1. 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

2. 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

3. Lubrecht Experimental Forest ▪ Timber ▪ Education ▪ Research ▪ Recreation ▪

4. 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. 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)

5. OverstoryUnderstory TPA135280 BA (ft 2 /ac)86.67.3 DF (%)5371 LP (%)710 PP (%)239 WL (%)147

6. 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 6 th 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

7. 3 Age Groups 3 Habitat Types 5 Composition classes LOGS

9. Study Design Summary 6 Installations Varied Site Conditions – Age – Site – Composition No Replication No Randomization Design will not facillitate statistically robust comparisons between treatments. 7080120 PSME/SYAL, CARU M1, M2, M3, PP PSME/LIBO, VAGL WL PSME/VACA LP

10. Data Set 3137 individual trees, measured 2-6 times since 1983, 12548 records. Tree Records by Species DFLPPPWL 3068327621441572 Tree Records by Thinning Intensity No ThinLevel 1Level 2Level 3 5556327621441572

11. Analysis - Data Set Goals Diameter growth model H:D model Volume growth model Compare with FVS growth predictions for local stands. Diameter Growth Model

12. 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

13. 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

14. Competition and Growth Competition (Basal Area/hectare)Growth (Annual Increment [cm]) Thinning Intensity Treatment

15. 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.

16. 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

17. Model Iterations - Detail StepFormulaInterceptCoeff.1Coeff.2Coeff.3R-squaredF-statisticp-value 1DBH~DBHt-1-0.01625961.047564 0.99542.91E+062.20E-16 2DBH~DBHt-1+TPHt-10.62451.032-3.17E-04 0.99591.62E+062.20E-16 3DBH~DBH.t-1+BA.t-10.773841.046783-2.73E-02 0.99631.81E+062.20E-16 4DBH~DBH.t-1+TPH.t-1+BA.t-10.90531.041-1.21E-04-2.34E-020.99631.22E+062.20E-16 5DBH~DBH.t-1+BA.t-1+Sp0.901081.044182.30E-02***0.99647.35E+052.20E-16 6DF -- DBH~DBH.t-1+BA.t-10.69261.03808-1.89E-02 0.99614.26E+052.20E-16 6LP -- DBH~DBH.t-1+BA.t-11.4102071.024712-4.19E-02 0.99141.20E+052.20E-16 6PP -- DBH~DBH.t-1+BA.t-10.7679521.04891-2.62E-02 0.99615.72E+052.20E-16 6WL -- DBH~DBH.t-1+BA.t-11.08051.0509-4.41E-02 0.99736.68E+052.20E-16

18. Growth Increment FormulaInterceptCoeff.1Coeff.2Coeff.3 (Species)R-squaredF-statisticp-value Inc~Inc.t-1 + BA.t-1 + Sp9.76E-020.8166-1.09E-030DF0.73395.59E+032.20E-16 -4.19E-02LP 2.20E-16 -1.62E-02PP 2.20E-16 -3.47E-02WL 2.20E-16

19. 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? 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?

20. 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