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 the frequency distribution of indicator scores capturing variability of attributes of interest. However, extraneous variability interferes with the status description by introducing bias into the frequency distributions. To examine 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 describe a procedure that removes this 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 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 nonconservative indicators, with nutrient indicators most affected by extraneous variance. Intermediate were trophic condition indicators (including sediment diatoms), fish species richness and individuals indicators, and zooplankton taxa richness and individuals indicators. 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).
Analysis of Variance Animals