Practicality, Versatility and Validity as

Guiding Principles in

Stand Model Development

Shongming Huang

Presented on January 31, 2006 at

“Post-harvest Stand Development Conference”

Jan. 31-Feb. 1, 2006

Edmonton, Alberta, Canada

Main Objectives

Modelling issues & priorities in Alberta

Highlights of GYPSY

– Growth and Yield Projection System

Model validation

Foundations for Sustainable

Forest Management (SFM)

Data

Model

Sustainable AAC

Regeneration

Protection

-you cannot manage it

-you cannot understand it

Importance of Models in SFM

If you cannot model it…

Decision-making Without Model Senior managers

Executive gurus

Deputy Gods…

Decision-making With Adequate Model

Model

Data and theory

Decision-Making With Inadequate Model(s)

Purposes & Fundamentals of Modelling

1. Solve real-world problems

2. Increase understanding

3. Advance science

Theory

Data

Modelling technique and approach

Experience

$$$

Purposes:

Fundamentals:

Modelling Approaches & Model Types

Whole stand

• V = Age

• V = HT & HT = Site index, Age

• Stocking adjustment

Diameter distribution

Individual tree distance-independent

Individual tree distance-dependent

Practical or empirical

Process (physiological)

Hybrid

Simulation, ecological, bio-geo-climatic, …

Searching the “Best”…

-No “best”

-Towards

hybridizing

Searching the “Best”…

-More “advanced”

-More requirements

-More complex

-Further and further

from reality…

Simplistic

Complex

Simplicity

The “Best” Model is…“Simple”

The “Best” Model…

…Solves Real-world Problems

Provide a solution

– Realistic

– Practical

– Scientifically defendable

-Regen surveys/standards, plans, policies & landscapes

Issue #1 – Linkage to Operations

Modeller

V = mc2 + oaf1 + oaf2 + OOPS…

Issue #2 - Post-harvest vs Natural Stands

Issue #3 - Pure vs Mixed-species Stands

Issue #4 - Spatial vs Non-spatial

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Natural

Post-harvest

Modelling Issues…

#5 - Data – quality, minimum standards, spatial

#6 - Ground vs photos (AVI)

#7 - Enhanced vs non-enhanced

#8 - Integration with other factors

-Silviculture, genetics

-Forest health, protection

-Ecosystems, ecoregions, landscapes

-Climate change

-Others… So many issues, so little time…

1. Post-harvest stands

2. Pure & mixed-species stands

3. Linked to operations, esp. regen surveys/standards

4. Spatial capabilities

GYPSY Priorities

Models…

5. Linked to enhanced forest management

6. Linked to photo inventory (AVI)

7. Some integration capabilities

1. Top height-site index models

2. Percent stocking models

3. Mortality models

4. Crown cover models

5. Diameter increment models

6. Volume models

GYPSY Key Functions

7. Approximation routines

8. Localization routines

Percent Stocking – Spatial Clumpness

PS = # of 10 m2 plots with tree(s) / total # of 10 m2 plots

10 m2

Percent Stocking – Data Lodgepole pine (PL)

SB

SW

AW

PS Model Example – Up to 4 Species Mixed

)505050)ln()]1[log(exp(1

)505050)ln()]501[log(exp(150

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swsbawplpl

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PSxbPSxbPSxbSIbtotagebb

PSxbPSxbPSxbSIbbbPSPS

PL starting at

80% with 0, 20,

50, 80, 100% AW

SW starting at

80% with 0, 20,

50, 80, 100% AW

Post-harvest Lodgepole pine (PL)

Photo by: Rory Thompson Photo by: Laurance Aiuppy

GYPSY Mortality Model - Data

Lodgepole pine (PL)

SB

SW

AW

Spatial Mort Models – 4 Species Mixed PL Mort = PL (site, age, density, PS) + AW, SW, SB (densities, PSs)

SW Mort = SW (site, age, density, PS) + AW, PL, SB (densities, PSs)

GYPSY Crown Cover (CC) Model -Predict CC – dissolved, un-dissolved & overlaps

-Predict CC changes & link to photos (AVI)

PSP #1 - 1960 PSP #1 - 2002

1. Post-harvest & natural, pure & mixed stands

2. Mixed-species stand succession

3. Linked to regen surveys/standards

4. Spatial & non-spatial options

5. Linked to enhanced management (thinning)

6. Linked to AVI

7. Backward & forward projections

8. No ‘tree number-specific’ prediction; not landscape-

level yet; limited integration; no wood quality yet…

GYPSY Capabilities & Limits

Importance of Model Validation

-Verify “scientific” research

-Alberta may turn into a desert in 80 years, ditto for Sask.

-By 2080 B.C. could be a garden of pecans, sugar cane, and cotton

-By 2050 warming may doom million species

-Sea level may raise 7 meters in 1000 year

-Massive destruction…perhaps unmatched in times of peace

-Forests may be bad for planet

Verify “scientific” research…

Now:

-Large swaths of Ontario’s boreal forests are likely to die over the

next century due to climate change

20 years ago:

-Large areas of Ontario’s boreal forests are likely to die over the

next 30 years due to acid rain

Without validation: Research Science

Science – a state of knowing, a system of knowledge,

proven or verifiable

Research – activities based on or directed at science

Research science

Research “junk” science?

Research showed more researches are needed

Model Validation

30% science

10% ‘junk’

10% ‘junk’ science

10% create problems

20% ‘a la mode’ & hobby-horse research

20% showed more researches ($$$) are needed

YOUR MODEL AS SEEN BY…

Yourself - modeler

Model user Model validator

Your pupil

Separate data

Graphical validity

Validation stats - fit index, prediction errors

Theory

Individual & system behaviours

Operational

Backward & forward projections

Model Validation Guidelines

Model Validation

error

error

Forward projection Backward projection

error

error

Backward & forward projections

Model Validation

Example - PL Mortality (Fit Index = 0.95)

Example - PL Mortality Error Trajectories

Silviculture - “processes” vs “outcome”

Genetics, forest health, protection

Climate change

– May enhance lodgepole pine site productivity

– May increase lodgepole pine biomass

Others - economics, wood quality…

– No “acid rain” please

Areas for Model Integration Model = (site, density, spatial, age, species mix, ecoregion)

Next Steps and Expected Timelines

More validation

Finalize the components

Complete the α-version

Complete the β-version in 2 years

More integration and validation

Release in 4 years

A picture is worth 1000 words

A model is worth 1000 pictures

Slave Lake, Alberta in year 3000

(climate model #2, day-dream version)