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Front Cover -- Integrated Population Models: Theory and Ecological Applications with R and JAGS -- Integrated Population Models: Theory and Ecological Applications with R and JAGS -- Copyright -- Contents -- Foreword -- Preface -- WHO SHOULD READ THIS BOOK? -- CONVENTIONS IN THIS BOOK -- COMPUTING -- THE IPMBOOK PACKAGE -- BOOK WEB PAGE -- Acknowledgments -- SPECIAL THANKS BY MICHAEL -- SPECIAL THANKS BY MARC -- LITERATURE CITED -- 1 - INTRODUCTION -- 1.1 POPULATION MODELING IN POPULATION ECOLOGY AND MANAGEMENT -- 1.2 THE TWO-STEP APPROACH TO POPULATION MODELING -- 1.3 INTEGRATED POPULATION MODELS -- 1.4 DEVELOPING INTEGRATED POPULATION MODELS WITH THE BUGS LANGUAGE -- 1.5 THIS BOOK -- 1.5.1 WHY THIS BOOK? -- 1.5.2 STRUCTURE AND OVERVIEW OF THIS BOOK -- 1.5.3 THE IMPORTANCE OF SIMULATION -- 1.5.4 USE OF THIS BOOK IN COURSES AND FOR TEACHING -- 1 - THEORY OF INTEGRATED POPULATION MODELS -- 2 - BAYESIAN STATISTICAL MODELING USING JAGS -- 2.1 INTRODUCTION -- 2.2 PARAMETRIC STATISTICAL MODELING -- 2.2.1 DESCRIPTION OF CHANCE PROCESSES IN PROBABILITY -- 2.2.2 PARAMETRIC STATISTICAL MODELS FOR INFERENCE ABOUT CHANCE PROCESSES -- 2.3 MAXIMUM LIKELIHOOD ESTIMATION IN A NUTSHELL -- 2.4 BAYESIAN INFERENCE -- 2.5 BAYESIAN COMPUTATION -- 2.6 BUGS SOFTWARE: WINBUGS, OPENBUGS, JAGS, AND NIMBLE -- 2.7 USING JAGS TO FIT SIMPLE STATISTICAL MODELS FROM R: GENERALIZED LINEAR AND GENERALIZED LINEAR MIXED MODELS -- 2.7.1 POISSON GENERALIZED LINEAR MODELS -- 2.7.2 BERNOULLI GENERALIZED LINEAR MODELS -- 2.7.3 BINOMIAL GENERALIZED LINEAR MODELS -- 2.7.4 MULTINOMIAL GENERALIZED LINEAR MODELS -- 2.7.5 CATEGORICAL GENERALIZED LINEAR MODELS -- 2.7.6 NORMAL LINEAR REGRESSION OR GAUSSIAN GENERALIZED LINEAR MODELS -- 2.7.7 GENERALIZED LINEAR MODELS WITH GAUSSIAN RANDOM EFFECTS -- 2.8 FITTING GENERAL INTEGRATED MODELS IN JAGS -- 2.9 WHY WE HAVE BECOME BAYESIANS….

Introduction -- Introduction to Bayesian analysis of a statistical model -- WinBUGS -- A first session in WinBUGS : the "model of the mean" -- Running WinBUGS from R via R2WinBUGS -- Key components of (generalized) linear models : statistical distributions and the linear predictor -- t-Test : equal and unequal variances -- Normal linear regression -- Normal one-way ANOVA -- Normal two-way ANOVA -- General linear model (ANCOVA) -- Linear mixed-effects model -- Introduction to the generalized linear model : Poisson "t-test" -- Overdispersion, zero-inflation, and offsets in the GLM -- Poisson ANCOVA -- Poisson mixed-effects model (Poisson GLMM) -- Binomial "t-Test" -- Binomial analysis of covariance -- Binomial mixed-effects model (binomial GLMM) -- Nonstandard GLMMs 1 : site-occupancy species distribution model -- Nonstandard GLMMs 2 : binomial mixture model to model abundance -- Conclusions

Front Cover -- Applied Statistical Modelling for Ecologists -- Copyright Page -- Dedication -- Contents -- Foreword -- Acknowledgments -- 1 Introduction -- 1.1 Statistical models and why you need them -- 1.2 Why linear statistical models? -- 1.3 Why go beyond the linear model? -- 1.4 Random effects and why you need them -- 1.5 Why do you need both Bayesian and non-Bayesian statistical inference? -- 1.6 More reasons for why you should really understand maximum likelihood -- 1.7 The data simulation/model fitting dualism -- 1.8 The "experimental approach" to statistics -- 1.9 The first principle of modeling: start simple! -- 1.10 Overview of the ASM book and additional resources -- 1.11 How to use the ASM book for self-study and in courses -- 1.12 Summary and outlook -- 2 Introduction to statistical inference -- 2.1 Probability as the basis for statistical inference -- 2.2 Random variables and their description by probability distributions -- 2.2.1 Discrete and continuous random variables -- 2.2.2 Description of random variables by probability distributions -- 2.2.3 Location and spread of a random variable, and what "modeling a parameter" means -- 2.2.4 Short summary on random variables and probability distributions -- 2.3 Statistical models and their usages -- 2.4 The likelihood function -- 2.5 Classical inference by maximum likelihood and its application to a single-parameter model -- 2.5.1 Introduction to maximum likelihood -- 2.5.2 Confidence intervals by inversion of the likelihood ratio test -- 2.5.3 Standard errors and confidence intervals based on approximate normality of the maximum likelihood estimates -- 2.5.4 Obtaining standard errors and confidence intervals using the bootstrap -- 2.5.5 A short summary on maximum likelihood estimation -- 2.6 Maximum likelihood estimation in a two-parameter model.

Front Cover -- Applied Statistical Modelling for Ecologists -- Copyright Page -- Dedication -- Contents -- Foreword -- Acknowledgments -- 1 Introduction -- 1.1 Statistical models and why you need them -- 1.2 Why linear statistical models? -- 1.3 Why go beyond the linear model? -- 1.4 Random effects and why you need them -- 1.5 Why do you need both Bayesian and non-Bayesian statistical inference? -- 1.6 More reasons for why you should really understand maximum likelihood -- 1.7 The data simulation/model fitting dualism -- 1.8 The "experimental approach" to statistics -- 1.9 The first principle of modeling: start simple! -- 1.10 Overview of the ASM book and additional resources -- 1.11 How to use the ASM book for self-study and in courses -- 1.12 Summary and outlook -- 2 Introduction to statistical inference -- 2.1 Probability as the basis for statistical inference -- 2.2 Random variables and their description by probability distributions -- 2.2.1 Discrete and continuous random variables -- 2.2.2 Description of random variables by probability distributions -- 2.2.3 Location and spread of a random variable, and what "modeling a parameter" means -- 2.2.4 Short summary on random variables and probability distributions -- 2.3 Statistical models and their usages -- 2.4 The likelihood function -- 2.5 Classical inference by maximum likelihood and its application to a single-parameter model -- 2.5.1 Introduction to maximum likelihood -- 2.5.2 Confidence intervals by inversion of the likelihood ratio test -- 2.5.3 Standard errors and confidence intervals based on approximate normality of the maximum likelihood estimates -- 2.5.4 Obtaining standard errors and confidence intervals using the bootstrap -- 2.5.5 A short summary on maximum likelihood estimation -- 2.6 Maximum likelihood estimation in a two-parameter model.

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