"Statistics for Biology and Health" is a series of books edited by M. Gail, K. Krickeberg, J. M. Samet, A. Tsiatis, and W. Wong. The series includes a variety of texts covering different aspects of statistical methods in biology and health, such as clinical research, animal abundance estimation, surrogate endpoints, frailty models, survival analysis, and more. One of the books in the series is "Mixed Effects Models and Extensions in Ecology with R," co-authored by Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith. This book provides an in-depth exploration of mixed effects models and their applications in ecological data analysis, with a focus on R programming and statistical methods. The book includes case studies, examples, and practical guidance for analyzing ecological data, particularly nested data, heterogeneity of variance, spatial and temporal correlation, and zero-inflated data. It also covers generalized linear models (GLMs), generalized additive models (GAMs), and Bayesian methods. The book is intended for researchers and students in ecology, environmental science, and related fields who are interested in statistical analysis of ecological data. The authors emphasize the importance of using appropriate statistical methods to analyze ecological data and provide practical examples and code for implementation in R. The book also includes a comprehensive list of references and an index for easy navigation. The authors thank their colleagues, students, and reviewers for their contributions to the book and acknowledge the support of various organizations and individuals who provided data for the case studies. The book is structured into 23 chapters, covering topics such as linear regression, additive modeling, heterogeneity, mixed effects modeling, violation of independence, exponential family distributions, GLMs and GAMs for count data, zero-truncated and zero-inflated models, generalized estimation equations, GLMM and GAMM, estimating trends for Antarctic birds, land-use change impacts, amphibian roadkills, deep-sea pelagic bioluminescent organisms, phytoplankton time series data, American foulbrood affecting honey bee larvae, age determination techniques for cetaceans, spatial distribution of koalas, and a comparison of GLM, GEE, and GLMM applied to badger activity data. The book also includes a section on required pre-knowledge for linear regression and additive modeling, as well as information theory and multi-model inference. The authors provide a detailed explanation of statistical methods and their applications in ecological research, with a focus on practical implementation using R. The book is a valuable resource for researchers and students in ecology, environmental science, and related fields who are interested in statistical analysis of ecological data."Statistics for Biology and Health" is a series of books edited by M. Gail, K. Krickeberg, J. M. Samet, A. Tsiatis, and W. Wong. The series includes a variety of texts covering different aspects of statistical methods in biology and health, such as clinical research, animal abundance estimation, surrogate endpoints, frailty models, survival analysis, and more. One of the books in the series is "Mixed Effects Models and Extensions in Ecology with R," co-authored by Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith. This book provides an in-depth exploration of mixed effects models and their applications in ecological data analysis, with a focus on R programming and statistical methods. The book includes case studies, examples, and practical guidance for analyzing ecological data, particularly nested data, heterogeneity of variance, spatial and temporal correlation, and zero-inflated data. It also covers generalized linear models (GLMs), generalized additive models (GAMs), and Bayesian methods. The book is intended for researchers and students in ecology, environmental science, and related fields who are interested in statistical analysis of ecological data. The authors emphasize the importance of using appropriate statistical methods to analyze ecological data and provide practical examples and code for implementation in R. The book also includes a comprehensive list of references and an index for easy navigation. The authors thank their colleagues, students, and reviewers for their contributions to the book and acknowledge the support of various organizations and individuals who provided data for the case studies. The book is structured into 23 chapters, covering topics such as linear regression, additive modeling, heterogeneity, mixed effects modeling, violation of independence, exponential family distributions, GLMs and GAMs for count data, zero-truncated and zero-inflated models, generalized estimation equations, GLMM and GAMM, estimating trends for Antarctic birds, land-use change impacts, amphibian roadkills, deep-sea pelagic bioluminescent organisms, phytoplankton time series data, American foulbrood affecting honey bee larvae, age determination techniques for cetaceans, spatial distribution of koalas, and a comparison of GLM, GEE, and GLMM applied to badger activity data. The book also includes a section on required pre-knowledge for linear regression and additive modeling, as well as information theory and multi-model inference. The authors provide a detailed explanation of statistical methods and their applications in ecological research, with a focus on practical implementation using R. The book is a valuable resource for researchers and students in ecology, environmental science, and related fields who are interested in statistical analysis of ecological data.