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showed that a forest model driven solely by rate of tree growth, which was
considered equivalent to fuel accumulation, likewise fit a power model for fire
size and frequency. Since their model was driven solely by internal ecosystem
processes, a phenomenon known as self-organized behavior to illustrate the lack
of exogenous drivers, they concluded that fuel accumulation alone was a wide-
spread driver of fire regimes, and referred to this as self-organized criticality.
However, a great many biological data sets with a large variance will fit a power
law relationship. We contend that very different fire regimes show a similar power
law relationship between number of fires and size of fires, and the mechanisms are
likely to be very different. For example, in forests with surface fire regimes, where
fuels are often limiting to fire spread, fire frequency may alter fuels in ways that
impact potential fire size. But in shrubland crown fire regimes where external
factors such as extreme weather are a more important determinant of fire spread,
fire frequency may affect fire size through the probability of ignitions during
severe fire weather.
The self-organized behavior of fire regimes was questioned by Boer
et al.
(
2009
)
who found that fire behavior could be accounted for by external factors such as
the length and intensity of weather events, which also show a power law distribu-
tion. However, Carlson & Doyle (
2002
) proposed that complex systems can be
best modeled when viewed as driven by both endogenous and exogenous factors.
They developed the highly optimized tolerance (HOT) model, aimed at optimizing
the trade-offs between system yield and tolerance to risks. Moritz
et al.
(
2005
) used
this model with southern California fire history data and found a far better fit
than with the self-organized criticality model.
Fire Behavior
Fuels, weather and topography are the major determinants of fire behavior.
Under severe fire weather conditions of high wind and low humidity fuels become
less of a determinant of fire behavior (Cary
et al.
2006
; Bradstock
et al.
2010
) and
steep rugged terrain may have similar effects. However, under other conditions,
fuels control fire behavior by a combination of inherent structural characteristics,
moisture content, live or dead status, and their quantity and arrangement at a
local and landscape scale.
Fuel Attributes
Characterizing fuels in MTC regions in meaningful ways that reflect different fire
regimes is complicated. Although primary productivity (
Fig. 2.1
) is a key deter-
minant of fuel loads, not all biomass represents available fuel. In savannas most of
the aboveground herbaceous biomass may be available fuel whereas in some
closed-canopy forests fuels include only the sloughed-off leaves and stems that
