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FVS, State - Transition Model Assumptions, and Yield tables – an Application in National Forest Planning. Eric Henderson Analyst, Hiawatha National Forest, Michigan. Presentation Overview. The Hiawatha National Forest Forest Planning Management Questions Models used to answer questions - PowerPoint PPT Presentation
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FVS, State - Transition Model Assumptions, and Yield tables – an
Application in National Forest Planning Eric Henderson
Analyst, Hiawatha National Forest, Michigan
Presentation Overview The Hiawatha National Forest Forest Planning Management Questions Models used to answer questions FVS application – an example Conclusions
The Hiawatha National Forest Located in Michigan’s Upper Peninsula Approximately 900,000 acres Recent plan revision completed Spring, 2006
Ecological Setting – Hiawatha 8 Ecological Land Types (ELTs) identified
Distinct ecological function Successional pathways Disturbances Climax Vegetation group
Seral Classes Within each ELT all possible seral classes are
identified (Approx. 130 total) Example – within ELT 10/20:
J1 - Regenerated jack pine : 0 – 4.5 feet tall J2 - Seed/Sap jack pine: 4.5 feet – 5” DBH J3 - Pole jack pine: 5” – 9” DBH J4 - Mature jack pine: 9”- 18” DBH J5 - Overmature jack pine: 18”+ DBH (improbable)
Key Vegetation Management Questions:
1. What do we want the forest to look like (desired future conditions)?
Manager/specialist – derived Set for each seral class
2. What are the natural processes that affect vegetative conditions?
3. How do we move to desired condition from our current status?
Models to address Questions 2 and 3 What are the natural processes to consider?
Vegetation Dynamics Development Tool (VDDT) Simple state-and-transition model Easy to change and evaluate assumptions
How to move to desired conditions? Spectrum linear programming model
Can emulate the function of a state-transition model (VDDT)
Selects optimal management strategy to move to desired condition
VDDT – A state-transition model
Inputs:
• Successional pathways and probabilities
• Disturbance pathways and probabilities
• Starting Conditions
Spectrum – model inputs (Model II/Model III formulation) Successional paths and probabilities Disturbance paths and probabilities Starting Conditions Treatment options Economic information (including timber
values and volumes) Goals (Desired Future Conditions) Constraints (e.g. non-declining yield)
Data Sources Succession
Expert opinion, empirical data, scientific study (?) Disturbance paths and probabilities
Expert opinion, historical data, scientific study Starting Conditions
Forest Database Treatment options
Silviculturist Economic information
Historical data Goals (Desired Future Conditions)
Managers Constraints (e.g. non-declining yield)
Plan directives, Laws, etc.
Goal of this studyProvide analysis to support or strengthen
model succession assumptions
Provide analysis to support or strengthen model growth and yield assumptions
Why are the assumptions important?A few reasons: Growth rates affect management rotation
lengths Some disturbance probabilities linked to
structure and/or size classifications Forest plan vegetation goals set for each state
(model “box”)
Successional Pathways
Example: the aspen “A” trajectory 5 size classes (states) Min/max goals set for each state Ages associated with each state – how good are our
expert-derived assumptions?
How did we assess our assumptions? We used FVS to simulate stand growth and capture
state “switches” FIA data stratified by Ecological Land Type and dominant
covertype (source: FIA forest type call, GIS intersection with ELT map)
FIA-derived age was analyzed and outliers were modified or adjusted to strengthen starting point
Other calibration files developed: maximum BA, max SDI, diameter growth rate, defect, maximum tree sizes
Algorithmic keyfiles developed to capture seral class at each age
FVS was also used to develop yield tables
Ex: One Species Even-aged stand
No
NoNo
Yes
No No No
Yes YesYes
No
Yes
No
Yes
No
Yes
Determine whether stand is species of
concern
BA of species < 30% of total?
At each time step
Determine Size Class 1
Remove stand from analysis
TPA of species < 20% of
total
Avg Hght 30%-70% tree < 4.5
ft?
TPA under 4.5 ft <
TPA over 4.5 ft?
Size Class 1
Determine Size Class 2-5
Size Class 2 has
greatest BA?
Size Class 3 has
greatest BA?
Size Class 4 has
greatest BA?
Size Class 2 Size Class 3 Size Class 4 Size Class 5
Other algorithms developed Even-aged multi-species seral types Uneven-aged multi-species seral types
Results Succession State Key metrics
Average seral class Mode Number of plots
Yield tables from historic data vs. FVS-derived yield tables
Output file Remove outliers Remove succession Quantify outputs
_STAGE ,_SZCLASS 5 ,1 ,2 ,3 ,10 ,1 ,2 ,3 ,2 ,15 ,1 ,2 ,3 ,2 ,20 ,2 ,3 ,3 ,2 ,25 ,2 ,3 ,4 ,2 ,30 ,3 ,3 ,4 ,3 ,3 ,35 ,3 ,3 ,4 ,3 ,3 ,40 ,3 ,4 ,4 ,3 ,3 ,45 ,3 ,4 ,4 ,3 ,3 ,4 ,50 ,3 ,4 ,4 ,4 ,3 ,4 ,55 ,3 ,4 ,4 ,5 ,4 ,4 ,3 ,1 ,4 ,60 ,3 ,4 ,4 ,2 ,4 ,4 ,4 ,3 ,1 ,4 ,65 ,3 ,4 ,4 ,2 ,4 ,6 ,4 ,4 ,4 ,1 ,4 ,70 ,3 ,4 ,4 ,2 ,4 ,6 ,4 ,4 ,4 ,1 ,4 ,75 ,4 ,4 ,4 ,2 ,4 ,6 ,4 ,4 ,4 ,1 ,4 ,4 ,80 ,4 ,4 ,4 ,3 ,4 ,6 ,4 ,4 ,4 ,4 ,4 ,4 ,85 ,4 ,4 ,4 ,3 ,4 ,6 ,4 ,4 ,4 ,4 ,4 ,4 ,90 ,4 ,4 ,4 ,3 ,4 ,6 ,4 ,4 ,4 ,4 ,4 ,4 ,95 ,4 ,4 ,4 ,3 ,4 ,6 ,4 ,4 ,4 ,4 ,4 ,4 ,100 ,4 ,4 ,4 ,3 ,4 ,4 ,4 ,4 ,4 ,4 ,4 ,105 ,4 ,3 ,4 ,4 ,4 ,4 ,4 ,4 ,4 ,110 ,4 ,3 ,4 ,4 ,4 ,4 ,4 ,4 ,115 ,4 ,3 ,4 ,4 ,4 ,4 ,4 ,4 ,120 ,4 ,4 ,4 ,4 ,4 ,4 ,4 ,4 ,125 ,4 ,4 ,4 ,4 ,4 ,3 ,4 ,4 ,130 ,4 ,4 ,5 ,4 ,3 ,4 ,4 ,135 ,4 ,4 ,5 ,4 ,3 ,4 ,4 ,140 ,4 ,4 ,5 ,4 ,4 ,4 ,4 ,145 ,4 ,4 ,5 ,4 ,4 ,4 ,150 ,4 ,4 ,5 ,5 ,4 ,4 ,155 ,5 ,5 ,4 ,160 ,5 ,5 ,165 ,5 ,170 ,5 ,
Simple Outputs Graphs1020 Aspen Average
0
1
2
3
4
5
6
0 50 100 150 200
Age
Siz
e C
lass
Size Class
10/20 Aspen Mode
0
1
2
3
4
5
6
0 50 100 150 200
Age
Siz
e C
lass
MODE
1020 Aspen Plot Count
0
2
4
6
8
10
12
0 50 100 150 200
Age
Nu
mb
er o
f P
lots
Num Points
Output graphs – compare assumptions1020 Aspen Average
0
1
2
3
4
5
6
0 50 100 150 200
Age
Siz
e C
lass
Size Class
Expert Assmp
10/20 Aspen Mode
0
1
2
3
4
5
6
0 50 100 150 200
Age
Siz
e C
lass
MODE
Expert Assmp
1020 Aspen Plot Count
0
2
4
6
8
10
12
0 50 100 150 200
Age
Nu
mb
er o
f P
lots
Num Points
Changes on the Hiawatha Of 26 successional pathways
8 were modified to reflect better information generated by FVS
The other 18 remained the same; FVS provided a basis of support for those assumptions
Yield Table Comparisons10/20 Aspen CC Total y = 1E-05x3 - 0.0035x2 + 0.3503x
R2 = 0.9668
y = 6E-06x3 - 0.0021x2 + 0.2218x
R2 = 0.3294
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0 50 100 150 200
Age
MB
F
FVS Output
TRACS
equation
Poly. (FVS Output)
Poly. (TRACS)
Changes on the Hiawatha 121 Yield tables developed
84 derived from FVS runs 37 derived from historic data/expert opinion
Mostly used where there were too few FIA plots
Conclusions Expert opinion on successional states mostly
supported FVS runs shed light on “gray areas” where model
succession assumptions were adjusted FVS provided good information for 70% of the
yields used in the Spectrum model
Better model assumptions lead to a better forest plan and more informed decisions