1. FVSCLIM: Prognosis Re-Engineered to Incorporate Climate Variables Robert Froese, Ph.D., R.P.F. School of Forest Resources and Environmental Science Michigan Technological University, Houghton MI 49931 Again

2. This presentation has four parts Introduction Approach Relevance Performance The issue, the question and the model formulations examined The methods and the data sets How do revisions affect fit and prediction accuracy? Does the approach have merit, and what are the next steps?

3. This presentation has four parts The issue, the question and the model formulations examined The methods and the data sets How do revisions affect fit and prediction accuracy? Does the approach have merit, and what are the next steps? Introduction Approach Relevance Performance

4. This presentation has four parts The issue, the question and the model formulations examined The methods and the data sets How do revisions affect fit and prediction accuracy? Does the approach have merit, and what are the next steps? Introduction Approach Relevance Performance

5. This presentation has four parts The issue, the question and the model formulations examined The methods and the data sets How do revisions affect fit and prediction accuracy? Does the approach have merit, and what are the next steps? Introduction Approach Relevance Performance

6. Wykoff’s (1990) Basal Area Increment Model is the subject of this research DDS = DBH 2 t+10 - DBH 2 t but actually.. DDS = DBH 2 t - DBH 2 t-10 BAI = π/4 (DBH 2 t - DBH 2 t-10 ) DI = (DBH 2 + DDS) 0.5 - DBH ln(DDS) = f (SIZE + SITE + COMPETITION)

7. Last year I presented results of a validation study of Wykoff’s model

8. “How and Where does Wykoff’s Basal Area Increment Model Fail?” “I appreciate the opportunity to review your paper. The title certainly grabs your attention, especially if your name is Wykoff and you spent many years developing the subject model.” I wrote it up as a manuscript… Bill replied:

9. The Prognosis BAI model is a multiple linear regression on the logarithmic scale Wykoff 1990

10. Wykoff (1997) proposed a number of revisions to the model formulation Wykoff 1990 Wykoff 1997

11. Froese (2003) proposed replacing climate proxies with climate variables Wykoff 1997 Froese 2003

12. The approach involves two parts evaluating model revisions –Fit Wykoff (1990), Wykoff (1997) and Froese (2003) to the new FIA data –Compare fit and lack-of-fit statistics of different model formulations testing on independent data –generate predictions for independent testing data –compare bias of prediction residuals across model formulations –Compare results using equivalence tests Introduction Approach Relevance Performance

13. Froese (2003) pretended to be a physiologist ANP: total annual precipitation GSL: growing season length (days with nighttime minimum temperature greater than 0°C) GSP: total precipitation during the growing season GST: mean daily temperature during the growing season GSV: mean daily water vapour pressure deficit during the growing season

14. Froese (2003) also pretended to be a climatologist

15. Changing model formulation had small effect on fit statistics Introduction Approach Relevance Performance Fit to the FIA data:

16. The Froese (2003) model provided biologically-rational behaviour Biologically reasonable sign and magnitude of model coefficients Extrapolation issues remain to be resolved Douglas-fir on median site

17. Testing revealed that every formulation over- predicts on the validation data Tested on the Region 1 data:

18. The 1990 formulation failed to be validated for the monitoring data

19. The 1997 model performed better, but was still not validated in this situation

20. The 2003 model performed similarly to the 1997 model but was also not validated

21. The substitution of climate variables for proxies is validated using equivalence tests

22. The model is not appropriately responsive to small and suppressed trees Results for Pseudotsuga menziesii

23. Some results are encouraging, some suggest that more work is needed Are we (am I) splitting hairs? –Is an RMSE reduction of 2% useful? Does it really matter if RMSE reductions are small? Can we come up with better DDS model formulations? What’s wrong with predictions for small trees? Have I modelled climate effects on growth or climate effects on genes? Introduction Approach Relevance Performance