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Michael Mazur WACFWRU, USGS-BRD, University of Washington SAFS

Quantifying the influence of diel optical conditions and prey distributions on visual foraging piscivores in a spatial-temporal model of growth rate potential. Michael Mazur WACFWRU, USGS-BRD, University of Washington SAFS. Objectives and road map.

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Michael Mazur WACFWRU, USGS-BRD, University of Washington SAFS

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  1. Quantifying the influence of diel optical conditions and prey distributions on visual foraging piscivores in a spatial-temporal model of growth rate potential Michael Mazur WACFWRU, USGS-BRD, University of Washington SAFS

  2. Objectives and road map Investigate how alterations in diel optical conditions and prey distributions influence the variation in growth of piscivorous cutthroat trout in Lake Washington Model structure Models within the model Data collection and inputs Results and model corroboration Conclusion

  3. Spatially explicit growth potential model Prey distribution Prey supply Foraging model Growth rate Predator demand Temperature Bioenergetics model

  4. Foraging Model Fish are primarily visual oriented foragers (Ali 1959)

  5. Search Volume = ‘cylinder’ Reaction Distance Swim speed x foraging duration Search Volume = ∏ x RD2 x (SS x time)

  6. Encounter Rate = Search Volume x Prey Density RD RD = f(depth, light, turbidity)

  7. Because RD and SS are functions of light Piscivores trade-off between light and prey

  8. Foraging model is a tool for filtering prey densities down into the amount of prey available for a predator Foraging sequence P(Capture) = P(Encounter) * P(Attack) * P(Success given attack) * P(Retain) all prey morphology space time Visual feeding fishes Light and Turbidity perceptual field available prey

  9. Spatially explicit growth potential model Prey distribution Prey supply Foraging model Growth rate Predator demand Temperature Bioenergetics model

  10. Bioenergetics, coverts consumption into growth Mass Balance Approach -Theoretical basis in laws of thermodynamics Consumption = Metabolism + Waste + Growth Metabolism (respiration, active metabolism, specific dynamic action) Waste (egestion, excretion) Consumption Growth

  11. Road map Model structure Models within the model Data collection and inputs Results and model corroboration Conclusion

  12. Prey densities Hydroacoustic estimates of Temporal-spatial prey densities Month/season Diel Areas of the lake Mid-water trawl estimates of species identification and size of prey

  13. Area 1 Distribution of Prey Area 2 Area 3 Area 4 Area 5

  14. Seasonal & Diel prey densities Summer Spring Winter Prey fish (40-150 mm) Day Fall Crepuscular Urban light pollution Night stickleback

  15. Prey fish Density Winter 2003 Day 0 30 60 Night 0 30 60

  16. Day Prey fish densities Night Spring 2002 Summer 2002 Fall 2002 Winter 2003 Spring 2003 Summer 2003 Fall 2003

  17. Road map Model structure Models within the model Data collection and inputs SE Results and corroboration Conclusion

  18. Growth potential One mid-lake transect Smelt reach 40 mm

  19. Winter 2003 Day 0 30 60 Night 0 30 60 Growth Potential (g/g/day)

  20. Day Night Growth Potential Spring 2002 Summer 2002 Fall 2002 Winter 2003 Spring 2003 Summer 2003 Fall 2003 Growth Potential (g/g/day)

  21. No consistent trends Area 4 generally highest Daytime estimate

  22. Back calculated Annual growth Agrees with GP estimates Delayed response Cutthroat trout condition Winter and spawning may contribute

  23. Constant RD increased the value of dark deep water habitat to the growth of cutthroat trout

  24. Conclusions • The growth potential model was able to transform general prey abundances into a quantifiable characteristic of the environment with implications for both predators and prey • Light-dependent foraging models improve the predictive capability of growth potential models • The growth potential model reflected annual changes in growth and seasonal shifts in condition for cutthroat trout • Despite variable prey densities among areas of the lake, cutthroat trout growth was predicted to be more dependent on vertical variability in foraging opportunity

  25. Acknowledgments: David Beauchamp Pat Nielsen, John Horne, Danny Grunbaum, Dan Yule, Chris Luecke Beauchamp grad students- Jen McIntyre! Lab and field help- Andy Jones, Chris S., Mike, Jo, Jim, Steve, Robert, Nathanael, Angie, Mistie, Chris B., Kenton, Shannon, Bridget, Lia Coop Unit- Chris Grue, Verna, Martin, Dede, Barbara WDFW- Chad Jackson, Casey Baldwin Tom Lowman Funding: Utah Coop Unit, UDWR WACFRU, King County (SWAMP) City of Seattle, City of Bellevue

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