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

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Output graphs – compare assumptions1020 Aspen Average

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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

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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