A Logistic Regression Model for Predicting the Upstream Extent of Fish Occurrence Based on Geographical Information Systems Data

Abstract

Regulations governing human activities in streams and riparian zones frequently differ depending on whether or not a stream reach supports fish. Fish presence or absence is usually determined by sampling or by assuming the presence of fish if the stream exhibits certain physical characteristics. Field surveys of fish occurrence in streams are time consuming and expensive. Inference of fish presence from simple thresholds of physical attributes, such as gradient or channel width alone, is inaccurate. We attempted to improve the accuracy and efficiency of this determination by developing a geographical information systems (GIS)-based predictive model. A 10-m digital elevation model incorporated field data on fish distribution from 517 streams in western Washington State and GIS-derived representations of the physical characteristics of stream networks. A model predicting the upstream extent of fish occurrence was derived using logistic regression models coupled with a heuristic “stopping rule.” Candidate variables included stream gradient, upstream basin area, elevation, and mean annual precipitation. When assessed against independent survey data, 91.9% of the occupied fish habitat was correctly classified by the model. Errors were generally small, but occasional large errors did occur and were most frequently associated with barriers to fish movement. Smaller errors occurred in marginal habitats, streams of low topographic relief, and streams that originated from headwater ponds. Use of this type of model, coupled with targeted field survey in areas most likely to be associated with model error, would greatly improve the efficiency and accuracy of current classification schemes.