Intermediate Statistics for Ecology & Evolutionary Biology
A Practical Guide
Preface
Purpose and Approach
This book addresses a critical gap in ecological statistical education. Most existing texts on generalized linear models suffer from significant limitations: - Excessive mathematical complexity - Minimal practical guidance - Outdated computational approaches - Lack of comprehensive example workflows
Our approach is fundamentally different. This book is designed to: - Provide clear, conceptual understanding of statistical models - Teach practical skills for data analysis in R - Demonstrate complete analytical workflows from data import to model interpretation and visualization
Why This Matters
Statistical knowledge is only valuable when it can be effectively applied. Ecological researchers need more than theoretical understanding—they need the ability to: - Select appropriate statistical models - Clean and prepare data - Fit models correctly - Diagnose model performance - Interpret and communicate results - Create informative visualizations
What Makes This Book Different
- Concept-Driven Learning
- Focus on understanding the core purpose of each statistical model
- Explain when and why to use specific modeling approaches
- Minimize mathematical notation
- Prioritize intuitive explanations
- Comprehensive R Workflow
- Step-by-step guidance through entire analytical processes
- Real-world ecological datasets
- Complete code examples for each analysis
- Best practices for data manipulation, modeling, and visualization
- Use of modern R packages and techniques
- Practical Skill Development
- Learn to translate ecological questions into statistical models
- Develop critical thinking about data analysis
- Build reproducible research skills
- Understand model limitations and appropriate use cases
Target Audience
This book is for: - Graduate students in ecology and evolutionary biology - Researchers transitioning to more advanced statistical methods - Ecologists seeking to improve their data analysis skills - Conservation biologists - Wildlife managers
Computational Tools
We use R as our primary analysis environment, focusing on: - Tidyverse for data manipulation - Modern statistical modeling packages - Comprehensive visualization techniques - Reproducible research practices
Learning Objectives
By the end of this book, readers will be able to: - Understand the fundamental principles of generalized linear models - Choose appropriate statistical models for different data types - Implement complete analyses in R - Critically evaluate statistical models - Communicate statistical results effectively
Book Structure
The book follows a progressive approach, building statistical skills: 1. Logistic Regression 2. Poisson Regression 3. Negative Binomial Regression 4. Zero-Inflated Models 5. Beta Regression 6. Multinomial Regression 7. Mixed Models 8. Model Selection Techniques
Each chapter provides: - Conceptual introduction - Ecological context - Detailed R code examples - Model interpretation guidelines - Practical exercises
A Final Note
Statistical analysis is a skill learned through practice. This book is not about memorizing formulas, but about developing the ability to approach ecological data thoughtfully and analytically.