403
where MaxFire = 1547 hectares and is 10 % of the area sum of all shrubland–wood-
land ecological systems. Equation 13.6 combines two Gompertz functions to
accommodate negative and positive values of PDSI. The first part of Eq. (13.6) after
MaxFire, representing fine fuels production, is a classic Gompertz function where a
weighted sum is applied to soil moisture during 2 previous years (70 % of PDSI in
year t−1 and 30 % of PDSI in year t−2). Wetter years (PDSI > 0) increase the value
of this function (fine fuels accumulation) to a maximum of one. The first part is
multiplied by the second function representing the current year, which is one minus
another Gompertz function bound between zero and one. Increasingly drier soil
moisture (PDSI < 0) causes the second part of Eq. (13.6) to increase to a maximum
of one (maximum ignition probability). The PDSI values from the scenarios without
and with climate change were used to calculate future area burned. Equation 13.6 is
not the final temporal multiplier, however, because it is not divided by its average.
In the absence of climate change effects, yearly values of Eq. (13.6) were divided by
their temporal average over 75 years, whereas each yearly value of Eq. (13.6) with
climate change was divided by the no-climate change average to reflect the hypoth-
esis of altered levels.
References
Abatzoglou JT, Kolden CA (2011) Climate change in western US deserts: potential for increased
wildfire and invasive annual grasses. Rangel Ecol Manag 64(5):471–478
Bagchi S, Briske DD, Bestelmeyer BT et al (2013) Assessing resilience and state-transition models
with historical records of cheatgrass Bromus tectorum invasion in North American sagebrush-
steppe. J Appl Ecol 50(5):1131–1141
Barrett T (2001) Models of vegetation change for landscape planning: a comparison of FETM,
LANDSUM, SIMPPLLE, and VDDT. Gen Tech Rep RMRS-GTR-76-WWW. USDA Forest
Service, Rocky Mountain Research Station, Ogden, UT, p 14
Bestelmeyer BT, Herrick JE, Brown JR et al (2004) Land management in the American Southwest:
a state-and-transition approach to ecosystem complexity. Environ Manag 34(1):38–51
Blankenship K, Smith J, Swaty R et al (2012) Modeling on the grand scale: LANDFIRE lessons
learned. In: Kerns B, Shlisky AJ, Daniel CJ (eds) First landscape state-and-transition simula-
tion modeling conference, 14–16 June 2011. Gen Tech Rep PNW-GTR-869. USDA, Forest
Service, Pacific Northwest Research Station, Portland, OR, pp 43–56
Blankenship K, Provencher L, Frid L et al (2013) Human dimensions of state-and-transition simu-
lation model applications to support decisions in wildland fire management. In: Proceedings of
3rd Human Dimensions of Wildland Fire Conference, Seattle, WA, 17–19 April 2012.
International Association of Wildland Fire, Missoula, MT, pp 28–33
Bradley BA (2009a) Regional analysis of the impacts of climate change on cheatgrass invasion
shows potential risk and opportunity. Glob Change Biol 15(1):196–208
Bradley BA (2009b) Assessing ecosystem threats from global and regional change: hierarchical
modeling of risk to sagebrush ecosystems from climate change, land use and invasive species
in Nevada, USA. Ecography 33(1):198–208
Bradley BA, Curtis CA, Chambers JC (2015) Bromus response to climate and projected changes
with climate change. In: Germino MJ, Chambers JC, Brown CS (eds) Exotic brome-grasses in
arid and semi-arid ecosystems of the Western US: causes, consequences and management
implications. Springer, New York, NY (Chapter 9)
13 State-and-Transition Models: Conceptual Versus Simulation Perspectives...