Scaling and uncertainty: in search of conceptual linkages and practical approaches

Abstract

The authors of Scaling and Uncertainty Analysis in Ecology have cast a broad net in reviewing and synthesizing these two all-encompassing topics. Scaling has become a major ecological issue in the fifteen years since Levin (1992) defined the problem of pattern and scale as a central theme in ecology, as evidenced by the number of books on this topic that have already been published (e.g., Schnieder 1994; Bissonette 1997; Peterson and Parker 1998; Waring and Running 1998; Gardner et al. 2001). Similarly, the need to quantify uncertainty has been the driving force behind the development and application of statistical methods in ecology, and uncertainty analysis has always been an important aspect of ecological simulation modeling. As with scaling, uncertainty is a pervasive theme in nearly all ecological research and has been the subject of recent books focusing on spatial uncertainty in ecology and natural resource management (Mowrer and Congalton 2000; Hunsaker et al. 2001). Scaling and Uncertainty Analysis in Ecology takes the unique perspective of addressing these two important topics simultaneously, providing an opportunity to consider how they are interlinked. The first section of the book consists of several background chapters describing concepts and methods related to scaling and uncertainty. Chapter 1 (Wu and Li) provides a useful framework for understanding the multitude of scale-related concepts and definitions based on a three-tiered model of dimensions, kinds, and components of scale. Chapter 2 (Wu and Li) similarly characterizes and classifies the different quantitative methods and models used in scaling research. The next two chapters provide background on the topic of uncertainty. Chapter 3 (Li and Wu) primarily addresses techniques for uncertainty analysis of ecological simulation models. Chapter 4 (Berk and de Leeuw) provides an overview of statistical approaches for analyzing hierarchical, multi-scale datasets (e.g. patches nested within landscapes) and spatially autocorrelated phenomena. Additional chapters in the concepts and methods section address downscaling species distribution patterns to estimate local abundance (Chapter 5, He and Reed), upscaling experimental results from model systems to real landscapes (Chapter 6, Bradford and Reynolds), and upscaling results from plot-level studies to heterogeneous landscapes (Chapter 7, Peters et al.). Finally, Chapter 8 (Wessman and Bateson) provide a thorough discussion of scale-related considerations in applying satellite remote sensing to measure earth-surface patterns and processes. The second section of Scaling and Uncertainty Analysis in Ecology provides several case studies that illustrate the myriad ways in which scaling techniques M. C. Wimberly (&) Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA e-mail: michael.wimberly@sdstate.edu