Assessing Linkages in Stream Habitat, Geomorphic Condition, and Biological Integrity Using a Generalized Regression Neural Network1. (25th February 2013)

• Record Type: Journal Article
• Authors: Mathon, Bree R.; Rizzo, Donna M.; Kline, Michael; Alexander, Gretchen; Fiske, Steve; Langdon, Richard; Stevens, Lori
• Is Part Of: Journal of the American Water Resources Association. Volume 49:Number 2(2013:Apr.)
• Page Start: 415
• Page End: 430
• Publication Date: 2013-02-25
• Subjects: Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/jawr.12030
• Languages: English
• ISSNs: 1093-474X

Abstract

<abstract abstract-type="main" xml:lang="en" id="jawr12030-abs-0001"> <title>Abstract</title> <p>Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1, 292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% <italic>vs</italic>. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.</p> </abstract>