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Applied Hierarchical Modeling In Ecology
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Book Synopsis Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS by : Marc Kery
Download or read book Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS written by Marc Kery and published by Academic Press. This book was released on 2020-10-10 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS, Volume Two: Dynamic and Advanced Models provides a synthesis of the state-of-the-art in hierarchical models for plant and animal distribution, also focusing on the complex and more advanced models currently available. The book explains all procedures in the context of hierarchical models that represent a unified approach to ecological research, thus taking the reader from design, through data collection, and into analyses using a very powerful way of synthesizing data. Makes ecological modeling accessible to people who are struggling to use complex or advanced modeling programs Synthesizes current ecological models and explains how they are inter-connected Contains numerous examples throughout the book, walking the reading through scenarios with both real and simulated data Provides an ideal resource for ecologists working in R software and in BUGS software for more flexible Bayesian analyses
Book Synopsis Hierarchical Modeling and Inference in Ecology by : J. Andrew Royle
Download or read book Hierarchical Modeling and Inference in Ecology written by J. Andrew Royle and published by Elsevier. This book was released on 2008-10-15 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site
Book Synopsis Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS by : Marc Kéry
Download or read book Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS written by Marc Kéry and published by Academic Press. This book was released on 2015-11-14 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information
Book Synopsis Introduction to Hierarchical Bayesian Modeling for Ecological Data by : Eric Parent
Download or read book Introduction to Hierarchical Bayesian Modeling for Ecological Data written by Eric Parent and published by CRC Press. This book was released on 2012-08-21 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.
Book Synopsis Joint Species Distribution Modelling by : Otso Ovaskainen
Download or read book Joint Species Distribution Modelling written by Otso Ovaskainen and published by Cambridge University Press. This book was released on 2020-06-11 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Book Synopsis Introduction to WinBUGS for Ecologists by : Marc Kery
Download or read book Introduction to WinBUGS for Ecologists written by Marc Kery and published by Academic Press. This book was released on 2010-07-19 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)
Book Synopsis Hierarchical Modeling and Analysis for Spatial Data by : Sudipto Banerjee
Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
Book Synopsis Bayesian Population Analysis Using WinBUGS by : Marc Kéry
Download or read book Bayesian Population Analysis Using WinBUGS written by Marc Kéry and published by Academic Press. This book was released on 2012 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R
Book Synopsis Mathematical Modeling in Economics, Ecology and the Environment by : N.V. Hritonenko
Download or read book Mathematical Modeling in Economics, Ecology and the Environment written by N.V. Hritonenko and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problems of interrelation between human economics and natural environment include scientific, technical, economic, demographic, social, political and other aspects that are studied by scientists of many specialities. One of the important aspects in scientific study of environmental and ecological problems is the development of mathematical and computer tools for rational management of economics and environment. This book introduces a wide range of mathematical models in economics, ecology and environmental sciences to a general mathematical audience with no in-depth experience in this specific area. Areas covered are: controlled economic growth and technological development, world dynamics, environmental impact, resource extraction, air and water pollution propagation, ecological population dynamics and exploitation. A variety of known models are considered, from classical ones (Cobb Douglass production function, Leontief input-output analysis, Solow models of economic dynamics, Verhulst-Pearl and Lotka-Volterra models of population dynamics, and others) to the models of world dynamics and the models of water contamination propagation used after Chemobyl nuclear catastrophe. Special attention is given to modelling of hierarchical regional economic-ecological interaction and technological change in the context of environmental impact. Xlll XIV Construction of Mathematical Models ...
Book Synopsis Spatial Capture-Recapture by : J. Andrew Royle
Download or read book Spatial Capture-Recapture written by J. Andrew Royle and published by Academic Press. This book was released on 2013-08-27 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package. Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic Every methodological element has a detailed worked example with a code template, allowing you to learn by example Includes an R package that contains all computer code and data sets on companion website
Book Synopsis Introduction to Systems Ecology by : Sven Erik Jorgensen
Download or read book Introduction to Systems Ecology written by Sven Erik Jorgensen and published by CRC Press. This book was released on 2016-04-19 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Possibly the first textbook to present a practically applicable ecosystems theory, Introduction to Systems Ecology helps readers understand how ecosystems work and how they react to disturbances. It demonstrates—with many examples and illustrations—how to apply the theory to explain observations and to make quantitative calculations and predictions. In this book, Sven Erik Jørgensen takes a first step toward integrating thermodynamics, biochemistry, hierarchical organization, and network theory into a holistic theory of systems ecology. The first part of the book covers the laws of thermodynamics and the basic biochemistry of living organisms, as well as the constraints they impose on ecosystems. To grow and develop, however, ecosystems have to evade these thermodynamic and biochemical constraints, so the second part of the book discusses the seven basic properties that enable ecosystems to grow, develop, and survive: They are open systems, far from thermodynamic equilibrium. They are organized hierarchically. They have a high diversity. They have high buffer capacities toward changes. Their components are organized in cooperative networks, which allows for sophisticated feedback, regulation mechanisms, and higher efficiencies. They contain an enormous amount of information embodied in genomes. They have emerging system properties. This timely textbook also looks at how systems ecology is applied in integrated environmental management, particularly in ecological modeling and engineering and in the assessment of ecosystem health using ecological indicators. Acknowledging that there is still much room for improvement, it will inspire ecologists to develop a stronger and more widely applicable ecosystem theory.
Book Synopsis Mixed Effects Models and Extensions in Ecology with R by : Alain Zuur
Download or read book Mixed Effects Models and Extensions in Ecology with R written by Alain Zuur and published by Springer Science & Business Media. This book was released on 2009-03-05 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.
Book Synopsis Applied Hierarchical Modeling in Ecology by : Manuel Marton Marc Kéry
Download or read book Applied Hierarchical Modeling in Ecology written by Manuel Marton Marc Kéry and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Integrated Population Models by : Michael Schaub
Download or read book Integrated Population Models written by Michael Schaub and published by Academic Press. This book was released on 2021-11-12 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrated Population Models: Theory and Ecological Applications with R and JAGS is the first book on integrated population models, which constitute a powerful framework for combining multiple data sets from the population and the individual levels to estimate demographic parameters, and population size and trends. These models identify drivers of population dynamics and forecast the composition and trajectory of a population. Written by two population ecologists with expertise on integrated population modeling, this book provides a comprehensive synthesis of the relevant theory of integrated population models with an extensive overview of practical applications, using Bayesian methods by means of case studies. The book contains fully-documented, complete code for fitting all models in the free software, R and JAGS. It also includes all required code for pre- and post-model-fitting analysis. Integrated Population Models is an invaluable reference for researchers and practitioners involved in population analysis, and for graduate-level students in ecology, conservation biology, wildlife management, and related fields. The text is ideal for self-study and advanced graduate-level courses. Offers practical and accessible ecological applications of IPMs (integrated population models) Provides full documentation of analyzed code in the Bayesian framework Written and structured for an easy approach to the subject, especially for non-statisticians
Book Synopsis Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan by : Franzi Korner-Nievergelt
Download or read book Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan written by Franzi Korner-Nievergelt and published by Academic Press. This book was released on 2015-04-04 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco
Book Synopsis Bayesian Hierarchical Models by : Peter D. Congdon
Download or read book Bayesian Hierarchical Models written by Peter D. Congdon and published by CRC Press. This book was released on 2019-09-16 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Book Synopsis Data Analysis Using Regression and Multilevel/Hierarchical Models by : Andrew Gelman
Download or read book Data Analysis Using Regression and Multilevel/Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
