Post-fire land degradation of Pinus sylvestris L. woodlands after 14 years

land degradation & development

Land Degrad. Develop. 21: 145–160 (2010)

Published online 16 June 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/ldr.925

POST-FIRE LAND DEGRADATION OF PINUS SYLVESTRIS L. WOODLANDSAFTER 14 YEARS

F. PEREZ-CABELLO*, P. IBARRA, M. T. ECHEVERRIA AND J. DE LA RIVA

Departamento de Geografıa y Ordenacion del Territorio, Facultad de Filosofıa y Letras,Universidad de Zaragoza, C/Cerbuna 12,50009 Zaragoza, Spain

Received 21 October 2008; Revised 15 March 2009; Accepted 15 April 2009

ABSTRACT

This study analysed medium-term degradation in Pinus sylvestris L. woodland affected by wildfire. To investigate recovery, a range ofpedological (including thickness of the organic horizon, soil structure and texture, organic matter content, pH and the percentage ofcarbonates), geomorphological and vegetation parameters were assessed in comparable burnt and control plots. Main findings suggest that firein the medium-term leads to severe degradation of P. sylvestris L. woodland ecosystems. The main consequences of the passage of fire are: (1)transformation of P. sylvestris L. woodlands into shrublands dominated by Genista scorpius (L.) DC, Buxus sempervirens L. and herbaceousspecies; (2) decrease in the thickness of the O horizon and its degradation and (3) increase in soil erosion features, due to the detachment of soilparticles by rain-splash or overland flow and their transport downslope. These results could help to provide guidelines for the restoration ofburnt areas to minimise the effects of wildfires. Copyright # 2009 John Wiley & Sons, Ltd.

key words: P. sylvestris L.; wildfire effects; recover; land degradation; Pre-Pyrenees

INTRODUCTION

In recent decades, there have been numerous reports on the

consequences of fire for Mediterranean ecosystems, on a

variety of temporal and spatial scales. Key reviews include

those of Trabaud (1987), Neary et al. (1999), Pausas and

Vallejo (1999), Terradas (1996), Gonzalez-Perez et al.

(2004), Certini (2005), Shakesby and Doerr (2006), Buhk

et al. (2007), Cerda and Robichaud (2008), etc.

Fire-induced changes to vegetation cover consumption,

litter-duff (partially decomposed organic matter beneath the

litter layer) and the physical, chemical and biological

properties of the soil usually cause an increase in erosion

rates and disorganisation of the vegetation structure, altering

successional patterns. These effects and processes are

considered among the most important problems affecting

forest degradation in Mediterranean ecosystems, where

wildfire is a frequent disturbance and plays a prominent role

(Pausas and Vallejo, 1999). However, in recent decades the

problem has compounded because of a significant increase

in the number of wildfires and the area affected (Terradas,

1996; Pinol et al., 1998; Pausas et al., 2008).

* Correspondence to: F. Perez-Cabello, Departamento de Geografıa yOrdencion del Territorio, Facultad de Filosofıa y Letras, Universidad deZaragoza, C/Cerbuna 12, 50009 Zaragoza, Spain.E-mail: [email protected]

Copyright # 2009 John Wiley & Sons, Ltd.

The most vulnerable vegetation component to forest

wildfires is the canopy, and the most immediate effects are

burning, charring and dehydration from the heat of fire.

Major degradation processes in the medium- and long-term

include permanent changes in the vegetation community

composition, decreased vegetation cover and the alteration

of landscape patterns. In spite of these effects, resprouting

and seedling establishment by many Mediterranean eco-

system plants leads to rapid revegetation and reestablish-

ment of communities similar in character to those prior to

fire. In Mediterranean ecosystems this recovery is referred to

as an autosuccession process (Hanes, 1971; Papio, 1988;

Naveh, 1990; Trabaud, 1990, 1998; May, 1991; Ne’eman

et al., 2004). Vegetation recovery processes have important

hydrological consequences, as post-fire regrowth and

recovery of natural ground cover reduce water and sediment

losses (Robichaud and Brown, 2000; Cerda and Lasanta,

2005) by decreasing the length of time soil is exposed to

erosion processes.

The immediate consequences of fire on soil properties

have been intensively studied (Giovannini and Lucchesi,

1983; Imeson et al., 1992; Mataix-Solera et al., 2007;

Arcenegui et al., 2008). In spite of the differences in the

methods used in these studies, there is general agreement as

to the most significant effects, most of which are linked to

the disappearance of the vegetation canopy and to fire

146 F. PEREZ-CABELLO ET AL.

intensity in the first centimetres of the ground. Alterations to

soil organic matter (Mangas et al., 1992; Molina and

Sanroque, 1996; Gonzalez-Perez et al., 2004; Czimczik and

Masiello, 2007; Knicker, 2007; Santın et al., 2008), soil

textural and structural features (Andreu et al., 2001), and the

movement of nitrogen and phosphorous are among the most

notable effects observed.

Consequently, the destruction of vegetation and alteration

of soil properties are the most important degradation factors

affecting hydrological processes governing runoff/infiltra-

tion and sediment yield (Helvey, 1980; Soto et al., 1991;

Dieckmann et al., 1992; Soler and Sala, 1992; Meyer and

Wells, 1997; Meyer et al., 2001; Moody and Martin, 2001;

Cerda and Doerr, 2005). However, because of the large

number of factors involved, fire effects are highly variable

and characterised by a wide range of possible outcomes

(Neary et al., 1999) and few reliable generalisations

(Wondzell and King, 2003). Notable among the main

factors determining the degree of degradation are fire

intensity, fire recurrence, vegetation resilience, post-fire

management and post-fire climatic conditions.

Although fire effects are relatively well studied, there

have been few reports on long-term comparisons of burnt

and control zones. Such studies are essential to understand

the ecosystem’s resilience, and to direct the design of

restoration programmes and conservation efforts.

Fire is an important ecological factor in the sub-

Mediterranean mountains of the Pre-Pyrenees, with pine

(Pinus sylvestris L.) woodland being one of the plant

communities most affected by this form of perturbation.

The P. sylvestris L. plant communities are secondary

communities that spontaneously have replaced the clear-

ings of sub-Mediterranean Quercus gr. cerrioides (Willk.

and Costa) woodlands in Pre-Pyrenees environments

characterised by high continentality and humid or sub-

humid precipitation conditions. This designation (Q. gr.

cerrioides) is the most usual in this territory for the most

frequent introgression between Q. faginea and Q. humilis

(Villar et al., 1997). The response of P. sylvestris L. to high

light conditions, and rapid initial regrowth, make

P. sylvestris L. a first-rate coloniser. The different levels

of degradation displayed by such communities are

functions of regeneration over time and interactions with

environmental conditions.

The object of the study is to assess the degradation of the

communities involved considering soil, geomorphology and

vegetation. After implementing the methodology, the first

results on the state of recovery of P. sylvestris L. woodlands

were obtained. These results could help to improve future

post-fire management of P. sylvestris L. woodlands in sub-

Mediterranean Pre-Pyrenean ranges. Moreover, our meth-

odological approach could inform other studies on fire and

pine woodland recovery.


MATERIALS AND METHODS

Study Area

The study involved a number of areas in the Pre-Pyrenean

ranges in the north of the province of Huesca (Aragon,

Spain), which were burnt in 1985, 1986 and 1990. Theses

ranges are located at an elevation of 450–2000 masl, in a

transition zone between continental Mediterranean (to the

south) and Atlantic (to the north) influences (Figure 1). The

area has an extensive history of land use and is especially

prone to wildfires (Perez-Cabello, 2002). It is predominantly

woodland with areas for cereal growing. The predominance

of woodland in this sector is due to the fact that in the last

half of the 20th century there was a significant natural plant

recovery process in successive stages as farming practices

ceased. Sometimes, the recovery of vegetation has been

‘accelerated’ by woodland repopulations that have increased

the levels of flammability and combustibility of the plant

formations. The origin and growth of wildfire in the study

area is connected with typical causes for Mediterranean

regions (Amatulli et al., 2007): (1) the decrease in

population and the abandonment of traditional forestry

uses in mountainous areas, (2) the abundance of inflam-

mable plant communities and overly dense structure and

(3) the climatic conditions (prolonged drought and electrical

storms). Indeed, according to recent Government of Aragons

statistics, most fires are caused by lightning and agricul-

tural–human activities. While the fires produced by light-

ning occur exclusively in the summer months, which is a

period when progagation conditions are move favourable,

the agricultural–human activities ones mostly occur in the

winter.

The climate can be generally defined as sub-Mediterra-

nean, with short summer drought periods and cold winters,

and with different levels of continental influence (Creus-

Novau, 1983). The mean annual rainfall ranges from 750 to

1000 mm with an equinoctial rainfall pattern. The mean

annual temperature ranges from 10 to 128C. The bedrock is

generally made up of limestone and sandstone, with areas

dominated by calcareous marl and surface colluviums

associated with the fluvial network restricted to the middle

basin of the Gallego River. As far as the types of soil are

concerned, there are leptic calcisols (FAO, 1998) in the

steepest areas and humic calcisols in the flattest and shadiest

areas with a higher organic matter content. Humic or

calcaric regosols are found on marly or colluvial materials,

depending on their organic matter content. Humic, mollic or

calcaric leptosols are often located on hard steep lithologies.

Calcic kastanozem, soils with a mollic A–horizon very rich in

organic matter and an accumulation of deep carbonates are in

the shadiest areas (Perez-Cabello, 2002). This area’s most

characteristic vegetation consists of oak and pine woodland,

and shrublands dominated by Buxus sempervirens L. and

LAND DEGRADATION & DEVELOPMENT, 21: 145–160 (2010)

https://www.researchgate.net/publication/223455684_Mapping_lightninghuman-caused_fires_occurrence_under_ignition_point_location_uncertainty?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

https://www.researchgate.net/publication/222686415_Postfire_Erosional_Processes_in_the_Pacific_Northwest_and_Rocky_Mountain_Regions?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

https://www.researchgate.net/publication/222493800_Fire_effects_on_belowground_sustainability_A_review_and_synthesis?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

https://www.researchgate.net/publication/222300923_Temporal_changes_in_soil_aggregates_and_water_erosion_after_wildfire_in_a_Mediterranean_pine_forest?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

Figure 2. Diagram of a site.

Figure 1. Location of study area and wildfire perimeters: (1) 1990 Nofuentes fire; (2) 1986 Castillo Mango fire; (3) 1985 Los Fils fire; (4) 1986 Sierra del Aguilafire and (5)1986 Sierra de Aineto fire.

POST-FIRE DEGRADATION OF P. SYLVESTRIS WOODLANDS 147

Genista scorpius L. (DC). Geo-botanically, the main classes

identified are: Echinosparto horridi-Pineto Sylvestris

sigmetum, Buxo sempervirentis-Querceto pubescentis sig-

metum, Violo willkommii-Querceto fagineae sigmetum

(Rivas-Martınez, 1987).

Field Sampling and Data Analysis

As a consequence of the extensive P. sylvestris L. woodland

cover in this area and the large number and size of wildfires

that have burnt these plant communities, 11 sites located at

five different wildfires were selected: one site for the 1986

Castillo Mango fire, four sites for the 1986 Sierra de Aineto

fire, one site for the 1986 Sierra del Aguila fire, three sites for

the 1985 Los Fils fire and two sites for the 1990 Nofuentes

fire (Figure 1). Each site includes two plots: burnt and

control (Figure 2). The burnt plots are established within the

boundaries of the wildfires recorded. The control plots are

located outside the burnt areas, although very close to them,

and they have similar environmental features to the burnt

plots.

Plot selection was carried out in two phases: (1) spatial

search operations using a Geographical Information System

(GIS) and the environment dataset of GEOFOREST


Research Group (GED), and (2) field campaigns to

investigate the sites selected with the GIS. Since 1997,

GEOFOREST Research Group of Zaragoza University has

performed spatial information collection in the study area

(Pre-Pyrenean ranges) with regard to environmental features

(climatic conditions, topographical information from a

Digital Elevation Model, vegetation types and structural

features, wildfire areas, etc.) including Landsat satellite data

set. Using different spatial analysis techniques, burnt and



control plots with similar characteristics were selected at five

wildfires (indicated above). Subsequently, the plots selected

using GIS were visited to check the level of similarity

between two plots at each site, and so as not to include areas

affected by logging, afforestation, grazing or any other type

of modification of the natural reconstruction line after fire.

A total of 22 plots (11 sites), each one 100 m2, were

surveyed inside burnt and control areas of pine woodland.

Characteristics can be consulted in Table I. Plots with north-

facing aspects at altitudes ranging from 968 to 1470 m were

preferentially selected, and only two sites had a south-facing

aspect (AI5 and AI8). The average slope was 20 per cent and

ranged from 15 to 37 per cent. The soils were shallow, with

two main types of parent material: sandstones and silt, and

cretaceous limestones. Most of them have horizon A below

25 cm depth distributed on three types: CA1, NO2, NO4,

AI4, AI5, AI11, PI5 (between 25 and 15 cm); FI1, AI8

(between 15 and 7 cm); FI2, FI5 (under 7 cm). Based on

Munsell soil colour charts, browns (7�5YR4/2, 10YR4/3)

were the most common types in the A–horizon.

The role of burn severity on post-fire processes is well-

known. A description of the severity at the sites was

performed using GED satellite data. Satellite images are

very useful for analysing damage intensity because the

radiometric response is altered following fire due to the

sudden decrease in vegetation recovery, the increase in soil

exposure and changes in soil properties (Jakubauskas et al.,

1990; Dıaz-Delgado et al., 2003). For old wildfires, remote

sensing is the only useful source of information when there

is no information on fire effects.

Therefore, a database of five summer Landsat-TM images

with information from 1984, 1986, 1987, 1990 and 1991 was

used to depict burn severity. The 1984 image captures pre-

fire spectral features in the areas burnt in 1985 and 1986,

while the 1986 and 1987 images capture post-fire spectral

features in these areas. The 1990 image captures pre-fire

information concerning the 1990 Nofuentes fire, and the

1991 image records the burnt area following fire.

Images were geometrically rectified using a second-order

polynomial model included in ERDAS IMAGINE 8�71.

Moreover, to compensate for variations in the sensor

radiometric response, digital values from images were

converted to spectral reflectance values by normalising the

topographic and atmospheric effects. Atmospheric effects

were removed using the dark-object subtraction method

(Chavez, 1996); conversion to reflectance was performed by

a method proposed by Pons and Sole-Sugranes (1994).

Burn severity was mapped calculating the normalised

burnt ratio (NBR) (Key and Benson, 2005). This index

integrates two spectral bands: NIR (from 0�76 to 0�90 mm)

and mid-infrared (SWIR) (from 2�08 to 2�35 mm) combined

as shown in Equation (1). Moreover, the ~NBR is obtained

to provide a quantitative measure of change, by subtracting


the NBR derived after burning from the NBR derived from

before burning (Equation 2)

NBR ¼ NIR� SWIR

NIRþ SWIR(1)

DNBR ¼ NBRpre�fire � NBRpost�fire (2)

According to the FIREMON methodology (Key and

Benson, 2005), the continuous dNBR data set can be

stratified into ordinal classes or severity levels: unburnt

(DNBR values below 100), low severity (from 100 to 270),

low-moderate severity (from 270 to 440), moderate-high

severity (from 440 to 660) and high severity (values above

660). Table I shows values extracted from burnt plots.

Generally speaking, burn severity values range from

moderate-high severity (six sites) to high severity (five

sites). Figure 3 shows spatial distribution of DNBR from

wildfires. The distribution of values in the Sierra de Aineto

fire is detailed. The location of the plots for this site (Pico del

Aguila wildfire, PI) is also included.

Three indicator types were quantified in evaluating the

magnitude of the degradation: (i) soil loss features, (ii)

pedological parameters and (iii) vegetation regeneration

processes (floristic composition and physiognomic

parameters). One-way analysis of variance (ANOVA) was

used to identify significant differences between burnt and

control plots. Moreover, as the controlling role of the

lithology on the type of soil and the influence of the severity

of the fire on the recovery after fire are well-known,

differences between burn severity classes and bedrock were

performed taking into account the absolute difference

between burnt and control plots. In this case, non-parametric

statistical analysis was performed (Mann–Whitney U-test).

Visual criteria were used to identify the hydrological

erosion processes described in the FAO methodology for the

evaluation of soil degradation (FAO, 1980). The perceptual

spatial incidence of the following erosive features was

assessed: accumulation of fine material over roots and

branches; the presence of exposed roots and scars; and the

formation of erosion pavements, erosion pedestals and

drainage incisions in flow lines (rills and gullies).

The pedological parameters were categorised in the FAO

Guidelines for soil description (FAO, 2006) and included

depth of the organic horizon (O horizon); shape, size and

grade of soil structure; particle size distribution; soil

texture, pH and carbonates; and organic matter content of

the A–horizon (the first 5 cm). Samples of soil were

collected from the O and A–horizons for laboratory analysis

of chemical and physical properties. The samples were air-

dried and the mineral horizons were separated into coarse

and fine fractions using a 2 mm sieve. Particle size

distribution was assessed using the Robinson pipette; pH


https://www.researchgate.net/publication/241687027_Landscape_Assessment_Ground_measure_of_severity_the_Composite_Burn_Index_and_Remote_sensing_of_severity_the_Normalized_Burn_Ratio?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

Tab

leI.

Gen

eral

char

acte

rist

ics

of

the

plo

ts

Wil

dfi

reS

ITE

cod

eP

lots

XY

Alt

itu

de

(mas

l)A

spec

tS

lop

e(%

)D

NB

RD

NB

RH

ori

zon

_A

dep

th(c

m)

Tex

ture

Bed

rock

Co

lor

dry

19

85

Lo

sF

ils

1F

IB

urn

t6

89

44

64

69

11

71

11

30

N2

47

00

Hig

h1

5A

C-L

Lim

esto

nes

7.5

YR

5/4

19

85

Lo

sF

ils

1F

IC

on

tro

l6

89

34

04

69

11

44

11

30

N2

51

5A

C-L

Lim

esto

nes

5Y

R5

/31

98

5L

os

Fil

s2

FI

Bu

rnt

68

98

88

46

91

19

31

04

5N

E1

07

95

Hig

h5

F-L

Lim

esto

nes

10

YR

3/3

19

85

Lo

sF

ils

2F

IC

on

tro

l6

89

96

84

69

11

37

10

43

NE

10

3A

C-L

Lim

esto

nes

7,5

YR

4/2

19

85

Lo

sF

ils

5F

IB

urn

t6

89

72

44

69

12

90

10

70

NE

54

40

Mo

der

ate

Hig

h7

F-A

CL

imes

ton

es7

,5Y

R4

/3

19

85

Lo

sF

ils

5F

IC

on

tro

l6

89

58

64

69

12

73

11

14

NE

77

AC

Lim

esto

nes

7,5

YR

5/4

19

86

Sie

rra

de

Ain

eto

4A

IB

urn

t7

27

02

64

69

34

21

12

00

N1

35

04

Mo

der

ate

Hig

h2

0F

-AC

San

dst

on

es&

silt

s7

,5Y

R5

/3

19

86

Sie

rra

de

Ain

eto

4A

IC

on

tro

l7

26

85

74

69

34

37

11

86

N1

52

0F

-AC

San

dst

on

es&

silt

s7

,5Y

R5

,5/4

19

86

Sie

rra

de

Ain

eto

5A

IB

urn

t7

27

02

04

69

29

75

12

17

S2

24

36

Mo

der

ate

Hig

h1

8F

-AC

San

dst

on

es&

silt

s1

0Y

R6

/3

19

86

Sie

rra

de

Ain

eto

5A

IC

on

tro

l7

27

04

84

69

28

52

11

93

S2

21

8F

-AC

-LS

and

sto

nes

&si

lts

10

YR

7/4

19

86

Sie

rra

de

Ain

eto

8A

IB

urn

t7

27

00

04

69

30

00

12

22

SE

18

43

6M

od

erat

eH

igh

7F

-AC

-LS

and

sto

nes

&si

lts

10

YR

6/3

19

86

Sie

rra

de

Ain

eto

8A

IC

on

tro

l7

29

95

04

69

29

00

13

26

S1

51

2F

-LS

and

sto

nes

&si

lts

10

YR

5/3

19

86

Sie

rra

de

Ain

eto

11

AI

Bu

rnt

72

95

50

46

92

65

01

33

4N

18

56

4M

od

erat

eH

igh

15

F-A

CS

and

sto

nes

&si

lts

10

YR

4/3

19

86

Sie

rra

de

Ain

eto

11

AI

Co

ntr

ol

72

92

63

46

92

92

11

27

7N

O1

52

0A

CS

and

sto

nes

&si

lts

10

YR

4/3

19

86

Cas

till

oM

ang

o1

CA

Bu

rnt

68

10

99

46

96

05

31

03

2N

E2

56

09

Mo

der

ate

Hig

h1

5F

-LL

imes

ton

es7

,5Y

R5

/3

19

86

Cas

till

oM

ang

o1

CA

Co

ntr

ol

68

08

52

46

96

05

31

08

0N

E2

51

5F

-AC

Lim

esto

nes

10

YR

5/3

19

86

Pic

od

elA

gu

ila

5P

IB

urn

t7

14

40

04

68

66

00

15

45

N3

57

92

Hig

h2

0A

C-L

Lim

esto

nes

7,5

YR

5/4

19

86

Pic

od

elA

gu

ila

5P

IC

on

tro

l7

14

51

64

68

66

65

14

70

N3

72

0A

CL

imes

ton

es7

,5Y

R5

/4

19

90

No

fuen

tes

2N

OB

urn

t6

73

93

34

69

98

75

10

40

NE

25

85

8H

igh

25

AC

San

dst

on

es&

silt

s7

.5Y

R4

/4

19

90

No

fuen

tes

2N

OC

on

tro

l6

73

79

34

69

96

86

11

19

NE

27

25

AC

San

dst

on

es&

silt

s7

,5Y

R5

/2

19

90

No

fuen

tes

4N

OB

urn

t6

75

36

14

70

00

14

10

12

N2

58

51

Hig

h2

5A

C-L

San

dst

on

es&

silt

s7

.5Y

R5

/4

19

90

No

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Copyright # 2009 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, 21: 145–160 (2010)


Figure 3. Special distribution of severity values, DNBR. Those for the Sierra del Pico del Aguila fires are enlarged.


and carbonates were measured with a pH meter and a

Bernard calcimeter, respectively; and organic matter content

was determined by chemical analysis following oxidation

with potassium dichromate in a sulphuric acid medium. The

structure grade was categorised as one of the four types:

without structure¼ 0, weak¼ 1, moderate¼ 2 and

strong¼ 3. The aggregate size was measured and aggregate

shape also was categorised: granular¼ 1, sub-angular

blocky¼ 2, angular blocky¼ 3 and columnar¼ 4.

Vegetation regeneration processes were assessed using

floristic inventories according to the Braun-Blanquet

method (Braun-Blanquet, 1979), modified by incorporation

of the Bertrand (1966) for layer analysis. Each species’

percentage cover was measured in relation to both the plot

area (100 m2) and the layer where it was located.

Abundance–dominance indices were applied to layers and

to the species present in each, taking into account the area of

the layer in which they were recorded. Consequently,

although a layer may have had an abundance–dominance

index of 1, the different species present could have indices

close to 5. This adaptation of the method resulted in precise

information on the floristic composition and structure of the

plant communities. A chart that includes the indicator types,

the features observed and methods used in each plot is

shown in Figure 4.

RESULTS AND DISCUSSION

Vegetation Structure and Floristic Composition

Vegetation cover was significantly different between control

and burnt plots (a¼ 0�05) (Table II). Fourteen years after fire

the accumulated percentage cover by layers was lower in

burnt than in control plots (126�8� 7�6 per cent of Standard


Error and 170�4 � 10�8 per cent, respectively) (Table III).

The standard deviation (SD) is lower in burnt plots than in

control plots, which means a higher level of homogeneity

among the plant formations replacing the P. sylvestris L. In

general, all sites lost vegetation cover, except for one (FI5),

where it increased, and two sites show a decrease of more

than 120 per cent (PI5 and AI8) (Table VI), when variations

of 33 per cent are usually the norms.

The physiognomic structure in control plots showed a

dominant tree layer (60�4 per cent) and a rather well-

developed layer 4 (31�8 per cent). Other layers were poorly

developed because of light restriction and the dominant

effect of pine in addition to the chemical and physical effects

of litter, including the exhaustion of mineral nutrients. In

contrast, the vegetation cover in layers 1 and 2 was higher in

burnt than control plots, whereas layers 4 and 5 were not

represented in burnt plots, as expected. Therefore, as a

consequence of fire in P. sylvestris L. plant communities a

reversed vegetation structure was generated.

Taking into account absolute differences (Table VI), we

can see that all sites show severe losses in upper layers (4 and

5), and important increases in lower layers (1 and 2), except

the PI5 site and, especially, the AI8 site. The latter site has a

south-facing aspect and a moderate-high severity, and it has

a low-vegetation-recovery level and an important increase in

soil losses, as we will see later.

The effects of fire on floristic composition were also very

significant. The plant communities in control plots were

dominated by P. sylvestris L. (var. pyrenaica Svob.) (35–45

years old) (average 47�35� 12�9 per cent), especially in the

upper layers (Table III). Layers 2 and 3 were dominated by

B. sempervirens L., followed by Juniperus communis L. and

leguminous species, including G. scorpius L. (DC) and

Echinospartum horridum (Vahl) Rothm. Herbaceous species


https://www.researchgate.net/publication/50339156_Filosociologia_Bases_para_el_Estudio_de_las_Comunidades_Vegetales?el=1_x_8&enrichId=rgreq-16a30d44-7088-4131-8a77-fdd1f865f6a7&enrichSource=Y292ZXJQYWdlOzIyOTU1MTc4MjtBUzoxNTE5MDU5NzIwMDI4MTdAMTQxMzIyODYxNDUzOA==

Figure 4. Indicator types, features and methods used.


were mostly those adapted to shade, and included Hepatica

nobilis (Schreb), Fragaria vesca L. and Hedera helix L.

Burnt plots were primarily recolonised by plant species

including Rosa sp., Equinospartum horridum (Vahl) Rothm.

and, especially, G. scorpius L. (DC). However, while

Table II. One-way analysis of variance

Sum of squares F Significance

Vegetation cover 10472�73 10�80 0�004Pinus sylvestris 11610�42 124�89 0�000Echinospartum horridum 605�32 6�82 0�017Genista scorpius 1227�02 9�22 0�007Buxus sempervirens 24�89 0�22 0�643Herbaceous species 1449�91 7�37 0�013pH 0�03 0�09 0�761Carbonates 110�28 1�02 0�326Shape aggregates 1�14 2�47 0�131Size agreggates 102�56 2�15 0�158Structure grade 0�73 2�58 0�124Sands (%) 10�23 0�14 0�716Silts (%) 127�68 0�89 0�356Clays (%) 65�64 0�61 0�445O horizon 120�56 17�48 0�000Organic matter content 18�51 1�35 0�259Soil loss features 472�91 4�74 0�042


G. scorpius L. (DC) and E. horridum (Vahl) Rothm. have

risen many points, B. sempervirens L. had dropped at most

sites. Taking absolute differences into account (Table VI),

only two plots (AI11 and FI5) show increases above 10 per

cent. In layer 1, the cover of herbaceous species of the

Brachypodium, Bromus, Festuca and Helictotrichon genera

were notable. These species restrict competitors because of

their capacity for vegetative spread by means of rhizomes.

Many of these species are stimulated by specific forms of

perturbation (Grime, 1989). Other species linked to stony

habitats (e.g. F. vesca L. and Hieracium pilosela L.) and

some other competitive and restriction-tolerant species

(Galium aparine L., Ranunculus repens L., Primula veris L.)

were also present. In the layer 3, P. sylvestris L. was only

recorded in one of the sites (FI5) (14�2 per cent); therefore,

similar to other authors’ findings (Retana et al., 2002; Nunez

et al., 2008), P. sylvestris L. shows very low recruitment in

burnt plots.

The failure of P. sylvestris L. to recover in burnt plots

appears to be related to two factors. First, P. sylvestris L.

regrowth strategies are characterised by absence of

serotinity and the poor viability of P. sylvestris L. seeds.

The seeds remain viable for only about 4 years if they are not in

suitable sites (Tapias and Gil, 2000), and they are not able to

resist the high temperatures of intense summer wildfires


able III. Descriptive statistics for vegetation features

egetation featuresxpressed onercentages

Mean SD SE Minimum Maximum

egetation coverBurned 126�82 25�52 7�69 90�00 180�00Control 170�45 35�88 10�88 130�00 235�00

ayer 1Burned 71�36 16�89 5�09 40�00 95�00Control 38�18 27�95 8�43 10�00 90�00





inus sylvestrisBurned 1�41 4�25 1�28 0�00 14�20Control 47�35 12�96 3�91 32�20 73�70

uxus sempervirensBurned 21�58 11�42 3�44 8�20 44�30Control 23�71 9�72 2�93 8�00 42�10

chinospartum horridumBurned 11�47 12�95 3�90 0�00 31�90Control 0�98 3�12 0�94 0�00 10�40

enista scorpiusBurned 16�31 16�18 4�88 0�00 54�90Control 1�37 2�05 0�62 0�00 5�70

erbaceous speciesBurned 36�27 14�12 4�26 18�40 58�80Control 20�04 13�94 4�20 2�90 45�20


T

Vep

V

L

L

L

L

L

P

B

E

G

H

(Habrouk et al., 1999). Second, the rapid growth of herbaceous

species and the effectiveness of the vegetative reproduction of

some underbrush species (B. sempervirens L., G. scorpius L.

(DC) and E. horridum (Vahl) Rothm.) results in their

competitive dominance during recolonisation.

Following a fire, the inhibitory effect of pine litter on

germination of other species is eliminated. Therefore,

established local seedbeds and seeds from nearby fruiting

species occupy the surface immediately and inhibit the

development of geomorphological processes. Moreover, the


rapid regrowth of herbaceous perennials, such as Helicto-

trichon cantabricum (Lag.) Gervais and Brachypodium

pinnatum L., enhance nutrient retention after fire, and

E. horridum (Vahl) Rothm. inhibits the regrowth of

P. sylvestris L.

Burn severity is an important factor conditioning post-fire

vegetation regeneration (Gimeno et al., 2000). Therefore,

the degradation level may be linked to the immediate

consequences of fire on the ecosystems. Regarding the role

of this factor, Table VII contains statistics associated with

the Mann–Whitney U-test for two independent samples, and

Figures 5 and 6 show mean values of the absolute differences

between control and burnt plots by site taking burnt severity

levels and bedrock types into account. No statistically

significant differences between burn severity levels were

recorded. However, in high-severity burnt plots, most of the

vegetation features recorded more absolute differences

between control and burnt plots (a higher degradation level

of pine woodland) than in moderate high-severity plots.

With regard to floristic composition, there was a higher

invasion of G. scorpius L. (DC) and herbaceous species at

high-severity sites. And, at the same time, there was a greater

loss of surface area covered by B. sempervirens L. Both

aspects lead us to think that there is more degradation at a

taxonomic composition level. Similar changes are observed

for vegetation structural features in connection with the

cover of the upper (4 and 5) and lower layers (1–3). As far as

bedrock is concerned, no statistically significant differences

were recorded. Nevertheless, more inversion was observed

(in other words, more increase in the lower layers and more

decrease in upper layers), and also more floristic degradation

at sandstones sites.

New environmental conditions following fire result in the

establishment of different post-fire plant communities. Four-

teen years after fire, the anatomical and physiological

variables of pine woodland, and the landscape’s environmen-

tal features, have not been sufficient to return these ecosystems

to pre-fire conditions, and fire has therefore disrupted the

ecological succession process, causing a high degree of altera-

tion to both structural and floristic composition. In this respect,

autosuccession processes, described for other sub-Mediterra-

nean communities, are not recognised in plant communities

dominated by P. sylvestris L. In this case, the reconstruction

process involves a succession towards a degraded scrub

ecosystem dominated by communities adapted to fire.

Regarding the role played by severity and lithology, no

statistically significant influence was recorded. However, as

far as severity is concerned, more degradation is noticed at

the sites affected by higher levels.

Soil Properties

Table IV shows descriptive statistics for soil properties

assessed in this study. In control plots, the O horizon was


Figure 5. Mean values of absolute differences between plots (burnt versus control) depending on the type of severity.


easily differentiated and the thickness was variable, ranging

from 2�5 to 15 cm (average 5�95� 1�06 cm): it was thickest

in NO4, while the two south-facing aspect sites (AI5 and

AI8) recorded the lowest values (around 3 cm). The most


common texture types of the fine earth fraction were clay

loam and silty clay. However, the SD of the sand, silt and

clay percentages were high because of micro-environmental

differences among the sites. While silt and clay percentages


Figure 6. Mean values of absolute differences between plots (burnt versus control) depending on the type of dominant lithology.


show SD values above 10 per cent, sand shows SD below 6

per cent and percentage ranges from 14 to 34 per cent. The

most frequent aggregate shapes were angular and sub-

angular blocky (2�3� 0�21), and the aggregates were very

variable in size, with fine and medium (5–10 mm) being


common. Aggregate structure was moderate-soft/weak

(1�9� 0�5). The pH was quite uniform in the various control

plots (average 7�2� 0�13) and indicative of neutral soils

(perhaps slightly basic). However, some plots (e.g. those in

Los Fils area) were acidic (pH 6�1), in contrast to the


Table IV. Descriptive statistics for soil property parameters

Pedological parameters Plots Mean SD SE Minimum Maximum

O horizon (cm) Burned 1�27 1�15 0�35 0�0 3�0Control 5�95 3�53 1�06 2�5 15�0

pH Burned 7�17 0�63 0�19 5�9 8�0Control 7�24 0�46 0�14 6�1 7�8

Carbonates (%) Burned 4�79 9�11 2�88 0�0 28�9Control 9�38 11�47 3�46 0�0 31�4

Organic matter (%) Burned 7�33 2�63 0�79 2�5 11�2Control 9�21 4�60 1�46 2�1 17�4

Shape� Burned 1�86 0�64 0�19 1�0 3�0Control 2�32 0�72 0�22 1�0 4�0

Size (mm) Burned 6�82 3�55 1�07 2�5 15�0Control 11�14 9�11 2�75 2�5 35�0

Structure grade�� Burned 1�54 0�52 0�16 1�0 2�0Control 1�91 0�54 0�16 1�0 3�0

Sands (%) Burned 19�73 10�77 3�25 8�0 40�0Control 21�09 5�84 1�76 14�0 34�0

Silts (%) Burned 44�45 13�99 4�22 25�0 75�0Control 39�64 9�50 2�86 30�0 62�0

Clays (%) Burned 35�82 10�37 3�13 14�0 48�0Control 39�27 10�41 3�14 15�0 51�0

�Aggregate shape categorisation: granular¼ 1, sub-angular¼ 2, angular¼ 3 and columnar¼ 4.��Structure grade categorisation: without structure¼ 0, weak¼ 1, moderate¼ 2 and strong¼ 3.


typically basic pH found in the south-facing soils of the

Sierra de Aineto area. This is because of the greater intensity

of mineralising processes occurring in south-facing areas, as

a consequence of higher solar radiation and ground

temperatures. The organic matter content was quite variable

(range 2�13–17�4 per cent); the highest values were found in

plots in Los Fils area, where the soils formed on cretaceous

limestones were very shallow and stony. The percentage of

total carbonates was low (average 9�38� 3�45 per cent) and

quite variable (range 0–31�4 per cent).

Compared to burnt plots, significant differences were

found in the depth of the O horizon (Table II), with this

horizon in control plots being thicker than in burnt plots:

there was an average of almost 5 cm of difference in

thickness between control and burnt plots. Moreover, as a

result of wildfire, the O horizon depth was similar in all burnt

plots (range 0–3 cm; SD¼ 1�14), whereas more variability

was seen in control plots (SD¼ 3�53). Wildfire uniforms this

feature removing important differences among the sites. On

analysing absolute differences between control and burnt

plots (Table VI), all sites show negative balances, and the

1990 Nofuentes and 1985 Los Fils wildfire sites recorded

larger differences compared with the control plots.

Continuing with the burnt plots, but now focussing on the

A–horizon, no statistically significant differences were

detected in the features considered (Table II). No major

differences were found in texture parameters between the A–

horizons of control and burnt plots, but the latter had higher

silt and lower clay percentages than the control plots did.


The sand percentage was very similar for both control and

burnt plots (Table IV). However, on analysing absolute

differences, most sites show a slight decrease in sand

(around 7–8 per cent) and just one, AI11, recorded an

important increase (almost 20 per cent). Moreover, the

increase in silt and the decrease in clay were not uniform in

burnt plots, thus maintaining the pre-fire variability. With an

increase of 34 per cent in silts and a decrease of 28 per cent

in clays, FI2 is the site with the most important changes.

According to Mataix-Solera and Guerrero (2007),

modifications in the texture of the soils affected by fire

are usually caused by possible thermal fusion of clay-sized

particles, with a percentage increase in silt and sand size

(Ulery and Graham, 1993), or by the rise in erosion rates and

fall in the stability of the aggregates, which brings about a

percentage increase in coarses (Llovet et al., 1994). In our

case, taking into account that this is not an analysis of the

direct consequences of fire, but rather the final balance of

the interactions between the consequences of fire and the

environment’s response processes over an extensive period

of time, there is a slight increase in the percentage of coarses

compared with that of clays. There is also a rise in variability

in connection with the control areas, which proves there are

discretional textural alterations. This also occurs with the

non-generalised increase in the erosion activity, since this is

the factor that largely explains the variability of percentages

in burnt plots. However, more homogeneity is observed in

the burnt plots (SD decrease in all cases) in the rest of the soil

parameters measured in the A–horizon.



With regard to aggregate features, there were no

substantial differences between control and burnt plots,

although the shape categories and size were lower in burnt

plots; in this regard, Andreu et al. (2001) reported that in

P. halepensis L. environments, located in the Mediterranean

coast of Spain, there was a very significant increase in small

aggregates (to 0�1 mm) after fire. The soil structure tended to

be weaker in burnt plots, although the results were not

statistically significant.

Despite the fact that there is generally an increase in pH

after fire due to the contribution of carbonate, oxide and

basic cation ash (Ulery et al., 1993), there was no

significant difference in pH between control (average pH

7�2� 0�13) and burnt plots (average pH 7�1� 0�19). The

leaching of basic ions and the formation of humus means

that pH increases are not persistent over time (Mataix-

Solera and Guerrero, 2007). Consequently, after 14 years,

the immediate effects of fire were neutralised in connection

with pH levels. With respect to soil carbonates, there was a

substantial decrease in burnt plots (average, 4�7� 2�8 per

cent) compared with control plots (average, 9�3� 3�4 per

cent). An important but non-significant decrease in the

organic matter content of the A–horizon was observed,

with an average 2 per cent difference from control being

noted. Taking absolute differences into account, the severe

decrease in organic matter content (around 10 per cent)

shown by a south-facing aspect site (AI8) due to the non-

normal high percentage in control plots is important.

Losses of organic matter as a result of heat has led to

changes in aggregate shape, structure grade and consist-

ence, although non-systematically. According to Molina

et al. (1999), decalcification and a decrease in organic

matter indicate a degradation process and, as many authors

state (Giovannini et al., 1990; Kutiel and Inbar, 1993;

Table V. Descriptive statistics for soil loss features

Soil losses features expressed on percentages Plots M

Accumulation of material over rootsand branches

Burned 1

Control 0Scars Burned 2

Control 0Erosion pavements Burned 4

Control 0Erosion pedestal Burned 0

Control 0Exposed roots Burned 0

Control 0Rills Burned 3

Control 1Surface soil losses Burned 11

Control 2


DeBano, 2000; Andreu et al., 2001; Mataix-Solera et al.,

2002), it can alter the stability of soil aggregates and

increase soil loss processes.

Regarding burn severity, most of the features analysed in

high-severity burnt plots recorded more absolute differences

between control and burnt plots than in moderate-high-

severity plots. However, as with vegetation, no statistically

significant differences between classes were recorded which

can be explained by various arguments: (i) the severity levels

recorded are not sufficiently contrasted to produce a

contrasted result in the parameters researched; (ii) the size

of the sub-samplings does not make it possible to make

excessively conclusive judgments and, finally, (iii) the

possible difference in the response derived from burn

severity has been cancelled out after 14 years of recovery.

According to Prosser and Williams (1998) with regard to the

latter, the increase in hydrological and geomorphological

activity following wildfire tends to occur during the

‘window’ of disturbance.

As far as bedrock is concerned, no statistically significant

differences were recorded. Nevertheless, while changes in

the percentages of silts, clays and sands are not very

significant in sandstones, there is more variability in the

limestones, especially in the case of the clays (important

gain). A higher decrease in OM was recorded. The results in

connection with the O horizon thickness must be considered

with some caution given how important they are in all the

data of two sites (NO and AI).

Soil Loss Indicators

Table V shows descriptive statistics for soil loss indicators.

The area of water erosion features was reduced in control

plots (2�09� 0�9 per cent), seven plots did not show any

water erosion features and just two sites show values above 5

ean SD SE Minimum Maximum

�09 2�47 0�74 0�00 7�00

�18 0�60 0�18 0�00 2�00�73 2�37 0�71 0�00 5�00�64 1�57 0�47 0�00 5�00�00 6�18 1�86 0�00 20�00�36 0�81 0�24 0�00 2�00�00 0�00 0�00 0�00 0�00�00 0�00 0�00 0�00 0�00�64 1�12 0�34 0�00 3�00�00 0�00 0�00 0�00 0�00�64 4�39 1�32 0�00 15�00�09 2�02 0�61 0�00 5�00�36 13�80 4�16 0�00 40�00�09 3�015 0�91 0�00 8�00


Table VI. Absolute differences between control and burnt plots

4AI 5AI 8AI 11AI 1CA 5FI 2NO 4NO 2FI 1FI 5PI

Accumulation of materials (%) 0�00 �2�00 7�00 0�00 0�00 5�00 0�00 0�00 0�00 0�00 0�00Scares (%) 3�00 0�00 5�00 5�00 5�00 0�00 0�00 0�00 5�00 0�00 0�00Erosion pedestals (%) 2�00 �2�00 10�00 20�00 0�00 5�00 0�00 0�00 5�00 0�00 0�00Erosion pavements (%) 0�00 0�00 0�00 0�00 0�00 0�00 0�00 0�00 0�00 0�00 0�00Root exposure (%) 0�00 2�00 3�00 2�00 0�00 0�00 0�00 0�00 0�00 0�00 0�00Rills (%) 0�00 0�00 15�00 5�00 5�00 5�00 0�00 0�00 0�00 �2�00 0�00Surface soil losses (%) 5�00 �2�00 40�00 32�00 10�00 15�00 0�00 0�00 10�00 �2�00 0�00O horizon (cm) �2�00 �2�50 �2�50 �4�00 �2�50 �6�00 �1�50 �14�00 �8�00 �3�50 �5�00Organic matter (%) 3�23 4�47 �10�63 �1�89 �1�69 — �1�22 �1�65 �3�54 �1�55 �1�42pH �0�90 �0�20 0�70 �0�60 �0�10 �0�20 �0�50 0�20 0�60 �0�20 0�40Sands (%) �3�00 7�00 �13�00 19�00 �5�00 �1�00 8�00 �6�00 �6�00 �9�00 �6�00Silts (%) 2�00 �10�00 �6�00 �5�00 16�00 10�00 �9�00 8�00 34�00 2�00 11�00Clays (%) 1�00 3�00 19�00 �14�00 �11�00 �9�00 1�00 �2�00 �28�00 7�00 �5�00Carbonates (%) �9�02 �2�22 — �1�79 �3�51 �0�85 �11�81 9�80 1�32 �31�27 0�00Size (mm) 0�00 — 0�00 �12�50 �7�50 �5�00 �7�50 5�00 0�00 — �20�00Structure grade 0�00 — 0�00 �1�00 0�00 0�00 0�00 0�00 �1�00 0�00 �2�00Layer 1 (%) 65�00 15�00 15�00 20�00 60�00 65�00 25�00 55�00 75�00 �10�00 �20�00Layer 2 (%) 5�00 35�00 �40�00 10�00 10�00 20�00 30�00 20�00 0�00 15�00 5�00Layer 3 (%) �5�00 0�00 �35�00 5�00 5�00 55�00 25�00 0�00 0�00 15�00 �10�00Layer 4 (%) �10�00 �25�00 �75�00 �60�00 �10�00 �70�00 �25�00 �30�00 �10�00 �5�00 �25�00Layer 5 (%) �85�00 �70�00 �10�00 �30�00 �85�00 �20�00 �80�00 �70�00 �85�00 �60�00 �70�00Vegetation cover (%) �30�00 �45�00 �145�00 �55�00 �20�00 50�00 �25�00 �25�00 �20�00 �45�00 �120�00Pinus sylvestris (%) �49�20 �53�80 �37�00 �32�20 �64�10 �59�50 �43�90 �44�70 �50�70 �37�20 �33�10Buxus sempervirens (%) �6�80 0�20 �5�80 24�40 �3�40 10�30 �11�20 �4�00 �24�00 �8�60 5�50Echinospartum horridum (%) 29�60 10�10 17�10 31�90 0�00 0�00 5�60 18�00 0�00 0�00 3�10Genista scorpius (%) �0�80 25�00 11�20 0�00 20�60 21�10 8�40 5�10 54�90 18�70 0�10Herbaceous species (%) 22�20 15�50 23�80 �26�80 21�90 25�70 0�40 31�00 16�00 25�40 23�50

Table VII. Statistics associated with the Mann–Whitney test

Severity Lithologic

U Mann-Whitney Z Significance U Mann–Whitney Z Significance

Surface soil losses 6 �1�67 0�10 14�5 �0�09 0�93O horizon 9 �1�11 0�27 8 �1�29 0�20Organic matter 12 �0�10 0�92 9 �0�64 0�52pH 10 �0�92 0�36 9 �1�11 0�27Sands 9 �1�11 0�27 9 �1�11 0�27Silt 10�5 �0�82 0�41 1�5 �2�47 0�01Clay 14�5 �0�09 0�93 8 �1�28 0�20Carbonates 10 �0�52 0�60 11 �0�31 0�75Size 9�5 �0�13 0�90 6�5 �0�88 0�38Structure grade 9�5 �0�77 0�44 9�5 �0�77 0�44Layer 1 12 �0�55 0�58 13�5 �0�28 0�78Layer 2 14 �0�18 0�85 11�5 �0�64 0�52Layer 3 14 �0�18 0�85 10�5 �0�83 0�41Layer 4 9 �1�12 0�26 8 �1�30 0�19Layer 5 11 �0�74 0�46 13 �0�37 0�71Vegetation cover 14 �0�18 0�85 8�5 �1�19 0�23Pinus sylvestris 10 �0�91 0�36 10 �0�91 0�36Buxus sempervirens 6 �1�64 0�10 15 0�00 1�00Echinospartum horridum 10 �0�93 0�35 0 �2�80 0�01Genista scorpius 15 0�00 1�00 7 �1�46 0�14Herbaceous species 12 �0�55 0�58 10 �0�91 0�36

Copyright # 2009 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, 21: 145–160 (2010)



per cent (AI5 and FI1). There was no dominant indicator,

although surface flow and runoff were the main processes.

We are talking about tree ecosystems with an important

vegetation cover level. In contrast, erosion processes in burnt

plots affected an average of 11� 4�1 per cent of the surface

(9 per cent more than in control plots), and statistically

significant differences were detected between burnt and

control plots (Table II). Rills and erosion pavements were

the most frequently detected indicators. Gullies were not

observed, perhaps, because there were no pre-existing

forms. Taking into account these resulting landforms,

concentrated flow has not been a post-fire process in this

type of ecosystem. Therefore, surface flow was the main

process by which material was transported downslope.

Decreases in vegetation cover, particularly ground veg-

etation, litter, organic matter and carbonates, are the most

important factors leading to increased post-fire erosion.

However, there was high variability (SD¼ 13�8), indi-

cating that increased soil loss is not a generalised process

after fire in these ecosystems. If absolute differences are

analysed, three sites show a moderate increase (around 12

per cent), two of them recorded severe increases in surface

soil losses features (up to 30 per cent) and six shows no

increase. AI8, with a south-facing aspect, displays the

largest increase (40 per cent). Moreover, this site shows

the lowest vegetation recovery (90 per cent). Therefore, the

more surface soil losses there are, the more vegetation cover

decreases. In this regard, the topography aspect has been

categorised as an important environmental factor regarding

post-fire recovery (Perez-Cabello, 2002), so post-fire

environmental conditions are more precarious on south-

facing hillsides and erosion levels tend to be higher

(Marques and Mora, 1992; Andreu et al., 2001). On the

other hand, on shaded hillsides, vegetation regeneration is

more accelerated, stabilising erosion values and soil loss

(Perez-Cabello, 2002).

CONCLUSIONS

This study has focussed on the diagnosis, 14 years after fire,

of the level of degradation of P. sylvestris L. woodlands

affected by forest fires. Based on the assessment of three

types of degradation indicators (vegetation, soil and

geomorphological features), we conclude that P. sylvestris

L. woodlands in the Pre-Pyrenees sub-Mediterranean area

are subject to considerable degradation after fire. Analysis of

indicators 14 years after wildfires showed the low resilience

of P. sylvestris L. woodlands. With regard to vegetation,

major floristic and physiognomic differences were detected,

and they are evidenced by the persistence of changed

vegetation structures as shrubland, a process initiated during

post-fire colonisation of the burnt stand, and with the low

recovery of P. sylvestris L. because of its inefficient


regeneration mechanism; P. sylvestris L. depend only on

seed dispersal from nearby seed bearers for recovery after

fire. Following a fire, pine woodlands shift to shrubland

dominated by E. horridum (Vahl) Rothm., G. scorpius L.

(DC) and many herbaceous species. Therefore, in the case of

pine woodland, the process of reconstruction involves a

partial reestablishment of disturbed communities, with a

succession towards a degraded scrub ecosystem dominated

by communities adapted to fire. Moreover, the important

increase in shrubland areas and its horizontal continuity

emphasises the risk of more frequent and recursive wildfires

in the future (Velez, 2000).

As far as soil is concerned, no statistically significant

differences were apparent between control and burnt plots,

but degradation was evident in burnt plots. The most

important change was the decrease in thickness of the O

horizon, in carbonates, in the size of aggregates and in

organic matter, and the increase in silt percentages. These

soil changes are linked specially to changes in ground cover

vegetation, and explain the increase in soil loss indicators in

some burnt spaces, so forest fires result in a general fall in

infiltration rates and an increase in overland flow and soil

water erosion. However, except in the percentages of sands

and silt and in soil loss features, all the other properties

sampled have more homogeneous values in the burnt plots

than in the control plots. This direct or indirect homogen-

isation process of fire has also been observed in the structure

of new plant formations colonising the burnt areas, although

not in the case of floristic composition, basically due to the

gap left by the pine. Regarding the role played by fire

severity and the type of lithology, no statistically significant

differences have been recorded between burn severity levels

and bedrock types. However, most of the features analysed

in high-severity burnt plots recorded more absolute

differences between control and burnt plots, which can be

interpreted as a higher level of degradation than in moderate-

high severity plots.

From the point of view of restoration, it is important to

define strategies that decrease the combustibility of the shrub-

lands replacing the pines. A new fire in the burnt areas would

involve much more serious consequences for these ecosys-

tems and would severely slow down their recovery. Therefore,

it is a good idea to perform actions early on to aid regeneration,

such as selective clearing of shrubland, sowing or planting

using techniques that are not aggressive with the soil.

The use of methods similar to those employed in this

study in other plant communities affected by fires that

occurred a long time ago would enable us to compare the

different level of degradation represented by fire in middle

mountain ecosystems. The diagnoses put forward could

guide forest management, especially in those areas with very

little capacity for environmental recovery. Consequently,

this paper helps to define simple methods that can be used for



diagnoses in areas that have suffered forest fires on

Mediterranean Type Ecosystems.

ACKNOWLEDGEMENTS

This work has been supported by the research projects

CGL2008-01083/CLI & CGL2008-02301/CLI, financed

by the Spanish Commission of Science and Technology,

and ‘Programa de Grupos de Investigacion’ (Research

Group Program) financed by the Aragon Government.

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Post-fire land degradation of Pinus sylvestris L. woodlands after 14 years