Thinning intensity studies and growth modeling of Montana mixed conifer forests at the - PowerPoint Presentation

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Thinning intensity studies and growth modeling of Montana mixed conifer forests at the University of Montana’s Lubrecht Experimental Forest

Thomas Perry

Research ForesterApplied Forest Management ProgramCollege of Forestry and ConservationUniversity 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

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 grazingSmall thinning studies established

Timber Management Era Begins: 1960’s.Road building increasesClearcutting

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 presentMPB 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

Understory

TPA

135

280

BA (ft

2

/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 measurementIntent

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).Implementation6 sites

4 thinning levels (treatment) per site3-7 plots per treatment

3 Age Groups

3 Habitat Types

5

Composition classes

LOGS

Site

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

120PSME/SYAL, CARUPonderosa pine

Study Design Summary

6 Installations

Varied Site Conditions

AgeSiteComposition

No ReplicationNo RandomizationDesign 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 modelH:D modelVolume growth modelCompare with FVS growth predictions for local stands.

Diameter Growth Model

Modeling Process- Overview

Stepwise processPredicting diameter Previous diameterDensity measures

Species effectsSpecies specific modelsLinear modeling in RDBH =

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 BABetter 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

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 MontanaDr. Aaron

Weiskittel: Universisty of MaineDr. Chris Keyes: University of MontanaKevin Barnett: University of MontanaWoongsoon Jang: University of Montana