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Modelling Natural Regeneration in Mountain Pine Beetle Affected Stands
A Hybrid Model Approach
Derek Sattler,M.Sc. Candidate
Faculty of Forestry. University of British Columbia, Vancouver, Canada.
Mountain Pine Beetle (MPB) Epidemic Lodgepole Pine (Pinus contorta var. latifolia)
Cumulative Volume Killed in All 'Pine' Units
0
250
500
750
1000
2000 2005 2010 2015 2020 2025year
tim
ber
vo
lum
e (1
0000
00's
of
m^
3)
Projected Kill
Observed Kill
Mil
lio
ns
of
m3
~ 80%
Dendroctonus ponderosae
Cumulative Volume Killed on the Timber Harvesting Landbase
Source: BC MoF, 2005
Stand Dynamics Post-MPB Attack
Highly variable snag fall rates (5 – 15 years)
Expect to see small tree release
Changing light dynamics
15-20 year regeneration delay
Challenge to model regeneration Uncertainty in Yield Projections
Candidate Growth Models:
1) SORTIE-ND Forest Ecology Model
2) PrognosisBC
Forest Management Tool
Input data: tree list, site info
Small treesHt then DBH growth
Large TreesDBH then Ht growth
MortalityCompetition, dbh, etc
Change in Crown
Regeneration
results
Thinning
smoothing
PROGNOSISBC Model Flow
Project Specific PrognosisBC Advantages
1) Calibrated using local data
2) Designed for complex, mixed stands
3) Includes Site factors – transportable
4) Government supported model
PrognosisBC Project Disadvantages
1) Poor results with Regeneration Submodel
2) No Post-MPB specific Mortality Submodel
3) Not Spatially Explicit
(i.e., Clumped vs. Even distribution)
SORTIEND Model Flow
Input data: tree list, location
Seedling/SaplingsDiameter then Ht
Large TreesDBH then Ht growth
Change crown size
Mortality
Regeneration
results
Light
Stem Map
Thinning
SORTIE Project Specific Advantages:
1. Episodic Mortality Behaviour
2. One year cycles for simulated runs
3. Post-MPB specific snag fall down function
4. Light mediated model
Project Specific Disadvantages:
1. Has not been calibrated for study area
2. Less precision in G & Y estimates
3. Over-simplified crown allometry
4. Used Less (?)
adbhCsCrownRadiu 1
bHeightCtCrownHeigh 2
Hybrid Model (SORTIE + PrognosisBC)
Advantages of Hybrid Approach:
1) Natural Regen Following MPB – Dynamic- Process-based Model
2) Tree Growth through Empirical Model
3) Uses Existing Models
Hybrid Model FlowSortie-ND
O/S + U/S tree list (from field data)
-
Time 1 (MPB attack)
Defined by ?
PrognosisBC
O/S + U/S tree list (from field data)
Sortie-ND
New O/S + U/S tree list following simulation
New Seedlings
PrognosisBC
New O/S+ U/S tree list following projection
Imputation from SORTIE
Time 2 (Post MPB attack)
PrognosisBC
O/S + U/S + New Seedlings projected in Prognosis
Regeneration submodel ‘off’
Time 3
Preliminary Results
Tested SORTIE-ND using CFS data (R. Scott)
(1987, 2001) SORTIE behaviour selection:
O/S + U/S + Initial Mortality + Subsequent Mortality– Non-spatial Seed dispersal– Number of Seeds = f (Basal Area parent trees) – Proportional Seedling Establishment– Light dependent mortality
Ht Class = 0.1-0.5cm
0
2000
4000
6000
8000
0 2000 4000 6000 8000
Observed Stems Per Hectare
Pre
dic
ted
SP
H
Lodgepole PineOther ConifersDeciduous trees.
a)
Ht Class = 1.0-1.5
0
500
1000
1500
0 500 1000 1500
Observed Stems Per Hectare
Pre
dic
ted
SP
H
c)
Species 0.1-0.5 1.0-1.5 0.1-0.5 1.0-1.5Pine -3159 -154 3999 391Conifers -75 -7 270 16Deciduous 94 180 204 383
BIAS RMSE
n = 9 stands
Lodgepole PineOther ConifersDeciduous trees.
Modifications to SORTIE-ND
1. Bath seed rain function
2. Height/DBH allometry
3. Light-dependent mortality
4. Crown allometry
Crown allometry
Crown Ratio (CR):
Xe
aRC
1ˆ
Crown Allometry Results
Pseudo – Rsquare
Model lnCCF H/D TPH H Slope Elevation
0.34 0.07 0.12 0.03 0.25 0.01 0.01
Standard Error of Estimate (SEE)
Model lnCCF H/D TPH H Slope Elevation
0.17 0.20 0.20 0.21 0.18 0.21 0.21
ElevationgSlopefSPHeCCFd
HtcDHbCR
ln
/0
11ln
Next Steps for the Hybrid Model
1. Crown Width Model
2. Other SORTIE-ND parameter adjustments• Using new dataset
3. Identification of ‘Hand-off’ point
4. Efficient Linkage (SORTIE to Prognosis)
Outstanding Questions
1. How to determine hand-off point between SORTIE-ND and PrognosisBC?
2. Does the Hybrid Model improve upon MSN results?
3. Does the Hybrid Model improve upon SORTIE alone, Prognosis alone?
• How to test this?
Acknowledgments
Data For Preliminary Analyses:
Natural Resource Canada (Brad Hawkes) - MBPI
Funding:
British Columbia Forest Science Program
Supervisor:
Dr. Valerie LeMay
Committee Members:
Peter Marshall, Bruce Larson, Dave Coates
Preliminary Analysis: Prognosis Technical Support:
Robyn Scott Donald Robinson, ESSA