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United States Department of Agriculture

Agricultural Research Service

Title: Simple and Weighted Averaging Approaches to Scaling: When Can Spatial Context Be Ignored?

Authors
item Lane, Diane - UNIVERSITY OF ILLINOIS
item Bestelmeyer, Brandon
item Parton, Bill - COLORADO STATE UNIV
item Mitchell, Katherine - NEW MEXICO STATE UNIV

Submitted to: US-International Association for Landscape Ecology
Publication Type: Abstract Only
Publication Acceptance Date: April 23, 2002
Publication Date: April 23, 2002
Citation: LANE, D.R., BESTELMEYER, B.T., PARTON, B., MITCHELL, K. SIMPLE AND WEIGHTED AVERAGING APPROACHES TO SCALING: WHEN CAN SPATIAL CONTEXT BE IGNORED?. US-INTERNATIONAL ASSOCIATION FOR LANDSCAPE ECOLOGY. 2002. ABSTRACT P. 82.

Technical Abstract: Scaling from plots to landscapes based on simple or weighted averaging techniques can be powerful and accurate when applied to the appropriate problems. Simple averaging approaches are appropriate when conditions are homogeneous through space and constant through time. Modeling of forest or grassland stands by averaging results from multiple small plots is an example of a simple averaging approach. The assumption of homogeneity is often violated, however, when scaling up to landscapes with topographic or edaphic variability. Weighted averaging approaches can address this variability by simulating the landscape as a collection of discrete patches. For example, estimates of carbon or nitrogen flux across a large region from the CENTURY model are based on a weighted averaging of unique input polygons with distinct combinations of soils and climates. Weighted averaging assumes that the spatial context of patches across a landscape does not influence conditions within a patch. In animal ecology, for example, weighted averaging approaches assume that limitations to patch perception, selection, or dispersal are unimportant over the scale of interest. This paper will draw on examples from plant, animal, and ecosystem ecology to identify when spatial extrapolation can be satisfied by simple or weighted averaging approaches.

Last Modified: 7/31/2014
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