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Transcript of Thinning intensity studies and growth modeling of Montana mixed conifer forests at the University of...
Thinning intensity studies and growth modeling of Montana mixed conifer
forests at the University of Montana’s Lubrecht Experimental Forest
Thomas PerryResearch Forester
Applied Forest Management ProgramCollege of Forestry and Conservation
University of MontanaMissoula, MT
Applied Forest Management Program
Developing and promoting silvicultural tools and techniques for the restoration and renewal of western
forests.
http://www.cfc.umt.edu/AFMP/default.php
Lubrecht Experimental Forest▪ Timber ▪ Education ▪ Research ▪ Recreation ▪
The LandbasePre-acquisition period: pre-1937.• Owned by Anaconda Timber Company.• Explotive harvesting; stand re-generating disturbance.
Early Lubrecht Years: 1938-1960’s.• Focus on managing uncontrolled grazing• Small thinning studies established
Timber Management Era Begins: 1960’s.• Road building increases• Clearcutting implemented; Greenough Ridge, Stinkwater Creek, Old
Coloma Road.
Transition to Stand Tending: 1970’s.• Timber sales primarily salvage, thinning and some overstory
removal.
Stand Tending Period: 1980’s-2000’s• Diameter in many stands is large enough for viable commercial
thinning. Large scale thinning program implemented.• Viable pulp markets encourage continued thinning through 1980’s
and 1990’s.
Pine Beetle Salvage: 2000’s to present• MPB salvage operations account for more and more harvest
volume.
1100m-1900m (3630ft-6270ft)
8500ha (21,000 acres)
Douglas fir (Psme)Ponderosa pine (Pipo)Western larch (Laoc)Lodgepole pine (Pico)
Overstory
DF
PP
WL
LP
HW
OSW
Understory
DF
PP
WL
LP
HW
OSW
4-8 8-12 12-16 16-20 20-24 24-28 28-320
10
20
30
40
50
60
70
OSWHWLPWLPPDF
Diameter ClassTr
ees p
er A
cre
Overstory UnderstoryTPA 135 280BA (ft2/ac) 86.6 7.3DF (%) 53 71LP (%) 7 10PP (%) 23 9
WL (%) 14 7
The Levels of Growing Stock Thinning Network (LOGS)
• History– Established in 1983, measured at 5 year intervals
until 2003, then six years elapsed until the 6th measurement
• Intent– Establish permanent growth and yield plots for a
range of sites, species, and stand densities.– Compare several alternative stand density
measures computed for the same stands.– Evaluate multi-resource productivity in side by
side comparison (timber, range, wildlife, watershed, recreation).
• Implementation– 6 sites– 4 thinning levels (treatment) per site– 3-7 plots per treatment
3 Age Groups3 Habitat Types5 Composition classes
LOGSSite Name Code Stand Age Habitat Type Species Composition
Baker Road M1 120 PSME/SYAL, CARU Ponderosa pine, Douglas fir, Western larch
Coyote Park WL 70 PSME/LIBO, VAGL Western larch
Gate of Many Locks M2 120 PSME/SYAL, CARU Douglas fir, Ponderosa pine
Section 12 LP 80 PSME/VACA Lodgepole pine
Shoestring M3 120 PSME/SYAL, CARU Douglas fir, Ponderosa pine
Upper Section 16 PP 120 PSME/SYAL, CARU Ponderosa pine
LP M1 M2 M3 PP WL0
5
10
15
20
25
30
WLPPLPDF
Basa
l Are
a (m
2/he
ctar
e)
No Thin 10x10 14x14 20x200
500
1000
1500
2000
2500
3000
LPM1M2M3PPWL
Tree
s per
Hec
tare
No Thin 10x10 14x14 20x200
5
10
15
20
25
30
35
40
45
50
LPM1M2M3PPWL
Basa
l Are
a (m
2/he
ctar
e)
No Thin 10x10 14x14 20x200
5
10
15
20
25
30
LPM1M2M3PPWLQ
MD
(cm
)
Study Design Summary
• 6 Installations• Varied Site Conditions
– Age– Site– Composition
• No Replication• No Randomization• Design will not facillitate
statistically robust comparisons between treatments.
70 80 120
PSME/SYAL, CARU
M1, M2, M3, PP
PSME/LIBO, VAGL WL
PSME/VACA
LP
Data Set
3137 individual trees, measured 2-6 times since 1983, 12548 records.
Tree Records by Species
DF LP PP WL
3068 3276 2144 1572
Tree Records by Thinning Intensity
No Thin Level 1 Level 2 Level 3
5556 3276 2144 1572
Analysis - Data Set Goals
• Diameter growth model• H:D model• Volume growth model• Compare with FVS
growth predictions for local stands.
Diameter Growth Model
Modeling Process- Overview
• Stepwise process• Predicting diameter – Previous diameter– Density measures– Species effects
• Species specific models• Linear modeling in R
DBH =
DBH t-1
DBH t-1 + TPH t-1
DBH t-1 + BA t-1
DBH t-1 + BA t-1 + Sp
Time series of basal area; level 1
Time series of basal area; level 3 Time series of basal area; level 4
Time series of basal area; level 2
Competition and Growth
Competition (Basal Area/hectare) Growth (Annual Increment [cm])
Thinning Intensity Thinning Intensity
Treatment Treatment
Variables-Why Drop Treatment ?
• Treatment tried to create 4 levels of thinning intensity and residual density.
• Thinning intensity, residual density, and species composition varied too much for distinctions by treatment to be meaningful.
• A better option was to use actual density per plot to describe competition for individual trees.
• Use a measured variable rather than a categorical variable that did not adequately reflect stand conditions.
Variables-Density
• Trees per Hectare versus Basal Area– Expected stronger
correlation using BA– Better measure of
competition than TPH since same levels of TPH could have wide ranges of competitive stress based on QMD
Model Iterations - DetailStep Formula Intercept Coeff.1 Coeff.2 Coeff.3 R-squared F-statistic p-value
1 DBH~DBHt-1 -0.0162596 1.047564 0.9954 2.91E+06 2.20E-16
2 DBH~DBHt-1+TPHt-1 0.6245 1.032 -3.17E-04 0.9959 1.62E+06 2.20E-16
3 DBH~DBH.t-1+BA.t-1 0.77384 1.046783 -2.73E-02 0.9963 1.81E+06 2.20E-16
4 DBH~DBH.t-1+TPH.t-1+BA.t-1 0.9053 1.041 -1.21E-04 -2.34E-02 0.9963 1.22E+06 2.20E-16
5 DBH~DBH.t-1+BA.t-1+Sp 0.90108 1.04418 2.30E-02 *** 0.9964 7.35E+05 2.20E-16
6 DF -- DBH~DBH.t-1+BA.t-1 0.6926 1.03808 -1.89E-02 0.9961 4.26E+05 2.20E-16
6 LP -- DBH~DBH.t-1+BA.t-1 1.410207 1.024712 -4.19E-02 0.9914 1.20E+05 2.20E-16
6 PP -- DBH~DBH.t-1+BA.t-1 0.767952 1.04891 -2.62E-02 0.9961 5.72E+05 2.20E-16
6 WL -- DBH~DBH.t-1+BA.t-1 1.0805 1.0509 -4.41E-02 0.9973 6.68E+05 2.20E-16
Growth Increment
Formula Intercept Coeff.1 Coeff.2 Coeff.3 (Species) R-squared F-statistic p-value
Inc~Inc.t-1 + BA.t-1 + Sp 9.76E-02 0.8166 -1.09E-03 0 DF 0.7339 5.59E+03 2.20E-16 -4.19E-02 LP 2.20E-16 -1.62E-02 PP 2.20E-16 -3.47E-02 WL 2.20E-16
Wrap Up• Good fit with diameter based
model.• Utilizes 80% of data set.• Strong autocorrelation.
• Increment model is less autocorrelated.
• Utilizes 100% of data set.• Weak fit without good data
describing environmental and morphological parameters.
How useful is a diameter based model predicting a fixed growth period?
While not biologically valid, will it perform across a local landscape?
For the increment model – What could be done to account for more of the variability in the model?
Will increased site and stand factors limit the portability of this model?
Is the dataset powerful but not useful or is it a diamond in the rough?
What would you do with this data?
• Acknowledgements– Dr. David Affleck: University of Montana– Dr. Aaron Weiskittel: Universisty of Maine– Dr. Chris Keyes: University of Montana– Kevin Barnett: University of Montana– Woongsoon Jang: University of Montana