The Influence of Natural and Anthropogenic Perturbations on

Lake Riparian Forest and Coarse Woody Debris

Modeling Linkages Between Aquatic and Terrestrial Ecosystems

September 26, 2002

Greg Sass

This is a collaborative effort!

• NSF-Biocomplexity Project• Dr. Monica Turner• Dr. Stephen Carpenter• Isaac Kaplan, Anna Sugden-Newbery, Anthony

Yannarell, Theodore Willis, Greg Sass• Scott van Egeren, Michelle Parara

Biocomplexity

Riparian forest, land, people, and lakes

http://limnology.wisc.edu

Click on research link, follow to biocomplexity

web page

Relationship between CWD density and shoreline development in N. Wisconsin

lakes

CWDDensity(no./km)

Shoreline DevelopmentFrom Christensen et al. 1996

-Also truefor MNmacrophytes!

Relationship between fish growth and coarse woody debris (CWD) in N. Wisconsin

lakes

logGrowthRate(mm/yr)

CWD Density (no./km)

High Development

Low Development

Undeveloped

High Development

Low Development

Undeveloped

From Schindler et al. 2000

How are changes on land and land/water interface reflected in the

adjacent lake ecosystem?

• Does the riparian forest influence fish populations?

– The riparian forest is linked to fish through Coarse Woody Debris (CWD)

CWD abundance influenced by:

• Forest structure (Harmon et al. 1986, Hely et al. 2000)

– Successional state

• Natural and anthropogenic disturbance (Christensen et al. 1996, Guyette and Cole 1999 , Hely et al 2000)

– Windthrows– Logging– Lakeshore development

Photo courtesy of Michelle Parara

Why model CWD dynamics?

• These are big systems with slow (and fast) dynamics!! Photo courtesy of Michelle Parara

923 year-old white pine in

Swan Lake, Ontario

Guyette and Cole 1999

Three Aspects Compose the Linked Terrestrial-Aquatic

Model

Terrestrial Terrestrial-AquaticInterface

Aquatic

Riparian Model Fish Model

Main Goals of the Wood Model

1. Create CWD via riparian forest that can be affected by both natural and anthropogenic processes

2. Simulate realistic CWD densities that can be used to test hypotheses/ask questions about effect of CWD on fish communities

Conceptual Structure of the Wood Model

Saplings

Adults

Snags CWD

Graduation

Trees that die and stay upright

Trees that die and fall immediately

Falling

Recruitment

Loss to decay and deep water

Falling away from water

Falling away from water

Two Pools of Trees:

• “Softwoods”– Representative of early

succession canopy– Paper birch (Betula

papyrifera), aspens (Populus spp)

• “Hardwoods”– Representative of mid-late

succession canopy– White pine (Pinus strobus),

sugar maple (Acer saccharum)

Big tooth aspen (Populus grandidentata)

Paper birch(Betula papyrifera)

White pine (Pinus strobus)

Sugar maple (Acer saccharum)

Riparian Model Formulas• SAPLINGS

Si(t+1) = Si(t) + {Ai(t)ri * (1-αjiAj(t) – αjiAi(t))} - Si(t)gi

• ADULT TREES

Ai(t+1) = Ai(t) + Si(t)gi - mi Ai(t);

• STANDING SNAGS

SSi(t+1) = SSi(t) + (1-Li) mi Ai(t) - fi SSi(t);

• COARSE WOODY DEBRIS

Di(t+1) = Di(t) + (γ fi SSi(t)) + {γ fi SSi(t) Li mi Ai(t)} - (a11 + a21) Di(t) ;

“Shading” terms

Conceptual Structure of the Wood Model

Hardwood Saplings

Adult Hardwoods

Graduation

Recruitment

Softwood Saplings

Adult Softwoods

Graduation

Recruitment

“Shading”(+) (+)

(-)(-)

Model Scenarios• Baseline Scenario

– Riparian forest density from Turner 2001 and Christensen et al. 1996

– CWD values from undeveloped Little Rock Lake in Vilas County, WI

• Windthrow Scenario– 65% instantaneous death of

hardwood and softwood adults and snags

• Clearcut Scenario– 95% instantaneous death of

hardwood and softwood adults and snags

• Development Scenario– 1% annual loss of adult

hardwoods and softwoods– 5% annual loss of Snags and

CWD

Little Rock Lake

Adult Tree and CWD Dynamics During Baseline Scenario

0

200

400

600

0 50 100 150 200 250 300 350 400 450 500Years

# L

og

s/km

sh

ore

line

0

500

1000

1500

2000

# T

ree

s/k

m S

ho

relin

e

HardwoodsSoftwoods

Adult Trees

CWD

Adult Tree and CWD Dynamics During Windthrow Scenario

0

500

1000

1500

2000

# T

ree

s/k

m S

ho

relin

e

0

200

400

600

0 50 100 150 200 250 300 350 400 450 500Years

# L

og

s/k

m s

ho

relin

e

HardwoodsSoftwoods

Adult Trees

CWD

CWD Dynamics During Windthrow Scenario

0

200

400

600

800

0 100 200 300 400 500Years

Har

dw

oo

d +

So

ftw

oo

d

CW

D/k

m S

ho

relin

e

CWD Abundance (all Trees) Following FireDisturbance

Hely et al. 2000

0

500

1000

1500

2000

# T

rees

/km

Sh

ore

lin

e

0

200

400

600

0 100 200 300 400 500

Time (years)

# L

og

s/k

m S

ho

relin

eAdult Tree and CWD Dynamics

During Clearcut ScenarioHardwoodsSoftwoods

Adult Trees

CWD

CWD Dynamics During Clearcut Scenario

0

500

1000

1500

2000

2500

0 100 200 300 400 500

Time (years)

Ha

rdw

oo

d +

So

ftw

oo

d

CW

D/k

m S

ho

relin

e

Adult Tree and CWD Dynamics During Development Scenario

0

200

400

600

0 50 100 150 200 250 300 350 400 450 500Years

# L

og

s/k

m s

ho

relin

e

0

500

1000

1500

2000

# T

ree

s/k

m S

ho

relin

e

HardwoodsSoftwoods

Adult Trees

CWD

CWD Dynamics during Development Scenario

0

200

400

600

800

0 50 100 150 200 250 300 350 400 450 500

Years

Har

dw

oo

d +

So

ftw

oo

d

CW

D/k

m S

ho

relin

e

Can the model mimic ‘real’ history?

Skidding Logs, Upper Chippewa Basin, Circa 1890

Lakeshore DevelopmentLast ~50 years

Taming the Northwoods

0

200

400

600

0 100 200 300 400 500

Time (years)

# L

og

s/k

m S

ho

relin

e

0

500

1000

1500

2000

# T

rees

/km

Sh

ore

lin

e

HardwoodsSoftwoods

Adult Trees

CWD

Clear cut Development

CWD Dynamics in Clearcut + Development Scenario

0

200

400

600

0 100 200 300 400 500

Time (years)

# L

og

s/k

m S

ho

relin

e

Summary of Wood Model

• Model is simple, but fairly realistic

• Windthrows and clearcuts have long-term effects on CWD pool

• Development a powerful force

Conclusions

• This ecosystem-level model is a useful tool for creating questions about CWD inputs/removals.– Can we devise ways to observe long-term changes in

riparian forest and CWD structure?– Does indiscriminate thinning actually occur?– How long does it take for the CWD pool to recover?– How do changes in CWD abundance affect fish

communities?

• How can we obtain answers to these questions?

Biocomplexity Cross-lakes Crew

• Led by Michelle Parara and Scott van Egeren

• Riparian forest/CWD analysis: Anna Sugden-Newbery

Modeling linkages between terrestrial

and aquatic ecosystems part II:

The influence of riparian forest dynamics on aquatic

food webs

Isaac Kaplan, Tanya Havlicek, Pieter Johnson, Brian Roth,Greg Sass, Anna Sugden-Newbery, Theodore Willis, Anthony Yannarell, Monica Turner, and Steve Carpenter

Biocomplexity

Riparian forest, land, people, and lakes

Relationship between fish growth and coarse woody debris in N. Wisconsin lakes

logGrowthRate(mm/yr)

Coarse Woody Debris Density (no./km)

High Development

Low Development

Undeveloped

High Development

Low Development

Undeveloped

From Schindler et al. 2000

Conceptual Model

Forestcoarsewoody debris

Growth

Humans

Senescence

Windthrow

Development

Decay/Physical Transport

AquaticFood Web

Fishing

Terrestrial Terrestrial-Aquaticinterface

Aquatic

Questions• How does the aquatic food web respond to

stable levels of coarse woody debris?

• How does the food web respond to perturbations?- windthrow, development, fishing

• How can we learn about effects of coarse woody debris on fish predation and growth rates in experimental lakes?

Fish biomass dynamics model

juv. piscivore

adult piscivore

benthivore

insects

Hypothesized Effects of Coarse Woody Debris on Fish Community

Insect Abundance

0

0.2

0.4

0.6

0.8

1

015

030

045

060

075

090

0

logs /km shoreline

kg

of

tric

ho

pte

ran

s +

o

do

na

tes

/ha

Response of Vulnerability and Hiding

0

0.5

1

1.5

2

0 200 400 600 800logs/km of shoreline

par

amet

er v

alu

e

vulnerability ofbenthivore

hiding ofbenthivore

vulnerability ofjuv. piscivore

hiding of juv.piscivore

Fish Model: Benthivore Biomass Equation

dB/dt=G-mB2-P2 –P3

G=fishC*g1 +BugC*g2

Bt+1=Bt-harvest

---------------------------------------------------Functional Response:

Piscivory

B1-V1 V1

B2

hiding vulnerable

v1

h1 predators

c12

Hypothesis 1:

Similar piscivore and benthivore behavioral response to logs

Response of Vulnerability and Hiding

0

0.5

1

1.5

2

0 200 400 600 800logs/km of shoreline

par

amet

er v

alu

e

vulnerability ofbenthivore

hiding ofbenthivore

vulnerability ofjuv. piscivore

hiding of juv.piscivore

Fish Biomass at Steady State

0

5

10

15

20

25

30

35

50 500 1000 500 andfishing

kg/ h

ecta

re benthivore

juv. piscivore

adult piscivore

logs / km of shoreline

Hypothesis 2:

Benthivore is less dependent on refuge than piscivore

Response of Vulnerability and Hiding

0

0.5

1

1.5

2

0 100 200 300 400logs/km of shoreline

par

amet

er v

alu

e

vulnerability ofbenthivore

hiding ofbenthivore

vulnerability ofjuv. piscivore

hiding of juv.piscivore

Fish Biomass at Steady State

0

10

20

30

40

50

50 500 1000 500 andfishing

kg/ h

ecta

re benthivore

juv. piscivore

adult piscivore

logs / km of shoreline

BenthivoreJuvenile PiscivoreAdult PiscivoreCoarse Woody Debris

Windthrow

Fish Biomass Response to Windthrow

years

100 200 300 400 500

kg o

f fi

sh /

hec

tare

0

10

20

30

Lo

gs /km

of sh

orelin

e

0

100

200

300

400

500

600

700

Fish Biomass Response to Windthrowand Fishing

years

100 200 300 400 500

kg o

f fi

sh /

hec

tare

0

10

20

30L

og

s /km o

f sho

reline

0

100

200

300

400

500

600

700

fishing starts

BenthivoreJuvenile PiscivoreAdult PiscivoreCoarse Woody Debris

Development

Fish Biomass Response to Development

years

100 200 300 400 500

kg o

f fi

sh /

hec

tare

0

10

20

30

Lo

gs /km

of sh

orelin

e

0

100

200

300

400

500

600

Fish Biomass Response to Development and Fishing

years

100 200 300 400 500

kg o

f fi

sh /

hec

tare

0

10

20

30L

og

s /km o

f sho

reline

0

100

200

300

400

500

600

fishing starts

Conclusions

• Coarse woody debris could be a major driver of fish community dynamics (and we will test this)

• Effect of development is much greater than effect of windthrow

• For benthivore, moderate reductions in coarse woody debris may be balanced by fishing on piscivore

• Help Greg chop down trees this winter

Work in ProgressField experiments in N. Wisconsin: • Removal of coarse woody debris from Little Rock Lake• Addition of coarse woody debris to Camp Lake• Observations of growth and abundance• Estimation of predation and vulnerability parameters and

hypothesis testing

Acknowledgements

Michele Parara, Scott VanEgeren, and the Biocomplexity Field Crew

This work is funded by the National Science Foundation under Cooperative Agreement #DEB-0083545

Increasing vulnerability

of benthivore

Increasing vulnerability

of benthivore

050

100150

200250

0

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10000

10

20

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40

inflection pt. for benthivore

Benthivore Response to CWD

logs / km

kg

of

fis

h /h

a

0100

200300

0

500

10000

20

40

60

inflection pt. for benthivore

Piscivore Response to CWD

logs / km

kg

of

fis

h /h

a