A Framework and Methods for Simplifying Complex Landscapes to Reduce Uncertainty in Predictions

TitleA Framework and Methods for Simplifying Complex Landscapes to Reduce Uncertainty in Predictions
Publication TypeBook Chapter
Year of Publication2006
AuthorsPeters DC, Yao J, Huenneke L., Havstad K, Herrick JE, Rango A., Schlesinger W.H.
Book TitleScaling and Uncertainty Analysis in Ecology: Methods and Applications
Pagination131-146
PublisherSpringer, Dordrecht
CityThe Netherlands
Accession NumberJRN00436
ARIS Log Number150094
Keywordsecosystems, linear, spatial temp oral heterogeneous
Abstract

Many of our most pressing ecological problems, such as the conservation of biodiversity, spread of invasive species, patterns in carbon sequestration, and impacts of disturbances (e.g., fire) must be addressed at the landscape scale (see Law et al., Chapter 9, Groffman et al., Chapter 10, Urban et al., Chapter 13). However, much of our information about these problems comes from plot-scale studies that must be extrapolated to the landscape. Because landscapes are complex, this extrapolation is not always straightforward or easy to accomplish (Turner et al. 1989a, Wu and Li, Chapter 2, Braford and Reynolds,Chapter 6). Landscape complexity results from the processes, factors, and their interactions that occur across a range of spatial and temporal scales. The problem is further complicated by the presence of contagious or neighborhood processes that connect different parts of a landscape. Dispersal of seeds by wind or animals, fire, and erosion and deposition of soil and nutrients by wind and water are examples of spatial or contagious processes that influence ecosystem dynamics. Landscape complexity makes it difficult to understand and predict ecosystem dynamics across spatial scales with high levels of confidence or certainty. Our goal is to develop a conceptual framework and operational approach to simplifying complex landscapes in order to minimize both prediction errors and costs associated with measurement, analysis, and prediction.

URL/files/bibliography/06-051.pdf
DOI10.1007/1-4020-4663-4_7