fgya 2006 01 prsnttn postharveststanddevconference practicalityversatilityandvalidityasguidingprinci
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https://foothillsri.ca/sites/default/files/null/FGYA_2006_01_Prsnttn_PostHarvestStandDevConference_PracticalityVersatilityandValidityasGuidingPrinciplesinStandModelDevelopment.pdfTRANSCRIPT
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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
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Main Objectives
Modelling issues & priorities in Alberta
Highlights of GYPSY
– Growth and Yield Projection System
Model validation
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Foundations for Sustainable
Forest Management (SFM)
Data
Model
Sustainable AAC
Regeneration
Protection
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-you cannot manage it
-you cannot understand it
Importance of Models in SFM
If you cannot model it…
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Decision-making Without Model Senior managers
Executive gurus
Deputy Gods…
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Decision-making With Adequate Model
Model
Data and theory
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Decision-Making With Inadequate Model(s)
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Purposes & Fundamentals of Modelling
1. Solve real-world problems
2. Increase understanding
3. Advance science
Theory
Data
Modelling technique and approach
Experience
$$$
Purposes:
Fundamentals:
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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, …
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Searching the “Best”…
-No “best”
-Towards
hybridizing
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Searching the “Best”…
-More “advanced”
-More requirements
-More complex
-Further and further
from reality…
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Simplistic
Complex
Simplicity
The “Best” Model is…“Simple”
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The “Best” Model…
…Solves Real-world Problems
Provide a solution
– Realistic
– Practical
– Scientifically defendable
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-Regen surveys/standards, plans, policies & landscapes
Issue #1 – Linkage to Operations
Modeller
V = mc2 + oaf1 + oaf2 + OOPS…
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Issue #2 - Post-harvest vs Natural Stands
Issue #3 - Pure vs Mixed-species Stands
Issue #4 - Spatial vs Non-spatial
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PLOT=4 LPSPSUB=1 MEASURE=4
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Natural
Post-harvest
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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…
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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
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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
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Percent Stocking – Spatial Clumpness
PS = # of 10 m2 plots with tree(s) / total # of 10 m2 plots
10 m2
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Percent Stocking – Data Lodgepole pine (PL)
SB
SW
AW
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PS Model Example – Up to 4 Species Mixed
)505050)ln()]1[log(exp(1
)505050)ln()]501[log(exp(150
36251432
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swsbawplpl
swsbawplplpl
PSxbPSxbPSxbSIbtotagebb
PSxbPSxbPSxbSIbbbPSPS
PL starting at
80% with 0, 20,
50, 80, 100% AW
SW starting at
80% with 0, 20,
50, 80, 100% AW
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Post-harvest Lodgepole pine (PL)
Photo by: Rory Thompson Photo by: Laurance Aiuppy
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GYPSY Mortality Model - Data
Lodgepole pine (PL)
SB
SW
AW
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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)
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GYPSY Crown Cover (CC) Model -Predict CC – dissolved, un-dissolved & overlaps
-Predict CC changes & link to photos (AVI)
PSP #1 - 1960 PSP #1 - 2002
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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
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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
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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
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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
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YOUR MODEL AS SEEN BY…
Yourself - modeler
Model user Model validator
Your pupil
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Separate data
Graphical validity
Validation stats - fit index, prediction errors
Theory
Individual & system behaviours
Operational
Backward & forward projections
Model Validation Guidelines
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Model Validation
error
error
Forward projection Backward projection
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error
error
Backward & forward projections
Model Validation
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Example - PL Mortality (Fit Index = 0.95)
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Example - PL Mortality Error Trajectories
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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)
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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
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A picture is worth 1000 words
A model is worth 1000 pictures
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Slave Lake, Alberta in year 3000
(climate model #2, day-dream version)
Fgya 2006 02 prsnttn postharveststanddevconference developmentcalibrationevaluationandapplicationofs
Afgo 2010 10 prsnttn confpresforestsandcarbonpositivefeedbacktoclimatechangeoropportunitiesforclimat
Fgya 2006 02 prsnttn postharveststanddevconference incorporatinggeneticgainintostandlevelmodeling ya
Fgya 2006 01 prsnttn postharveststanddevconference arobustgrowthmodellingstrategyforpredictionofgene
Fgya 2007 08 prpsl friaa0708provincialprojectsinitiativemonitoringanddecisionsupportforforestmanagem