Wildfire Selectivity for Land Cover Type: Does Size Matter?
Citation: Barros AMG, Pereira JMC (
Wildfire Selectivity for Land Cover Type: Does Size Matter?
Ana M. G. Barros 0
Jose M. C. Pereira 0
Gil Bohrer, The Ohio State University, United States of America
0 Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa , Lisboa , Portugal
Previous research has shown that fires burn certain land cover types disproportionally to their abundance. We used quantile regression to study land cover proneness to fire as a function of fire size, under the hypothesis that they are inversely related, for all land cover types. Using five years of fire perimeters, we estimated conditional quantile functions for lower (avoidance) and upper (preference) quantiles of fire selectivity for five land cover types - annual crops, evergreen oak woodlands, eucalypt forests, pine forests and shrublands. The slope of significant regression quantiles describes the rate of change in fire selectivity (avoidance or preference) as a function of fire size. We used Monte-Carlo methods to randomly permutate fires in order to obtain a distribution of fire selectivity due to chance. This distribution was used to test the null hypotheses that 1) mean fire selectivity does not differ from that obtained by randomly relocating observed fire perimeters; 2) that land cover proneness to fire does not vary with fire size. Our results show that land cover proneness to fire is higher for shrublands and pine forests than for annual crops and evergreen oak woodlands. As fire size increases, selectivity decreases for all land cover types tested. Moreover, the rate of change in selectivity with fire size is higher for preference than for avoidance. Comparison between observed and randomized data led us to reject both null hypotheses tested (a = 0.05) and to conclude it is very unlikely the observed values of fire selectivity and change in selectivity with fire size are due to chance.
Wildfire is a ubiquitous disturbance in many ecosystems of the
world [1,2]. Unlike other ecological disturbances, such as cyclones
or earthquakes, fires feed on complex organic molecules
transforming them into organic and mineral products, thus acting as an
evolutionary force that shapes fire-prone ecosystems and plays a
pivotal role in maintaining their structure and function .
What is common to any fire-prone ecosystem is that its fire
regime - broadly described in terms of fire occurrence, spread,
behavior and effects - results from non-linear processes controlled
by the interactions and feedbacks between fire, land use,
vegetation attributes, climate, landscape characteristics and
ignition patterns. Understanding the mechanisms that determine
where and when fires occur, what limits their growth and intensity,
and what will be the ecosystem response is an essential goal of fire
ecology and management . Past studies addressed the
relationships between climate, fuel, topography, in what is
commonly described as the fire behaviour triangle [4,710]. Over
the past years, in Southern California and in the Mediterranean
Basin regions, the relative contribution of fuel vs climate has been
the focus of attention and remains an issue under debate, with
strong arguments on both sides and important management
implications [8,1114]. If, in fact, fuels (age, load and continuity)
are determinant for the occurrence of severe fire seasons, then fuel
management may be efficient in reducing fire hazard .
However, if area burned tends to coincide with episodes of severe
weather, poorly constrained by fuel landscape properties, then
investments in large scale fuel management programs are unlikely
to succeed [16,17]. Furthermore, future climate change is likely to
exert stronger impact in fire regimes that are primarily
climatedriven. The relative dominance of fire regime controls can also
change in time. A recent study  identified a major shift in the
fire regime of the Western Mediterranean Basin over the past 130
years, with a change point circa 1970. In the decades after 1970,
number of fires doubled and burned area increased by one order
of magnitude. The authors show that the main driver of this shift
was the increase in fuel amount due to rural abandonment.
Additionally, it was shown that climatic conditions were poorly
related to pre-1970s fires and strongly related to post-1970s fires,
suggesting that fires are currently less fuel-limited and more
drought-driven than before the 1970s. Similar results were
found by  for the eastern Mediterranean region using
historical fire data between 1894 and 2010. Trend analysis of
number of fires and air temperature showed a statistically
significant increase, particularly after the mid-1970s, and strong
correlations between fire occurrence (number of fires and burnt
area) and mean maximum and the absolute maximum air
The landscape in the Mediterranean basin is highly dynamic,
humanized and fire-prone, therefore land use/land cover changes
(LULC) have strong effects on fire hazard through changes in
vegetation structure, fuel load, and fuel composition [12,2022]. A
better understanding of the relationship between fire and fuel
provides the basis for analyses that attempt to quantify land cover
proneness to fire or, alternatively, fire selectivity towards different
land cover types. The concept of fire selectivity, also known as fire
selection ratio, is derived from seminal work of , whom used the
metaphor of fire as global herbivore capable of significantly alter
biomass in flammable ecosystems . The interpretation of fire
selectivity is straightforward - fires are considered selective if they
burn through a certain land cover type disproportionally to its
availability. Selectivity is positive (preference) if the land cover is
burned proportionally more than available and is negative
(avoidance) if the land cover class is burned proportionally less
than available. When a land cover type is consumed
proportionally to its availability the fire is considered indifferent to that land
Previous analyses of fire selectivity towards different land cover
types in Portugal suggest that shrublands and conifer stands are
more fire prone, while agricultural areas are less fire prone [20,23
26]. Within forest types  found that the most fire prone forest
type is maritime pine, followed by eucalypt and unspecified
broadleaf species, while stone pine is the least fire prone. Land
cover proneness to fire and terrain was analyzed by . In
agreement with other analyses, shrublands and steep slopes were
found to be the most fire prone land cover/topographic setting.
Broad scale analysis of fire selectivity to land cover and topography
in southern European countries suggested that shrublands and
grasslands were preferred by fire, with forests showing
intermediate values of selectivity. Results also show that north-facing
slopes steeper than 25% were less susceptible to burning . In a
study for Sardignia, Italy,  showed that the mean fire size of
grasslands and shrublands is significantly larger than expected
using a random null model, whereas in urban areas and
permanent crops there is significant resistance to fire spread. In
central Spain, and based on data from 16 years,  concluded
that pine woodlands showed significant and positive fire selectivity,
whereas deciduous woodlands showed significant and negative
selectivity. Their study also shows that fires positively selected
areas closer to towns and roads, and that selectivity to topographic
variables - slope and aspect - was less marked than for land cover
and proximity to towns and roads .
Despite overall agreement between fire selectivity analyses, their
conclusions may be hindered by the effects of other drivers of fire
spread, e.g. fuel connectivity, structure and load, landform
position, level of suppression and weather conditions, which can
overwhelm the importance of land cover type [24,32].
Furthermore,  elaborates on the shortcomings associated with nature
of fire data and fire selectivity analyses that may affect the findings
of some studies. The authors propose assessing fire selectivity
through a null based model based on fire size and shape that is
independent of spatial relationships or any other distributional bias
of fire data [30,31]. Under severe weather conditions fires are
expected to become larger and, to a certain extent, independent of
land cover patterns [4,33,34], i.e. it is likely that as fire size
increases, selectivity becomes less important, as other drivers, e.g.
weather, become increasingly important . The effect of fire size
on fire selectivity was addressed by , whom analyzed selectivity
in distinct fire size classes. The authors concluded that large
(w5 km2 and 15 km2) and very large (w15 km2) fires are not
significantly selective for land cover, while small fires (5 km2) are
unequivocally selective.  also examined the size-dependent
variation in the degree of fire selectivity, by dividing fires into small
(v100 ha) and large (w100 ha) and found no differences in the
land cover selection indexes between both groups.
The present study aims to discern to what extent fire selectivity
towards different land cover varies as a function of fire size. We
used quantile regression to determine how selectivity responds to
increasing fire size, for different land cover types. Quantile
regression allows studying the edges of the response variable
distribution (selectivity), conditioned on values of the predictor
variable (fire size). In this context, for any given land cover type
lower quantiles correspond to avoidance, while higher quantiles
correspond to preference for that specific land cover. Under the
assumption that land cover types are differently prone to fire, we
expect to find different rates of change in both upper and lower
quantiles, among different land cover types. These rates of change
in selectivity will be quantified by the slope of the estimated linear
regression quantiles and compared with a null random model, to
test the hypotheses that land cover proneness to fire is independent
from fire size. The present study is meant to contribute to the
current debate on the importance of weather vs fuel as
determinants of fire regime attributes [35,36], with implications
for the land use planning and fuel management in the
wildlandurban interface. From the fire management stand-point, in land
cover types with higher rates of change in selectivity escalating fire
behavior is likely to be quicker, thus with potential for severe fire
behavior. Based on that knowledge, more accurate assessment of
potential fuel management practices and improved fire
suppression strategies can be developed.
Data and Methods
Study area and data
The study area, with approximately 89,100 km2, corresponds to
mainland Portugal, located in south-western Europe (Figure 1).
The Portuguese climate is temperate, with lower mean annual
temperature along the coast and in the northern half of the
country . Precipitation is higher in the interior highlands of
northern Portugal than in the southern half of the country. The
dry season, corresponding to about 6% of the mean annual
precipitation, is concurrent with the fire season and takes place
during the summer months, usually June to September .
According to the latest revision of the National Forest Inventory
(2010), forested lands (plantations or woodlands) are the most
common land cover type, representing 40% of Portugals
mainland surface area. From the latter, 27% corresponds to
maritime pine stands (Pinus pinaster), 23% to blue gum stands
(Eucalyptus globulus) and 23% to cork oak (Quercus suber).
Maritime pine stands occur mainly in the northern half of the
country, while blue gum stands are predominant in the western
half, central and southern of Portugal. Evergreen oak woodlands
of cork oak and holm oak (Quercus rotundifolia) are abundant in
the south-western and south-eastern portions of the country,
respectively. Agriculture covers one third of the Portuguese
mainland and is widely present throughout the study area,
particularly in the central costal plain and along main river
valleys and alluvial plains. Shrublands represent 22% of Portugals
surface and are primarily found in eastern half of the country and/
or in mountainous regions with low population density.
Wildfire is the most influential process shaping short-term
landscape dynamics in the study area [24,38]. Portuguese
pyrogeography is quite heterogeneous in terms of fire ignitions and
burned areas. Ignitions are spatially concentrated and more
frequent in coastal and peri-urban areas, but due to higher
population density, accessibility and landscape fragmentation,
seldom originate large burned areas . Large fires, responsible
for the vast majority of the burned area occur in the northern
interior part of the study area, where lower demographic pressure
coupled with higher fuel connectivity and fuel loads contribute to
occurrence of most extensive burning . The majority of area
burned (80%) in Portugal is due to fire events occurring during a
small number (10%) of summer days, when the typical
atmospheric circulation pattern is characterized by an upper level
ridge located over the Iberian Peninsula and conducive to severe
fire weather [40,41].
The Portuguese fire atlas, derived from satellite imagery,
includes fire perimeters from 1975 to 2009 . Annual fire
perimeter maps were obtained with semi-automatic supervised
classification of single-date, post-fire season Landsat imagery,
performed with the Classification and Regression Trees (CART)
algorithm of . We relied primarily on near-infrared and
midinfrared channels, and thus were able to avoid atmospheric
correction of the data. In each year, we performed relative
radiometric calibration of the eight Landsat images required to
form the mosaic covering the entire mainland of Portugal,
ensuring consistent performance of the image classification rules
induced with CART. Accuracy of the data was assessed by
comparing mapped burned area with field statistics gathered on
the ground by the National Forest Authority, and by the National
Civil Protection Authority, for over 3000 administrative units. The
Portuguese fire perimeter database can be accessed at www.icnf.pt.
We used fire data from the 19901994 fire seasons, with spatial
resolution of 30 m and minimum mapping unit of 5 ha (Figure 2).
In the five years considered in this study, a total of 5,712 fire
perimeters were mapped (Table 1). Overall burned area over this
period was 442,924 ha, with mean fire size of 77 ha (Table 1).
To characterize land cover in the study area we used the 1990
land cover map (COS90) . COS90 is a land cover
classification map for mainland Portugal, derived from aerial
photography collected between June and September of 1990
(Figure 2). It has a minimum mapping unit of 1 ha and is available
in vector format at www.igeo.pt. Similar to other studies that have
addressed relationships between land cover and wildfire in
Portugal [20,23,2628], the choice of time frame for the analysis
was opportunistic, due to limited land cover data availability at an
adequate spatial resolution for the objectives of this study. The
original COS90 legend, which is hierarchical, with the most
detailed level containing 76 classes, was simplified to include the
following classes: annual crops, evergreen oak woodlands, eucalypt
stands, maritime pine stands and shrublands/grasslands (Figure 2).
Over the study period, a small proportion (2.5%) of the burned are
was burned twice. Using the burned area maps of 19901993, we
reclassified all burned areas to shrublands for analysis in the
following year, with exception of annual crops, which maintained
the same class after the fire [27,45].
Selectivity index: Jacobs index
Analysis of resource selection often relies on selection indices
that summarize information on resource use and availability - see
 for an extensive review of commonly used selectivity indexes.
In this study we follow a type III design, where used and available
resources are defined for each fire observation. The definition of
available resources is a key component in resource selection
analyses because it will determine the amount and accessibility of
what is available and it is strongly affected by scale . Resource
selectivity was described by  as a hierarchical system of choices
in which higher level choices condition lower level choices - e.g.
the definition of an animals home range will determine the
Figure 2. Land cover map (right) and spatial distribution of fire perimeters (left). The land cover map is based on a simplification of the
COS90 legend. Annual crops correspond to 10% of the study area, whereas evergreen oak woodlands, account for 18% of the study area. Forests are
mostly Eucalyptus globulus plantations (Eucalypt, 6%) and Pinus pinaster stands (Pine, 15%). Shrublands and grasslands account for 16% of the study
area. The fire perimeter map represents the annual fire perimeter distribution per year between 19901994. Areas that burned twice during the study
period account for 2.5% of the overall burned area.
amount of resources that will be available to it . In this system,
resources consumed can only be determined after defining
resources available . The analogy can be adapted to fire
selectivity, where the spatial location of an ignition is the higher
order process that conditions the type, amount and accessibility of
land cover available, once that ignition becomes a spreading fire.
We define available area as twice the used area, by delineating a
buffer around each fire perimeter with the same area as the fire
During the study period, 19901994, total number of fires and burnt area
corresponds to 5,712 and 442,924, respectively.
[26,29]. We define used area as the area burned by the fire
(Figure 3). Our choice is justified in two ways: 1) buffer size is the
same size of the fire, so that sampling effort is equivalent for both
and 2) used area (fire size) is included in the available area because,
by definition, used resources are a subset of available resources
The use of selectivity ratios to assess differential degree of
burning of land cover types fire is common in the literature
[20,23,2628,30,31,49]. Most studies have used Savages forage
ratio as a selectivity index . The ratio theoretically varies
between 0 (avoidance) and ? (preference), taking the value of 1
when proportion used equals proportion available - no preference
or avoidance by fire j of land cover type i. It follows that the forage
ratio will present a larger region for preference than for avoidance,
which may introduce bias in the statistical analysis. To minimize
such potential bias we chose to use Jacobs index, which has a
bounded scale between -1 and 1 and is symmetric around 0
(indifference). For fire j and resource i, Jacobs selectivity index is
defined as :
where r is the proportion of land cover type i used by fire j and p is
the proportion of land cover type i available to fire j. Jacobs index
ranges between -1 (extreme avoidance) and 1 (extreme preference),
taking the value 0 under indifference [51,52].
In this study, each observation consists of a fire (resource used)
and its buffer area (resource available). For each observation, a
value of the index was calculated for each land cover type
contained within the fire+buffer boundaries. Therefore, the
number of observations in each land cover class will depend on
the number of fire perimeters (or buffers) intersecting each class.
Averaging fire selectivity over all fires allows for inference
regarding the fire proneness of a specific land cover class.
Linear quantile regression and randomization tests
Quantile regression is used to estimate and draw inferences
about conditional quantile functions [53,54]. A regression quantile
of 0.10 will estimate a function of the predictor variables, such that
10% of the observations are below it, while a regression quantile of
0.5 (median) splits the frequency distribution into two parts, each
containing equal number of observations [55,56]. Quantile
regression is particularly useful when one is interested in modeling
the effect of covariates along or near the upper boundary of the
conditional distribution of responses, instead of the mean response
modeled by common statistical techniques, such as ordinary least
squares. An excellent primer on quantile regression for ecologists
can be found in .
We used linear quantile regression to examine the relationship
between fire size and fire proneness for different land cover types.
Quantile regression is particularly useful in this case because the
edges of the selectivity index distribution have distinctive
interpretations. For any given land cover class, lower quantiles
represent fires that burn through a specific land cover
proportionally less than available (lower end of the distribution
avoidance region), while upper quantiles represent fires that burn
proportionally more than available (upper end of the distribution
preference region). Therefore, we are interested in understanding
how the predictor (fire size) affects the distribution in these
particular regions of the response variable distribution, rather than
the mean response of fire selectivity to changes in fire size.
Additionally, quantile regression analysis does not require a priori
segmentation of fire observations according to size[26,31].
Steepness of the slope of each quantile regression line quantifies
the rate of change in fire selectivity as fire size increases.
We computed and plotted estimates of linear quantile regression
functions for the first significant upper and lower quantiles of fire
selectivity. To deal with clumped discrete values of -1 and 1 we
jittered these values by adding ui, which is i.i.d. U[0,0.01], thus
replacing discrete values of -1 and 1 with a smoothed response
. Identification of significant quantiles was done in increments
of t = 10 and significance was tested for a = 0.05. We present
parameter estimates and their respective p-value (95% confidence
level). All calculations and plots were performed in R, using the
quantreg package .
Figure 6. Quantile regression for the first significant upper and lower quantiles (a = 0.05) between fire size and fire selectivity
(Jacobs index). Significant quantiles in the avoidance region (lower) varied between 50th and 10th quantile in evergreen oak woodlands and
shrublands, respectively. In the preference region all land cover types presented significant regressions for the 90th quantile. Estimated parameters
and significance for each model are presented in Table 2.
To test whether fire selectivity and changes in selectivity as
function of fire size differ from random we tested the observed
values of fire selectivity and quantile regression slope against a null
model of randomly placed fires. The objective of the null model is
twofold: 1) to test whether observed selectivity and fire-size
dependencies are not due to an artifact of sampling design, in
which large fires are larger sampling units; 2) assess the impact of
landscape configuration in the observed patterns of fire selectivity
small fires are more likely to occur within a single land cover type
than larger fires, thus are more likely to exhibit strong selectivity
preference or avoidance .
To test the null hypothesis that fire selectivity does not differ
from selectivity obtained from a spatially random distribution of
fires, we randomly re-positioned fire observations from the 1990
fire season. This corresponded to 1670 fire perimeters, which
where rotated and translated, to obtain 500 maps of randomly
distributed fire perimeters with the same shape and size
distribution as the original data (Figure 4). We assumed that fire
is selective to a given land cover type if its observed mean
selectivity (average of Jacobs ratio for all observations in that land
cover type) was greater/smaller than 5% of 500 random fire
distributions (one-sided test). P-values are calculated as the ranking
position of the observed value in the simulated distribution. The
same procedure was followed to test the null hypothesis that rate of
change in fire proneness (slope in estimated upper/lower quantiles)
does not differ from that which would be expected by chance
Results showed that fires exhibit different degrees of selectivity
towards the various land cover types (Figure 5). Median fire
selectivity showed that annual crops, evergreen oak woodlands and
eucalypt plantations tend to be avoided by fire, while pine stands
and shrublands tend to be preferred (Figure 5). The ranking of
land cover types according to fire proneness, from less to most fire
prone is: annual crops (20.78), evergreen oak woodlands (20.38),
eucalypt plantations (20.53), pine stands (0.11) and shrublands
(0.35). This is in agreement with other studies that obtained similar
rankings using Savages forage ratio [20,23,26,27].
All land cover types exhibit reductions in fire preference (upper
quantile) as fire size increases (Figure 6). Slope values were
negative and significant (a = 0.05) for the 90th quantile in all land
cover types. Annual crops and pine stands showed the highest and
lowest rate of change (decrease) in fire preference with fire size, as
described by the slope of the estimated quantile regression, 20.31
and 20.17, respectively (Table 2).
Figure 6 shows, for each land cover type, the first significant
quantiles in the avoidance (lower) and preference (upper) regions.
It is worth mention that significant quantiles associated with fire
avoidance varied according to the land cover type tested - the first
significant quantile for evergreen oak woodlands was the median
(50th quantile) while for annual crops it was the 40th quantile.
Eucalypt plantations, pine stands and shrublands had significant
regressions at the 30th, 20th and 10th quantiles, respectively. On
the other hand, in the preference region, all land cover types
presented significant regressions for the 90th quantile (Figure 6).
With the exception of evergreen oak woodlands, all land cover
types exhibit reductions in avoidance as fire size increases (as
shown by the positive slope values for estimated regression in the
lower quantiles, Table 2). Rate of change in avoidance is positive
and similar in pine stands and shrublands, 0.288 and 0.269,
respectively. Decrease in avoidance for annual crops is the lowest
of all, with a slope of 0.074. Evergreen oak woodlands displayed
Annual crops 40th
Annual crops 90th
Pine stands 20th
Pine stands 90th
For each land cover type we present the first significant upper and lower
quantiles (a = 0.05). Also reported are the results of the t-test for assessing the
significance of the regression parameters (a = 0.05).
negative slope, 20.11), suggesting increasing avoidance with fire
size (Table 2).
Comparison between the randomly placed fire perimeter
dataset and actual locations led to the rejection of the null
hypothesis (a = 0.05) that mean fire selectivity does not differ from
mean fire selectivity obtained through a spatially random
distribution of fires (Figure 7). The relationship between fire
selectivity and fire size was tested through two distinct null
hypotheses that compare slope of lower/upper quantiles in
observed and randomized data. For all land cover types tested,
we rejected the null hypothesis that slope in avoidance quantiles
does not differ from that obtained for the same quantiles with a
randomized dataset (Figure 7). This hypothesis was not tested for
shrubs because all quantiles below the median in the 1990 fire data
were non-significant (a = 0.05). With the exception of eucalypt
plantations (p-value = 0.06), we also rejected the null hypothesis
that slope in preference quantiles does not differ from slope
obtained with randomized data (a = 0.05, Figure 7).
According to this analysis, rates of change in selectivity as a
function of fire size differ for preference and avoidance. For all
land cover types tested, with exception of preference in eucalypt
indicate the position of the real (observed) p-value in the randomized distribution. P-value is calculated as the ranking position of the observed value
in the simulated distribution.
plantations (p-value = 0.06), we reject the null hypothesis
(a = 0.05). This suggests that for all other land cover types, the
observed values of fire selectivity are significantly different from
those that would be observed if fires were to occur at random
locations in the landscape. Likewise, as fire size increases, changes
in fire selectivity occur with magnitude (slope of the quantile
regression estimates) that is significantly different from what would
be observed due to chance alone. This suggests that change in
selectivity as function of fire size, is in fact, a property of fire
selectivity, and not a statistical artifact due to increase of sampling
unit size in large fires. This is partially in agreement with previous
research, since  found differences in fire selectivity with fire
size, while  did not. However, comparison between both
studies and with our study should be done with caution, because
both  and  used discrete and distinct fire size thresholds to
distinguish between small, large and very large fires.
Results showed that fire selectivity is generally higher for
shrublands, pine stands and eucalypt plantations than for annual
crops and agroforestry lands. This agrees with previous studies that
identified patterns of strong fire preference for shrublands . In
a similar analysis, using Savages forage ratio and the 19901994
fire perimeter database, found that shrublands were the only
land cover that burned more than expected. In their analysis,
forests (both conifer and broadleaved) ranked second, with crops
and agroforestry systems being the land covers least preferred
In general, all land cover types showed changes in fire
preference (90th quantile) as fire size increases. Such changes
corresponded to reductions in selectivity towards indifference
(Jacobs index = 0), hence the negative slope estimated for the
linear quantile regression at t = 0.90 (significant at a = 0.5).
Analysis of quantile regression in the avoidance region (lower
quantiles of fire selectivity distribution) showed more diverse
results. Significant quantiles vary according to land cover types
from t = 0.5 in evergreen oak woodlands, to t = 0.10 in
shrublands. In general, we observed that as selectivity increases, the
lower the first significant quantile. This may be due to the fact
that, on average, land cover types with lower selectivity (annual
crops, evergreen oak woodlands) have a significant number of fire
observations with Jacobs index equal to 21. This corresponds to
situations where the land cover in question was available but not
used, representing perfect avoidance. When data are populated
with a significant number of low value observations, then it
becomes necessary to increase t to obtain significant changes in
fire selectivity as fire size increases (Figure 6). Despite the variable
t, all land cover types exhibit some degree of change in fire
selectivity as function of fire size. Such change is positive and its
magnitude, described by the slope of the quantile regression, is
higher in pine stands (0.288), shrublands (0.268), and eucalypt
plantations (0.196), and lower in annual crops (0.074), thus
suggesting that fire size has a stronger avoidance reducing effect in
forest land cover types. Evergreen oak woodlands exhibit a distinct
pattern, with avoidance increasing as fire size increases (20.105).
Our results suggest that while small fires appear to be selective
towards land cover, either through avoidance or preference, this
effect fades as fires become larger. This agrees with previous work
by , whom found that fire selectivity changes as a function of
fire size. However, we observed that changes in selectivity are
distinct according to the land cover type considered. Assuming
that fire size is a proxy for severe meteorological conditions, we
would expect to observe similar reduction in fire selectivity for all
land cover types tested, however our results do not support this
hypothesis. The effect of fire size on fire selectivity is clear for most
forest types, but less pronounced for annual crops and evergreen
oak woodlands. The estimated quantiles in the avoidance region
show that selectivity of annual crops and oak woodlands is less
affected by fire size than other land cover types. Low fire selectivity
for evergreen oak woodlands and for rainfed cereal crops, mostly
present in the southern half of Portugal, is likely to result from the
low fuel loadings characteristic of these land use types. In northern
Portugal, where population density is higher and settlement
patterns are scattered, irrigated agriculture is more common, and
low fire selectivity for croplands probably is due to a scarcity of
available fuel, combined with more aggressive fire suppression in
the extensive urban-cropland intermix. Moreover, it can be
argued that during severe fire events, these areas remain as
priorities in terms of fire suppression or eventually become more
pressing, thus contributing for the relatively stable pattern of fire
avoidance with increasing fire size .
In the ranking of increasing land cover proneness to fire,
eucalypt stands rank between the least fire prone, annual crops,
and the most fire prone, shrublands. In many parts of Portugal,
eucalypts are cultivated in monospecific plantations for paper and
pulp production, and such areas are under intensive fire
management and protection. Due to their economic value and
active management, fire incidence is generally lower, and
suppression effectiveness in such areas may contribute to mitigate
the effect of severe weather conditions.
Given the dynamic nature of fire drivers in the past decades in
the western Mediterranean Basin , analysis of a longer time
series would help characterize the significance of land cover as fire
spread driver over time. Moreover, a greater understanding of the
importance of fire regime drivers at national and local scale could
be obtained if weather conditions were used as a predictor, instead
of fire size. Comparing fire seasons occurring under variable
weather conditions would allow for testing the hypothesis of a
cause-effect relationship between fire weather and selectivity.
Another important advantage of using weather data, rather than
fire size, would be the ability to determine thresholds in
meteorological variables that determine changes in selectivity,
thus providing insight into meteorological conditions that act as
turning points in fire behavior. For the same land cover type, fire
behavior (and size) will vary depending on the characteristics of
the surface fuels complex and local topography. This highlights the
benefits of a regional analysis to identify the site-specific scale
thresholds (e.g. thresholds in fire size) above which selectivity is
altered. Such an analysis would also aid managers, by informing
where fire drivers are more likely to be
fuel-vs.weatherdominated, hence supporting preventive measures, such as fuel
reduction or increasing vigilance and preparedness when severe
fire weather is forecast. Additionally, including co-variates such as
distance to roads or villages and landform position could be useful
to fully understand what drives changes in selectivity for distinct
land cover types.
The authors acknowledge the significant input from Dr. Brian S. Cade.
Thanks are also due to Gerardo Saldana for his help with the randomization
procedure and to Ana Sa and Renata Pinto for their help processing the
data. The authors also thank the significant comments and suggestions from
two anonymous reviewers.
Conceived and designed the experiments: AB JMCP. Performed the
experiments: AB JMCP. Analyzed the data: AB JMCP. Wrote the paper:
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