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
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
Spatially explicit growth potential model
Prey distribution
Prey
supply
Foraging model
Growth rate
o
Temperature C
6 12 18
0
20
30
40
50
Depth (m)
10
Temperature
Bioenergetics model
Predator
demand
Foraging Model
Fish are primarily visual oriented foragers (Ali 1959)
0.08 NTU - 0.55 NTU
120
Lake trout model
Reaction distance (cm)
100
80
Cutthroat trout model
60
Rainbow trout model
40
Lake trout 0.08 NTU
Rainbow trout 0.08 NTU
Cutthroat trout 0.08 NTU
20
Lake trout 0.55 NTU
Rainbow trout 0.55 NTU
Cutthroat trout 0.55 NTU
0
0
10
20
30
40
Light (Lx)
50
60
70
80
Search Volume = ‘cylinder’
Reaction Distance
Swim speed x foraging duration
Search Volume = ∏ x RD2 x (SS x time)
Encounter Rate = Search Volume x Prey Density
RD
RD = f(depth, light, turbidity)
Because RD and SS are functions of light
Piscivores trade-off between light and prey
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
Visual feeding fishes
Light and Turbidity
time
perceptual field
available prey
Spatially explicit growth potential model
Prey distribution
Prey
supply
Foraging model
Growth rate
o
Temperature C
6 12 18
0
20
30
40
50
Depth (m)
10
Temperature
Bioenergetics model
Predator
demand
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
Road map
Model structure
Models within the model
Data collection and inputs
Results and model corroboration
Conclusion
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
Area 1
Area 3
Area 4
Depth (m)
Area 2
0
10
20
30
40
50
60
0
10
20
30
40
50
60
0
10
20
30
40
50
60
0
10
20
30
40
50
60
0
10
20
30
40
50
60
February, Area 1
< 70 mm
70 - 150 mm
> 150 mm
February, Area 2
Distribution of Prey
February, Area 3
February, Area 4
February, Area 5
Density (Fish / 1000 m3)
Area 5
0
2
4
6
8
10
12
14
Reaction distance (cm)
0
0
20 40 60
20 40 60
Spring
Winter
20 40 60
20 40 60
Summer
Depth (m)
15
30
RD
Fall
45
Seasonal & Diel
prey densities
Day
Prey fish (40-150 mm)
60
0
Depth (m)
15
Crepuscular
30
45
Urban light
pollution
60
0
Depth (m)
15
Night
30
45
60
Prey fish
0 4 8 12 16
4 8 12 16
4 8 12 16
Prey fish / 1000 m3
4 8 12 16 20
sockeye fry
sockeye ps
0+ smelt
1+ smelt
stickleback
stickleback
Winter 2003
Prey fish Density
Day
0
30
60
Night
0
30
60
Day
Prey fish densities
Night
0
0
30
30
60
0
60
0
30
30
60
0
60
0
30
30
60
60
0
0
30
30
60
0
60
0
30
30
60
Area 3 only
0
60
0
30
30
60
Area 3 only
0
60
0
30
30
60
Area 3 only
Prey fish / m3
60
Spring 2002
Summer 2002
Fall 2002
Winter 2003
Spring 2003
Summer 2003
Fall 2003
Road map
Model structure
Models within the model
Data collection and inputs
SE Results and corroboration
Conclusion
Growth potential
0
20
40
Spring 2002 (night)
One mid-lake transect
60
20
40
Summer 2002 (night)
Depth (m)
60
Smelt reach 40 mm
20
40
Fall 2002 (night)
60
20
40
Winter 2003 (night)
60
20
40
Spring 2003 (night)
60
-0.002
0
0.003
0.008
0.013
0.02
Growth Potential (g/g/day)
Winter 2003
Day
0
30
60
Night
0
30
60
Growth Potential (g/g/day)
Day
Night
Growth Potential
0
0
30
30
60
0
60
0
30
30
60
0
60
0
30
30
60
60
0
0
30
30
60
0
60
0
30
30
60
Area 3 only
0
60
0
30
30
Area 3 only
60
0
60
0
30
30
60
Area 3 only
Growth Potential (g/g/day)
60
Spring 2002
Summer 2002
Fall 2002
Winter 2003
Spring 2003
Summer 2003
Fall 2003
0.3
0.2
Day
0.3
Night
May 2002
0.2
Proportion positive growth cells
0.1
0.1
0.3
0.3
August 2002
0.2
0.2
0.1
0.1
0.3
0.3
October 2002
0.2
0.2
0.1
0.1
0.3
0.3
February 2003
0.2
0.2
0.1
0.1
0.3
0.2
0.3
May 2003
0.2
*
* * *
No data available
0.1
0.0
5
4
3
0.1
0.0
2
1
No consistent trends
5
4
3
2
Lake Area (South to North)
1
Area 4 generally highest
Daytime estimate
winter 04
fall 03
summer 03
spring 03
winter 03
fall 02
summer 02
spring 02
3.75
220
Cutthroat trout condition
Slope
200
3.25
180
3.00
160
140
Back calculated growth age 3-4
Back calculated
Annual growth
Agrees with GP estimates
120
100
0.25
Cutthroat trout growth potential
Delayed response
0.20
0.15
Cutthroat trout condition
0.10
Winter and spawning may
contribute
0.05
0.007
winter 04
fall 03
summer 03
spring 03
winter 03
fall 02
summer 02
0.00
spring 02
Proportion of positive cells
2.75
Growth (g/year)
3.50
240
May 2003
Constant 0.5 m RD
Night
0
30
Constant RD
increased the value
of dark deep water
habitat to the growth
of cutthroat trout
60
0
Light-dependent RD
Night
30
60
Growth Potential (g·g-1·day-1)
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
Acknowledgments:
David Beauchamp
Pat Nielsen, John Horne, Danny Grunbaum, Dan Yule, Chris Luecke
Tom Lowman 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