|Yao, Jin - NEW MEXICO STATE UNIV|
|Huenneke, Laura - NEW MEXICO STATE UNIV|
|Schlesinger, William - DUKE UNIVERSITY|
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: June 2, 2003
Publication Date: March 6, 2006
Citation: Peters, D.P.C., Yao, J., Huenneke, L.F., Havstad, K.M., Herrick, J.E., Rango, A., Schlesinger, W.H. 2006. A framework and methods for simplifying complex landscapes to reduce uncertainty in predictions. In: Wu, J., Jones, B., Li, H., Loucks, O.L., editors. Scaling and Uncertainty Analysis in Ecology: Methods and Applications. The Netherlands: Springer, Dordrecht. p. 131-146. Interpretive Summary: Extrapolation of information from small to large areas is particularly difficult in spatially and temporally variable ecosystems. Because these ecosystems consist of a mosaic of sites differing in spatial variability and degree of connections, we expect that a combination of extrapolation approaches is needed. In this paper, we develop a conceptual framework and operational approach to simplifying complex landscapes in order to minimize error in predictions. We illustrate our approach for arid and semiarid landscapes at the Jornada Experimental Range using variation in aboveground net primary production.
Technical Abstract: Extrapolation of information from sites to landscapes or regions is especially problematic in spatially and temporally heterogeneous ecosystems. Although linear extrapolations are the easiest and most cost-effective, other approaches are necessary when spatial location and contagious or neighborhood processes are important. Because landscape and regions consist of a mosaic of sites differing in spatial heterogeneity and degree of connectedness, we expect a combination of scaling approaches is needed to characterize these areas. Our goal was to develop a conceptual framework and operational approach to simplifying complex landscapes in order to minimize uncertainty in predictions. We illustrate our approach for arid and semiarid landscapes where spatial variation in carbon dynamics, in particular aboveground net primary production, is a timely and important problem.