High-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades

Abstract

Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in southeastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in southeastern Australia.

Full text

RESEARCH ARTICLE
High-severity wildfires in temperate
Australian forests have increased in extent
and aggregation in recent decades
Bang Nguyen TranID
1,2
*, Mihai A. Tanase
1,3
, Lauren T. Bennett
4
, Cristina Aponte
1,5
1School of Ecosystem and Forest Sciences, University of Melbourne, Richmond, Victoria, Australia,
2Faculty of Environment, Vietnam National University of Agriculture, Trauquy, Gialam, Hanoi, Vietnam,
3Department of Geology, Geography and Environment, University of Alcala, Alcala de Henares, Spain,
4School of Ecosystem and Forest Sciences, The University of Melbourne, Creswick, Victoria, Australia,
5National Institute for Research and Development in Forestry “Marin Dracea”, Voluntari, Ilfov, Romania
*ntran6@student.unimelb.edu.au
Abstract
Wildfires have increased in size and frequency in recent decades in many biomes, but have
they also become more severe? This question remains under-examined despite fire severity
being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and
associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than
1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity
burned areas between 1987 and 2017. High-severity maps were generated from Landsat
remote sensing imagery. Total and proportional high-severity burned area increased
through time. The number of high-severity patches per year remained unchanged but vari-
ability in patch size increased, and patches became more aggregated and more irregular in
shape. Our results confirm that wildfires in southern Australia have become more severe.
This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-
adapted temperate forests are more likely to be burned at high severities relative to historical
ranges, a trend that seems set to continue under projections of a hotter, drier climate in
south-eastern Australia.
Introduction
Wildfire shapes landscape patterns and ecosystem processes as it determines both vegetation
distribution and structure [1,2]. Changes in wildfire activity may alter mortality and regenera-
tion patterns, initiating new successional pathways that ultimately lead to shifts in vegetation
composition and landscape attributes [3]. Many studies over the past decades have reported a
change in wildfire activity including increases in the frequency, size, and duration of wildfires,
as well as the length of the fire season [4–8]. Such increases have been linked to climate change,
which influences key fire drivers like fuel accumulation and availability [9–11]. Models based
on climate change projections suggest that this trend in increasing fire activity will continue
into the future [3,12–15] posing threats to forest resilience, including shifts to lower density
forests or non-forest states [16–18].
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OPEN ACCESS
Citation: Tran BN, Tanase MA, Bennett LT, Aponte
C (2020) High-severity wildfires in temperate
Australian forests have increased in extent and
aggregation in recent decades. PLoS ONE 15(11):
e0242484. https://doi.org/10.1371/journal.
pone.0242484
Editor: Krishna Prasad Vadrevu, University of
Maryland at College Park, UNITED STATES
Received: April 18, 2020
Accepted: November 3, 2020
Published: November 18, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
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editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0242484
Copyright: ©2020 Tran et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files

Fire severity is a wildfire attribute that quantifies the degree of environmental change
caused by fire including immediate fuel consumption and carbon emissions and longer-term
impacts on vegetation mortality, successional pathways, and soil substrate [19]. Wildfire sever-
ity is spatially heterogeneous and can range from partial litter consumption and light scorching
of understorey vegetation to near complete mortality of canopy trees [19–21]. Fire severity and
the spatial configuration of severity classes have critical implications for fire-related resilience
and potential degradation of ecosystems [21–25]. Wildfire severity is related to fire intensity,
which is driven by fuel, climate, and weather [26–29]. As such, fire severity, as for other com-
ponents of fire regimes, has likely been affected by changing climates in recent decades [30,
31]. In contrast to the large number of studies that have documented recent increases in wild-
fire area and frequency [9,32–34], comparatively fewer studies, mostly focused on North
America forests, have investigated trends in fire severity, some indicating increases while oth-
ers indicating no change or decreases [35–37]. Changes in wildfire severity can influence eco-
logical processes by affecting the trajectory of postfire vegetation succession, leading to
reductions in forest cover and even conversions to non-forested vegetation [38,39]. A better
understanding of changes in fire severity is crucial to foresee the future pathways of forest sys-
tems [40–44].
Australia is one of the most fire-prone countries worldwide [45,46] with 30.4 million hect-
ares burned across Australia in 2019–2020 alone [47]. Studies have highlighted how climate
change has and will continue to impact Australian fire weather and fire activity [31,48,49]
with fires predicted to become larger and more frequent [50–52]. Whether fires have also
become more severe remains largely undocumented. This study’s principal objective was to
examine patterns in high-severity fires in temperate forests of the state of Victoria, south-east-
ern Australia over the last three decades. Specifically, we addressed three questions: 1) Has the
area burnt by high- severity fire in temperate forests of Victoria increased in the last 30 years?;
2) Has the spatial configuration of high-severity patches in the landscape changed in the last
30 years; and 3) Are the observed trends consistent across bioclimatic regions?
Materials and methods
Study area and forest types
This study was conducted across the state of Victoria, south-eastern Australia, an area that
encompasses 237,659 km
2
, ranges from 0 to 1986 m a.s.l in elevation and comprises several
geographical bioregions with differing geology, soils, climate, and predominant vegetation
(Table 1 and Fig 1) [53]. Climate across Victoria is temperate with warm to hot summers
(average maximum temperature between 16˚C and 30˚C; [54]). The annual mean temperature
ranges from 12.6˚C in the south-east region to 14.7˚C in the north and north-west regions of
the state [55]. The mean annual precipitation varies from 500 to 2,200 mm, with precipitation
over 1000 mm in the mountainous areas of the Great Dividing Range [56]. Over the past few
decades, Victoria has become warmer and drier, consistent with global trends, and these
trends are likely to continue [57–59].
Vegetation affected by the studied wildfires was predominantly comprised of a range of
Eucalyptus forests of varying composition, structure and post-fire regeneration strategies [60]
(Table 1). These included Mallee, with low canopy height (7 m) and sparse canopy cover
(25%), Woodlands with medium canopy height (15 m) and sparse canopy cover, Open forests,
with medium to tall canopy height (10–30 m) and mid-dense canopy cover (30–70%) and
Closed forests, with tall canopy height (30 m) and dense canopy cover (70–100%) [61]. Obli-
gate seeder tree species are dominant in Closed forest whereas resprouter eucalypts (basal or
epicormic) are dominant in all other forest types [60,62,63].
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Funding: The authors would like to acknowledge
the financial support of the Melbourne Research
Scholarship program, the Vietnam International
Education Cooperation Department (VIED)
scholarship, and the Integrated Forest Ecosystem
Research program, supported by the Victorian
Department of Environment, Land, Water and
Planning. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist

Fire history dataset
We used the wildfire history data available from the Victorian Department of Environment,
Land, Water & Planning (‘DELWP’; [64]). Data contained the spatial extent of wildfires since
1926 and, for the most recent fires (from 1998 onward), the start date of the fire. For this study
we selected the subset of wildfires that occurred between 1987 and 2017 and that had a mini-
mum burned area of 1000 ha to ensure the fire size was sufficient to include multiple fire-
severity levels. That amounted to 211 wildfires that were used to assess changes in the number
of fires per year and mean fire size between 1987 and 2017. Each fire was classified according
to its dominant bioregion [53]. For the purpose of assessing changes in fire severity, 32 of the
211 wildfires were discarded because pre- or post-fire remote sensing images were unavailable,
and 11 were discarded because clouds covered more than 25% of the fire affected area, which
may affect the spatial metrics assessed in our study. In total, a subset of 162 wildfires, with at
least two fires per year over the past three decades, was used to generate fire-severity maps and
analyse changes in severity patterns.
Remote sensing dataset and spectral indices
Wildfire severity of the selected 162 fires was mapped using Landsat TM, ETM+ and Landsat 8
imagery (30 m spatial resolution, all from Landsat Collection 1, Tier 1). Pre- and post-fire
Table 1. Characteristics of the bioregions in the study area affected by the selected 162 fires.
Bioregion Major forest
types
a
Height
(m)
Projective
Foliage Cover
(%)
Regeneration
strategy
b
Elevation
(m)
MAT
(˚C)
MAP
(mm)
No of
fires
Total burnt
area (ha)
Total high-
severity burnt
area (ha)
AA Australian
Alps
High Altitude
Shrubland/
Woodland
15 10–30 R 844–1996 4.5–12.6 712–1996 9 1,426,791 290,073
Riverine
Woodland/Forest
15 10–30 R
MDD Murray
Darling
Depression
Lowan Mallee 7 10–30 R 265–690 12.8–17.2 265–702 52 514,689 358,238
Riverine
Woodland/Forest
15 10–30 R
SCP South East
Coastal Plain
Riverine
Woodland/Forest
15 10–30 R 492–1260 11.4–14.9 494–1306 10 40,375 8,745
SEC South East
Corner
Moist Forest 30 70–100 S 664–1184 7.3–15.2 656–1292 17 170,045 18,700
Riverine
Woodland/Forest
15 10–30 R
SEH South Eastern
Highlands
Grassy/Heathy
Dry Forest
10–30 10–30 R 681–1922 6.6–14.8 645–1942 17 995,133 170,452
Moist Forest 30 70–100 S
VM Victorian
Midlands
Forby Forest 15–30 30–70 R 418–1411 8.5–15.3 418–1490 46 404,363 156,083
VVP Victorian
Volcanic Plain
Moist Forest 30 70–100 S 477–1026 11–14.9 476–1026 11 165,003 79,022
Bioregion name and acronym [53], major forest types in each bioregion affected by the selected wildfires, height, projective foliage cover and regeneration strategy of the
dominant species in each forest type, elevation range, mean annual temperature (MAT) and annual precipitation (MAP) range [65]; Number of wildfires included in
this study (i.e. 162 wildfires greater than 1000 ha, occurred between 1987 and 2017 and with available Landsat imagery) and their cumulative total [64] and high-severity
burnt area (as estimated in this study).
a
Major forest types were adopted from EVD names and associated structural data [66]. Dominant tree species were derived from the Ecological Vegetation Classes
(EVC) benchmarks database [67];
b
R: resprouter; S: obligate seeder, classifications based on predominant fire-response traits of dominant tree species [62,68,69].
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images were selected for each wildfire based on the recorded fire start dates, which were pre-
dominantly in the summer months (December to February). Images were selected within two
months before and after the fire to minimise differences in forest phenology and general atmo-
spheric conditions at the time of acquisition. When only the fire year but not start date was
recorded (~13% of the fires), we conducted a visual inspection of all images available for the
fire season, identified the image where the fire scar was first visible and selected that image and
the previous one as post- and pre-fire images respectively for that event. A total of 347 Landsat
images including 228 scenes of Landsat 5 (TM), 36 scenes of Landsat 7 (ETM+), and 83 scenes
of Landsat 8 (OLI/TIRS) were selected and obtained through the US Geological Survey
(USGS) EarthExplorer at http://earthexplorer.usgs.gov as higher level surface reflectance prod-
ucts for each fire. The images were masked for clouds and shadows using the Fmask algorithm
[70], which has an accuracy of about 96% [71].
Four spectral indices, namely NBR, NDVI, NDWI, and MSAVI, and their temporal dif-
ferences (i.e. delta versions, which calculate the change between pre-fire and post-fire spec-
tral index values) were computed for each of the 162 wildfires. These indices are
commonly used to assess fire severity [72–76] and were identified by the authors, in a pre-
vious study, as the optimal spectral indices for mapping fire severity in the forest types of
the study area [77].
Fig 1. Map of study area. (i) Victoria highlighted (grey) in the map of Australia; (ii) Locations of study areas within the state of Victoria in south-eastern
Australia. Red points rrepresent the centroids of the 162 wildfires investigated in this study. Colours relate to bioregions (Acronyms are defined in Table 1).
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Fire severity mapping
Severity of the wildfires in Victoria has not been consistently recorded, with historic fire sever-
ity mapping only available for nine years in the period between 1998 and 2014 [78]. To gener-
ate fire severity maps for the 162 selected wildfires ensuring the consistency of the
classification we used a Random Forest model based on spectral indices that had been previ-
ously trained and validated by the authors for the same study area [61]. The reference fire-
severity dataset used for training and validation was comprised of 3730 plots from eight large
wildfires (>5,000 ha) that occurred between 1998 and 2009 and covered 13 forest types differ-
ing in species composition, canopy cover, canopy height and regeneration strategy. These for-
est types match those affected by the 162 wildfires of this study. Fire severity of the 3730
reference plots had been assessed in situ or visually interpreted on very high resolution ortho-
photos by the Department of Environment, Water & Planning (DELWP) [78]. Severity was
classified as Unburnt: less than 1% of eucalypt and non-eucalypt crowns scorched; Low sever-
ity: light scorch of 1–35% of eucalypt and non-eucalypt crowns; Moderate severity: 30–65% of
eucalypt and non-eucalypt crowns scorched; or High severity: 70–100% of eucalypt and non-
eucalypt crowns burnt [79]. Overall, the reference data included a minimum of 20 plots for
each forest type and fire-severity class combination. The Random Forest model was trained
with 60% of the data and used 12 predictor variables, which included the four optimal SI indi-
ces (dNBR, dNDVI, dNDWI, and dMSAVI) and their pre- and post- fire values. Model accu-
racy was tested on the remaining 40% of the data that had been left for model validation.
Accuracy for high-severity mapping was very high, with a commission error (plots wrongly
attributed to high severity) of 0.06 and an omission error (high severity plots incorrectly classi-
fied) of 0.18.
Metrics of high-severity fire
Based on the high-severity maps of each of the 162 wildfires, we calculated eight landscape
metrics to characterize the extent and spatial configuration of the high-severity burned area.
Extent metrics included total and proportional high-severity burned area. Spatial configura-
tion metrics were calculated at the patch level, i.e. areas of high-severity fire surrounded by dif-
ferent severities within the wildfire boundary. Spatial configuration metrics included two
patch size metrics (mean patch size, coefficient of variation of patch size), two fragmentation
metrics (number of patches, and edge density—a measure of shape complexity) and two aggre-
gation metrics (clumpiness and normalized landscape shape index–NLSI, S1 Table of S1 File).
Edge density is the ratio between the total length (m) of the edges of the high-severity patches
and the fire size (i.e. total wildfire area burnt at any severity; ha). Low edge density values rep-
resent simple shape (e.g. circular) and/or large patches, while large values indicate irregular
and/or less continuous patches [80]. Clumpiness and NLSI, both unitless, quantify patch
aggregation. The former is based on the likelihood of adjacent pixels belonging to the same
class, whereas the later measures the deviation from the hypothetical minimum edge length of
the class. Increasing levels of aggregation (i.e. increasing clumsiness and decreasing NLSI) rep-
resent more compact and simpler-shaped patches [80,81]. These metrics describe different
aspects of landscape configuration but were not completely independent and therefore should
be interpreted jointly (S1 Table of S1 File). Spatial pattern metrics were obtained using the
‘landscapemetric’ package [82] in the R statistical software [83].
Data analysis
Linear regression models were used to evaluate the trends in high-severity fire metrics from
1987 to 2017, with individual fires as the sampling unit. We built two groups of models, a
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state-wide model (n = 162 fires) and separate bioregion models. The response variables for
both groups of models were the extent or landscape configuration metrics of the high-severity
burned area. Predictor variables included year and fire size (i.e. total wildfire area, ha) as fixed
effects and bioregion as a random effect, which was only included in the state-wide mixed
effects models. Fire size was included as covariate in all models as it can be related to burn pat-
terns [27] and was not correlated with fire year (Pearson’s r = -0.01). Data were transformed
when needed to meet assumptions of normality (S1 Table of S1 File). All statistical tests were
conducted in the statistical programming language R [83].
Results
Changes in area and proportion of high-severity fire over time
Based on the fire history dataset (n = 211), the number of wildfires per year larger than 1000
ha between 1987 and 2017 increased significantly (P= 0.012), a trend that was mostly due to
an increase since 2000 (Fig 2). In contrast, we detected no significant change in total fire size
(i.e. all fire severities combined) over that period.
Between 1987 and 2017 the area burnt by high-severity fire increased significantly (P
Year
<0.001) even when accounting for total fire size (P
Fire size
<0.001; Fig 3 and S1 Fig of S1
File). The same trend was observed for the proportion of the area burnt by high-severity fire
(P
Year
<0.001; Fig 3). Estimated changes in the area and the proportion of area burnt by
high-severity fire over time by bioregions were positive and significant (or marginally sig-
nificant 0.05 <P<0.1) in all cases (Fig 3 and S2-S3 Figs of S1 File). The studied bioregions
supported quite distinct forest types, from wet, tall, and highly productive to dry, open, and
less productive. This suggests that the observed increases in the area burnt by high-severity
fire was ubiquitous across regions and did not depend on local environmental conditions or
forest types.
Fig 2. Changes in the number of fires per year and fire size between 1987 and 2017. Data includes all wildfires �1000 ha from DEWLP fire history dataset
(n = 211) [64]. Solid black line indicates significant relationship (P<0.05), dashed grey line indicates no significant relationship.
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Changes in spatial patterns of high-severity fire
We detected no changes in fragmentation of wildfires between 1987 and 2017 as evidenced by no
significant increases in the number of high-severity patches, a result that was consistent across all
bioregions (Figs 4and 5and S4 Fig of S1 File). In contrast, edge density, which is related to patch
shape complexity, increased over time across Victoria (P
Victoria
= 0.006), although this trend was
only (marginally) significant for the SEC, VM, VVP bioregions (0.05 <P
Year
<0.1; Fig 5 and S5
Fig 3. Changes in the area and proportional area of high-severity fire from 1987 to 2017. Left panels: Area and proportional area burnt by high-severity fire
in each of 162 wildfires (line represents significant relationship between variables). Right panels: Standardized coefficients for high-severity area (top, log
transformed) and the proportion high-severity area (bottom, arcsine transformed) indicating the relationship between area burnt and time. Each panel displays
results for a single model for all regions (“Victoria”) and for individual bioregions (Acronyms of bioregions are defined in Table 1); Dot points represent mean
estimated coefficient along with the 90
th
(solid line) and 95
th
(dashed line) percentile intervals. Coefficients denote significant changes when interval does not
include zero.
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Fig of S1 File). While mean high-severity patch size did not change significantly, the coefficient of
variation of patch size, which was related to fire size, increased in all models (P
Year
<0.05 and P
Fire
size
<0.001; Figs 4and 5and S6-S7 Figs of S1 File). Accordingly, we detected an increase in the size
of the largest patch (P
Year
= 0.005; S8 and S9 Figs of S1 File). The level of patch aggregation mea-
sured through increased clumpiness and/or decreased Normalized Landscape Shape Index
(NLSI), also increased from 1987 to 2017 (Figs 4and 5and S10 and S11 Figs of S1 File). This
trend, which was significant both at the state and bioregion level, suggests the patterns in high-
severity fire changed from a more random, highly-dispersed distribution of patches towards
fewer, larger patches of irregular shape that were more aggregated within the fire boundaries.
Discussion
Our study assessed for the first-time changes in high-fire severity patterns since 1987 in Victo-
ria, south-eastern Australia. We detected an increase in the area burnt at high-severity during
that period and a shift in the landscape configuration of high-severity patches, which was con-
sistent across most bioregions, encompassing a broad range of forest types.
The area of high-severity fire has increased
Our results showed an increasing trend in both total and proportion of high-severity burned
area between 1987 and 2017 across various temperate forests types in south-eastern Australia.
Fig 4. Changes in high-severity spatial metrics over time. Each subplot displays a scatterplot between the Year of the fire and the defined high-severity spatial
metric. Dots represent each of the 162 wildfires. Values are the results for single mixed effects models where Year and Fire size are fixed effects and Bioregion is
a random effect. Lines represent significant (solid black) or not significant (dashed grey) linear relationships.
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Fig 5. Estimated coefficients for high-severity spatial metrics by bioregions. Each panel displays results for a single
model for all regions (“Victoria”) and for individual bioregions (Acronyms of bioregions are defined in Table 1); Dot
points represent mean estimated coefficient along with the 90th (solid line) and 95th (dashed line) percentile intervals.
Coefficients denote significant changes when interval does not include zero. Spatial metrics were log transformed
(Number of Patches, Mean Patch Area, Variation Patch Area, NLSI) or arcsine transformed (Edge Density).
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Our findings are in contrast to similar studies conducted in the US where either an increase in
fire severity was not detected [37,84] or the detected increase was due to increasing fire size
[36]. Our results also show a covariation between fire size and the extent of the area burned by
high-severity fire, a pattern that has been documented before in several north American forests
[4,27,85–87].
The increasing trends in total and proportion of high-severity burned area at the state level
were consistent across all bioregions, indicating that these changes occurred irrespective of
forest type and climatic region. This is in contrast to the mixed fire-severity trends assessed
across regions in North America [37,88], which have been argued to be related to fire suppres-
sion policies masking climate-change effects [84,88].
Changes in the area of high-severity fire like those described here have been predicted to
occur as a result of climate change since decades ago [89–91]. Our results confirm for the first
time that wildfires in south-east Australia are indeed becoming more severe and, given projec-
tions of a hotter, drier climate [59], this pattern seems set to continue in coming decades.
Trends in landscape configuration: Aggregation of high-severity patches
Our results showed changes in the landscape configuration of high-severity patches that were
consistent at the state level and across bioregions. While we did not detect a significant shift in
patch number or mean patch size, we noted an increase in patch size variability, patch shape
complexity (measured as edge density) and patch aggregation (as evidenced by trends in clum-
piness and NLSI). These changes suggest that the areas burned by high-severity fire have
become more aggregated, more irregular in shape, and have a larger area occupied by the larg-
est patch. Similar changes in spatial patterns of high-severity fire have also been reported in
fire-severity research in North America [27,88,92], where increasing patch aggregation was
related to the increased proportion of high-severity area [42].
Implications of increasing high-severity fire for temperate forests in south-
east Australia
Our quantified increases in high-severity burned area can lead to concerns about the resilience
of Victoria’s temperate forests [20,93,94], similar to those expressed for other forest types else-
where [4,92,95]. High-severity fire influences ecosystem dynamics with effects on vegetation
succession [25,96,97], biogeochemical processes [21,26,98], geomorphic processes [99,100],
and habitat availability and biodiversity [23,101,102]. Recent high-severity fires within our
study area have led to increased mortality of fire-tolerant eucalypt trees and to an increase in
the density of young trees vulnerable to subsequent fires [20,63,103]. If increasing trends in
the extent of high-severity fire detected in our study continue, this indicates potential for
large-scale changes in key structural attributes of even the most fire-tolerant forests.
High-severity fire impacts can be modulated by the size, shape, and configuration of high-
severity patches. For instance, patch size and aggregation can influence runoff connectivity
and post-fire sediment yields and affect the distribution of low- and moderate-severity patches
that serve as refuges for fire-sensitive species [104–106]. Patch size and spatial configuration
can also affect dispersal and subsequently influence vegetation succession potentially leading
to forest-type conversions [107–109]. Delays in tree re-establishment following high-severity
fires has been detected in non-serotinous forests of the United States and Canada due to a
rapid and extensive shrub establishment via persistent soil seedbanks [109,110]. Eucalypt for-
ests in south-eastern Australia, including those affected by the studied wildfires, are dominated
by either resprouter species that survive most fires, or obligate seeder species that rely on a can-
opy seedbank to regenerate after fire [63,111]. Seed dispersal in both resprouters and obligate
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seeders eucalypt forests is limited to one or two tree heights, with seeds lacking attributes to
facilitate animal or wind dispersal [112]. Resprouters’ seed viability decreases with fire inten-
sity [113] and therefore regeneration in high-severity patches may depend on dispersal from
adjacent moderate-severity or unburned patches (although see [20] indicating prolific regener-
ation from seed of resprouter eucalypts after a single high-severity wildfire). Increases in high-
severity patch size though aggregation as observed in this study could hinder post-fire tree
establishment by increasing distances from seed source and also altering the regeneration abi-
otic environment [114] contributing to feedbacks that result in an increased risk of forest-type
conversion [115,116]. Spatial configuration of high-severity patches can also influence regen-
eration of obligate seeder forests burnt by recurrent fires in quick succession (~20 years;
[103]). In such circumstances, trees regenerating after the first fire would not have yet pro-
duced meaningful quantities of viable seed before a second fire [117], and eucalypt regenera-
tion would rely on seed dispersal from adjacent patches. Lack of tree regeneration after short-
interval fires in obligate seeder forests has been observed in the last decades with aerial sowing
being required to address post-fire recovery in obligate seeder forests [118]. This highlights the
impact that the observed changes in fire regimes have had on the resilience of eucalypt forests
in south-eastern Australia [63,103].
Conclusions
Changes in high-severity fire, its extent and spatial configuration, can alter a range of ecosys-
tem processes that interactively determine post-fire recovery, including the conversion to non-
forest alternative states. Our analysis showed an increase in both the total and proportion of
high-severity burned area in Victoria between 1987 and 2017. Over that period, high-severity
patches have become more aggregated and more irregular in shape. These trends were consis-
tent across bioregions encompassing a diversity of forest types. Shifts in the spatial patterns of
high-severity fire over time may have cascading effects on forest ecology, highlighting the
increased threat posed by changing fire regimes to forests ecosystems.
Supporting information
S1 File.
(DOCX)
Acknowledgments
We acknowledge the support of many staff and students from the School of Ecosystem and
Forest Sciences at the University of Melbourne, including valuable comments and advice to
improve this manuscript.
Author Contributions
Conceptualization: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Data curation: Bang Nguyen Tran, Cristina Aponte.
Formal analysis: Bang Nguyen Tran.
Funding acquisition: Bang Nguyen Tran.
Investigation: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Methodology: Bang Nguyen Tran, Mihai A. Tanase, Cristina Aponte.
Project administration: Bang Nguyen Tran.
PLOS ONE
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | https://doi.org/10.1371/journal.pone.0242484 November 18, 2020 11 / 17

Resources: Bang Nguyen Tran, Lauren T. Bennett.
Software: Bang Nguyen Tran, Mihai A. Tanase.
Supervision: Mihai A. Tanase, Lauren T. Bennett, Cristina Aponte.
Validation: Bang Nguyen Tran.
Visualization: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Writing – original draft: Bang Nguyen Tran.
Writing – review & editing: Bang Nguyen Tran, Mihai A. Tanase, Lauren T. Bennett, Cristina
Aponte.
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