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 study Provide 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 Yes No Yes 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 2Size 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 Graphs

Output graphs – compare assumptions

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 Comparisons

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