Presenting the current state of the art in scaling and uncertainty in ecology, Wu et al's Scaling and Uncertainty Analysis in Ecology is the first book of its kind — explicitly considering uncertainty and error analysis as an integral part of scaling.

Primarily, the book draws together a series of important case studies to provide a comprehensive review and synthesis of the most recent concepts, theories and methods in scaling and uncertainty analysis. It compares current definitions and ideas concerning scale within a coherent framework, and examines two key scaling approaches: similarity-based scaling, which is rooted in the idea of similitude or self-similarity; and dynamic model-based scaling, which emphasizes processes and mechanisms.

With case studies focusing on issues ranging from population to ecosystem processes; from biodiversity to landscape patterns; and from basic research to multidisciplinary management and policy-making, the book will appeal to both researchers and practitioners working on landscape issues. It will also provide a valuable resource for graduate students and professional trainees in ecology, environmental policy, resource management and global change science.