4 Discussion
Ecoregions within a given biome have similar fuel moisture thresholds
(EFT) associated with observed landscape fires (Figures 1, 3). There is,
however, significant variability in the biogeography of EFTs (Figures
2a, 3), which suggests that a single fuel moisture threshold cannot be
interpreted as a universal determinant of flammability. This global
variability is sensibly explained by eco-climatological variables like
precipitation and temperature. In addition to contextualizing the EFTs
in relation to these eco-climatological variables, they need to be
considered in concert with the inflection point slope estimates (IPSEs)
and fire activity as outlined below. These considerations are important
in understanding the likely effects of climate change on fire regimes,
as well as determining those ecoregions and biomes where the risk of
fire activity is increasing. We propose identification and use of these
EFTs is critical to understanding the future of global pyrogeography.
4.1 The biogeography of flammability
thresholds
The global averages for our EFTs correspond closely to the extreme
values of between 8 and 12% identified or used in prior research
(Wotton, 2008; Slijepcevic et al., 2015; Flannigan et al., 2016; Nolan
et al., 2016; Boer et al., 2017; Filkov et al., 2019; Ellis et al.,
2022). This suggests that those commonly used EFTs as derived from the
Canadian FWI system and ERA5 reanalysis data do, on average, reflect
ecological switches controlling fire ignition and spread for many Earth
environments. Despite this, relying on a global average oversimplifies
local fuel-fire relationships and ignores our understanding of how the
fibre saturation point can vary between landscapes and vegetation types
(Fernandes et al., 2008; Alvarado et al., 2019). Biome-level differences
in EFTs suggest that a generalized threshold will often under- or
over-estimate the localised fuel moisture-fire relationship, with nearly
70% of the Earth’s surface holding an EFT above or below that range of
8 and 12%. The geographic distribution of EFTs (Figure 2a) as well as
the differences between and within distinct biome EFT distributions
(Figure 3) highlight where this oversimplification fails to capture the
true association between fuel moisture and fire. This includes, for
instance, all boreal forests (Q1: 13.9%, Q3: 16.6%) and tundra (Q1:
17.7%, Q3: 19.8%) ecoregions, as well as those North AmericanPinus banksiana and Pinus contorta forests where the FWI
system originates (Van Wagner, 1987). For ecoregions or biomes such as
these, the general threshold grossly under-estimates the true fuel
moisture content shown to be associated with fire (Figure 3).
The need for biogeographically-variable EFTs is logical given the role
water availability – including the moisture content of both live and
dead fuels – plays in fuel accumulation within different types of fire
regimes (Murphy et al., 2013). A universal threshold oversimplifies the
distinct biology of vegetation and how vegetation in different
ecoregions may have evolved with fire. A bespoke threshold can more
accurately capture the existing variation in live and dead fuel moisture
content associated with the phylogeographic and structural dimensions of
vegetation and the fuel array (e.g., Pausas and Keeley, 2009; Keeley et
al., 2011; Alvarado et al., 2019). For example, fuel-limited biomes like
desert or tundra maintain arid conditions that prevent the growth of
burnable biomass. Moisture-limited temperate forest systems, on the
other hand, experience years- to decades-long lagged growth typical of
the classic negative exponential fuel accumulation curve (i.e., Olson,
1963), while the most productive tropical forests tend to retain enough
water that they rarely burn (Murphy et al., 2013). For these
moisture-limited biome types, fuel moisture plays the key role in
whether the landscape can ignite and maintain a wildfire.
4.2 Interpreting the ecoregion flammability
threshold
The EFT method was applied using global scale data and is therefore an
ecological generalization. Although there is variability within each
ecoregion due to terrain, vegetation, and fuel structure, EFTs are still
biologically meaningful across ecoregions and should be interpreted asrepresentative thresholds. That is, when the average DFFMC nears
or falls below our identified EFT for a given ecoregion, that reflects
an average state of susceptibility across that landscape rather than a
strict measure of the moisture content of all fuel particles. It’s also
important to acknowledge some EFTs are inaccurate or imprecise. Sources
of error include the short length of the underlying remotely sensed fire
records and the differential importance of fuel moisture as a fire
trigger across the productivity gradient (Figure 5a-c). For instance,
the modelled P(BA < DFFMC) relationships for some
ecoregions feature gradual shifts in the cumulative proportion of burnt
area over the full range of DFFMC (supporting information). In other
cases, we identified EFTs well above the upper limits of the fibre
saturation point (i.e., > 30%: Fernandes et al., 2008).
Both these cases could reflect a lack of available fire data within that
ecoregion, differences in local land-use practices (e.g., savanna or
agricultural lands: Le Page et al., 2010; Andela et al., 2017), that the
role of fuel moisture for fire ignition and spread in that ecoregion is
less important (e.g., Alvarado et al., 2019), or a combination of these
factors. Furthermore, the ERA5 grid scale resolution (0.25°) may be too
coarse to accurately capture and differentiate the climatology of small
ecoregions from surrounding regions. This includes most flooded
grasslands and savanna and tropical and subtropical coniferous forests
given their small size, number, and limited geographic range (supporting
information).
Our results show that eco-climatic factors explain a large proportion of
the variation in the EFTs amongst the ecoregions (Figure 4a-b, Table 1
and supporting information). Overlaying biome means onto the NMDS
ordination, for example, shows that the lowest EFTs in desert
environments (Q1: 4.2%; Q3: 8%) are strongly associated with
precipitation seasonality and low overall precipitation variables. The
highest EFTs in tundra environments (Q1: 17.7%, Q3: 19.8%) are notably
associated with high percent herbaceous cover and lower temperatures.
Higher-latitude temperate forests – particularly those with higher EFTs
like boreal forests (Q1: 13.9%, Q3: 16.6%) and coniferous forests (Q1:
9.3%, Q3: 17.2%) – are associated more strongly with a combination of
precipitation, temperature, and seasonality. It is important to note,
however, that the precipitation NMDS axis includes the effects of NPP
and percent tree cover, which conforms to pyrogeographic theory between
productivity and global fire activity (Pausas and Bradstock, 2007;
Pausas and Ribeiro, 2013; Jones et al., 2022). For example,
low-productivity deserts and xeric shrublands feature the most extreme
combination of EFTs, IPSEs, and productivity on Earth (median of 0.17 t
C ha-1 year-1; Figures 5b-c).
However, fire activity in these environments is controlled by
intermittent periods of high productivity rather than moisture content
(Archibald et al., 2009; Bradstock, 2010; Kelley et al., 2019).
The relationship between fire activity, fuel moisture, and fuel
availability invites consideration of climate change. Climate change can
manifest itself directly (via fuel moisture) and indirectly (via
production of phytomass). Those environments with abundant fuel and
higher EFTs are often those most vulnerable to climatic change. Tropical
and subtropical rainforests, for example, feature high moisture and high
fuel and are at risk of catastrophic change, largely driven by human
land-use impacts on fire regimes (Le Page et al., 2010; Canadell et al.,
2021). Mediterranean forests are among the least productive forests in
the world (Figure 5a-c) and could shift towards savanna or desert
environment as drying trends continue and leave fuel accumulation unable
to keep up with increasing fire intensity (Pausas and Paula, 2012;
Pausas and Bond, 2020). The most arid desert landscapes rarely support
enough fuel for fire spread (Bradstock, 2010; Murphy et al., 2013; Bedia
et al., 2015), and are unlikely to have fire regimes driven by
anthropogenic climate change except along biome transition lines
(Archibald et al., 2009; Senande-Rivera et al., 2022). At the upper
limits of the intermediate fire-productivity zone, the most important
intersection supports higher productivity (> 4.5 t C
ha-1 year-1), higher fire activity
(> 0.5 FAI), and extreme values of both EFT and IPSE
(Figures 5b-c). This area reflects some of the most at-risk environments
under climatic change, including many higher-latitude temperate
broadleaf and boreal forests susceptible to ecological collapse under
drying climate trends (Ellis et al., 2022; Senande-Rivera et al., 2022),
as well as more productive, moisture-limited tropical savanna ecoregions
(e.g., Alvarado et al., 2019).
One important advance our identified EFTs provide is the elimination of
the persistent uncertainty in defining fire season onset – a key
pyrogeographic parameter that further defines fire regimes and pyromes.
At the continental scale, for example, Australia’s fire-prone tropical
and temperate ecoregions feature fire season onsets driven by a distinct
latitudinal climate gradient (Murphy et al., 2013; Williamson et al.,
2016). The evident drying trend in fuel moisture for wetEucalyptus forests along this gradient places those ecoregions at
risk of a potential collapse due to increasing fire frequency (Bowman et
al., 2014; Furlaud et al., 2021; McColl-Gausden et al., 2022).
Worldwide, boreal forests, Mediterranean forests, and both temperate and
tropical broadleaf and coniferous forests are all at risk due to
increasing burnt area, fire frequency or both (Westerling, 2016; Forkel
et al., 2019; Kelley et al., 2019; Abatzoglou et al., 2021; Ellis et
al., 2022). Our EFTs can be used to detect the onset of a fire season in
real time in environments like these, which is of primary importance to
tracking the effects of climate change on different landscapes’ fire
activity, as well as determining the allocation of forest management and
firefighting resources.
Specific EFTs will be useful in understanding changes in fire seasons
for individual ecoregions, biomes, and specific vegetation types
globally. The identification of changing length, intensity, and extremes
of fire seasons using meteorological data has been an important line of
evidence of changing global fire risk. However, previous analyses have
used generalised or assumed thresholds to which this study provides a
key innovation. Additionally, the expansion of the active fire season is
not just occurring at the seasonal boundaries, but into the night-time
– a period previously assumed to provide relief from rapid wildfire
spread (Balch, et al., 2022). To advance our understanding of shifting
fire regimes under anthropogenic stressors, identifying the biogeography
of EFTs as we have done is a prerequisite for defining the bounds of the
fire seasons, as well as conducting trend analyses sensibly informed by
the local fuel-fire relationship. Such trend analyses can be used to
identify and mange those most at-risk environments for ecological
collapse under predicted future fire regimes.