Andrew O. Finley

Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R. Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skills – data wrangling, visualization, and reproducible workflows – while the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis. Key Features: · Introduces R and the tidyverse using forestry-specific datasets and management questions · Develops reproducible workflows for data import, cleaning, transformation, visualization, and reporting · Presents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage sampling · Implements classical estimators using transparent, script-based computing rather than black-box software · Integrates spatial data handling and mapping for areal sampling frames and field-based applications · Emphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventories Introduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis.

Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R. Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skills – data wrangling, visualization, and reproducible workflows – while the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis. Key Features: · Introduces R and the tidyverse using forestry-specific datasets and management questions · Develops reproducible workflows for data import, cleaning, transformation, visualization, and reporting · Presents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage sampling · Implements classical estimators using transparent, script-based computing rather than black-box software · Integrates spatial data handling and mapping for areal sampling frames and field-based applications · Emphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventories Introduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis.

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated.This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference.Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated.This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference.Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.