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Assessing Linkages in Stream Habitat, Geomorphic Condition, and Biological Integrity Using a Generalized Regression Neural Network
Author(s) -
Mathon Bree R.,
Rizzo Donna M.,
Kline Michael,
Alexander Gretchen,
Fiske Steve,
Langdon Richard,
Stevens Lori
Publication year - 2013
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/jawr.12030
Subject(s) - watershed , habitat , biological integrity , scale (ratio) , regression , biological data , environmental science , streams , linear regression , index of biological integrity , artificial neural network , regression analysis , computer science , data mining , environmental resource management , hydrology (agriculture) , ecology , machine learning , cartography , geography , statistics , geology , mathematics , biology , computer network , genetics , geotechnical engineering
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% vs . 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.