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