A review of potential methods of determining critical effect size for designing environmental monitoring programs

Authors

  • Kelly R. Munkittrick,

    Corresponding author
    1. Canadian Rivers Institute and Department of Biology, University of New Brunswick, Saint John, New Brunswick E2L 4L5
    • Canadian Rivers Institute and Department of Biology, University of New Brunswick, Saint John, New Brunswick E2L 4L5
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  • Collin J. Arens,

    1. Canadian Rivers Institute and Department of Biology, University of New Brunswick, Saint John, New Brunswick E2L 4L5
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  • Richard B. Lowell,

    1. Environment Canada, National Environmental Effects Monitoring Program, 11 Innovation Boulevard, Saskatoon, Saskatchewan S7N 3H5
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  • Greg P. Kaminski

    1. Environment Canada, National Environmental Effects Monitoring Office, 351 St. Joseph Boulevard, Gatineau, Quebec K1A 0H3
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  • Published on the Web 2/6/2009.

Abstract

The effective design of field studies requires that sample size requirements be estimated for important endpoints before conducting assessments. This a priori calculation of sample size requires initial estimates for the variability of the endpoints of interest, decisions regarding significance levels and the power desired, and identification of an effect size to be detected. Although many programs have called for use of critical effect sizes (CES) in the design of monitoring programs, few attempts have been made to define them. This paper reviews approaches that have been or could be used to set specific CES. The ideal method for setting CES would be to define the level of protection that prevents ecologically relevant impacts and to set a warning level of change that would be more sensitive than that CES level to provide a margin of safety; however, few examples of this approach being applied exist. Program-specific CES could be developed through the use of numbers based on regulatory or detection limits, a number defined through stakeholder negotiation, estimates of the ranges of reference data, or calculation from the distribution of data using frequency plots or multivariate techniques. The CES that have been defined often are consistent with a CES of approximately 25%, or two standard deviations, for many biological or ecological monitoring endpoints, and this value appears to be reasonable for use in a wide variety of monitoring programs and with a wide variety of endpoints.

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