May 9, 2021

Download Ebook Free Hierarchical Modeling And Inference In Ecology

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
Author : J. Andrew Royle,Robert M. Dorazio
Publisher : Elsevier
Release Date : 2008-10-15
Category : Science
Total pages :464
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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

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 Date : 2020-10-10
Category : Nature
Total pages :820
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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

Models of the Ecological Hierarchy

Models of the Ecological Hierarchy
Author : Anonim
Publisher : Newnes
Release Date : 2012-12-31
Category : Science
Total pages :594
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In the application of statistics to ecological inference problems, hierarchical models combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are applied in this book to a wide range of problems ranging from the molecular level, through populations, ecosystems, landscapes, networks, through to the global ecosphere. Provides an excellent introduction to modelling Collects together in one source a wide range of modelling techniques Covers a wide range of topics, from the molecular level to the global ecosphere

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 Date : 2012-08-21
Category : Mathematics
Total pages :427
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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 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.

Bayesian Inference

Bayesian Inference
Author : William A Link,Richard J Barker
Publisher : Academic Press
Release Date : 2009-08-07
Category : Science
Total pages :354
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This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analytical software and examples Leading authors with world-class reputations in ecology and biostatistics

Hierarchical Modelling for the Environmental Sciences

Hierarchical Modelling for the Environmental Sciences
Author : James S. Clark,Alan E. Gelfand
Publisher : OUP Oxford
Release Date : 2006-05-04
Category : Science
Total pages :216
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New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.

Occupancy Estimation and Modeling

Occupancy Estimation and Modeling
Author : Darryl I. MacKenzie,James D. Nichols,J. Andrew Royle,Kenneth H. Pollock,Larissa Bailey,James E. Hines
Publisher : Elsevier
Release Date : 2017-11-17
Category : Science
Total pages :648
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Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. Provides authoritative insights into the latest in occupancy modeling Examines the latest methods in analyzing detection/no detection data surveys Addresses critical issues of imperfect detectability and its effects on species occurrence estimation Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation

Spatial Dynamics and Ecology of Large Ungulate Populations in Tropical Forests of India

Spatial Dynamics and Ecology of Large Ungulate Populations in Tropical Forests of India
Author : N. Samba Kumar
Publisher : Springer Nature
Release Date : 2021
Category :
Total pages :129
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Bayesian Disease Mapping

Bayesian Disease Mapping
Author : Andrew B. Lawson
Publisher : CRC Press
Release Date : 2008-08-05
Category : Mathematics
Total pages :368
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Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and

Bayesian Models

Bayesian Models
Author : N. Thompson Hobbs,Mevin B. Hooten
Publisher : Princeton University Press
Release Date : 2015-08-04
Category : Science
Total pages :320
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Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models

Spatial Capture-Recapture

Spatial Capture-Recapture
Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
Publisher : Academic Press
Release Date : 2013-08-27
Category : Science
Total pages :612
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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

A Robust-design Formulation of the Incidence Function Model of Metapopulation Dynamics Applied to Two Species of Rails

A Robust-design Formulation of the Incidence Function Model of Metapopulation Dynamics Applied to Two Species of Rails
Author : Benjamin Brewster Risk
Publisher : Unknown
Release Date : 2009
Category :
Total pages :116
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Community Ecology

Community Ecology
Author : Anonim
Publisher : Unknown
Release Date : 2008
Category : Botany
Total pages :129
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Journal of the American Statistical Association

Journal of the American Statistical Association
Author : Anonim
Publisher : Unknown
Release Date : 2008
Category : Statistics
Total pages :129
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Bayesian Hierarchical Models to Untangle Complex Evolutionary Histories

Bayesian Hierarchical Models to Untangle Complex Evolutionary Histories
Author : Erik William Bloomquist
Publisher : Unknown
Release Date : 2009
Category :
Total pages :350
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