By Jianguo Wu, K. Bruce Jones, Harbin Li, Orie L. Loucks
Presenting the present cutting-edge in scaling and uncertainty in ecology, Wu et al’s Scaling and Uncertainty research in Ecology is the 1st publication of its sort – explicitly contemplating uncertainty and mistake research as a vital part of scaling.
Primarily, the publication attracts jointly a sequence of vital case stories to supply a accomplished evaluate and synthesis of the newest thoughts, theories and strategies in scaling and uncertainty research. It compares present definitions and concepts relating scale inside of a coherent framework, and examines key scaling techniques: similarity-based scaling, that's rooted within the thought of similitude or self-similarity; and dynamic model-based scaling, which emphasizes techniques and mechanisms.
With case experiences concentrating on concerns starting from inhabitants to atmosphere methods; from biodiversity to panorama styles; and from easy examine to multidisciplinary administration and policy-making, the booklet will entice either researchers and practitioners engaged on panorama matters. it is going to additionally supply a worthy source for graduate scholars trainees in ecology, environmental coverage, source administration and international switch science.
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Additional resources for SCALING AND UNCERTAINTY ANALYSIS IN ECOLOGY
WU AND H. LI 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. 1 The Concept of Similarity The concept of similarity has been essential in scaling-related studies. In general, similarity exists between two systems whenever they share some properties that can be related across the systems by a simple conversion factor (Blöschl and Sivapalan 1995).
Thus, extrapolation by lumping uses the same local model at the target scale, with highly aggregated values of parameters and inputs. Simply put, lumping is about averaging over space. If grain elements are of the same size, arithmetic averaging is usually used. But if grain elements are of different sizes, area-weighted averaging should be used, instead. The local model is defined at the scale of a grain element or patch in the case of increasing extent, and at the size of the smaller grain element whose aggregates form the larger grain element in the case of coarse-graining.
O’Neill et al. 1991, Li and Wu 2004, Hay et al. 2001, 2003, Hall et al. 2004, Wu 2004). As emphasized in Wu and Loucks (1995) and Wu (1999), these spatial hierarchies should be constructed with consideration of both pattern and process, such that they are neither completely arbitrary, nor simply products of spatial analyses. 2). This can be accomplished by changing grain, extent, or both. 2. Illustration of the scaling ladder approach in which scaling up (or down) is implemented by changing model grain size, extent, or both across successive domains of scale (redrawn from Wu 1999).
SCALING AND UNCERTAINTY ANALYSIS IN ECOLOGY by Jianguo Wu, K. Bruce Jones, Harbin Li, Orie L. Loucks
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