Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB - Softcover

Kéry; Kellner

Softcover

ISBN 10: 0443137153 ISBN 13: 9780443137150

Publisher: Elsevier, 2024

Synopsis

**2025 PROSE Award Finalist in Environmental Science**

Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.

This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.

The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.

• Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences
• Covers the foundations of modern applied statistical modelling
• Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB
• Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference
• Details the principles of model building, model checking and model selection
• Adopts a “Rosetta Stone” approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines
• Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150

About this title

About the Authors

Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences.

Dr. Ken Kellner is an Assistant Research Professor at Michigan State University, MI, United States. Prior to his current position, he completed a Ph.D. in forest ecology at Purdue University, IN, United States, and a postdoc at West Virginia University, WV, United States. Ken's research has covered a wide range of topics including forest management, plant demography, and avian and mammal conservation. He has published this research in more than 40 peer reviewed publications. In addition, Ken is particularly focused on the development of open-source software tools for ecological modeling. He has developed or contributed to several software packages that are widely used by ecologists and featured in several books, including the successful R packages jagsUI, unmarked, and ubms.

From the Back Cover

Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.

This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.

The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.

Edition details

• Publisher: Elsevier
• Publication date: 2024
• ISBN 10: 0443137153
• ISBN 13: 9780443137150
• Binding: Paperback
• Language: English
• Number of pages: 550