Thinning intensity studies and growth
modeling of Montana mixed conifer
forests at the University of Montana’s
Lubrecht Experimental Forest
Thomas Perry
Research Forester
Applied Forest Management Program
College of Forestry and Conservation
University of Montana
Missoula, MT
Applied Forest Management Program
http://www.cfc.umt.edu/AFMP/default.php
Developing and promoting silvicultural tools and
techniques for the restoration and renewal of western
forests.
Lubrecht
Experimental Forest
▪ Timber ▪ Education ▪ Research ▪ Recreation ▪
The Landbase
Pre-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.
1100m-1900m
(3630ft-6270ft)
8500ha
(21,000 acres)
Douglas fir (Psme)
Ponderosa pine (Pipo)
Western larch (Laoc)
Lodgepole pine (Pico)
•
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.
70
Overstory
60
50
DF
Trees per Acre
PP
WL
LP
OSW
40
HW
LP
30
WL
HW
PP
20
OSW
DF
10
Understory
0
4-8
8-12
12-16
16-20
20-24
24-28
28-32
Diameter Class
DF
PP
WL
LP
HW
OSW
Overstory Understory
TPA
135
280
2
BA (ft /ac)
86.6
7.3
DF (%)
53
71
LP (%)
7
10
PP (%)
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
LOGS
3 Age Groups
3 Habitat Types
5 Composition classes
30
Baker Road
Coyote Park
Code
Stand Age
25
Basal Area (m2/hectare)
Site Name
Gate of Many Locks
Section 12
Shoestring
Upper Section 16
20
15
Habitat Type
M1
120
WL
70
PSME/LIBO, VAGL
M2
120
PSME/SYAL, CARU
Species Composition
PSME/SYAL, CARU Ponderosa pine, Douglas fir, Western larch
WL
Western larch
PP Ponderosa pine
Douglas fir,
LP
10
LP
80
PSME/VACA
DF Lodgepole
M3
120
PSME/SYAL, CARU
Douglas fir, Ponderosa pine
PP
120
PSME/SYAL, CARU
Ponderosa pine
5
0
LP
M1
M2
M3
PP
WL
pine
3000
2500
45
35
LP
2000
M1
M2
1500
M3
1000
PP
WL
500
30
LP
0
No Thin
10x10
14x14
M1
20x20
M2
25
M3
20
PP
WL
30
15
25
10
5
0
QMD (cm)
Basal Area (m2/hectare)
40
Trees per Hectare
50
LP
20
M1
M2
15
M3
10
PP
No Thin
5
10x10
14x14
20x20
WL
0
No Thin
10x10
14x14
20x20
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
PSME/SYAL,
CARU
PSME/LIBO,
VAGL
PSME/VACA
120
M1, M2,
M3, PP
WL
LP
Data Set
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
3137 individual trees, measured 2-6 times since 1983,
12548 records.
Analysis - Data Set Goals
•
•
•
•
Diameter growth model
H:D model
Volume growth
model
Diameter
Growth
Compare
with FVS
Model
growth predictions for
local stands.
Modeling Process- Overview
• Stepwise process
• Predicting diameter
– Previous diameter
– Density measures
– Species effects
• Species specific models
• Linear modeling in R
DBH t-1
DBH t-1 + TPH t-1
DBH =
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 2
Time series of basal area; level 4
Competition and Growth
Competition (Basal Area/hectare)
Treatment
Thinning Intensity
Growth (Annual Increment [cm])
Treatment
Thinning Intensity
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 - Detail
Step
Formula
1 DBH~DBHt-1
2 DBH~DBHt-1+TPHt-1
3 DBH~DBH.t-1+BA.t-1
4 DBH~DBH.t-1+TPH.t-1+BA.t-1
5 DBH~DBH.t-1+BA.t-1+Sp
Intercept
Coeff.1
Coeff.2
Coeff.3
-0.0162596 1.047564
0.6245
R-squared F-statistic p-value
0.9954 2.91E+06 2.20E-16
1.032 -3.17E-04
0.9959 1.62E+06 2.20E-16
0.77384 1.046783 -2.73E-02
0.9963 1.81E+06 2.20E-16
0.9053
0.9963 1.22E+06 2.20E-16
1.04418 2.30E-02 ***
0.9964 7.35E+05 2.20E-16
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
6 DF -- DBH~DBH.t-1+BA.t-1
0.90108
1.041 -1.21E-04 -2.34E-02
0.6926
Growth Increment
Formula
Inc~Inc.t-1 + BA.t-1 + Sp
Intercept
9.76E-02
Coeff.1
Coeff.2
0.8166 -1.09E-03
Coeff.3 (Species)
0 DF
-4.19E-02 LP
-1.62E-02 PP
-3.47E-02 WL
R-squared F-statistic p-value
0.7339 5.59E+03 2.20E-16
2.20E-16
2.20E-16
2.20E-16
Wrap Up
How useful is a diameter based model
predicting a fixed growth period?
• Good fit with diameter based
model.
While
not biologically valid, will it
perform
across
landscape?
• Utilizes
80%a local
of data
set.
• Strong
autocorrelation.
For
the increment
model – What could
be done to account for more of the
variability
in themodel
model?is less
• Increment
autocorrelated.
Will increased site and stand factors limit
• Utilizes
100%
of model?
data set.
the
portability
of this
• Weak fit without good data
Is the
dataset powerful
but not useful
describing
environmental
and or
is it morphological
a diamond in theparameters.
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