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PREDICTING HABITAT RESPONSE TO FLOW USING GENERALIZED HABITAT MODELS FOR TROUT IN ROCKY MOUNTAIN STREAMS
Author(s) -
Wilding T. K.,
Bledsoe B.,
Poff N. L.,
Sanderson J.
Publication year - 2014
Publication title -
river research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.679
H-Index - 94
eISSN - 1535-1467
pISSN - 1535-1459
DOI - 10.1002/rra.2678
Subject(s) - habitat , streams , hydrology (agriculture) , trout , environmental science , ecology , flow (mathematics) , geology , computer science , fishery , mathematics , biology , fish <actinopterygii> , computer network , geometry , geotechnical engineering
Dams and water diversions can dramatically alter the hydraulic habitats of stream ecosystems. Predicting how water depth and velocity respond to flow alteration is possible using hydraulic models, such as Physical Habitat Simulation (PHABSIM); however, such models are expensive to implement and typically describe only a short length of stream (10 2 m). If science is to keep pace with development, then more rapid and cost‐effective models are needed. We developed a generalized habitat model (GHM) for brown and rainbow trout that makes similar predictions to PHABSIM models but offers a demonstrated reduction in survey effort for Colorado Rocky Mountain streams. This model combines the best features of GHMs developed elsewhere, including the options of desktop (no‐survey) or rapid‐survey models. Habitat–flow curves produced by PHABSIM were simplified to just two site‐specific components: (i) Q95h (flow at 95% of maximum habitat) and (ii) Shape . The Shape component describes the habitat–flow curves made dimensionless by dividing flow increments by Q95h and dividing habitat (weighted usable area) increments by maximum habitat. Both components were predicted from desktop variables, including mean annual flow, using linear regression. The rapid‐survey GHM produced better predictions of observed habitat than the desktop GHM (rapid‐survey model explained 82–89% variance for independent validation sites; desktop 68–85%). The predictive success of these GHMs was similar to other published models, but survey effort to achieve that success was substantially reduced. Habitat predicted by the desktop GHM (using geographic information system data) was significantly correlated with the abundance of large brown trout ( p < 0.01) but not smaller trout. Copyright © 2013 John Wiley & Sons, Ltd.