Classifying the biological condition of small streams: an example using benthic macroinvertebrates

Abstract

AbstractThe ability to classify the biological condition of unsurveyed streams accurately would be an asset to the conservation and management of streams. We compared the ability of 5 modeling methods (classification and regression trees, conditional inference trees, random forests [RF], conditional random forests [cRF], and ordinal logistic regression) to predict stream biological condition (very poor, poor, fair, or good) based on benthic macroinvertebrate Index of Biotic Integrity data taken from the Maryland Biological Stream Survey. Predictor variables included land use and land cover (e.g., impervious surface, row-crop agriculture, and population density) and landscape measures (annual precipitation and watershed area). We included 1561 sites on small nontidal streams in the Maryland portion of the Chesapeake Bay watershed. We used 1248 sites (80%) as a training data set to build models and 313 sites (20%) as an independent evaluation data set. RF and cRF models most accurately predicted observed in...