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Application of the Probability-based Maryland Biological Stream Survey to the State's Water Quality Standards Program
Mark T. Southerland 1, Jon H. Vølstad 1, Ronald J. Klauda 2, Charles A. Poukish 3 and Matthew C. Rowe 3
1 Versar, Inc., Columbia, Maryland
2 Maryland Department of Natural Resources, Annapolis, Maryland
3 Maryland Department of the Environment, Baltimore, Maryland
The Clean Water Act presents a daunting task for states required to assess and restore all of their waters. The reality is that monitoring cannot be conducted on every foot or even mile of stream in a state. The only way to obtain representative information on all stream miles is to conduct probability-based (random) sampling. In Maryland, statewide random sampling at the 8-digit watershed scale (approximately 100 mi 2) is practical over 3-5 years. At the same time, water quality restoration actions are usually undertaken at the site or smaller watershed scale. To meet these conflicting needs, the Maryland Departments of Natural Resources (DNR) and the Environment (MDE) have developed a close partnership to maximize the use of random and targeted Maryland Biological Stream Survey (MBSS) data to identify (1) impaired waters (303d list) using biocriteria based on fish and benthic macroinvertebrate Indices of Biotic Integrity (IBIs), (2) high-quality waters, and (3) likely stressors affecting these waters.
We conducted analyses using subsets of the 1,500 MBSS sites sampled in 2000-2004 that describe the variability of IBI scores at the 75-m segment, reach, 12-digit watershed, and 8-digit watershed levels. Average variability for fish and benthic IBI scores from replicate samples as measured by the coefficient of variation increased with increasing spatial scale, demonstrating that site IBI scores are not representative at watershed scales. We conclude that large-scale stream restoration priorities should not be based on single site scores, however precise, but rather on random sampling or other information that describes entire stream networks.
Keywords: MBSS, IBI, random sampling, water quality standards, 303d list