To understand, manage, and assess the effects of change on the Earth's ecosystems requires a troika of measurement, translation, and prediction of patterns with changes in scale. This problem is familiar to ecologists: Pielou (1969) discussed issues of spatial sampling and data analysis nearly 40 years ago; O'Neill (1979) explored the transmutation of information across levels of complexity; Allen and Starr (1982) provided new perspectives from hierarchy theory; and Levin (1992) galvanized interest with his 1989 Robert H. MacArthur Award Lecture to the Ecological Society of America. The unresolved issues revolve around the complex response of ecosystems to change; the insufficiency of measurements in time and space; and the diversity of approaches now being employed by geographers, geologists, atmospheric scientists, and ecologists.
Scaling and Uncertainty Analysis in Ecology, an edited volume resulting from a workshop specifically designed to address these issues, has been ably produced by Jianguo Wu, Bruce Jones, Harbin Li, and Orie Loucks. These four editors have a broad range of backgrounds and experience in the relevant theory and practice. Their stated goals were to review and make sense of the many approaches to scale transformation and prediction; to address the effects of uncertainties on this process; and to provide a synthesis useful for management, planning, and decisionmaking. The text achieves the first two objectives with a thorough review and careful definition of terms, but the final objective—to make theory and methods useful and accessible to a broad audience—remains tantalizingly out of reach.
The 18 chapters of this book are arranged into three sections. The first section reviews concepts and defines terms. Chapters 1 and 2, by Wu and Li, are well-constructed overviews with clear expositions of issues concerning extrapolation across scale. Those unfamiliar with this subject will appreciate these chapters and the perspective they provide. The third chapter, also by Li and Wu, reviews the history and methods employed in the analysis of uncertainties of model predictions. There has been a recent resurgence of interest in uncertainty analysis, making this review timely and useful. Because complex systems often have many variables with high uncertainties, this chapter leaves the reader with the pessimistic view that reliable predictions may be beyond current capabilities. In fact, a central point of earlier work revolved around the fact that only a few variables are usually responsible for most of the uncertainties associated with predictions. The importance of this result is that it focuses future studies on measuring specific processes that will most increase our confidence in predictions. This feedback between prediction and measurement should be an organic component of all ecological studies.
The remaining chapters of this section provide a diverse set of approaches to scale-dependent analysis and prediction. The discussion of multilevel statistical models by Richard A. Berk and Jan de Leeuw provides access to these methods for the ecological community; the contrasting requirements of nonspatial, spatially implicit, and spatially explicit methods, reviewed by Debra P. C. Peters and colleagues, are fundamental to the problems of spatial prediction; and the discussion of landscape prediction by Carol A. Wessman and C. Ann Bateson succinctly summarizes all spatial extrapolations by stating that “heterogeneity and non-linearity are the two factors determining the magnitude of scaling errors and bias.”
The second section of the book presents a series of case studies. As in most edited volumes, there is much of interest here, but it is difficult to extract governing principles or unifying themes that will resolve the problems of measurement, scale, and prediction. There are four chapters on nutrient dynamics, each with different scale-dependent perspectives; two chapters on avian habitat issues; two chapters on landscape analysis; and a single chapter on policy issues associated with water quality. Undoubtedly the reader will find these examples interesting, but will be forced to parse and select among the different approaches. Because the stated goal of this volume was “to provide a synthesis useful for management, planning and decision making,” it would have been helpful to provide at the start of each chapter a bulleted list of topics considered, methods employed, and principles explained. A nice overall chapter outline is provided late in the text (table 18.1, p. 332), but this summary is limited to an indexed list of keywords that does not exhaustively cover the concepts and applications essential for planning and management.
The final section comprises a single synthesis chapter written by the four editors. This chapter emphasizes the importance of scale and uncertainty for prediction. Although most of the discussion consists of caveats and warnings, a systematic and pluralistic philosophy is outlined. The final conclusions provide a useful summary, echoing observations found early in the text:
In general, only when the scales of observation and analysis are properly chosen, may the characteristic scale of the phenomenon of interest be detected correctly; only when the scales of experiments and models are appropriate, may the results of experiments and models be relevant; only when the scale of implementation of policies is commensurate with the intrinsic scale of the problem under consideration, may the policies be effective. (p. 7)
Scaling and Uncertainty Analysis in Ecology is an expensive book to add to a personal library—the cloth edition costs more than $100, and the paperback about $50. Nevertheless, it may be a wise purchase for those seeking a coherent introduction to the issues of scale, measurement, and prediction. I am pleased to have a copy on my shelf, and I plan on referring to it often for its exposition of concepts and for the diversity of examples presented.