African Journal of Biotechnology Vol. 7 (22), pp. 4122-4129, 19 November, 2008
Available online at http://www.academicjournals.org/AJB
DOI: 10.5897/AJB08.782
ISSN 1684–5315 © 2008 Academic Journals
Full Length Research Paper
Fire behavior in Mediterranean shrub species (Maquis)
Bülent Sağlam1*, Ertuğrul Bilgili1, Ömer Küçük3 and Bahar Dinç Durmaz2
1
Artvin Çoruh University, Faculty of Forestry, 08000 Artvin, Turkey.
Karadeniz Technical University, Faculty of Forestry, 61080 Trabzon, Turkey.
3
Kastamonu University, Faculty of Forestry, 37200 Kastamonu, Turkey.
2
Accepted 17 October, 2008
The prediction of fire behavior in fire prone ecosystems is of vital importance in all phases of fire
management including fire prevention, presuppression, suppression and fire use. This paper deals with
an experimental burning exercise conducted in the Mediterranean region in Turkey. A series of 18
experimental fires were carried out in tall maquis fuels in Asar District, Antalya, southwestern Turkey.
The site was selected for its structural homogeneity. But, there was an apparent variation in the fuel
loadings in different plots. Weather conditions were within reasonable ranges during the burns. Wind
-1
speed ranged from 4.8 to 14.4 km h , relative humidity from 16 to 76% and air temperature from 23.7 to
-1
36°C. Of the fire behavior characteristics, rate of spread ranged from 0.38 to 7.35 m min , fuel
-2
-1
consumption from 1.57 to 3.05 kg m , and fire intensity from 188.72 to 5906.48 kW m . Rate of spread
was related to wind speed, relative humidity, moisture content of live fuels and vegetation cover. Fuel
consumption was related to fuel loading and wind speed, and fire intensity was related to wind speed,
moisture contents of live fuels and mean vegetation height and vegetation cover. Results obtained in
this study should be invaluable in fire management planning.
Key words: Fire behavior, fuel biomass, Maquis, shrubland, Turkey.
INTRODUCTION
Fire is a major determining factor of the alteration and the
structure of fire-prone ecosystems. In the Mediterranean
region, shrub fuels are known for their flammability and
tendency to sustain high intensity fire even at moderate
fire danger situations (McCaw, 1995; Fogarty, 1996;
Plucinski and Catchpole, 2002). The shrub fuels, known
as maquis, grow extensively at low elevations adjacent to
open forests of oak and pine and as an understorey in
these forest types in the Mediterranean and Aegean
Regions in Turkey. Maquis occupies about 6 million
hectares (OGM, 2006), an area representing 30% of the
country’s forested lands. Maquis is found in fire prone
areas and is highly adapted to frequent forest fires
(Neyi çi, 1987). Thus, the prediction of fire behavior has
a vital importance for the ecology of maquis in the face of
fire and for the assessment of fire danger and implemen-
*Corresponding author: E-mail: saglambul@gmail.com. Tel:
+90-533 6915749. Fax: +90-466 2126951.
tation of fire management planning.
Maquis vegetation type is composed of several species
such as Arbutus andrachne L., Pistacia lentiscus L.,
Quercus coccifera L., Phillyrea latifolia L., and Cistus
creticus L. The abundance of different species in the
composition results in a great variance in the structure
and composition of the fuel complex. Burning characteristics differ greatly among different species (Neyi çi,
1987; Santoni et al., 2006; Morandini et al., 2006), thus
rendering the fire danger potential and fire behavior
characteristics highly variable. Although a series of fire
behavior studies are available for the Mediterranean
shrub fuels (De Luis et al., 2004; Morandini et al., 2006;
Santoni et al., 2006), fire behavior in maquis fuels are not
well understood and there is a lack of fire behavior data
in maquis fuels (Bilgili and Saglam, 2003) in Turkey. The
objective of this paper is to determine fire behavior
characteristics based on varying weather conditions in tall
shrub species (maquis) in the Mediterranean region. The
results generated from this study should be invaluable in
all phases of fire management planning and decision
making processes.
Sağlam et al.
MATERIAL AND METHODS
Description of the study area
The area is situated in Asar District of Antalya State Forest Enterprise (36o 56' N; 30o 36' E), and has an elevation of 270 m above
sea level. The area is mainly level with a maximum slope of 3o. The
climate of the study area is Mediterranean climate with long hot
summer and mild short winters. Annual average max and min
temperatures are 34.4o C in July and·5.6o C in January, respectively.
An average precipitation is 805 mm. Soils in the area are shallow
and loam and sandy loam of limestone origin. The vegetation is
open shrubland with an average height of 1.7 m. The understory is
composed of annual and perennial herbs and grasses. The
dominant plant species of the area were A. andrachne L., P.
lentiscus L., Q. coccifera L., P. latifolia L., C. creticus L. and, to a
lesser extent (less than 10%), Ceratonia siliqua L., Crataegus
monogyna Jacq., Rhamnus ssp. and Daphne sericea Vahl.
4123
necessary as all the variables satisfied the homoscedasticity homogeneity of the variance over the range of the data - and the
linearity assumption for the variables to be used in the analyses.
Then, using linear regression models, equations were generated for
predicting fire behavior. All selected equations were significant at
the 95% significance level. Statistical analyses were performed
using SPSS 11.0 for Windows (SPSS, 2001).
Height and vegetation cover
Average height and vegetation cover were determined in each
experimental plot before the burnings. The procedure for the
measurements was the same as that explained in the preburn fuel
sampling section above, with a difference that measurements were
made on only one side of the plot.
Fuel moisture contents
Preburn fuel sampling
Fuel sampling was carried out throughout the study area on twenty
four 3 × 3 m randomly located sample plots. Average height and
vegetation cover were determined before all the plants were cut in
each plot. The height was determined by taking the average of
eight readings made at 1 m intervals from the soil surface to the tip
of the branches along the two opposite sides of each plot. Shrub
vegetation cover was estimated by running two transects along the
two opposite sides of each plot and adding the distances the
transect runs over shrub crowns, and expressing these as a fraction
of the total transect length (Martin et al., 1981). The relationship to
calculate the percent shrub vegetation cover was of the form:
Shrub vegetation cover percent = (Shrub cover length / total
transect length) x 100
Following the measurements, all shrubs were cut at groundline in
each plot. Each plant was divided into components of leaf and
branches. Dead and live woody parts were separated. All woody
parts were further separated into size classes by diameter: fine
branches (0 - 0.5 cm), medium branches (0.6 - 2.5 cm), thick
branches (> 2.5 cm in diameter), and available fuel (leaf + fine
branches (less than 0.6 cm in diameter) (Roussopoulos and
Loomis, 1979; Martin et al., 1981; Sağlam et al., 2008). The
biomass size classes are useful in calculating fire intensity and fuel
consumption. All dead and live fuel samples by size classes were
weighed to the nearest 0.1 g and subsamples were taken from
each class and weighed to the nearest 0.1 g in the study area.
Subsamples were then taken to the laboratory for further analyses.
All fuel samples transferred to the laboratory was oven dried to a
constant weight for 24 h at 100o C, and weighed to the nearest 0.1
g. Final leaf and branch biomass determinations were made on the
basis of oven dry measurements. Fuel loadings of the experimental
plots were estimated using the equations generated from these
measurements.
Moisture content of live and dead fuels was obtained from clipped
samples immediately before each burning. As the moisture content
levels may vary greatly among different species, samples were
taken from different plant species on a relative basis such that each
plant was represented according to its relative coverage in the
burning plot. Samples were weighed, taken to the lab and ovendried at 105°C for 24 h. Fuel moisture was expressed as a
percentage of dry weight.
Environmental variables and fire behavior
A series of 18 burning plots were established at the experimental
burning site. Each plot was more or less 0.06 ha (20 × 30 m), and
was delimited by a 5 m wide fire-break bulldozed to mineral soil to
enable easy access and facilitate fire control. A complete fire
weather station was established on the site 10 days prior to the
burnings. Air temperature, relative humidity, 2 m open wind speed
and precipitation were recorded at 13:00 local standard time.
Wind speed, relative humidity, and air temperature were recorded at 15 s intervals during each fire. Plots were burned over under
varying temperature, relative humidity, fuel moisture and wind
speed conditions. Fires were started with a drip torch to rapidly
establish a fire line along the windward edge of each plot. Fire
intensity was calculated using Byram’s equation (1959):
I = Hwr
Where, I is the fire line intensity (kW m-1), H is heat yield of the fuel
(kJ kg-1), w is the dry weight of the fuels consumed by the fire (kg
m-2) and r is the rate of spread of the flaming front (m s-1). In this
study, an energy content of 19000 kJ kg-1 was used based on the
relevant information (Brown and Davis, 1973; Alexander, 1982;
Bilgili and Saglam, 2003).
Statistical analysis
Postburn fuel sampling
Correlation and regression analyses were performed to relate fire
behavior characteristics to fuel properties and weather conditions.
Regression analyses considered fuels and weather conditions as
the independent variables, and fire behavior characteristics as the
dependent variables. Before the analyses, the variables were
tested for normality and as a result, no transformation was deemed
Postburn fuel loading was estimated after each fire to determine
fuel consumption. Remaining fuel in each plot was estimated by
clipping, oven drying and weighing all material form randomly
selected three 3 × 3 m sample plots. Fuel consumption was
calculated based on the difference between pre- and post-burn fuel
loadings.
4124
Afr. J. Biotechnol.
Table 1. Pre-burn fuel characteristics associated with the experimental fires.
Fire no
VC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Max
Min
Mean
SD
SE
N
0.70
0.95
0.75
0.75
0.90
0.70
0.80
0.90
0.90
0.85
0.90
0.60
0.95
0.85
0.80
0.60
0.80
0.90
0.95
0.60
0.81
0.11
0.03
18
a
H (m)
-2 a
Moisture Contents (%)
Live fuels
2. 50
3.00
2.00
2.60
2.00
2.50
2.30
1.80
2.00
2.20
1.75
2.00
2.50
2.75
2.75
2.00
2.50
2.75
3.00
1.75
2.33
0.37
0.09
18
60.30
78.00
88.50
90.00
72.10
78.60
82.10
78.60
95.30
118.80
164.00
130.10
133.20
126.80
125.60
125.60
125.60
142.50
164.00
60.30
106.43
29.24
6.89
18
Dead fuels
13.30
50.00
21.90
25.00
27.60
24.20
23.50
33.30
52.40
22.50
67.60
37.30
38.10
33.00
28.50
28.50
28.50
34.90
67.60
13.30
32.78
12.95
3.05
18
Fuel loadings (kg m )
FLlf
1.58
2.17
1.27
1.68
1.34
1.58
1.50
1.21
1.34
1.45
1.18
1.19
1.74
1.87
1.84
1.19
1.64
1.91
2.17
1.18
1.54
0.29
0.07
18
FLlm
1.05
1.63
0.84
1.15
0.93
1.05
1.04
0.80
0.93
1.02
0.77
0.74
1.30
1.35
1.29
0.74
1.15
1.40
1.63
0.74
1.07
0.25
0.06
18
FLa
1.68
2.37
1.52
1.80
1.71
1.68
1.73
1.59
1.71
1.75
1.56
1.33
2.07
2.05
1.96
1.33
1.83
2.13
2.37
1.33
1.77
0.27
0.06
18
FLt
3.38
5.13
2.81
3.69
3.15
3.38
3.40
2.80
3.15
3.37
2.71
2.47
4.20
4.29
4.10
2.47
3.71
4.48
5.13
2.47
3.48
0.73
0.17
18
Estimated (see text for explanation).
RESULTS
Pre-burn fuel characteristics for each plot are presented
in Table 1. Although the site was selected for its structural homogeneity, there was an apparent variation in the
fuel loadings in different plots. Live fine fuel biomass
-2
ranged from 1.18 to 2.17 kg m , live medium fuel
-2
biomass from 0.74 to 1.63 kg m , available fuel biomass
from 1.33 to 2.37 kg m-2 and total fuel biomass from 2.47
to 5.13 kg m-2.
Table 2 displays the observed variations in fire behavior values and fire weather conditions recorded on site
during each experimental fire. Experimental burnings
were conducted under relatively wide range of weather
conditions. Wind speed ranged from 4.8 to 14.4 km h-1,
relative humidity from 16 to 76% and air temperature
from 23.7 to 36°C. Variability in weather and fuel conditions was reflected in the associated fire behavior
parameters. Rate of spread ranged from 0.38 to 7.35 m
min-1, fuel consumption from 1.57 to 3.05 kg m-2, and fire
intensity from 188.72 to 5906.48 kW m-1.
Correlation and regression analyses were undertaken
to investigate the relationships between fire behavior
characteristics and associated fuel properties and
weather conditions. Table 3 displays the correlation
coefficients showing trends and relationships among the
independent and dependent variables. The relationships
that have the best fits to the predicted variables are given
in Table 4. Equations are presented with up to five independent variables as the additional independent variables
increased the percent variability explained by the
equation.
Rate of spread was highly correlated with wind speed (r
= 0.771; P<0.01), relative humidity (r = -0.683; P<0.01)
and air temperature (r = 0.586; P = 0.05). Wind speed
alone explained 57% of observed variation (P<0.01) in
the rate of fire spread. Squaring wind speed had a
positive effect and the addition of vegetation cover as the
second independent variable highly improved the percent
variability explained (R2 = 0.737; P<0.01). Relative humidity as the third independent variable improved the rate of
2
spread prediction significantly (R = 0.821; P<0.01).
Similarly, the addition of the moisture content of live fuels
as the third independent variable also, somewhat improved the percent variability explained in the rate of fire
spread (R2 = 0.832; P<0.01). The relationship between
Sağlam et al.
4125
Table 2. Fire behavior values and fire weather conditions associated with the experimental fires.
Fire
No.
RH
(%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Max
Min
Mean
SD
SE
N
66
52
40
65
76
28
28
28
31
31
60
24
25
19
17
16
17
22
76
16
35.8
19.3
4.5
18
T
(°C)
25.4
26.6
27.5
26.9
23.7
33.5
32.6
30.5
30.0
29.6
26.4
33.6
32.0
34.0
35.0
36.0
34.0
36.0
36.0
23.7
30.7
3.9
0.9
18
W
-1
(km h )
10.6
9.2
11.3
8.6
8.2
11.6
10.3
10.3
12.4
9.1
4.8
14.4
12.5
8.7
9.9
10.6
13.2
13.1
14.4
4.8
10.5
2.3
0.5
18
Rate of spread
-1
(m min )
Observed
Predicted
1.76
1.45
3.82
2.93
3.33
3.38
0.89
0.71
0.80
1.46
2.10
3.93
2.06
3.67
4.28
4.33
5.92
5.66
4.05
2.91
0.38
0.46
6.25
4.84
5.57
6.66
4.43
2.94
2.85
3.88
3.22
2.34
6.06
6.18
7.35
6.45
7.35
6.66
0.38
0.46
3.62
3.57
2.06
1.89
0.49
0.45
18
18
Fuel consumption
-2
(kg m )
Observed
Predicted
2.09
2.07
3.05
3.01
1.57
1.80
2.20
2.19
1.75
1.90
2.03
2.12
2.08
2.08
1.92
1.72
2.18
1.98
2.02
2.03
1.58
1.56
1.69
1.71
2.73
2.58
2.60
2.53
2.41
2.47
1.57
1.56
2.37
2.35
2.64
2.84
3.05
3.01
1.57
1.56
2.14
2.14
0.43
0.42
0.10
0.10
18
18
Observed ROS (m min-1)
Observed ROS (m min -1)
8
6
4
2
0
4
9
14
19
Wind speed (km h-1)
Figure 1. Relationship between wind speed and observed rates of
spread.
wind speed and observed rate of spread is shown in
Figure 1. The relationship between relative humidity and
observed rate of spread is shown in Figure 2.
Fuel consumption was significantly related to fuel bio-
Fire intensity
-1
(kW m )
Observed
Predicted
1124.12
903.02
3561.35
3294.62
1601.28
1779.63
597.86
955.12
433.67
1205.78
1307.81
1947.84
1311.33
1814.18
2533.57
1569.58
4014.61
3455.41
2499.44
1852.87
188.72
1905.63
3237.43
3600.20
4655.53
5551.46
3561.15
2227.55
2108.67
2710.14
1539.39
545.10
4396.25
4374.84
5906.48
5712.45
5906.48
5712.45
188.72
545.10
2476.59
2522.52
1622.79
1521.91
382.50
358.72
18
18
8
6
4
2
0
0
20
40
60
80
Relative humidity (%)
Figure 2. Relationship between relative humidity and observed rate
of spread.
mass and wind speed. Live fine, live moderate, available
and total fuel biomass individually explained 86, 89, 91
and 92% of the observed variation in fuel consumption,
4126
Afr. J. Biotechnol.
Table 3. Correlation matrix between the variables used in the analyses.
RH
T
MCl
MCd
VC
H
H2
H×VC
ROS
W
Flf
Flm
Fa
Ft
FC
FI
2
W
T
MCl MCd
VC
H
2
RH
1
-.933**
-.448
.058
.166
-.123
-.109
H
H×VC ROS
W
Flf
Flm
Fa
Ft
FC
FI
1
.496* 1
-.098 .482* 1
-.284 .073 .489* 1
.227 -.050 -.197 .155
1
.211 -.041 -.148 .184 .998**
-.019
-.683**
-.574*
-.080
-.079
-.018
-.064
-.191
-.598**
-.560*
.025 .027 .155 .677** .828** .844** 1
.586* .363 .131 .128 .169 .166 .237
1
.551* -.009 -.275 -.310 .131 .100 -.055 .771** 1
.150 -.019 -.040 .375 .972** .980** .934** .199 .048
1
.125 .004 .016 .473* .938** .948** .969** .238 .039 .992** 1
.024 .019 .149 .673** .831** .847** 1.00** .235 -.052 .936** .970** 1
.100 .011 .056 .532* .914** .926** .983** .241 .014 .982** .998** .984** 1
.194 .031 .114 .530* .863** .873** .948** .406 .169 .934** .955** .948** .960** 1
.530* .363 .198 .345 .397 .399 .523* .946** .648** .461 .508* .522* .517* .655** 1
.548* .099 -.166 -.306 .072 .044 -.095 .786** .986** -.004 -.011 -.093 -.033 .125 .656**
2
W
1
1
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).
RH, relative humidity (%); T, air temperature (°C); MCl, moisture contents of live fuels (%); MC d, moisture contents of dead fuels
2
(%);VC, vegetation cover; H, mean vegetation height (cm); H , mean vegetation height × mean vegetation height; H×VC, (mean
-1
-2
vegetation height × vegetation cover); W, wind speed (kph); ROS, rate of spread (m min ); Flf, live fine fuel loading (<0.6 cm; kg m );
-2
-2
-2
Flm, live medium fuel loading (0.6 - 2.5 cm; kg m ); Fa: total available fuel (<0.6 cm; kg m ); Ft, total fuel loading (kg m ); FI, fire
-1
-2
intensity (kW m ); FC, fuel consumption (kg m ).
DISCUSSION
The results presented in this study come from the efforts
dealing with the prediction of fire behavior in tall shrub
(maquis) fuels in Turkey. In that respect, the study makes
4
Observed FC (kg m-2)
respectively (P<0.01) (Table 4). The addition of the wind
speed as a second independent variable along with the
total fuel biomass somewhat improved the variability
explained (R2 = 0.94; P<0.01). The relationship between
total fuel loading and observed fuel consumption is
shown in Figure 3.
Fire intensity was closely related to wind speed (r =
0.648; P = 0.01), relative humidity (r = -0.598; P = 0.01)
and air temperature (r = 0.530; P = 0.05). Wind speed
alone explained 39% of the observed variation in fire
intensity (P<0.05). Figure 4 shows the relationship
between wind speed and observed fire intensity. The
addition of mean vegetation height multiplied by vegetation cover (H×VC) as the second and moisture content
of live fuels as the third independent variables improved
2
2
the fire intensity prediction significantly (R = 0.75, R =
0.82; respectively, P<0.01). Mean vegetation height as
the fourth independent variable somewhat improved the
observed variation of fire intensity (R2 = 0.87, P<0.01).
3
2
1
2
3
4
5
Total fuel loading (kg m-2)
6
Figure 3. Relationship between total fuel loading and observed fuel
consumption.
a valuable contribution to fire behavior analyses in maquis fuels in the Mediterranean region within the given
range of weather and fuel conditions. This study differs
from similar works conducted on maquis fuels (Bilgili and
Saglam, 2003) in terms of fuel characterization, composi-
Sağlam et al.
4127
Table 4. Regression equations for predicting fire spread, fuel consumption and fire intensity in maquis fuels based on the data
in this study.
Dependent
Variables
Model Form
1a) Y=a+bW
1b) Y=a+bW2 + cVC+dRH
ROS
1c) Y=a+bW + cVC+dMCl
2a) Y=a+bFlf
2b) Y=a+bFa
2c) Y=a+bFlm
FC
2d) Y=a+bFt
2e) Y=a+bFt+cW
2
3a) Y= a+bW2+cH×VC
3b) Y= a+bW2+cH×VC+dMCl
FI
2
3c) Y= a+bW +cH×VC+dMCl+eH
Constant and Coefficient
SE
a: -3.669
1.538
b: 0.695
0.144
a: -4.906
2.081
b: 0.032
0.006
c: 7.634
2.031
d: -0.038
0.013
a:-13.207
2.223
b: 0.804
0.094
c: 7.171
1.979
d: 0.24
0.007
a: 0.016
0.207
b: 1.379
0.132
a: -0.530
0.225
b: 1.510
0.126
a: 0.404
0.138
b: 1.627
0.126
a: 0.174
0.147
b: 0.564
0.041
a: -0.005
0.144
b: 0.567
0.036
c: 0.001
0.001
a:-4623.159
1067.234
b: 25.134
4.344
c: 2224.039
463.146
a: -6085.143
1044.575
b:
24.121
3.661
c: 2185.034
388.488
d: 15.524
5.727
a: -4427.973
1103.213
b: 26.460
3.245
c: 3500.113
615.945
d: 13.484
4.932
e: -1804.614
583.269
F
R2
Adj. R2
SEE
23.456
0.594
0.569
1.352
27.037
0.853
0.821
0.871
29.024
0.861
0.832
0.845
108.962
0.872
0.864
0.159
143.236
0.900
0.893
0.141
167.432
0.913
0.907
0.131
186.024
0.921
0.916
0.125
129.546
0.945
0.938
0.108
25.871
0.775
0.745
819.005
26.997
0.853
0.821
686.511
29.648
0.901
0.871
583.269
*Asymptotic Standard Error
position, structure and weather conditions. The results
were based on a total of 18 experimental fires. Differences in fire behavior were clearly shown to be a
function of wind speed, relative humidity, moisture
contents of live fuels, vegetation cover and fuel loadings.
The fuel characteristics, structure and composition of
this study differ from a recent work (Bilgili and Saglam,
2003) in maquis fuel type (e.g. mean vegetation height
and mean total live fuel load less than 2.5 cm and mean
total fuel load were 0.53 m, 1.63 and 2.64 kg m-2 in recent
-2
work, whereas these were 2.33 m, 2.61 and 3.48 kg m
in our study, respectively). While our study area includes
five dominant and four other species, recent work com-
prises only four dominant species.
The rate of spread is a result of the combined effect of
fuel, weather and environmental conditions on fire
behavior (Arca et al., 2007). The results obtained in the
present study indicated that wind speed and air temperature were positively correlated with the rate of fire
spread, but that relative humidity was negatively correlated. The dominant effect of wind on fire spread has
been reported previously for many vegetation types (e.g.
Bilgili and Saglam, 2003; Williams et al., 2003; Viegas,
2004). The positive effect of wind speed on rate of fire
spread is generally attributed to the increased supply of
oxygen to the fire (Trollope et al., 2004) and to the tilting
4128
Afr. J. Biotechnol.
Observed FI (kW m-1)
16
12
8
4
0
2000
4000
6000
Wind speed (km h-1)
Figure 4. Relationship between wind speed and observed fire
intensity.
of flames forward closer to the unburned fuels ahead,
thereby increasing the radiation impinging on the fuel,
and thus the rate of spread (Bessie and Johnson, 1995;
Santoni et al., 2006; Savadogo et al., 2007). However, a
threshold in wind speed is required for wind to influence
the rate of fire spread (Catchpole, 2002; Gambiza et al.,
2005). This is especially true when fuel continuity is low,
and dead surface fuels are low to nonexistent. The analysis of fuel, weather and site conditions in the present
study indicated that, coupled with the effects of fuel
moisture contents and relative humidity, the effect of wind
on the rate of fire spread was relatively low at low wind
speed values. Moreover, the lack of dead surface fuels or
discontinuity in surface fuels due to the underlying
landform composed of rocks or boulders in some
experimental plots decelerated the rate of spread
especially at low wind speed values. The initiation and
sustainable development of a shrubland fire requires fire
to spread vertically and then horizontally though the
shrub layer (Plucinski and Catchpole, 2002). In the
burning plots dominated by Arbutus andrachne, although
not statistically significant, low rates of spread were
observed. In the light of Bessie and Johnson’s (1995)
explanation this can be attributed to the absence of
vertical continuity of fuels due to lack of dead or live fuels
(ladder fuel) of A. andrachne, which carries the fire from
surface to crown.
Heterogeneities in species which have different flammability characteristics causes variation in the observed
fire behavior. Shrubs that burn easily can facilitate the
drying and burning of the surrounding vegetation that do
not burn easily through generating great amount of
energy (Santoni et al., 2006). Similarly, it was observed in
the present study that when Quercus coccifera surrounded Arbutus andrachne or Cistus creti-cus, these species
also burned easily due to the drying effects of Q.
coccifera burning and to the filling in the gap between
surface and crown, especially under relatively high wind
speed conditions.
Fuel consumption in the experimental fires was significantly related to fuel loading (live fine, live moderate,
available and total) and wind speed. In this regard, the
results of the present study agree well with other relevant
studies (Bilgili and Saglam, 2003; Savadogo et al., 2007).
Fuel consumed was related to the intensity of burnings.
The higher the intensity of fires, the larger the size of the
fuels consumed in the flaming front.
It is generally accepted that fire intensity is the most
important fire behavior property as it provides the amount
of heat released during the fire and is therefore a useful
indicator of its impact on plants (Alexander, 1982;
Gambiza et al., 2005; Savadogo et al., 2007). Fire
intensity in this study was related to wind speed, vegetation height×vegetation cover, moisture content of live
fuels and vegetation height. Fire intensities observed in
the present study ranged from 188.72 to 5906.48 (mean
2476.59 kW m-1). These results are comparable to and
agree well with the relevant literature (Trabaud, 1979;
Bilgili and Saglam, 2003). Differences may be attributed
to the vegetation composition and heterogeneities in the
study area and in burning conditions.
Fuel moisture contents are expected to have a
significant effect on ignitability (e.g. Wilson, 1985) and
combustion rate (Rothermel, 1972; Catchpole et al.,
1998). Moreover, the heterogeneity of vegetation was
also reported to play an important role in fire behavior
(Santoni et al., 2006). However, the effect of live or dead
fuel moisture contents on fire behavior could not be
established in this study due mostly to the great variation
in vegetation structure and composition. There was little
or no surface fuels and very little dead fuels, and live
fuels were an important fraction of the total fuel load.
Species such as Arbutus andrachne that do not
necessarily accumulate standing dead fuel also represent
discontinuity and, thus, lower moisture contents (Baeza
et al., 2002). Under these conditions, it may be concluded
that dead fuel moisture content has a limited influence on
the overall moisture content, and, as a result, it is logical
to expect that the effect of dead fuel moisture content on
fire spread will be limited (Fernandes, 2001).
Given that the study is based on a relatively small
number of fires with relatively narrow range of weather
and fuel conditions in an open area conditions, further
studies should be conducted under broader weather and
fuel conditions to analyze and understand fire behavior
more comprehensively in tall maquis fuels. However, the
relationships developed from this study are reasonably
good, and could be used as a tool in fire management
planning.
Sağlam et al.
ACKNOWLEDGEMENTS
This study was conducted with the cooperation and
efforts of many people. The authors greatly acknowledge
the assistance of Antalya State Forest District staff who
contributed to the field work. Special thanks are due to
State Forest Fire Command Unit staff that cooperated
and assisted throughout the study. This study was supported by The Scientific and Technological Research
Council of Turkey, Project No: TOGTAG-3375.
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