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
-Also true
for MN
macrophytes!
CWD
Density
(no./km)
Shoreline Development
From Christensen et al. 1996
Relationship between fish growth and
coarse woody debris (CWD) in N. Wisconsin
lakes
Undeveloped
Undeveloped
log
Growth
Rate
(mm/yr)
Low Development
Low Development
High Development
High Development
CWD Density (no./km)
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 yearold white
pine in
Swan Lake,
Ontario
Guyette and Cole 1999
Three Aspects Compose
the Linked Terrestrial-Aquatic
Model
Terrestrial Terrestrial-Aquatic
Interface
Riparian Model
Aquatic
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
Recruitment
Saplings
Falling away
from water
Graduation
Adults
Trees that die and
stay upright
Trees that die and
fall immediately
Snags
Falling away
from water
CWD
Falling
Loss to decay and
deep water
Two Pools of Trees:
• “Softwoods”
– Representative of early
succession canopy
– Paper birch (Betula
papyrifera), aspens
(Populus spp)
Big tooth aspen
(Populus grandidentata)
Paper birch
(Betula papyrifera)
• “Hardwoods”
– Representative of mid-late
succession canopy
– White pine (Pinus strobus),
sugar maple (Acer
saccharum)
White pine
(Pinus strobus)
Sugar maple
(Acer saccharum)
Riparian Model Formulas
• SAPLINGS
“Shading” terms
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) ;
Conceptual Structure of the
Wood Model
(+)
Hardwood
Saplings
“Shading”
(-)
Graduation
Recruitment
Adult
Hardwoods
Softwood
Saplings
(+)
(-)
Graduation
Adult
Softwoods
Recruitment
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
Little Rock Lake
• Development Scenario
– 1% annual loss of adult
hardwoods and softwoods
– 5% annual loss of Snags and
CWD
Adult Tree and CWD Dynamics
During Baseline Scenario
# Logs/km shoreline # Trees/km Shoreline
Hardwoods
Softwoods
2000
1500
Adult Trees
1000
500
0
600
400
CWD
200
0
0
50
100
150
200
250 300
Years
350
400
450
500
Adult Tree and CWD Dynamics
During Windthrow Scenario
# Logs/km shoreline # Trees/km Shoreline
Hardwoods
Softwoods
2000
1500
Adult Trees
1000
500
0
600
400
CWD
200
0
0
50
100
150
200 250
Years
300
350
400
450
500
CWD Dynamics During
Windthrow Scenario
Hardwood + Softwood
CWD/km Shoreline
800
600
400
200
0
0
100
200
300
Years
400
500
CWD Abundance (all Trees)
Following FireDisturbance
Hely et al. 2000
Adult Tree and CWD Dynamics
During Clearcut Scenario
# Logs/km Shoreline
# Trees/km Shoreline
Hardwoods
Softwoods
2000
1500
Adult Trees
1000
500
0
600
400
CWD
200
0
0
100
200
300
Time (years)
400
500
Hardwood + Softwood
CWD/km Shoreline
CWD Dynamics During
Clearcut Scenario
2500
2000
1500
1000
500
0
0
100
200
300
Time (years)
400
500
Adult Tree and CWD Dynamics
During Development Scenario
# Trees/km Shoreline
2000
# Logs/km shoreline
Hardwoods
Softwoods
600
1500
Adult Trees
1000
500
0
400
CWD
200
0
0
50
100
150
200
250 300
Years
350
400
450
500
CWD Dynamics during
Development Scenario
Hardwood + Softwood
CWD/km Shoreline
800
600
400
200
0
0
50 100 150 200 250 300 350 400 450 500
Years
Can the model mimic ‘real’ history?
Skidding Logs, Upper Chippewa Basin,
Circa 1890
Lakeshore Development
Last ~50 years
# Logs/km Shoreline
# Trees/km Shoreline
Taming the Northwoods
Hardwoods
Softwoods
2000
Clear cut
Development
1500
Adult Trees
1000
500
0
600
400
CWD
200
0
0
100
200
300
Time (years)
400
500
CWD Dynamics in Clearcut +
Development Scenario
# Logs/km Shoreline
600
400
200
0
0
100
200
300
Time (years)
400
500
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
Undeveloped
Undeveloped
log
Growth
Rate
(mm/yr)
Low Development
Low Development
High Development
High Development
Coarse Woody Debris Density (no./km)
From Schindler et al. 2000
Conceptual Model
Terrestrial
Terrestrial-Aquatic
interface
Aquatic
Growth
Senescence
coarse
woody debris
Forest
Aquatic
Food Web
Windthrow
Humans
Development
Decay/
Physical Transport
Fishing
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
adult piscivore
juv. piscivore
benthivore
insects
Hypothesized Effects of Coarse Woody Debris on Fish Community
Insect Abundance
kg of
trichopterans +
odonates /ha
1
0.8
0.6
0.4
0.2
0
90
0
75
0
60
0
45
0
30
0
15
0
0
logs /km shoreline
Response of Vulnerability and Hiding
parameter value
2
vulnerability of
benthivore
1.5
hiding of
benthivore
1
vulnerability of
juv. piscivore
0.5
hiding of juv.
piscivore
0
0
200
400
600
800
logs/km of shoreline
Fish Model: Benthivore Biomass
Equation
dB/dt=G-mB2-P2 –P3
G=fishC*g1 +BugC*g2
Bt+1=Bt-harvest
--------------------------------------------------Functional Response:
Piscivory
v1
B1-V1
hiding
V1
h1
vulnerable
c12
B2
predators
Hypothesis 1:
Similar piscivore and benthivore behavioral response to logs
Response of Vulnerability and Hiding
parameter value
2
vulnerability of
benthivore
1.5
hiding of
benthivore
1
vulnerability of
juv. piscivore
0.5
hiding of juv.
piscivore
0
0
200
400
600
800
logs/km of shoreline
kg/ hectare
Fish Biomass at Steady State
35
30
25
20
15
10
5
0
benthivore
juv. piscivore
adult piscivore
50
500
1000
logs / km of shoreline
500 and
fishing
Hypothesis 2:
Benthivore is less dependent on refuge than piscivore
Response of Vulnerability and Hiding
parameter value
2
vulnerability of
benthivore
1.5
hiding of
benthivore
1
vulnerability of
juv. piscivore
0.5
hiding of juv.
piscivore
0
0
100
200 300 400
logs/km of shoreline
Fish Biomass at Steady State
kg/ hectare
50
40
benthivore
30
juv. piscivore
20
adult piscivore
10
0
50
500
1000
logs / km of shoreline
500 and
fishing
Fish Biomass Response to Windthrow
Windthrow
30
600
500
20
400
300
10
200
100
0
100
Fish Biomass Response to Windthrow
and Fishing
200
300
400
0
500
years
30
600
500
20
400
300
10
200
100
0
100
200
300
years
400
0
500
fishing starts
Logs /km of shoreline
kg of fish / hectare
700
Benthivore
Juvenile Piscivore
Adult Piscivore
Coarse Woody Debris
Logs /km of shoreline
kg of fish / hectare
700
Fish Biomass Response to Development
Development
30
500
400
20
300
200
10
100
0
100
Fish Biomass Response to Development
and Fishing
200
300
400
0
500
years
30
500
400
20
300
200
10
100
0
100
200
300
years
400
0
500
fishing starts
Logs /km of shoreline
kg of fish / hectare
600
Benthivore
Juvenile Piscivore
Adult Piscivore
Coarse Woody Debris
Logs /km of shoreline
kg of fish / hectare
600
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 Progress
Field 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
Piscivore Response to CWD
Benthivore Response to CWD
60
kg of fish /ha
kg of fish /ha
40
30
40
20
20
10
0
1000
0
1000
500
logs / km
0
0
50
100
150
200
300
250
inflection pt. for
benthivore
500
200
logs / km
0 0
100
inflection pt. for
benthivore
Increasing vulnerability
Increasing vulnerability
of benthivore
of benthivore