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

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

5. Reaction Distance Swim speed x foraging duration Search Volume = ‘cylinder’ Search Volume = ∏ x RD 2 x (SS x time)

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

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

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

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

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

11. Modeling Process Thermal Experience Temporal Diet Composition Prey Energy Density Consumer Growth Predator Energy Density Bioenergetics Model Consumption Estimate Foraging model

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

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

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

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

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

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

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

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

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

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

25. No consistent trends Area 4 generally highest Daytime estimate

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

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

28. 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

29. 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 Funding: Utah Coop Unit, UDWR WACFRU, King County (SWAMP) City of Seattle, City of Bellevue Tom Lowman

32. Courtesy of Brant Allen, Tahoe Research Group

35. Visual Foraging Piscivores: Mechanistic level of Predator-prey interaction Investigate the Influences of natural environment Light Turbidity

36. Foraging sequence P(Capture) = P(Encounter) * P(Attack) * P(Success given attack) * P(Retain) Reaction Distance Predation Rate Light and Turbidity influence all aspects of the foraging sequence

37. Size-selective predation -size of prey in diet reflects vulnerable portion of prey population Common approach to determine The amount of vulnerable prey Gape limitation Size of prey in diet vs. the environment However, the vulnerability of mobile prey is further constrained Available refuge habitat predators and prey overlap is it possible to encounter a prey? (e.g. sensory)

38. Growth One of the most researched metrics in fisheries Often used to evaluate the quality of available habitat However, the focus is frequently on past conditions!

39. How do we become more predictive? Need to measure the “potential for growth” Growth = eating tissue *Knowledge of available forage *How consumed food converted into tissue

40. RD Encounter Rate = Search Volume x Prey Density Depth specific encounter rate = How many prey would be encountered