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.

Biological and water quality responses to hydrologic disturbances in third‐order forested streams

“…In addition, the flood sites and the debris flow site without a fish barrier supported fish communities as diverse as is typically seen in small streams in the western Cascade Mountains in the Pacific Northwest by 2002, 6 years after disturbance (Fransen et al , 2006). We did not have information on fish community composition prior to the storm, so it is uncertain whether the fish community has fully recovered to pre‐1996 storm condition.…”

Predictive Models for Differentiating Habitat Use of Coastal Cutthroat Trout and Steelhead at the Reach and Landscape Scale

“…Whereas this study's results focus on the approximate stream boundary separating CCT from steelhead, it does not determine the upstream limits of the species, which will be influenced by the presence of barriers and flow permanence. Either information by ground truth measures on barrier location is required (Torgersen et al 2004) or theoretical "stopping rules" related to stream gradient (Fransen et al 2006). Similarly, distribution models that incorporate temperature and other water quality parameters might help provide rules to differentiate when small streams become dominated by Dolly Varden and Bull Trout rather than CCT (Hicks 2002).…”

Modeling Habitat Selection of a Top Predator: Considering Growth and Physical Environments in a Spatial Context

“…Traditionally, emphasis has been placed on the role of physical characteristics in habitat selection of fishes, often through the combination of multiple parameters into a measure of habitat suitability (e.g., channel catfish Ictalurus punctatus: Layher and Maughan 1985;paddlefish Polyodon spathula: Crance 1987; brown trout Salmo trutta: Strakosh et al 2003). One application of suitability criteria has been to predict fish locations based on available habitat (Layher et al 1987;Fransen et al 2006;Sharma and Jackson 2008). Although they are valuable, physical habitat variables alone may be insufficient to predict habitat selection.…”