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THE STRUCTURE OF VARIATION AND ITS INFLUENCE ON THE ESTIMATION OF STATUS: INDICATORS OF CONDITION OF LAKES IN THE NORTHEAST, U.S.A.
Citation:
Kincaid, T. M., D P. Larsen, AND N. S. Urquhart. THE STRUCTURE OF VARIATION AND ITS INFLUENCE ON THE ESTIMATION OF STATUS: INDICATORS OF CONDITION OF LAKES IN THE NORTHEAST, U.S.A. ENVIRONMENTAL MONITORING AND ASSESSMENT. Springer Science and Business Media B.V;Formerly Kluwer Academic Publishers B.V., , Germany, 98:1-21, (2004).
Description:
One goal of regional-scale sample surveys is to estimate the status of a resource of interest from a statistically drawn representative sample of that resource. An expression of status is a frequency distribution of indicator scores capturing the variability of the attributes of interest. However, extraneous variability interferes with the status description by introducing bias into the frequency distributions. To address this issue, we used data from a regional survey of lakes in the Northeast U.S. collected by the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP). We employ a components of variance model to identify sources of extraneous variance pertinent to status descriptions of physical, chemical, and biological attributes of the population of lakes in the NE. We summarize the relative magnitude of four components of variance (lake-to-lake, year, interaction, and residual) for each indicator and illustrate how extraneous variance biases the status descriptions. We then use a procedure that removes bias from the status descriptions to produce unbiased estimates and introduce a novel method for estimating the "cost" of removing the bias (expressed as either increased sampling uncertainty or additional samples needed to achieve the target precision in the absence of bias). We also compare the relative magnitude of the four variance components across the array of indicators, finding in general that conservative chemical indicators are least affected by extraneous variance, followed by some non-conservative indicators, with nutrient indicators most affected by extraneous variance. Intermediate were trophic indicators (including sediment diatoms), fish species richness and individuals indicators, and zooplankton taxa richness and individuals indicators. Contrary to our expectations, we found no clear patterns in the relative magnitude of variance components as a function of several methods of aggregating fish and zooplankton indicators (e.g., level of taxonomy, or species richness vs. numbers of individuals).