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FVS, State Transition Model Assumptions, and Yield Tables – An Application in National Forest Planning

This presentation discusses the use of the Forest Vegetation Simulator (FVS) and state transition models to address forest planning management questions. It provides an example of FVS application in the Hiawatha National Forest and discusses the importance of assumptions in model succession and growth and yield. The study aims to support and strengthen these assumptions through analysis.

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FVS, State Transition Model Assumptions, and Yield Tables – An Application in National Forest Planning

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  1. FVS, State - Transition Model Assumptions, and Yield tables – an Application in National Forest Planning Eric Henderson Analyst, Hiawatha National Forest, Michigan

  2. Presentation Overview • The Hiawatha National Forest • Forest Planning Management Questions • Models used to answer questions • FVS application – an example • Conclusions

  3. The Hiawatha National Forest • Located in Michigan’s Upper Peninsula • Approximately 900,000 acres • Recent plan revision completed Spring, 2006

  4. Ecological Setting – Hiawatha • 8 Ecological Land Types (ELTs) identified • Distinct ecological function • Successional pathways • Disturbances • Climax Vegetation group

  5. 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)

  6. Key Vegetation Management Questions: • What do we want the forest to look like (desired future conditions)? • Manager/specialist – derived • Set for each seral class • What are the natural processes that affect vegetative conditions? • How do we move to desired condition from our current status?

  7. 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

  8. VDDT – A state-transition model • Inputs: • Successional pathways and probabilities • Disturbance pathways and probabilities • Starting Conditions

  9. 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)

  10. 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.

  11. Goal of this study Provide analysis to support or strengthen model succession assumptions Provide analysis to support or strengthen model growth and yield assumptions

  12. 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”)

  13. 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?

  14. 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

  15. At each time step Determine whether stand is species of concern BA of species < 30% of total? TPA of species < 20% of total Yes Yes No Determine Size Class 1 No TPA under 4.5 ft < TPA over 4.5 ft? Avg Hght 30%-70% tree < 4.5 ft? Yes Yes No No Determine Size Class 2-5 Size Class 2 has greatest BA? Size Class 3 has greatest BA? Size Class 4 has greatest BA? No No No No No Yes Yes Yes Remove stand from analysis Size Class 1 Size Class 2 Size Class 3 Size Class 4 Size Class 5 Ex: One Species Even-aged stand

  16. Other algorithms developed • Even-aged multi-species seral types • Uneven-aged multi-species seral types

  17. Results • Succession State Key metrics • Average seral class • Mode • Number of plots • Yield tables from historic data vs. FVS-derived yield tables

  18. Output file _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 , • Remove outliers • Remove succession • Quantify outputs

  19. Simple Outputs Graphs

  20. Output graphs – compare assumptions

  21. 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

  22. Yield Table Comparisons

  23. 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

  24. 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

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