Uncertainty analysis in ecological studies:An overview

Jianguo Wu, Harbin Li

Research output: Chapter in Book/Report/Conference proceedingChapter

47 Scopus citations

Abstract

Large-scale simulation models are essential tools for scientific research and environmental decision-making because they can be used to synthesize knowledge, predict consequences of potential scenarios, and develop optimal solutions (Clark et al. 2001, Berk et al. 2002, Katz 2002). Modeling is often the only means of addressing complex environmental problems that occur at large scales (Klepper 1997, Petersen 2000). For example, investigations of global climate change (Wobbles et al. 1999), regional assessments of net primary productivity and carbon dynamics (Jenkins 1999, Peters et al., Chapter 7, Law et al., Chapter 9), and landscape analysis of fire spread (Hargrove et al. 2000) rely heavily on simulation modeling at various scales. However, uncertainty in simulation modeling is often overlooked even though it is a fundamental characteristic of modeling that can be caused by incomplete data, limitations of models, and lack of understanding of underlying processes (Beck 1987, Reckhow 1994, Clark et al. 2001, Berk et al. 2002, Katz 2002, Stott and Kettleborough 2002, Urban et al., Chapter 13). If simulation results are to be useful, researchers must show the reliability of the model output by providing information about model adequacy and limitations, prediction accuracy, and the likelihood of scenarios (Clark et al. 2001, Katz 2002). Uncertainty affects every aspect of modeling (Reckhow 1994, Klepper 1997, Jansen 1998, Katz 2002, Stott and Kettleborough 2002, Urban et al., Chapter 13). Data may contain errors that result from problems with sampling, measurement, or estimation procedures (O?Neill and Gardner 1979, Regan et al. 2002). Incomplete data are a common problem, especially in spatial modeling at broad scales. Models are imperfect because they are simplifications of real systems and always have errors in their assumptions, formulation, and parameterization. Moreover, effects of these errors on model adequacy are often insufficiently evaluated (Beck 1987, Reckhow 1994). In fact, most large-scale models are not fully validated, partly because validation data are not available (sometimes no data can be collected under the existing technological and logistical constraints) and partly because techniques for validating spatial models have not been perfected. Although the importance of uncertainty in modeling is well recognized, few studies of ecological modeling provide critical information about uncertainty, confidence levels or likelihood associated with simulation results (Reckhow 1994, Clark et al. 2001, Rypdal and Winiwarter 2001, Katz 2002). This lack of discussion and reporting is unfortunate because predictions that are not accompanied by information about uncertainty are of limited value in policy- or decision-making. Researchers must adopt a new modeling philosophy that requires that uncertainty in models and modeling be understood, quantified when possible, and reduced to an acceptable level when feasible. Scaling is the translation or extrapolation of information from one scale to another in time or space or both (Bl?schl and Sivapalan 1995, Wu 1999, Wu and Li, Chapters 1 and 2). For example, scaling is needed to resolve most of the large-scale management problems because most of our knowledge and data is obtained by means of small-scale research. In the process of scaling, errors in data and models may be propagated into results. It is not adequate simply to ask how to scale: one must ask how to scale with known reliability and uncertainty even when ecological systems and models involved are often complex. Thus, uncertainty analysis is an essential part of scaling because it provides critical information about the adequacy of models or algorithms used in the scaling process and about the accuracy of scaling results (Katz 2002). In this overview, we will focus on the major concepts and techniques of uncertainty analysis associated with up-scaling methods (i.e., those that extrapolate information from fine scales to coarse scales; Wu and Li, Chapters 1 and 2). Specifically, we will identify sources of uncertainty in the scaling process and illustrate approaches to and techniques of uncertainty analysis. Because translating or extrapolating is usually done with the help of models (Wu and Li, Chapter 2), scaling can be regarded as a special case of modeling (i.e., modeling with changing scales). Therefore, most discussion of uncertainty in modeling is directly applicable to uncertainty in scaling. Also, it should be noted that most of the techniques of uncertainty analysis discussed below are more suitable for ecological models with low to intermediate complexity than for highly complex models like the general circulation models employed in climate change research (Allen et al. 2000, Forest et al. 2002, Stott and Kettleborough 2002).

Original languageEnglish (US)
Title of host publicationScaling and Uncertainty Analysis in Ecology
Subtitle of host publicationMethods and Applications
PublisherSpringer Netherlands
Pages45-66
Number of pages22
ISBN (Print)1402046642, 9781402046629
DOIs
StatePublished - Dec 1 2006

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ASJC Scopus subject areas

  • Environmental Science(all)

Cite this

Wu, J., & Li, H. (2006). Uncertainty analysis in ecological studies:An overview. In Scaling and Uncertainty Analysis in Ecology: Methods and Applications (pp. 45-66). Springer Netherlands. https://doi.org/10.1007/1-4020-4663-4_3