1. Introduction to the State and Transition Models for Southwest Oregon Forests Emilie Henderson May 11, 2015 9AM

2. State Class Naming Conventions

3. Transition Names

4. Methods: Calibration of Fire Probabilities Fire Types: Calibrated to FIA data / fuel beds. Overall fire return interval: Calibrated with MTBS. Spatial STSim: Patch-size distribution: MTBS Spatial Constraints – STSim: roads, developed areas don’t burn (although fires can cross roads with low probability). Estimated future fire trends: MC2

5. Notes Arrow width reflects relative importance of transition in the model as a whole. Not shown: – inter-pvt transitions (they exist, but make this too complicated to review) – Management transitions (ditto on complication) – Effects of climate change on fire transition probabilities

6. Dry Douglas-fir OSW_fdd

7. Intermediate White Fir OSW_fiw

8. Mountain Hemlock OSW_fmh

9. Dry Pine OSW_fpd

10. Dry Tanoak OSW_ftd

11. Moist Tanoak OSW_ftm

12. Ultramafic, Cool (Jeffrey Pine) OSW_fuc

13. Western Hemlock, Intermediate OSW_fwi

14. White oak OSW_fwo

15. Shrubland OSW_sc3 (taken from Landfire model: California Montane Chaparral)

16. Raw Fire Probabilities by State Class

17. No CCHadley No Management Restoration Management

18. Calibration with your current efforts Compare information from fire intensity layers (fil layers – did I get that acronym right, Don?) with information on the raw fire probabilities for WFSR, WFMS and WFNL. Spatial Constraints: Your treatment plans.

19. Things to Think About How to think about fire suppression in the futures? – Est. future: more fire, but more of it is NL. What might this mean for suppresion? The net effects of suppression are embedded within the baseline average fire return intervals (extracted from MTBS for the PVTs), but will this metric of how much fire is ‘normal’ operate the same way in a landscape with a better fuel profile?. – Will fire suppression be more effective if we can get the fuels under control? How could we model that?