes on the significance of integrating different knowledge sources (physical, ecological, health, and social) into formal spatiotemporal statistics and provides an array of practical procedures for incorporating these sources into composite space-time analysis, modeling, and estimation/prediction.
Presents the concepts and tools required in finite populations, and develops the Monte Carlo approach in infinite populations to analyze or design complex forest inventories. This book discusses design-based, model-assisted, and model-dependent inference as well as the design of optimal sampling schemes based on the anticipated variance.
Presents a review of statistical tools used in analyzing and addressing environmental issues. This book examines commonly used techniques found in USEPA guidelines and discusses their potential impact on decision-making. It advises when to go outside of standard statistical models when making difficult decisions.
This book shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecological sampling methods, including mark-recapture, adaptive, and removal sampling. The book explains the methods as simply as possible, keeping equations and their derivations to a minimum. Accessible to biologists, the text only assumes a basic knowledge of statistical methods. Data sets and R code are available on a supplementary website.
Future Sustainable Ecosystems: Complexity, Risk, Uncertainty provides an interdisciplinary, integrative overview of environmental problem-solving using statistics. It shows how statistics can be used to solve diverse environmental and socio-economic problems involving food, water, energy scarcity, and climate change risks.
Containing many recent developments available for the first time in book form, this concise and up-to-date work presents the statistical concepts and tools needed to conduct a modern forest inventory. It develops the Monte Carlo approach for both simple and complex sampling schemes and explores design-based, model-assisted, and model-dependent inference, including geostatistics and Kriging procedures. The book also explains the design of optimal sampling schemes based on anticipated variance, introduces the g-weight technique for variance estimation, and presentsthe stereological approach to transect sampling. In addition, it includes numerous case studies, simulations, and instructive problems with solutions.
Future Sustainable Ecosystems: Complexity, Risk, Uncertainty provides an interdisciplinary, integrative overview of environmental problem-solving using statistics. It shows how statistics can be used to solve diverse environmental and socio-economic problems involving food, water, energy scarcity, and climate change risks.
Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical mode
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