Perspectives and methods of scaling

Jianguo Wu, Harbin Li

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Scopus citations

Abstract

Transferring information between scales or organizational levels is generally referred to as "scaling" (Wu and Li, Chapter 1), and is inevitable in both basic research and its applications. Scaling is the essence of prediction and understanding both of which require cross-scale translation of information, and is at the core of ecological theory and application (Levin 1992, Levin and Pacala 1997, Wu 1999). While the importance of scaling in ecology has been acutely recognized in recent decades, how to conduct scaling across heterogeneous ecosystems remains a grand challenge (Turner et al. 1989, Wu and Hobbs 2002). A number of scaling approaches and methods have been developed and applied in different disciplines ranging from physics, engineering, biology, to social sciences. Two general scaling approaches can be distinguished: similarity-based scaling and dynamic model-based scaling methods (Bl?schl and Sivapalan 1995). Similarity-based scaling methods are rooted in the concepts and principles of While the previous chapter (Wu and Li, Chapter 1) discussed various concepts of scale and scaling, in this chapter we focus on the major characteristics of the two scaling approaches and several more specific upscaling and downscaling methods similarity and self-similarity and often characterized by relatively simple mathematical or statistical scaling functions, even though the underlying ecological processes of a phenomenon may be extremely complex. In contrast, dynamic model-based scaling methods use deterministic or stochastic models to simulate the processes of interest, and to transfer information across scales by either modifying the parameters and input variables of the same model or developing multiple-scaled models. In this case, information transfer between different scales is accomplished through manipulating the inputs, outputs, and formulations of dynamic models. In both approaches, it is important to properly identify scaling thresholds at which scaling relations often change abruptly, reflecting fundamental shifts in underlying processes or controlling factors and defining the domains of applicability of specific scaling methods. While the previous chapter (Wu and Li, Chapter 1) discussed various concepts of scale and scaling, in this chapter we focus on the major characteristics of the two scaling approaches and several more specific upscaling and downscaling methods within each approach. The purpose of this chapter is not to provide a recipe for scaling. Rather, we shall review scaling perspectives and methods in different disciplines, and provide a synthesis based on a common conceptual framework. By so doing, we expect that a more comprehensive and cohesive understanding of ecological scaling will emerge.

Original languageEnglish (US)
Title of host publicationScaling and Uncertainty Analysis in Ecology
Subtitle of host publicationMethods and Applications
PublisherSpringer Netherlands
Pages17-44
Number of pages28
ISBN (Print)1402046642, 9781402046629
DOIs
StatePublished - Dec 1 2006

ASJC Scopus subject areas

  • Environmental Science(all)

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