Maryland Biological Stream Survey: A state agency program to assess the impact of anthropogenic stresses on stream habitat quality and biota

Maryland Department of Natural Resources is conducting the Maryland Biological Stream Survey, a probability-based sampling program, stratified by river basin and stream order, to assess water quality, physical habitat, and biological conditions in first through third order, non-tidal streams. These streams comprise about 90% of all lotic water miles in the state. About 300 sites (75 m segments) are being sampled during spring and summer each year. All basins in the stare will be sampled over a three-year period, 1995-97. MESS developments in 1995-96 included (1) an electrofishing capture efficiency correction method to improve the accuracy of fish population estimates, (2) two indices of biotic integrity (IBI) for fish assemblages to identify degraded streams, and (3) land use information for catchments upstream of sampled sites to investigate associations between stream condition and anthropogenic stresses. Based on fish IBI scores at 270 stream sites in six basins sampled in 1995, 11% of non-tidal stream miles in Maryland were classified as very poor, 15% as poor, 24% as fair, and 27% as good. IBIs have not yet been developed for stream sites with catchment areas less than 120 hectares (23% of non-tidal stream miles). IBI scores declined with stream acid neutralizing capacity (ANC) and pH, an association that was also evident for fish species richness, biomass, and density. Low IBI scores were associated with several measures of degraded stream habitat, but not with local riparian buffer width. There was a significant negative association between IBI scores and urban land use upstream of sampled sites in the only extensively urbanized basin assessed in 1995. Future plans for the MESS include (I) identifying all benthic macroinvertebrate samples to genus, (2) developing benthic macroinvertebrate, herpetofaunal, and physical habitat indicators, and (3) enhancing the analysis of stream condition-stressor associations by refining landscape metrics and using multi-variate techniques.