Predicting fecal indicator organism contamination in Oregon coastal streams.
In this study, we used publicly available GIS layers and statistical tree-based modeling (CART and Random Forest) to predict pathogen indicator counts at a regional scale using 88 spatially explicit landscape predictors and 6657 samples from non-estuarine streams in the Oregon Coast Range. A total of 532 frequently sampled sites were parsed down to 93 pathogen sampling sites to control for spatial and temporal biases. This model's 56.5% explanation of variance, was comparable to other regional models, while still including a large number of variables. Analysis showed the most important predictors on bacteria counts to be: forest and natural riparian zones, cattle related activities, and urban land uses. This research confirmed linkages to anthropogenic activities, with the research prediction mapping showing increased bacteria counts in agricultural and urban land use areas and lower counts with more natural riparian conditions.
School of the Environment, Department of Environmental Science and Management, Portland State University, PO Box 751, Portland, OR 97207, USA. Electronic address: ppettus@gmail.com.
Paul
P
Pettus
School of the Environment, Department of Environmental Science and Management, Portland State University, PO Box 751, Portland, OR 97207, USA; Oregon Department of Environmental Quality, 811 SW Sixth Ave., Portland, OR 97204-1390, USA. Electronic address: foster.eugene@deq.state.or.us.
Eugene
E
Foster
School of the Environment, Department of Environmental Science and Management, Portland State University, PO Box 751, Portland, OR 97207, USA. Electronic address: bwyp@pdx.edu.
Yangdong
Y
Pan
D000383:Agriculture; D000818:Animals; D058491:Bacterial Load; D002417:Cattle; D002947:Cities; D005243:Feces / Q000382:microbiology*; D065928:Forests; D008962:Models, Theoretical; D009922:Oregon; D045483:Rivers / Q000382:microbiology*; D014871:Water Microbiology
Escherichia coli; Fecal coliform; Modeling; Pathogens; Water quality