title: Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators.
abstract: In Maryland. U.S., an interim framework has recently been developed for using biologically based thresholds, or 'biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40-50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.
authors.affiliation: Versar Inc., ESM Operations, Columbia, Maryland, USA. volstadjon@versar.com
authors.forename: J H
authors.initials: JH
authors.lastname: Vølstad
authors.forename: N E
authors.initials: NE
authors.lastname: Roth
authors.forename: G
authors.initials: G
authors.lastname: Mercurio
authors.forename: M T
authors.initials: MT
authors.lastname: Southerland
authors.forename: D E
authors.initials: DE
authors.lastname: Strebel
mesh_terms: D017753:Ecosystem; D004781:Environmental Exposure / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000032:analysis / Q000382:microbiology; D006801:Humans; D016015:Logistic Models; D008396:Maryland; D012044:Regression Analysis; D014871:Water Microbiology; D014876:Water Pollution / Q000032:analysis*