Recent advances in machine learning and predictive analytics have transformed our ability to identify patterns and forecast outcomes from complex data sets. Yet the central goal of much ecological and wildlife research remains explanation rather than prediction. Researchers seek to understand the processes that drive population dynamics, species distributions, behavior, and ecosystem change. Such questions require statistical methods that not only describe patterns, but also support inference about underlying mechanisms while explicitly accounting for uncertainty. 

Applied Statistics for Wildlife Biologists provides a clear and conceptually grounded introduction to statistical reasoning for students, researchers, and professionals in wildlife biology, ecology, and conservation science. Rather than focusing on mathematical formalism or procedural recipes, it emphasizes intuition, interpretation, and the logic of statistical inference – how we learn from data, quantify uncertainty, and draw conclusions. Core ideas such as sampling distributions, standard errors, confidence intervals, and statistical testing are introduced gradually and linked to real-world ecological questions, within a broader philosophy of science perspective. From t-tests to generalized linear models, the book shows how commonly used methods emerge from shared principles. Particular attention is given to study design, model interpretation, assumptions, and the conditions under which statistical conclusions are valid. R is used as a practical tool to implement models and visualize results, supporting the learning process without overshadowing the underlying concepts. Written from the perspective of a practicing wildlife biologist, the book aims to bridge the gap between statistical theory and applied ecological research, equipping readers with both practical tools and the mindset needed to make sound analytical decisions and interpret results with care.

Key features
•    Conceptually grounded introduction to statistical reasoning for wildlife biologists, ecologists, and    conservation scientists
•    Strong emphasis on sampling distributions, standard errors, confidence intervals, statistical testing and inference
•    Methods presented as a coherent framework, from t-tests and ANOVA to linear and generalized linear models
•    Realistic examples and case studies drawn from wildlife biology, ecology, and conservation research
•    Clear guidance on model interpretation, study design, assumptions, and the validity of statistical conclusions
•    Integration of R for data analysis, model fitting, visualization, and reproducible workflows
•    Written by a wildlife biologist for wildlife biologists, with a focus on practical research questions
•    Emphasis on developing statistical judgment rather than simply applying procedures

Luca Corlatti is a Senior Wildlife Ecologist at the Stelvio National Park, Italy, and an Affiliate Professor of Wildlife Ecology at the Albert Ludwig University of Freiburg, Germany. He also serves as Adjunct Professor at the University of Milan, Italy. He obtained an MSc in Forest and Environmental Sciences from the University of Padua, Italy, in 2006. After research experiences in Italy, Denmark, Sweden, Austria, and Scotland, he earned a PhD in Evolutionary Biology from the University of Siena, Italy, in 2013. In 2016, he was awarded a Marie Skłodowska-Curie Postdoctoral Fellowship in Behavioral Ecology at the Freiburg Institute for Advanced Studies, Germany. Between 2017 and 2021, he held a postdoctoral position with the Chair of Wildlife Ecology and Management at the University of Freiburg. He is Associate Editor of the journals Mammalian Biology and Wildlife Biology and a member of the Caprinae Specialist Group of the International Union for Conservation of Nature (IUCN). His research interests focus primarily on the ecology, behavior, and management of mountain-dwelling mammals.