Conclusions

Our model results may indicate some interesting landscape hypotheses in this and similar areas:

1. Fuel treatments in WUI may shift wildfire behavior as fires burn in grass, shrubs, and open forests, but not reduce overall wildfire probability. However, shift in behavior could be important because wildfires in grass, shrub, and open forest fuels are easier to control than those in closed, dense forests.

2. Efforts to increase multistory, dense forest habitats in these drier environments for particular wildlife habitats may prove difficult because increased wildfire and insect outbreak probabilities might offset gains from silvicultural manipulation. In our simulations, multistory large tree forests didn’t exceed about 15 percent of the landscape on average, and amounts declined from current conditions. Most individual model runs in our Monte Carlo set experienced boom and bust conditions such that this forest type occasionally crashed to less than 5 percent of the landscape area.

3. Scenario 3, which moved Federal general forests toward HRV conditions, generated the most stable landscape conditions, but individual simulations still produced occasional sharp declines in large tree forests due to severe wildfire years or insect outbreaks.

The models we used and the assumptions they embody reflect how we think the landscape disturbance and management processes might work to control landscape characteristics in the study area. Our models were based on expert opinion, the existing literature, and calibration by finer scale, stand-level silvicultural models. Calibration of annual wildfire year and insect outbreak sequences with historical drought and other climatic influences with empirical data from other sources, (e.g., dendroclimatology) is an area where future model improvements could be made. Stand treatment prescriptions we used need to be tested in the field to determine whether desired outcomes are achieved. In addition, processes in the future may produce results much different than our estimates because: (1) odd or unusual events could occur, (2) we may not understand the system sufficiently well, (3) there may be some undetected logical error in our models, (4) climate change may alter fire, insects/disease, and other disturbances, and (5) management direction may change.