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Applied Hierarchical Modeling in Ecology Analysis of Distribution Abundance and Species Richness in R and BUGS

Applied Hierarchical Modeling in Ecology  Analysis of Distribution  Abundance and Species Richness in R and BUGS
  • Author : Marc Kery
  • Publsiher : Academic Press
  • Release : 10 October 2020
  • ISBN : 0128097272
  • Pages : 820 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKApplied Hierarchical Modeling in Ecology Analysis of Distribution Abundance and Species Richness in R and BUGS

Summary:
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 for people who are struggling to use complex or advanced modeling programs Synthesizes current ecological models and explains how they are inter-connected Contains examples throughout the book, walking the reading through scenarios with both real and simulated data Presents an ideal resource for ecologists working in R, an open source version of S known for its exceptional ecology analyses, and in BUGS for more flexible Bayesian analyses


Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS

Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS
  • Author : Marc Kery,J. Andrew Royle
  • Publisher : Academic Press
  • Release : 10 October 2020
GET THIS BOOKApplied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS

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


Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
  • Author : J. Andrew Royle,Robert M. Dorazio
  • Publisher : Elsevier
  • Release : 15 October 2008
GET THIS BOOKHierarchical Modeling and Inference in Ecology

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


Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data
  • Author : Eric Parent,Etienne Rivot
  • Publisher : CRC Press
  • Release : 21 August 2012
GET THIS BOOKIntroduction to Hierarchical Bayesian Modeling for Ecological Data

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


Bayesian Population Analysis Using WinBUGS

Bayesian Population Analysis Using WinBUGS
  • Author : Marc Kéry,Michael Schaub
  • Publisher : Academic Press
  • Release : 07 March 2021
GET THIS BOOKBayesian Population Analysis Using WinBUGS

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


Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan
  • Author : Franzi Korner-Nievergelt,Tobias Roth,Stefanie von Felten,Jérôme Guélat,Bettina Almasi,Pius Korner-Nievergelt
  • Publisher : Academic Press
  • Release : 04 April 2015
GET THIS BOOKBayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

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


Hierarchical Modeling and Analysis for Spatial Data

Hierarchical Modeling and Analysis for Spatial Data
  • Author : Sudipto Banerjee
  • Publisher : CRC Press
  • Release : 17 December 2003
GET THIS BOOKHierarchical Modeling and Analysis for Spatial Data

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,



Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists
  • Author : Marc Kery
  • Publisher : Academic Press
  • Release : 19 July 2010
GET THIS BOOKIntroduction to WinBUGS for Ecologists

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


Mixed Effects Models and Extensions in Ecology with R

Mixed Effects Models and Extensions in Ecology with R
  • Author : Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith
  • Publisher : Springer Science & Business Media
  • Release : 05 March 2009
GET THIS BOOKMixed Effects Models and Extensions in Ecology with R

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.


Spatial Capture-Recapture

Spatial Capture-Recapture
  • Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
  • Publisher : Academic Press
  • Release : 27 August 2013
GET THIS BOOKSpatial Capture-Recapture

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



Spatial Ecology and Conservation Modeling

Spatial Ecology and Conservation Modeling
  • Author : Robert Fletcher,Marie-Josée Fortin
  • Publisher : Springer
  • Release : 15 February 2019
GET THIS BOOKSpatial Ecology and Conservation Modeling

This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation.


Ecological Models and Data in R

Ecological Models and Data in R
  • Author : Benjamin M. Bolker
  • Publisher : Princeton University Press
  • Release : 01 July 2008
GET THIS BOOKEcological Models and Data in R

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate


Hierarchical Linear Models

Hierarchical Linear Models
  • Author : Stephen W. Raudenbush,Anthony S. Bryk
  • Publisher : SAGE
  • Release : 07 March 2021
GET THIS BOOKHierarchical Linear Models

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: * An intuitive introductory summary of the basic


Handbook of Discrete-Valued Time Series

Handbook of Discrete-Valued Time Series
  • Author : Richard A. Davis,Scott H. Holan,Robert Lund,Nalini Ravishanker
  • Publisher : CRC Press
  • Release : 06 January 2016
GET THIS BOOKHandbook of Discrete-Valued Time Series

Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca