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This book presents a
comprehensive and up-to-date review and synthesis of concepts,
theories, methods and case studies in scaling and uncertainty analysis
in ecology and related fields. Various definitions and ideas
concerning scale are compared and contrasted in a coherent framework,
and two general scaling approaches, similarity-based scaling that is
rooted in the idea of similitude or self-similarity and dynamic
model-based scaling that emphasizes processes and mechanisms, are
discussed. The book is the first of its kind to explicitly
consider uncertainty and error analysis as an integral part of scaling.
The series of case studies included illustrate how scaling and
uncertainty analysis are being conducted in ecology and environmental
science, from population to ecosystem processes, from biodiversity to
landscape patterns, and from basic research to multidisciplinary
management and policy-making issues. While the theme of this book
focuses primarily on spatial scaling, several chapters deal as well
with aspects of temporal scaling. Although it is not intended to
be a handbook of “scaling recipes,” the book provides both examples and
a set of guidelines for scaling across heterogeneous ecological
systems. Overall, this book will help readers gain a fuller
understanding of the state-of-the-science of scale issues.
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