Introduction to Ecological Sampling

An Easy-to-Understand Treatment of Ecological Sampling Methods and Data AnalysisIncluding only the necessary mathematical derivations, Introduction to Ecological Sampling shows how to use sampling procedures for ecological and environmental ...

Author: Bryan F.J. Manly

Publisher: CRC Press

ISBN: 9781466555150

Category: Mathematics

Page: 228

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An Easy-to-Understand Treatment of Ecological Sampling Methods and Data AnalysisIncluding only the necessary mathematical derivations, Introduction to Ecological Sampling shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecolo

Environmental and Ecological Statistics with R

APPLIED ENVIRONMENTAL STATISTICS University of North Carolina TATISTICS Series Editors Doug Nychka Richard L. Smith ... Introduction to Ecological Sampling Steven P. Millard and Nagaraj K. Neerchal, Environmental Statistics with S Plus ...

Author: Song S. Qian

Publisher: CRC Press

ISBN: 9781498728737

Category: Mathematics

Page: 536

View: 161

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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. 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.

Bayesian Applications in Environmental and Ecological Studies with R and Stan

Series Editors Chapman & Hall/CRC Applied Environmental Series Douglas Nychka, Colorado School of Mines Alexandra ... Ganapati P. Patil Introduction to Hierarchical Bayesian Modeling for Ecological Data Eric Parent, Etienne Rivot ...

Author: Song S. Qian

Publisher: CRC Press

ISBN: 9781351018760

Category: Mathematics

Page: 419

View: 874

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Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features • An accessible overview of Bayesian methods in environmental and ecological studies • Emphasizes the hypothetical deductive process, particularly model formulation • Necessary background material on Bayesian inference and Monte Carlo simulation • Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more • Advanced chapter on Bayesian applications, including Bayesian networks and a change point model • Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Bringing Bayesian Models to Life

Chapman & Hall/CRC Applied Environmental Statistics Series Editors Douglas Nychka, Colorado School of Mines Alexandra ... Analysis in Ecology Thorsten Wiegand, Kirk A. Moloney Introduction to Ecological Sampling Bryan F.J. Manly, ...

Author: Mevin B. Hooten

Publisher: CRC Press

ISBN: 9780429516801

Category: Mathematics

Page: 426

View: 469

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Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.

Environmental and Ecological Statistics with R Second Edition

CHAPMAN. &. HALL/CRC. APPLIED. ENVIRONMENTAL. STATISTICS. Series Editors Doug Nychka Institute for Mathematics ... Introduction to Ecological Sampling Steven P. Millard and Nagaraj K. Neerchal, Environmental Statistics with S Plus Wayne ...

Author: Song S. Qian

Publisher: CRC Press

ISBN: 9781498728751

Category: Mathematics

Page: 439

View: 732

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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. 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.

Introduction to Hierarchical Bayesian Modeling for Ecological Data

APPLIED ENVIRONMENTAL STATISTICS University of North Carolina TATISTICS Published Titles Michael E. Ginevan and Douglas E. ... for Ecological Data Chapman & Hall/CRC Applied Environmental Statistics INTRODUCTION TO HIERARCHICAL BAYESIAN.

Author: Eric Parent

Publisher: CRC Press

ISBN: 9781584889205

Category: Mathematics

Page: 427

View: 815

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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statisti

Future Sustainable Ecosystems

CHAPMAN & HALL/CRC APPLIED ENVIRONMENTAL STATISTICS SeriesEditors Doug Nychka Institute for Mathematics Applied to ... Introduction to Ecological Sampling Steven P. Millard and Nagaraj K. Neerchal, Environmental Statistics with S Plus ...

Author: Nathaniel K Newlands

Publisher: CRC Press

ISBN: 9781315356662

Category: Mathematics

Page: 290

View: 789

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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. It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership. Discussions include a broad, integrated perspective on sustainability, integrated risk, multi-scale changes and impacts taking place within ecosystems worldwide. State-of-the-art statistical techniques, including Bayesian hierarchical, spatio-temporal, agent-based and game-theoretic approaches are explored. The author then focuses on the real-world integration of observational and experimental data and its use within statistical models. The book clarifies how complex adaptive systems theory frames sustainability as a probabilistic (i.e., stochastic) problem, highlighting the importance of adaptive policy, science and institutional arrangements, for strengthening ecosystem adaptation and resilience. The author elucidates how we must transform our thinking, illuminating the benefits and opportunities offered by the integrative risk approach to innovation and learning in the Cognitive/Risk Era. The book highlights the importance of statistics in guiding, designing and delivering real-world solutions and helping to unravel the complex array of tradeoffs, uncertainties, inter-dependencies and unforeseen risks.

Biometry for Forestry and Environmental Data

Chapman & Hall/CRC Applied Environmental Series Series Editors Douglas Nychka, Colorado School of Mines Alexandra Schmidt, ... Analysis in Ecology Thorsten Wiegand, Kirk A. Moloney Introduction to Ecological Sampling Bryan F.J. Manly, ...

Author: Lauri Mehtatalo

Publisher: CRC Press

ISBN: 9780429530777

Category: Mathematics

Page: 421

View: 875

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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. Features: · Describes the theory and applications of selected statistical methods and illustrates their use and basic concepts through examples with forestry and environmental data in R. · Rigorous but easily accessible presentation of the linear, nonlinear, generalized linear and multivariate models, and their mixed-effects counterparts. Chapters on tree size, tree taper, measurement errors, and forest experiments are also included. · Necessary statistical theory about random variables, estimation and prediction is included. The wide applicability of the linear prediction theory is emphasized. · The hands-on examples with implementations using R make it easier for non-statisticians to understand the concepts and apply the methods with their own data. Lot of additional material is available at www.biombook.org. The book is aimed at students and researchers in forestry and environmental studies, but it will also be of interest to statisticians and researchers in other fields as well.

Handbook of Spatial Point Pattern Analysis in Ecology

APPLIED ENVIRONMENTAL STATISTICS University of North Carolina TATISTICS Series Editor ... Statistical Tools for Environmental Quality Timothy G. Gregoire and Harry T. Valentine, Sampling Strategies for Natural ... CHAPMAN & HALL/CRC.

Author: Thorsten Wiegand

Publisher: CRC Press

ISBN: 9781420082548

Category: Mathematics

Page: 542

View: 410

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Understand How to Analyze and Interpret Information in Ecological Point Patterns Although numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Ideal for empirical ecologists who want to avoid advanced theoretical literature, the book covers statistical techniques for analyzing and interpreting the information contained in ecological patterns. It presents methods used to extract information hidden in spatial point-pattern data that may point to the underlying processes. The authors focus on point processes and null models that have proven their immediate utility for broad ecological applications, such as cluster processes. Along with the techniques, the handbook provides a comprehensive selection of real-world examples. Most of the examples are analyzed using Programita, a continuously updated software package based on the authors’ many years of teaching and collaborative research in ecological point-pattern analysis. Programita is tailored to meet the needs of real-world applications in ecology. The software and a manual are available online.

Individual based Methods in Forest Ecology and Management

Ecol Lett 8:1191–1200 Diggle PJ (2014) Statistical analysis of spatial and spatio-temporal point patterns, 3rd edn. CRC Press ... Chapman & Hall/CRC, Boca Raton, 474 p Grimm V, Railsback SF (2005) Individual-based modeling and ecology.

Author: Arne Pommerening

Publisher: Springer Nature

ISBN: 9783030245283

Category: Technology & Engineering

Page: 411

View: 225

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Model-driven individual-based forest ecology and individual-based methods in forest management are of increasing importance in many parts of the world. For the first time this book integrates three main fields of forest ecology and management, i.e. tree/plant interactions, biometry of plant growth and human behaviour in forests. Individual-based forest ecology and management is an interdisciplinary research field with a focus on how the individual behaviour of plants contributes to the formation of spatial patterns that evolve through time. Key to this research is a strict bottom-up approach where the shaping and characteristics of plant communities are mostly the result of interactions between plants and between plants and humans. This book unites important methods of individual-based forest ecology and management from point process statistics, individual-based modelling, plant growth science and behavioural statistics. For ease of access, better understanding and transparency the methods are accompanied by R code and worked examples.