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ENVIRONMENTALLY STRATIFIED SAMPLING DESIGN FOR THE DEVELOPMENT OF THE GREAT LAKES ENVIRONMENTAL INDICATORS
Danz, N. P., R. R. Regal, G. J. Niemi, J. R. Kelly, V. J. Brady, T. Hollenhorst, L. B. Johnson, G. E. Host, J. M. Hanowski, C. A. Johnston, J. Kingston, AND T A. Brown. ENVIRONMENTALLY STRATIFIED SAMPLING DESIGN FOR THE DEVELOPMENT OF THE GREAT LAKES ENVIRONMENTAL INDICATORS. ENVIRONMENTAL MONITORING AND ASSESSMENT. Elsevier Science Ltd, New York, NY, 102:41-65, (2005).
Ecological indicators must be shown to be responsive to stress. For large-scale observational studies the best way to demonstrate responsiveness is by evaluating indicators along a gradient of stress, but such gradients are often unknown for a population of sites prior to site selection. The availability of geographic information (GIS) data makes it possible to partially characterize environmental conditions for large geographic areas without visiting sites. We used geomorphological criteria to divide the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then calculated over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site-selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute samples across the range of environmental conditions in the basin. This design has broad applicability when the goal is to develop condition or diagnostic indicators using observational data from large-scale surveys.