Introduction
Peatland wildfires pose a significant global challenge due to their
potential for severe effects on ecosystem functioning and the detrimental
role they may play in climate change. Peatlands account for approximately
2.5 % of Earth's land-cover (Kaat and Joosten, 2009) and contain more than
600 Gt of stored carbon (Yu et al., 2010), equivalent to 25 % of global
soil organic carbon stocks (Mitra et al., 2005) and 75 % of all
atmospheric carbon (Kaat and Joosten, 2009). The degradation of this resource
is a potential positive feedback to climate change and smouldering wildfires
also have other significant environmental and human impacts such as
respiratory problems associated with the inhalation of noxious smoke, the
significant effort and costs involved in fire fighting, destruction of soil
seedbanks, widespread plant mortality and post-fire erosion and water
pollution problems (Watts and Kobziar, 2013). Increased fire risk and
severity with climate change means wildfires pose a particularly significant
threat to the ecological integrity and carbon stocks of peatlands (Turetsky
et al., 2015).
The majority of research on the effects of peatland wildfires has come from
tundra, boreal and tropical ecosystems (Turetsky et al., 2015). Temperate
peatlands are also an important carbon store and habitat type but many have
a long history of disturbance and management (e.g. Moore, 2002). British
peatlands are acknowledged to be of significant national and international
conservation importance though most have been subjected to a variety of land
management practices, including burning and grazing, over at least the last
two centuries (Bonn et al., 2009). Many peatlands have also been
significantly impacted by drainage (Holden et al., 2004) and nutrient
deposition from atmospheric pollution (Hogg et al., 1995). British peatland
habitats contain fire-prone vegetation including moorlands dominated by
Calluna vulgaris L. Hull (hereafter Calluna) and a variety of mire and bog communities associated
with Molinia caerulea (L.) Moench and Eriophorum spp. The majority of such habitats are underlain
either by deep peat deposits or by shallower organic soils that nevertheless
hold substantial amounts of carbon. Estimates suggest that around 88 t C ha-1
are stored in the soil and up to 2 t C ha-1 in the vegetation
of dwarf shrub dominated moorlands in the UK (Ostle et al., 2009). The
majority of the UK's 4.5 Tg of soil carbon stocks are stored in peat
deposits below heath, bog and moorland habitats (Bradley et al., 2005).
Managed burning is an important control on the structure of these habitats
with fires burnt regularly in both moorland and blanket bog habitats systems
(Bonn et al., 2009). Recommended burn rotations are 15–25 years for
Calluna-dominated moorlands whilst longer rotations or no burning are recommended
for wetter bog communities (Scottish Government, 2011). The role of fire in
peatland ecology has become a highly controversial subject with substantial
debate surrounding the effect of managed burning on ecosystem dynamics (e.g.
Grant et al., 2012). The situation is not helped by a lack of data on how
fire affects temperate peatland ecosystems such as those found in the UK. A
number of studies have been completed but mostly for low-severity
experimental prescribed burns (e.g. Davies et al., 2010) or for a few
individual wildfire events (e.g. Davies et al., 2013; Maltby et al., 1990;
Worrall et al., 2011). There is a consensus that wildfires pose a substantial
and growing threat in the context of a changing climate (Bonn et al., 2009).
In this context, data are urgently needed on both the scale of the wildfire
problem and the effects of such burns.
Summary of wildfires used in this study including variation in
biotic and abiotic conditions across the fire grounds and the monitoring
effort associated with each fire. Vegetation type is reported as National
Vegetation Classification (NVC) communities (Rodwell, 1991) with the NVC code
given in brackets. Paired CBI plots were those placed around the fire
perimeter to enable direct comparison of burnt and unburnt fuel loads and
soil gas fluxes. Stand-alone CBI plots were additional plots located within
the fire in order to give a more comprehensive overview of variation in burn
severity. Nearby unburnt comparison locations were not available for these
plots.
Fire name
Latitude
Date of
Burned area
Elevation
Soil
Vegetation
Paired CBI
Stand-alone
and location
and longitude
fire
(ha)
(m)
type
plots
CBI plots
Anglezarke (N England)
53.658∘ N, 2.569∘ W
29 Apr 2011
4144
270–380
Deep peat
Calluna vulgaris – Eriophorum vaginatum blanket mire (M19)
3
4
Mardsen (N England)
53.596∘ N, 1.976∘ W
9 Apr 2011
316
385–480
Deep peat
Calluna vulgaris – Eriophorum vaginatum blanket mire (M19) Calluna vulgaris – Vaccinium myrtillus heath (H12)
3
2
Loch Doon (SW Scotland)
55.214∘ N, 4.393∘ W
29 May 2011
No data
230–250
Shallow peat
Molinia caerulea – Potentilla erecta mire (M25a)
2
2
Wainstalls (N England)
53.777∘ N, 1.928∘ W
30 Apr 2011
82
385–420
Deep peat
Calluna vulgaris – Eriophorum vaginatum blanket mire (M19) Scattered Calluna vulgaris – Vaccinium myrtillus heath (H12)
3
3
Finzean (NE Scotland)
57.025∘ N, 2.702∘ W
30 Mar 2012
19
320–340
Rocky organic
Calluna vulgaris – Vaccinium myrtillus heath (H12)
3
0
In systems with peat or organic soils severe wildfires that ignite
carbon-rich deposits can lead to substantial, instantaneous losses of carbon
(Davies et al., 2013) and long-term changes to ecosystem function
(Maltby et al., 1990). Whilst the severe effects of smouldering peat fires are obvious,
such burns lie at one end of a spectrum of burn severity and not all fires
on peatlands necessarily ignite peat or cause ecological damage. Indeed,
carefully managed burning of peatlands can have a variety of ecosystem
benefits (Davies et al., 2008a). Differences in burn severity can be caused
by between and within site variation in fuel type and fuel structure as well
as by differences in fire weather conditions (e.g. fuel moisture content,
wind speed) across different burn days (Davies et al., 2010). In general this
may mean that managed fires, typically burnt during low-severity conditions,
have more limited effects than wildfires. Such patterns are not consistent as
wildfires can occur in a wide variety of conditions and not all have
particularly severe effects. Rather little effort has been made to try to
capture or understand the effects of such variation but this is vital in
order to monitor the amount of carbon released during wildfires and the
extent of the environmental change they cause.
This research was initiated following severe wildfires during the springs of
2011 and 2012. We aimed to assess how burn severity varied within and
between individual wildfires, and to define what the implications of such
variation might be for carbon emissions due to wildfire and on-going
development of fire danger rating systems such as the Met Office Fire
Severity Index (MOFSI; Kitchen et al., 2006). MOFSI is based on the Canadian
Fire Weather Index System (FWI System; Van Wagner, 1987) and has been
implemented in Wales and England in order to provide a forecast of
“exceptional” conditions when it becomes permissible to close open-access
land under the Countryside and Rights of Way Act 2000. To date there have
been limited efforts to examine the relationship between the FWI System and
fire severity in the UK. There is some evidence its moisture codes relate
fairly well to ground fuel (Legg et al., 2007) and peat (Krivtsov et al.,
2008) moisture content, and that it can do a tolerable job of discriminating
periods of increased wildfire risk (Legg et al., 2007). We aimed to
investigate the relationship between fire severity and all the sub codes and
indices of the FWI System but were particularly interested in its response
to variation in the DMC (Duff Moisture Code), the DC (Drought Code) and the
BUI (Build-up Index). The DMC and DC are designed to relate, respectively,
to the moisture content of duff (partly decomposed litter) and compacted
deeper organic layers. Such fuel layers bear some resemblance to the
moss and/or litter layers and peat deposits found in British peatland ecosystems.
The BUI integrates DMC and DC to provide an overall indication of fuel
availability. Our specific objectives were to develop a simple methodology
to assess variation in burn severity post hoc; assess the extent to which
burn severity and fuel consumption vary within and between wildfires; and to
investigate links between burning conditions (fuel type and fire weather)
and variation in burn severity and fuel consumption.
Material and methods
Study sites
Monitoring was completed on five different wildfires (Table 1) that burnt
British peatlands during the springs of 2011 and 2012. Sites were selected
from information on fires provided by land-managers, public and private
land-owners, government agencies and Fire and Rescue Services. We selected
five sites that represented fires displaying moderate to high burn severity
and the north-south and west-east range of bioclimatic conditions of the
British uplands.
Pre-fire biotic and abiotic conditions varied both within and between our
study sites (Table 1). Most locations in England were broadly classified as
mires on deep peat with vegetation dominated by Calluna and Eriophorum vaginatum L. along with species
such as Vaccinium myrtillus L., Deschampsia flexuousa (L.) Trin. and Trichophorum caespitosum (L.) Hartm. Vegetation was underlain by mats of
pleurocarpous mosses. A number of plot locations were recorded at noticeably
wetter locations. Here Calluna was less dominant, Eriophorum spp. and T. caespitosum occasionally very
abundant and ground layer vegetation included patches of Sphagnum. Sites in Scotland
represented opposite ends of the spectrum of peatland habitat types found in
Britain. Finzean was comparatively drier, had shallow, stony organic soils
and vegetation dominated by a mixture of Calluna and Pteridium aquilinum (L.) Kuhn. The site at Loch
Doon was a bog with true peat soils and vegetation dominated by Molinia caerulea (L.)
Moench, Myrica gale L. and Sphagnum spp.
Field data collection
Burn severity and fuel consumption sampling was performed approximately 6 months
after the fires occurred. Previous researchers have collected such
data as much as a year after fire (e.g. de Groot et al., 2009). Wildfires are
sporadic, unpredictable events meaning sites had not been surveyed prior to
the burns. Similar to other studies (e.g. Kasischke and Johnstone, 2005;
Hollis et al., 2007; de Groot et al 2009), we used paired plots with
burnt and/or unburnt subplots located across the fire perimeter (see Supplement
Fig. S1). Two or three paired plots were located within each fire
and chosen to represent the range of burn severities visible during a
detailed site reconnaissance with local stakeholders. Many peatlands in the
British uplands have a patchwork of fuel structures produced by managed
burning. We were therefore careful to ensure that subplots were established
where, following observation of stem basal diameters and stem density and
discussion with local land-managers, we were confident that pre-fire fuel
conditions across the fire-line were similar. Plots were also only
established in regions of the fireline known to have been actively
extinguished. In order to capture additional information about variation in
burn severity we established a number of unpaired plots within the interior
of each fire (Table 1). Unburnt areas were not available for comparison with
these plots and they were only used to explore variation in burn severity.
Fire weather
Variation in burning conditions between the fires was described using the
FWI System (Van Wagner, 1987). The FWI System requires daily data on wind
speed, temperature and humidity at 12 noon as well as 24 h accumulated
rainfall. These were extracted from the British Atmospheric Data Centre
database for the nearest weather station to each of the wildfires (mean
distance = 15 km, max = 31 km). Rainfall data were available from rain
gauges closer to the fire site than the nearest full weather station and we
used these to estimate precipitation (mean distance = 5 km, max = 10 km).
Available data on 24 h accumulated rainfall was 09:00–09:00 rather than
noon to noon though the difference is unlikely to be of importance. FWI
System values were calculated using the package “fume” (Santander
Meteorology Group, 2012) in R 3.1.2 (R Development Core Team, 2014). Some of
the moisture codes and indices of the FWI System have long lag times (52 days for the Drought Code) so values were calculated with a 90-day lead-in.
Assessing burn severity
To assess burn severity we adapted the Composite Burn Index (Key and Benson,
2006) which was developed in the USA to allow semi-quantitative assessment
of burn severity and ground-truthing of remotely sensed data (e.g. Miller
and Thode, 2007). The CBI uses a scoring system to visually estimate a fire's
impact on components of each of five fuel strata. For instance, assessment of
“substrates” considers consumption of downed fuels of a variety of size
classes (litter up to heavy fuels > 8 inches diameter),
consumption of duff layers and changes to the cover and colour of soil and
rock. Similarly to Schepers et al. (2014), we adapted the CBI to account for
the unique vertical structure and fuelbeds of treeless peatland habitats
and, specifically, to include the impact of fire on peat-building Sphagnum species.
We recorded severity in circular plots 20 m in diameter (Supplement
Fig. S1) according to two strata – substrates (soil, litter and
mosses) and the field layer (dwarf shrubs and graminoids; see Supplement
Table S1). All variables were rated on a scale of 1–3 with
individual ratings averaged within strata and then summed across the strata.
Any variable that was not relevant, or which could not be recorded, for a
particular plot was disregarded. A full protocol and data collection sheet
for using the peatland CBI methodology (pCBI) are provided in the
Supplement.
Estimating fuel consumption
We assessed fuel consumption in two pCBI burnt–unburnt paired plots for each
fire. Within both the burnt and unburnt subplots we randomly located two
fuel quadrats (0.25 m2) and five gas-flux chambers (0.12 m2). All
biomass above the top of the peat was harvested in each quadrat/chamber. A
total of 14 biomass estimates were thus available for each plot – seven
from burnt and seven from unburnt subplots. Harvested vegetation was
separated into the following categories: dwarf shrubs, graminoids, ferns
(P. aquilinum), pleurocarpous mosses and plant litter, Sphagnum spp., tussock bases of M. caerulea and/or
Eriophorum spp. and woody stems buried in the moss and litter. During analysis, the
first three categories were grouped into a surface fuel category whilst the
mosses, litter, tussock bases and buried stems were classified as ground
fuels. Material was dried for 48 h at 80 ∘C.
Fuel consumption in our wildfires was compared with values reported by Legg
et al. (2007) for 26 experimental prescribed burns in Calluna-dominated moorland
fuel types. Legg et al. (2007) used a non-destructive method, based on
visual obstruction of a measuring stick (Davies et al., 2008b), to estimate
pre-fire surface and ground fuel loads. Post-fire surface fuel loads were
estimated via destructive harvesting. We estimated ground fuel consumption
in these fires by using the reported mean change in moss/litter layer depth
following burning and in the equation in Davies et al. (2008b) which relates
moss/litter layer depth Dm, cm) to ground fuel biomass (Bg, g m-2; Eq. 1).
Bg=407+171×Dm
Data analysis
We analysed burn severity data at the plot-level, in essence treating each
plot as a separate observation of fire effects and burn severity. We believe
that this is valid because substantial variations in vegetation type and
fuel structure across the fire ground and changes in fire weather during the
course of the burn day mean fire behaviour can be considered independent at
each plot. This approach is frequently used in wildland fire research as
obtaining numerous observations of individual fires is often impossible
(e.g. Fernandes et al., 2000; de Groot et al., 2009). The relationship between
pCBI and FWI system codes was analysed graphically and using correlation
analysis (Pearson product-moment correlation in the “cor.test” function in
R 3.1.2; R Core Team, 2014).
We used a generalised linear mixed model (GLMM) with a normal error
distribution to investigate spatial variation in estimated fuel consumption.
The aim of our analysis was to partition variance in our data to understand
how fuel consumption varies at multiple scales (i.e. between fires, plots
within fires and within plots) and how this contributes to uncertainty in
estimates of fuel consumption. We were not interested in testing the
hypothesis that there is a difference in biomass between burnt and unburnt
plots as this is not particularly enlightening. The GLMM was run with plot
and fire site defined as random effects whilst status (burnt/unburnt)
and sample type (chamber/quadrat) were defined as fixed effects. Including
plot as a random effect accounts for the paired burnt-unburnt subplots
design of our experiment. We selected the best fitting model by comparing a
full model and a minimal model. The minimal model contained all sources of
variation intrinsic to the design: the main effects of status and sample
type, and random intercepts at the plot and fire levels. The full model
additionally allowed the effect of status to vary between sample types
(fitted as an interaction between status and sample type), and between plots
and fires (fitted as random slopes at the plot and fire levels). Analysis
started with the full model and simplification proceeded by null hypothesis
testing, dropping non-significant effects. Random effects were tested first,
using parametric bootstrapping with 10 000 replicates (Faraway, 2005),
dropping effects where P > 0.1. Fixed effects were then tested
using likelihood ratio tests, dropping effects where P > 0.05. We
justify using a less stringent significance level for random effects on the
basis that power for testing random effects is generally low with few random
effect levels, and incorrectly dropping a random effect due to a false
negative test result can result in over-precise (anti-conservative) fixed
effect estimates (Schielzeth and Forstmeier, 2009). We used parametric
bootstrapping with 10 000 replicates to estimate confidence intervals around
mean plot-level consumption. This process was used to fit separate models
for both ground and surface fuel consumption. Fuel consumption was
square-root transformed to improve the fit of the residuals to a normal
distribution. Log transformation was also considered but provided a poorer
fit (see Fig. S2 for dot plots showing the raw data distributions,
Supplement). There is debate in ecology about the usefulness of
P values (Ellison et al., 2014) and we do not report them. Rather we report
the explanatory power of the final selected models and the variance
explained by different levels of our experimental design. Thus, for the
final, reduced models we used the procedures described by Nakagawa and
Schielzeth (2013) and Johnson (2014) to calculate marginal and conditional
R2 values. These describe the explanatory power of the fixed effects and the
whole model (fixed + random effects) respectively. As an initial step in
this analysis we were also able to partition the variance in our data into
that related to the fixed effects and the random effects of plot and fire.
We assumed that residual variance was the result of within subplot variation
in load between samples.
We examined controls on mean fuel consumption by combining the estimates of
fuel consumption during wildfires produced by the GLMM analysis with
information available from the prescribed fires reported by Legg et al. (2007).
This allowed us to examine how mean ground and surface fuel
consumption varies over a wider range of fire weather conditions. We used
the “lm” function in R to model changes in the consumption and combustion
completeness of surface and ground fuels as a function of pre-fire fuel load
and fire weather.
Results
Variation in burn severity
There was substantial variation in burn severity both within and between
individual fires (Fig. 1). On average, mean pCBI varied 1.6 fold between
wildfires but up to 1.7 fold within fires. Variability in burn severity was
particularly substantial in the Anglezarke and Loch Doon wildfires.
Examining the relationship between plot vegetation community, fire weather
conditions and pCBI suggested potential interactions between these variables
(Fig. 2). In general, pCBI appeared to increase with higher DMC
(r = 0.80, P = 6.4 × 10-4) and DC (r = 0.68, P = 7.9 × 10-3)
values. Plots in drier Calluna-dominated communities (National Vegetation Community
H12) appeared to burn at high severities at lower DMC and DC values than
wetter bog and mire communities (NVC M19, M20, M25a). However, fire sites
with more varied vegetation community structure did not necessarily show the
greatest amount of variation in fire severity.
Variation in burn severity (peatland Composite Burn Index; pCBI)
within and between five UK wildfires and across all 25 paired and unpaired
pCBI plots. Error bars are 95 % confidence intervals for the mean.
![]()
The relationship between burn severity as estimated by the
peatland composite burn index, ecosystem type (National Vegetation
Classification community; Rodwell, 1991) and moisture codes of the Canadian
Fire Weather Index system. Only data for the 14 paired burnt-unburnt pCBI
plots were available. Codes shown are the Duff Moisture Code (DMC; relating
to loosely compacted organic layers of moderate depth) and the Drought Code
(DC; relating to the moisture content of peat and layers of organic soil).
Individual wildfires are shown as different symbol shapes, colours relate to
NVC vegetation community (see Table 1 for NVC community descriptions).
![]()
Variation in fuel consumption
Both surface and ground fuel consumption were best represented by a model
which included the fixed effects of plot status (burnt/unburnt), sample type
(quadrat/chamber), random intercepts for individual fires and plots within
fires, and random slopes for the effect of status within individual plots
(Table 2). Plot status had considerably greater explanatory power for
surface fuel loads compared to ground fuel loads where random factors
attributable to variation in load between fires, plots and samples explained
a greater proportion of the variance. There was considerable variation in
fuel consumption both within and between different wildfires (Fig. 3),
indeed variability within some fires was greater than that seen, on average,
between fires.
Summary of the linear mixed model analyses on surface and ground
fuel consumption showing. Top: the proportion of variance explained by each
component of the models (the marginal and conditional R2, respectively,
show the explanatory power of the fixed effects and the whole model); and
bottom: the magnitude of the fixed-effects' terms in the model where
“Estimate” is the increase in the square-root of fuel load in comparison
to the reference level (burnt or gas flux chamber for status and sample type
respectively). SE = standard error.
Model
Fixed effects
Random effects
Fixed + random effects
Residual
(marginal R2)
Fire
Plot
(conditional R2)
Surface fuels
48
5
24
77
23
Ground fuels
30
12
29
71
29
Model
Fixed effect
Estimate
SE
t
Surface fuels
Status – Unburnt
0.51
0.056
9.11
Sample – Quadrat
0.11
0.034
3.27
Ground fuels
Status – Unburnt
0.49
0.074
6.62
Sample – Quadrat
0.10
0.046
2.23
Estimated mean consumption of (left) surface, and (right) ground
fuels across two plots on each of five UK wildfires. Error bars are 95 %
confidence intervals estimated using parametric bootstrapping based on a
general linear mixed model analysis of variation in consumption (Table 3).
![]()
For surface fuels there was a positive linear relationship between pre-fire
fuel load and mean fuel consumption irrespective of fire type (Fig. 4).
Surface fuel consumption (Cs) was best predicted by pre-fire fuel load
(Ls; Radj2= 0.73, P = 1.79 × 10-11; Eq. 2).
None of the FWI System values were significant or substantially improved the
model fit. For ground fuels, the relationship between pre-fire fuel load and
mean fuel consumption was noticeably different with a positive, linear
relationship for wildfires but little change in consumption with load for
prescribed fires (Fig. 4). Ground fuel consumption and ground fuel
combustion completeness appeared to decline with ground fuel load (Fig. 4).
It proved difficult to develop a satisfactory model of ground fuel
consumption, but ground fuel combustion completeness (Pg) could be
predicted tolerably well as an asymptotic function of BUI (B;
Radj2= 0.77, P = 1.77 × 10-12; Eq. 3).
Cs=0.173+0.624×LsPg=-0.034+0.020×B.
The relationships between mean pre-fire fuel load and mean fuel
consumption for surface and ground fuels (top left and right respectively);
and mean pre-fire fuel load and mean combustion completeness of surface and
ground fuels (bottom left and right). Stars are experimental prescribed
burns (see Legg et al., 2007), all other symbols are wildfires. The colours
and shapes of the points for wildfires follows Fig. 3.
![]()
Discussion
Wildfires are variable in every aspect and the fires we were able to assess
do not capture the full range of possible conditions. Notably, none of our
fires displayed peat smouldering outside of isolated “hotspots”.
Nevertheless, this work represents the first multi-site attempt to
investigate the relationship between burning conditions and wildfires'
ecosystem effects on moorlands. Wildfires on peatlands are recognised as a
growing global challenge with the potential to develop into a significant
positive feedback to climate change (Kettridge et al., 2015). Scientists and
land-managers currently have a limited understanding of the extent and causes
of variation in the severity and ecological effects of temperate peatland
fires. Temperate peatlands, such as those found in the UK, are likely to be
at the forefront of the effects of climate change with some studies
suggesting considerable declines in their bioclimatic space (Gallego-Sala
and Prentice, 2013) and fundamental changes in state associated with even
moderate reductions in water tables (Kettridge et al., 2015). UK peatlands
are of particular management concern due to the substantial area that has
already been lost or degraded by changing land-management, and debates over
the effects of traditional managed burning on the ecosystem services they
provide (Bonn et al., 2009).
The growing peatland wildfire problem demands evidence to inform management
and solve on-going conflict about the impacts of burning. Our adapted
version of the CBI provides a method for rapid cataloguing of post-fire
effects and burn severity in UK peatland ecosystems. The pCBI method
appeared to function well and detected substantial differences in burn
severity between and within individual fires. Importantly, there was
evidence that increased pCBI can be attributed to reduced ground fuel layer
moisture content as higher burn severity in British peatland ecosystems was recorded at higher values of
DMC and DC (Fig. 2). Our results, and existing evidence that the DC may
relate to the potential for smouldering peat fires (Davies et al., 2013),
raise the prospect that it will be possible to forecast the potential for
damaging wildfires. There was also a suggestion that burn severity is a
function of ecosystem type, and associated site hydrology, as we recorded
higher severity burns in dry moorland sites than would be expected given the
intermediate DMC/DC values at which they occurred (Fig. 2). Sites with
thin organic soils may thus be at greater risk of severe and smouldering
wildfires than those with deeper peat and forecasts for such systems should
be developed separately.
Variation in fuel consumption
In general, the random factors in our GLMM that account for variation in
loads within plots and within and between fires, explained for as much, if
not more, of the variation in fuel loads across our survey than differences
between burnt/unburnt subplots. This was particularly true for ground fuels
where 70 % of the variance was attributable to spatial variation rather
than the effects of fire or sample type. For ground fuels, the higher
variance explained by random factors is possibly a function of the
substantial differences in their composition between sites and, at some
locations, between plots. Our sites included both bog communities with
substantial cover of Sphagnum spp. and drier sites with thin organic soils where
bryophyte communities were poorly-developed and ground fuels were dominated
by litter. When considering the wildfires alone, ground fuel consumption
showed a linear relationship with pre-fire fuel load though there was some
evidence of a possible interaction with ecosystem type. Shetler et al. (2008)
demonstrated that the presence of Sphagnum had a limiting effect on total
carbon release during fires in black spruce forest peatlands and combustion
completeness was lowest at Loch Doon, the wettest of our sites, where
Sphagnum spp. and Molinia tussocks comprised a substantial proportion of the ground fuel
load Fig. 4. However, this fire also occurred under the least severe fire
weather conditions (Fig. 2).
When we analysed ground fuel consumption for the wild and prescribed fires
together we were unable to develop a tolerably robust model. We hypothesise
that this was due to differences in combustion rates between ecosystem
types. Given that all our prescribed fires were in drier Calluna-dominated
heathlands, our current data set was not sufficient to model
ecosystem-specific rates. de Groot et al. (2009) examined variation in
ground fuel consumption, albeit in non-peatland systems, and also found
differences in the controlling relationships for different fuel types. We
were, however, able to predict combustion completeness based on BUI. These
results are significant because (i) it provides further evidence that the
moisture status of ground fuel layers is a critical control on burn severity
in peatlands; (ii) it further demonstrates that certain components of the FWI
System (DMC, DC and BUI) may be useful in forecasting potential burn
severity.
Surface fuel consumption also showed significant spatial variation, though
variability between plots explained a greater proportion of the variance
than that between fires (Table 2). Surface fuel consumption of shrubs and
graminoids was strongly related to pre-fire fuel load (Fig. 4, Eq. 2)
and there was no significant effect of fire weather conditions. This matches
some of our existing understanding of fire behaviour in moorland fuel types
(Legg et al., 2007). In the vast majority of cases a relatively constant
proportion of fuel is consumed as the fire spreads through the Calluna canopy
consuming fine fuel particles but leaving larger live basal stems unburnt.
Coarse woody, live fuels form a larger proportion of the fuel in older stands (Davies
et al., 2008b) but rarely burn except under exceptionally severe conditions.
This accounts for the decline in combustion completeness with increasing
fuel load (Fig. 4). The variability we recorded in fuel consumption within
and between our fires is likely to be attributable to (i) differences in fuel
load between ecosystems; and (ii) the highly-managed nature of many UK
peatlands where rotational patch burning produces a mosaic of fuel/habitat
loads across the landscape.
Assuming that the approximate carbon content of our fuels was 49 %
(Worrall et al., 2013), our data suggest that average carbon release from
the combustion of above-ground biomass by wildfires can range between 0.36
and 1.00 kg C m-2. This is somewhat greater than was seen
for the prescribed fires which saw C release rates of between 0.26
and 0.66 kg C m-2. Our wildfire C release rates are
considerably higher than the mean release of 0.15 kg C m-2 reported by
Clay and Worral (2011) for the single moorland wildfire they studied, but
both their result and ours falls within the range reported by Poulter et al. (2006)
for a temperate peatland wildfire in North Carolina, USA. Whether or
not leaving a peatland unburnt would increase the amount of carbon stored in
the landscape is difficult to judge from our data alone. Whilst unmanaged
peatlands may store greater amounts of C in surface and ground fuel layers
than those that are subject to regular managed burning, they may also be
more susceptible to large-scale wildfires because of their unmanaged fuel
loads (Allen et al., 2013). Our results show rates of fuel consumption during
such wildfire events will also be higher.