Quantifying the influence of diel optical conditions and prey distributions on visual foraging...
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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
RD
Encounter Rate = Search Volume x Prey Density
6 12 18
Dep
th (
m)
0
10
20
30
40
50
Temperature oCGrowth rate
Prey supply
Temperature
Foraging model
Spatially explicit growth potential model
Predator demand
Bioenergetics model
Prey distribution
Foraging Model
Fish are primarily visual oriented foragers (Ali 1959)
0.08 NTU - 0.55 NTU
Light (Lx)
0 10 20 30 40 50 60 70 80
Re
act
ion
dis
tan
ce (
cm)
0
20
40
60
80
100
120
Lake trout model
Rainbow trout model
Cutthroat trout model
Lake trout 0.08 NTU
Rainbow trout 0.08 NTU
Cutthroat trout 0.08 NTU
Lake trout 0.55 NTU
Rainbow trout 0.55 NTU
Cutthroat trout 0.55 NTU
Reaction Distance
Swim speed x foraging duration
Search Volume = ‘cylinder’
Search Volume = ∏ x RD2 x (SS x time)
RD
Encounter Rate = Search Volume x Prey Density
RD = f(depth, light, turbidity)
Piscivores trade-off between light and preyBecause RD and SS are functions of light
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 intothe amount of prey available for a predator
all prey
available prey
space
time
morphology
perceptual field
RD
Encounter Rate = Search Volume x Prey Density
6 12 18
Dep
th (
m)
0
10
20
30
40
50
Temperature oCGrowth rate
Prey supply
Temperature
Foraging model
Spatially explicit growth potential model
Predator demand
Bioenergetics model
Prey distribution
Consumption = Metabolism + Waste + GrowthMetabolism (respiration, active metabolism, specific dynamic action)Waste (egestion, excretion)
Consumption Growth
Mass Balance Approach-Theoretical basis in laws of thermodynamics
Bioenergetics, coverts consumption into growth
Road map
Model structure
Models within the model
Data collection and inputs
Results and model corroboration
Conclusion
Hydroacoustic estimates ofTemporal-spatial prey densities
Month/seasonDielAreas of the lake
Prey densities
Mid-water trawl estimates ofspecies identificationand size of prey
Area 1
Area 2
Area 3
Area 4
Area 5
February, Area 10
10
20
30
40
50
60
< 70 mm70 - 150 mm> 150 mm
February, Area 20
10
20
30
40
50
60
February, Area 3D
epth
(m
) 0
10
20
30
40
50
60
February, Area 40
10
20
30
40
50
60
February, Area 5
Density (Fish / 1000 m3)0 2 4 6 8 10 12 14
0
10
20
30
40
50
60
Distribution of Prey
Prey fish / 1000 m3
0 4 8 12 16
Dep
th (
m)
0
15
30
45
60
4 8 12 16 4 8 12 16 4 8 12 16 20
Prey fish
Dep
th (
m)
0
15
30
45
60
Reaction distance (cm)0 20 40 60 20 40 60 20 40 60 20 40 60
Dep
th (
m)
0
15
30
45
60
RD
sockeye frysockeye ps0+ smelt1+ smeltsticklebacksticklebackstickleback
Day
Crepuscular
Night
Urban lightpollution
Seasonal & Dielprey densitiesWinter Spring Summer
Fall
Prey fish (40-150 mm)
Winter 2003 Day0
30
60
Night
60
30
0
Prey fish Density
Day Night
Spring 2002
Fall 2003
Summer 2003
Spring 2003
Winter 2003
Fall 2002
Summer 2002
0
30
60
0
30
60
60
60
60
60
60
30
30
30
30
30
0
0
0
0
0
Area 3 only
Area 3 only
Area 3 only
0
0
0
0
0
0
0
30
30
30
30
30
30
30
60
60
60
60
60
60
60
Prey fish / m3
Prey fish densities
Road map
Model structure
Models within the model
Data collection and inputs
SE Results and corroboration
Conclusion
-0 .002 0 0.003 0.008 0.013 0.02
Growth Potential (g/g/day)
W inter 2003 (n ight)
0
20
40
60
40
20
Spring 2003 (n ight)
Sum m er 2002 (n ight)
Spring 2002 (n ight)
6 0
2 0
2 0
2 0
4 0
4 0
4 0
6 0
6 0
6 0
Fall 2002 (n ight)
Dep
th (m
)
One mid-lake transect
Smelt reach 40 mm
Growth potential
Winter 2003 Day0
30
60
Night
Growth Potential (g/g/day)60
30
0
0
30
60
0
30
60
60
60
60
60
60
30
30
30
30
30
0
0
0
0
0
Area 3 only
Area 3 only
Area 3 only
0
0
0
0
0
0
0
30
30
30
30
30
30
30
60
60
60
60
60
60
60
Day Night
Growth Potential (g/g/day)
Spring 2002
Fall 2003
Summer 2003
Spring 2003
Winter 2003
Fall 2002
Summer 2002
Growth Potential
May 2003
Lake Area (South to North)
12345
0.0
0.1
0.2
0.3
February 2003
0.1
0.2
0.3
October 2002
Pro
por
tion
posi
tive
gro
wth
cel
ls
0.1
0.2
0.3
August 2002
0.1
0.2
0.3
May 2002
0.1
0.2
0.3
* * ** No data available
Day
12345
0.0
0.1
0.2
0.3
0.1
0.2
0.3
0.1
0.2
0.3
0.1
0.2
0.3
0.1
0.2
0.3Night
No consistent trends
Area 4 generally highestDaytime estimate
Cutthroat trout condition
sprin
g 02
sum
mer
02
fall
02
win
ter
03
sprin
g 03
sum
mer
03
fall
03
win
ter
04
Slo
pe
2.75
3.00
3.25
3.50
3.75
Gro
wth
(g/
year
)
100
120
140
160
180
200
220
240
Cutthroat trout growth potential
sprin
g 02
sum
mer
02
fall
02
win
ter
03
sprin
g 03
sum
mer
03
fall
03
win
ter
04
Pro
port
ion
of p
ositi
ve c
ells
0.00
0.05
0.10
0.15
0.20
0.25
0.007
Back calculated growth age 3-4
Delayed response
Cutthroat trout condition
Back calculated Annual growthAgrees with GP estimates
Winter and spawning maycontribute
May 2003
Night
Night0
30
60
0
30
60
Constant 0.5 m RD
Light-dependent RD
Growth Potential (g·g-1·day-1)
Constant RDincreased the valueof dark deep waterhabitat to the growthof cutthroat trout
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 BeauchampPat 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, BarbaraWDFW- Chad Jackson, Casey Baldwin
Funding:Utah Coop Unit, UDWRWACFRU, King County (SWAMP)City of Seattle, City of Bellevue
Tom Lowman