1. Incorporating Genetics into Growth Models: Results from Block-Plot Trials of Douglas-Fir Peter Gould and Brad St Clair PNW Research Station Keith Jayawickrama and Terrance Ye Northwest Tree Improvement Cooperative Growth Model Users Group. November 13, 2009. Vancouver, WA

2. Tree Improvement raises questions How much have growth rates changed? What other traits have changed (e.g., height- diameter ratio, crown width)? How long do the effects last (beyond 20 yrs)? How do the changes interact with other aspects of stand development (e.g., density effects)? How good is our information and how can it inform models?

3. Breeding Values and Genetic Worth Breeding Value: predicted value of how much a parent can change a particular trait (percentage differences from woods-run). Genetic worth: value of a seedlot or seedling for a particular trait compared with woods- run. Both are calculated for a particular trait at a specific age. For a particular trait: GW = (BV M + BV F )/2

4. Predicting Breeding Values Ex. Molalla Breeding Program 375 parents tested. Usually road-side selection. Nine test sites planted in 1971. Single-tree plots Progeny measured at different times to determine genetic differences among parents after controlling for other factors.

5. How well can we predict breeding values?

8. Genetic-gain multipliers Genetics trials often focus on size, not growth rates. Genetic-gain multipliers, which can be used in models, are the change in growth rate. Ex. ΔD G = ΔD WR · M D M D = 1 + 0.34 · BV D M H = 1 + 0.31 · BV H

9. Testing “Realized” Gain “Family-Deployment” (PNW) and “Realized- Gain” (NWTIC) trials at age 15 yrs. full-sib families from parents with predicted BV for DBH, HT, and volume. GW = (BV M + BV F )/2 Block plots – FD: Single-family, mixed, woods-run (48-tree plots). One site (four replicates within site). – RG: Elite, intermediate, woods-run (100-tree plots). Five sites (six replicates with each site).

10. Family-Deployment Trial Genetic Worth (15 yrs) FamilyDBHHTVolume 122.216.758.4 219.211.654.6 38.86.221.2 47.12.910.9 53.22.28.7 63.7-4.68.2 72.07.24.0 8-3.74.1-2.7

11. Research Questions Do predicted gains match up with realized gains (DBH, HT, M D, M H )? How does gain interact with stand density (spacings = 0.9, 1.8, 3.6 m)? How has mortality affected gain?

12. Absolute Differences Percentage Differences DBH15 HT15 Inches Feet

13. Absolute Differences Percentage Differences DBH15 Inches

14. Absolute Differences Percentage Differences HT15 Feet

15. Absolute Differences Percentage Differences Mean DBH of smallest 25 percent of stems Inches Mean DBH of largest 25 percent of stems

16. 6’ x 6’ spacing 12’ x 12’ spacing Both spacings DBH15 Results from Realized-Gain Study (Ye et al. in review)

17. Results from Realized-Gain Study (Ye et al. in review)

18. Testing Multipliers 1.Calibrate ORGANON so that it is unbiased for the woods-run plot at each spacing in each rep (growth period = 7 to 15 yrs). 2.Run all plots using calibration for woods-run. 3.Calculate multipliers for each plot so that predicted growth would be unbiased.

19. ORGANON Calibration

21. Results Predicted M D

22. Results Predicted M H

23. Density Effects: Self-Thinning

24. Absolute Difference Percentage Difference Density Effects: Self-Thinning

25. There’s a lot of variability (but that’s what we expect). With enough observations (i.e., realized-gain trial), the genetic effects are fairly clear. Growth multipliers are in general agreement with predictions. More results over time. Conclusions