Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
Author: J. Andrew Royle,Robert M. Dorazio
Publsiher: Academic Press
Total Pages: 444
Release: 2008
ISBN 10: 9780123740977
ISBN 13: 0123740975
Language: EN, FR, DE, ES & NL

Hierarchical Modeling and Inference in Ecology Book Review:

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

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
Author: J. Andrew Royle,Robert M. Dorazio
Publsiher: Elsevier
Total Pages: 464
Release: 2008-10-15
ISBN 10: 0080559255
ISBN 13: 9780080559254
Language: EN, FR, DE, ES & NL

Hierarchical Modeling and Inference in Ecology Book Review:

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
Publsiher: Academic Press
Total Pages: 808
Release: 2015-11-14
ISBN 10: 0128014865
ISBN 13: 9780128014868
Language: EN, FR, DE, ES & NL

Applied Hierarchical Modeling in Ecology Analysis of distribution abundance and species richness in R and BUGS Book Review:

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

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data
Author: Eric Parent,Etienne Rivot
Publsiher: CRC Press
Total Pages: 427
Release: 2012-08-21
ISBN 10: 1584889195
ISBN 13: 9781584889199
Language: EN, FR, DE, ES & NL

Introduction to Hierarchical Bayesian Modeling for Ecological Data Book Review:

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 Models

Bayesian Models
Author: N. Thompson Hobbs,Mevin B. Hooten
Publsiher: Princeton University Press
Total Pages: 320
Release: 2015-08-04
ISBN 10: 1400866553
ISBN 13: 9781400866557
Language: EN, FR, DE, ES & NL

Bayesian Models Book Review:

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

Models of the Ecological Hierarchy

Models of the Ecological Hierarchy
Author: Ferenc Jordán,Sven Erik Jorgensen
Publsiher: Newnes
Total Pages: 562
Release: 2012
ISBN 10: 0444593969
ISBN 13: 9780444593962
Language: EN, FR, DE, ES & NL

Models of the Ecological Hierarchy Book Review:

"Based on selected papers covering the presentations at the 7th European Conference on Ecological Modelling, organized by ISEM and hosted by The Microsoft Research--University of Trento Center for Computational and Systems Biology from 30 May to 2 June, 2011 in Riva del Garde, Italy"--P. xi.

Hierarchical Modeling and Analysis for Spatial Data

Hierarchical Modeling and Analysis for Spatial Data
Author: Sudipto Banerjee
Publsiher: CRC Press
Total Pages: 472
Release: 2003-12-17
ISBN 10: 020348780X
ISBN 13: 9780203487808
Language: EN, FR, DE, ES & NL

Hierarchical Modeling and Analysis for Spatial Data Book Review:

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,

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
Publsiher: Academic Press
Total Pages: 820
Release: 2020-10-10
ISBN 10: 0128097272
ISBN 13: 9780128097274
Language: EN, FR, DE, ES & NL

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

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

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
Publsiher: Academic Press
Total Pages: 328
Release: 2015-04-04
ISBN 10: 0128016787
ISBN 13: 9780128016787
Language: EN, FR, DE, ES & NL

Bayesian Data Analysis in Ecology Using Linear Models with R BUGS and Stan Book Review:

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

Bayesian Disease Mapping

Bayesian Disease Mapping
Author: Andrew B. Lawson
Publsiher: CRC Press
Total Pages: 464
Release: 2018-05-20
ISBN 10: 1351271741
ISBN 13: 9781351271745
Language: EN, FR, DE, ES & NL

Bayesian Disease Mapping Book Review:

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.

Bayesian Inference

Bayesian Inference
Author: William A Link,Richard J Barker
Publsiher: Academic Press
Total Pages: 354
Release: 2009-08-07
ISBN 10: 0080889808
ISBN 13: 9780080889801
Language: EN, FR, DE, ES & NL

Bayesian Inference Book Review:

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 Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
Author: J. Andrew Royle
Publsiher: Unknown
Total Pages: 444
Release: 2008
ISBN 10:
ISBN 13: OCLC:317254441
Language: EN, FR, DE, ES & NL

Hierarchical Modeling and Inference in Ecology Book Review:

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
Publsiher: Elsevier
Total Pages: 648
Release: 2017-11-17
ISBN 10: 0124072453
ISBN 13: 9780124072459
Language: EN, FR, DE, ES & NL

Occupancy Estimation and Modeling Book Review:

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

Ecological Inference

Ecological Inference
Author: Gary King,Martin A. Tanner,Ori Rosen
Publsiher: Cambridge University Press
Total Pages: 421
Release: 2004-09-13
ISBN 10: 9780521542807
ISBN 13: 0521542804
Language: EN, FR, DE, ES & NL

Ecological Inference Book Review:

Publisher Description

Ecological Models and Data in R

Ecological Models and Data in R
Author: Benjamin M. Bolker
Publsiher: Princeton University Press
Total Pages: 396
Release: 2008-07-21
ISBN 10: 0691125228
ISBN 13: 9780691125220
Language: EN, FR, DE, ES & NL

Ecological Models and Data in R Book Review:

Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

Data Analysis Using Regression and Multilevel Hierarchical Models

Data Analysis Using Regression and Multilevel Hierarchical Models
Author: Andrew Gelman,Professor in the Department of Statistics Andrew Gelman,Jennifer Hill
Publsiher: Cambridge University Press
Total Pages: 625
Release: 2007
ISBN 10: 9780521686891
ISBN 13: 052168689X
Language: EN, FR, DE, ES & NL

Data Analysis Using Regression and Multilevel Hierarchical Models Book Review:

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Spatial Capture Recapture

Spatial Capture Recapture
Author: J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
Publsiher: Academic Press
Total Pages: 612
Release: 2013-08-27
ISBN 10: 012407152X
ISBN 13: 9780124071520
Language: EN, FR, DE, ES & NL

Spatial Capture Recapture Book Review:

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

Joint Species Distribution Modelling

Joint Species Distribution Modelling
Author: Otso Ovaskainen,Nerea Abrego
Publsiher: Cambridge University Press
Total Pages: 371
Release: 2020-04-30
ISBN 10: 1108492460
ISBN 13: 9781108492461
Language: EN, FR, DE, ES & NL

Joint Species Distribution Modelling Book Review:

A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.

Hierarchical Modelling for the Environmental Sciences

Hierarchical Modelling for the Environmental Sciences
Author: James Samuel Clark,Alan E. Gelfand
Publsiher: Oxford University Press on Demand
Total Pages: 205
Release: 2006
ISBN 10: 019856967X
ISBN 13: 9780198569671
Language: EN, FR, DE, ES & NL

Hierarchical Modelling for the Environmental Sciences Book Review:

New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.

Bayesian Analysis for Population Ecology

Bayesian Analysis for Population Ecology
Author: Ruth King,Byron Morgan,Olivier Gimenez,Steve Brooks
Publsiher: CRC Press
Total Pages: 456
Release: 2009-10-30
ISBN 10: 9781439811887
ISBN 13: 1439811881
Language: EN, FR, DE, ES & NL

Bayesian Analysis for Population Ecology Book Review:

Novel Statistical Tools for Conserving and Managing PopulationsBy gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space mode