Ecological Indicators for the Nation (2000) / Chapter Skim
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Appendix A: Variability, Complexity, and the Design of Sampling Procedures
Appendix A: Variability, Complexity, and the Design of Sampling Procedures
Pages 149-158
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Appendixes
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He was a member of the National Research Council Board on Environmental Studies and Toxicology, chairs the Committee to Assess the North American Research Strategy for Tropospheric Ozone (NARSTO) Program and is a member of the National Academy of Sciences/National Academy of Engineering Joint Committee on Cooperation in the Energy Futures of China and the United States.
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has greater effect on observed variability than does spatial or temporal extent of the data (Kratz 1995~. Parameters useful in monitoring programs designed to detect trends and patterns often have two, potentially conflicting characteristics.
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These approaches are characterized by: · comparison of "quasi-treatment" to "quasi-control" groups to resemble a designed experiment; · comparison of "treatment" groups with more than one "control" group to develop different contrasts with the "treatment" group; · comparison of "treatment" and "control" groups with important exogenous variables; if this is unfeasible, groups should be adjusted for differences using covariates in the analysis; and · use of a variety of measures/components to reduce the dependence of study results on a single aspect of the data and on assumptions inherent for single methods of analysis (Strauss 1990~. It is impossible to optimize both experimental and monitoring goals simultaneously, but long-term monitoring data can be used as key inputs in the development of estimates of the states of the systems being monitored, together with confidence levels associated with those estimates.
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For example, it would make no sense whatsoever to sample at daily frequencies to detect trends embedded within a 30-year cycle; even annual or biannual sampling would be too frequent. These considerations pose three problems: identifying reasonable long-term surrogates for current ecosystems to serve as first approximations to characterize oscillatory behavior, determining the optimal sampling frequency of an ecosystem property from these surrogates, and detecting changes in the behavior of an oscillating system.
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To determine the optimal sampling frequency and to examine how the system responds to rapid changes, the surrogate time series of data can then be analyzed using techniques of signal processing (Shannon and Weaver 1964, Simpson and Houts 1971, Wickerhauser 1994~. Fourier analysis and wavelet analysis are the two main techniques of interest.
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The output is then analyzed using Fourier analysis to determine the optimal frequency to sample an oscillating system under current climates. Many ecosystems are now well described by extensively tested simulation models that incorporate fundamental biological processes respon
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We first generate a long time series of productivity under current climate for northern Minnesota for a single plot of 0.01 ha on a silty clay loam soil with high water holding capacity (Figure A-1. Such a plot represents a standard forest inventory plot that would be resampled in a monitoring program such as the USES Continuous Forest Inventory and Analysis (CFIA)
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The oscillatory nature of the above equation can be clarified by recalling that ei2~= = cost + i sink. We essentially thereby obtain a series of cosine functions each of characteristic frequency and amplitude that describe the time series of productivity under northeastern Minnesota climate.
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Therefore, the current sampling frequencies of the CFIA and Forest Health Monitoring Programs appear to sample forests sufficiently often to characterize productivity, at least in northern Minnesota on clay soils. The above model run was for a silty clay loam soil.
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