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Using An Energy Gradient Metric To Predict Fish Habitat Selection

Using An Energy Gradient Metric To Predict Fish Habitat Selection. Eric McLeskey 12-02-03 CEE 6440 Term Project. Why Use an Energy Metric?. Drift feeding stream salmonids use velocity gradients to maximize energy intake and conserve energy.

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Using An Energy Gradient Metric To Predict Fish Habitat Selection

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  1. UsingAn Energy Gradient MetricTo Predict Fish Habitat Selection Eric McLeskey 12-02-03 CEE 6440 Term Project

  2. Why Use an Energy Metric? • Drift feeding stream salmonids use velocity gradients to maximize energy intake and conserve energy. • Most habitat studies (e.g., PHABSIM) use point velocity suitability curves. • Point velocity values do not describe the variability in the flow field around each point. • Ability to distinguish between two points in a flow field with the same point velocity.

  3. One Energy Metric • Is a measure of the spatial change in kinetic energy between two points in a flow field 1 1 Crowder, D.W. and P. Diplas. 2000. Evaluating spatially explicit metrics of stream energy gradients using hydrodynamic model simulations. Can. J. Fish. Aquat. Sci. 57: 1497-1507.

  4. Objectives • Apply the metric developed by Crowder and Diplas to a velocity data set. • Access grid values • Data conversion and interpolation outside of GIS • Conduct a sensitivity analysis for different values of s.

  5. Study Site • 250 m section of the Blacksmith Fork River, UT • Third order; 1750 m elevation • Grid resolution 0.3048 m (1ft)

  6. Applying the Metric Properly

  7. Assumptions • Only lateral velocity gradients important • Velocity vectors parallel with stream edge

  8. Creating the (x’,y’) Mesh

  9. Interpolating Velocities Using Terra Model Interpolated velocity point shapefile x,y, & velocity value table

  10. Interpolated Velocity Grid Used PFE text editor to assemble interpolated velocity values into an ASCII table. ASCII to Grid tool in ArcToolbox This grid was opened in ArcMap and a Visual Basic Macro was used to run the metric computations.

  11. Accessing Grid Values

  12. Metric Calculation for s = 2 ft • Visual analysis of fish locations • Correlated positive metric values

  13. Results • Moderate correlation for s of 2 & 3 • Overall low correlations • Highly productive stream • Uniform flow

  14. Possible Future Research • Include multiple size classes in analysis • Apply metric to another study site • Determine metric threshold value • Develop metric suitability curves

  15. Acknowledgements • Greg Guensch • INSE, Utah State University Staff • Craig Addley • Mark Winkelaar • Shannon Clemens

  16. Questions?

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