Predicting habitat response to flow using generalized habitat models for trout in Rocky Mountain streams

T.K. Wilding, B.P. Bledsoe, LeRoy POFF, Barbara J S Sanderson

    Research output: Contribution to journalArticle

    14 Citations (Scopus)

    Abstract

    Dams and water diversions can dramatically alter the hydraulic habitats of stream ecosystems. Predicting how water depth and velocityrespond to flow alteration is possible using hydraulic models, such as Physical Habitat Simulation (PHABSIM); however, such modelsare expensive to implement and typically describe only a short length of stream (102m). If science is to keep pace with development, thenmore rapid and cost-effective models are needed. We developed a generalized habitat model (GHM) for brown and rainbow trout that makessimilar predictions to PHABSIM models but offers a demonstrated reduction in survey effort for Colorado Rocky Mountain streams. Thismodel 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 maximumhabitat) and (ii) Shape. The Shape component describes the habitat–flow curves made dimensionless by dividing flow increments byQ95h 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 desktopGHM (rapid-survey model explained 82–89% variance for independent validation sites; desktop 68–85%). The predictive success of theseGHMs was similar to other published models, but survey effort to achieve that success was substantially reduced. Habitat predicted by thedesktop 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.
    Original languageUndefined
    Pages (from-to)805-824
    Number of pages20
    JournalRiver Research and Applications
    Volume30
    Issue number7
    DOIs
    Publication statusPublished - 2014

    Cite this

    Wilding, T.K. ; Bledsoe, B.P. ; POFF, LeRoy ; Sanderson, Barbara J S. / Predicting habitat response to flow using generalized habitat models for trout in Rocky Mountain streams. In: River Research and Applications. 2014 ; Vol. 30, No. 7. pp. 805-824.
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    abstract = "Dams and water diversions can dramatically alter the hydraulic habitats of stream ecosystems. Predicting how water depth and velocityrespond to flow alteration is possible using hydraulic models, such as Physical Habitat Simulation (PHABSIM); however, such modelsare expensive to implement and typically describe only a short length of stream (102m). If science is to keep pace with development, thenmore rapid and cost-effective models are needed. We developed a generalized habitat model (GHM) for brown and rainbow trout that makessimilar predictions to PHABSIM models but offers a demonstrated reduction in survey effort for Colorado Rocky Mountain streams. Thismodel 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 maximumhabitat) and (ii) Shape. The Shape component describes the habitat–flow curves made dimensionless by dividing flow increments byQ95h 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 desktopGHM (rapid-survey model explained 82–89{\%} variance for independent validation sites; desktop 68–85{\%}). The predictive success of theseGHMs was similar to other published models, but survey effort to achieve that success was substantially reduced. Habitat predicted by thedesktop GHM (using geographic information system data) was significantly correlated with the abundance of large brown trout (p < 0.01)but not smaller trout. Copyright {\circledC} 2013 John Wiley & Sons, Ltd.",
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    Predicting habitat response to flow using generalized habitat models for trout in Rocky Mountain streams. / Wilding, T.K.; Bledsoe, B.P.; POFF, LeRoy; Sanderson, Barbara J S.

    In: River Research and Applications, Vol. 30, No. 7, 2014, p. 805-824.

    Research output: Contribution to journalArticle

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    AU - Wilding, T.K.

    AU - Bledsoe, B.P.

    AU - POFF, LeRoy

    AU - Sanderson, Barbara J S

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    N2 - Dams and water diversions can dramatically alter the hydraulic habitats of stream ecosystems. Predicting how water depth and velocityrespond to flow alteration is possible using hydraulic models, such as Physical Habitat Simulation (PHABSIM); however, such modelsare expensive to implement and typically describe only a short length of stream (102m). If science is to keep pace with development, thenmore rapid and cost-effective models are needed. We developed a generalized habitat model (GHM) for brown and rainbow trout that makessimilar predictions to PHABSIM models but offers a demonstrated reduction in survey effort for Colorado Rocky Mountain streams. Thismodel 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 maximumhabitat) and (ii) Shape. The Shape component describes the habitat–flow curves made dimensionless by dividing flow increments byQ95h 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 desktopGHM (rapid-survey model explained 82–89% variance for independent validation sites; desktop 68–85%). The predictive success of theseGHMs was similar to other published models, but survey effort to achieve that success was substantially reduced. Habitat predicted by thedesktop 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.

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