Fire severity effects on ash chemical composition and water-extractable elements

Geoderma 191 (2012) 105–114

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Fire severity effects on ash chemical composition and water-extractable elements Paulo Pereira a,⁎, Xavier Úbeda b, 1, Deborah A. Martin c, 2 a b c

Department of Environmental Policy, Mykolas Romeris University Vilnius , Ateities st. 20, LT-08303 Vilnius, Lithuania GRAM (Mediterranean Environmental Research Group), Dept of Physical Geography and Regional Geographic Analysis, University of Barcelona, Montalegre, 6. 08001 Barcelona, Spain U.S. Geological Survey, 3215 Marine Street (E146), Boulder, CO 80303-1066, United States

a r t i c l e

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Article history: Received 15 June 2011 Received in revised form 31 December 2011 Accepted 2 February 2012 Available online 3 March 2012 Keywords: Ash properties Fire severity Ash colour Landscape recovery Ash chemical properties Water-extractable elements

a b s t r a c t The effects of fire in the landscape are commonly assessed through the evaluation of ash properties. Among other properties, colour is one of the methods more frequently used. However, little is known about the effect of fire severity on ash chemical and extractable elements. Ash is an important source of nutrients in terms of landscape recovery after fire. In this study we analysed the effects of fire severity (estimated using ash colour) on ash chemical properties, CaCO3, pH, Total Carbon (TC), Total Nitrogen (TN), C/N ratio and some ash water-extractable elements, such as Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Total Phosphorous (TP), Total Sulphur (TS) and Silica (Si) collected in Portugal (N = 102) after three wildfires that occurred in the same ecosystem, composed mainly of maritime pine, Pinus pinaster, and cork oak, Quercus suber. The results showed significant statistical differences among ash colour at a p b 0.05 for ash waterextractable K and Si, at a p b 0.01 for ash water-extractable Ca, Mg, Na and TS, and the major differences were observed (at a p b 0.001) for ash CaCO3, pH, TC, TN, C/N ratio and water-extractable TP. Ash CaCO3, pH and water-extractable TS increased with fire severity and ash TC, TN, C/N ratio and water-extractable TP showed a decrease. In the remaining elements, no trend is identified. Major concentrations of ash TC, TN, C/N ratio and water-extractable Ca, Mg and K were identified in very dark brown and black ash. CaCO3, pH and waterextractable TS were identified in higher quantities in light grey ash. These findings show that fire severity is an important determinant of the type and amount of water-extractable nutrients present in ash that later can be incorporated into the soil and become available for plant growth. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Fire severity is an important indirect measure of fire effects on vegetation and soil properties. This term was created to describe the effects of fire on ecosystems, especially after wildfires where direct information on fire intensity is absent (Keeley, 2009). It is an indicator of landscape recovery rate and post-fire erosion risk as observed elsewhere (Benavides-Solorio and MacDonald, 2001; Miller et al., 2011). Fire severity depends on complex interactions among temperature, meteorogical conditions prior and during the fire, geomorphology, aspect, slope (Alexander et al., 2006; Maingi and Hanry, 2007), burned species (Úbeda et al., 2009) , amount of fuel, fuel moisture, fuel structure and arrangement, type of ecosystem and season (Keeley, 2009; Verbyla et al., 2008;). Normally, fire intensity is used wrongly as a synonym of fire severity to describe the effects of the fire on the landscape and this leads to some confusion in the terminology. Recently

⁎ Corresponding author. Tel.: + 370 271 4551. E-mail addresses: [email protected] (P. Pereira), [email protected] (X. Úbeda), [email protected] (D.A. Martin). 1 Tel.: + 34 934037882; fax: + 34 93 4037892. 2 Tel.: + 1 303 541 3024. 0016-7061/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2012.02.005

Keeley (2009) and Pereira et al. (2010) have completed overviews and have suggested a correct use of both of these concepts. Fire severity has been assessed using a variety of methodologies, such as the minimum branch diameter remaining after fire (Perez and Moreno, 1998), fine fuel combustion (Davies et al., 2010), crown scorch (Ramage et al., 2010; Vega et al., 2008), relation to soil properties such as colour (Ketterings and Bigham, 2000), degree of changes in soil organic matter, soil structure, soil iron oxidation, soil hydrophobicity and soil reflectance, that can be observed throughout Near Infrared Spectroscopy (NIR) (Guerrero et al., 2007; Keeley, 2009; Keeley et al., 2008; Mataix-Solera and Doerr, 2004), remote sensing (Miller et al., 2009; Wang and Gleen, 2009) and aerial photo analysis (Hayes and Robenson, 2011; Odion et al., 2010). In addition to the above-mentioned fire severity indices, ash colour is also an important indicator of fire severity as observed elsewhere (Pereira et al., 2010; Pereira et al., 2011; Úbeda et al., 2009). The presence of ash is one of the key characteristics of burned areas. It is the organic and inorganic residue remaining after organic matter combustion. When organic matter is only heated to the point that vaporization of moisture takes place rather than combustion, the residue is not considered ash (Pereira et al., 2010). Roy et al. (2010) attempted to classify fire severity according to ash colour using a greyscale, however it is important to note that ash is not only black

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or white, and can be reddish or brownish, especially when produced at low temperature/severity, conditions that are likely to occur somewhere in a burned landscape. The previous study (Roy et al., 2010) did not consider other colours than those present in the grayscale, thus perhaps this method is not the most reliable for fire severity analysis. As such, studies that use other methods such as the Munsell colour chart (Bodi et al., 2011; Pereira et al., 2011; Úbeda et al., 2010) might be more suitable to analyse ash colour. The ash colour is an indirect estimator of the effects of fire on organic matter consumption. Brownish and reddish colours represent low fire severity, black to very dark grey moderate fire severity and dark grey to white ash, high fire severity (Úbeda et al., 2009). Presently, a great number of studies have been conducted on ash produced in boilers (Callesen et al., 2007; Hartmann et al., 2009; Misra et al., 1993; Pöykiö et al., 2009; Schumann and Sumner, 2000) and some in laboratory environments (Bodi et al., 2011; Gray and Dighton, 2006; Henig-Sever et al., 2001; Liodakis et al., 2009; Pereira, et al., 2009; Soto and Díaz-Fierros, 1993; Úbeda et al., 2009). Some studies have been carried out on ash produced in fires, however, the majority of these studies focused on the physical and hydrological properties of ash and effects on soil (Cerdà and Doerr, 2008; Gabet and Sternberg, 2008; Larsen et al., 2009; Woods and Balfour, 2008, 2010; Zavala et al., 2009). Only a small amount of attention has been paid to ash chemical properties and water-extractable elements (Blank and Zamudio, 1998; Bodi et al., 2011; Khanna et al., 1994) and these studies give little consideration to the effects of the fire severity on ash chemical properties and water-extractable elements. Since ash colour can be used as a measure of fire severity (Blank and Zamudio, 1998; Bodi et al., 2011; Khanna et al., 1994; Úbeda et al., 2009), the study of the implications of fire severity on ash chemistry and water-extractable components is of major importance because this information allows us to identify the type and amount of elements released into solution when ash is mixed with water. In addition, this can be an important step towards the understanding of the effect of ash produced at different severities on the soil solution and provide field verification of previous laboratory studies (Úbeda et al., 2009). In order to fill this gap in fire studies, the aim of this work was to study the ash chemical composition and its relation to ash colour and fire severity, especially CaCO3 and the combustion of the organic matter (Úbeda et al., 2009), pH, which influences the leachability of ash nutrients, Total Carbon (TC), Total Nitrogen (TN), the nutrient most affected by fire, owing to the low temperature of volatilization

(±200 °C) and C/N ratio, which shows the degree of organic matter mineralization (Neary et al., 2005). Previous studies analysing ash produced in the laboratory and collected after prescribed fires showed that CaCO3 was created at temperatures around 350–400 °C and pH increased with the exposure temperature and severity (Úbeda et al., 2009). TC and TN can increase in low severity prescribed fires (Pereira, 2010). Some water-extractable elements were analyzed, namely, Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Total Phosphorous (TP), Total Sulphur (TS) and Silica (Si), that are fundamental for plant nutrition (Varennes, 2003) and vegetation recovery after the fire.

2. Materials and methods 2.1. Study area and sites Ash samples were collected in three areas recently burned by wildfires that occurred between the end of July and the beginning of August of 2008 south of Lisbon, Portugal (Fig. 1): Quinta da Areia (38° 35′N, 09° 02′W, 42 m a.s.l), Quinta do Conde (38° 34′N, 09° 02′ W, 35 m a.s.l.) and Casal do Sapo (38° 33′N, 09° 02′W, 55 m a.s.l). All three areas are in similar ecosystems. The fire severity was assessed by the colour of the ash and classified following the increasing order: very dark brown (low severity), black (medium severity), very dark grey (medium severity), dark grey (high severity) and light grey (high severity). The Quinta do Conde plot was considered to represent medium severity (17% low severity, 70% medium severity and 13% high severity), and the other study plots represented medium to high severity conditions: Quinta da Areia (3% low severity, 53% medium severity and 44% high severity) and Casal do Sapo (53% medium severity and 47% high severity). The geologic substrate of the studied areas is mainly composed of Plio–Pleistocene dunes with low cementation and soils are classified as Podzols (FAO, 2006) with a higher content of sand, and low values of organic matter, pH, EC and cation exchange capacity (CEC) (Table 1). The average temperature is 14.8 °C and the annual precipitation is 639.2 ± 156.4 mm based on data collected at the meteorological station of Vila Nogueira de Azeitão (36° 31′N, 09° 01′W, 126 m a.s.l) during the period 1971–2000 The vegetation in the sampling areas was mainly composed of maritime pine, Pinus pinaster, and in some parts of cork oak, Quercus suber.

Fig. 1. Location of study sites.

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2.4. Statistical analysis

Table 1 Soil physical and chemical characteristics at the study sites.

Sand (%) Silt (%) Clay (%) Organic matter (%) pH Electrical conductivity (μm/cm3) Extractable Ca (mg/l) Extractable Mg (mg/l) Extractable Na (mg/l) Extractable K (mg/l) CEC (mg/l)

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Quinta do Conde

Quinta da Areia

Casal do Sapo

80 ± 4.33 8 ± 3.41 12 ± 3.22 4.06 ± 1.22 6.98 ± 0.12 0.114 ± 5.52

77 ± 5.12 9 ± 1.11 14 ± 3.13 3.07 ± 2.37 6.96 ± 0.19 0.091 ± 2.61

83 ± 2.19 10 ± 2.31 7 ± 2.97 6.16 ± 3.16 6.89 ± 0.10 0.070 ± 1.15

3.02 ± 1.17 0.36 ± 0.11 0.93 ± 0.09 3.07 ± 0.24 7.38 ± 2.17

1.89 ± 1.10 0.13 ± 0.10 1.11 ± 0.18 3.86 ± 1.04 6.99 ± 3.01

2.66 ± 1.56 0.21 ± 0.13 0.60 ± 0.11 3.29 ± 0.78 6.76 ± 3.12

2.2. Field sampling At each site, we laid out a grid on the second day after the fire, before the occurrence of any rain event and, we collected 32 samples in Quinta da Areia (15 × 35 m on a west-facing slope) , 30 in Quinta do Conde (6 × 13 m, flat area) and 40 (9 × 27 m, on a west-facing slope) in Casal do Sapo for a total of 102 samples. The collection of ash samples was carried out very carefully in order to avoid the mixture with mineral soil in an area of 1 m 2 around the sampling point. At each point we collected ±200 g of ash, with a spoon. Samples were stored in plastic bags without air and taken to the laboratory for analysis. All the sample values are a mean of two replicates.

One-way ANOVA was carried out to test whether the ash chemical composition varied significantly amongst the five ash colour classes. In case it did, the post-hoc Fisher test was applied to assess which colour classes differed significantly. The underlying assumptions of normality and homogeneity of variances were tested using the Kolmogorov–Smirnov test and the Brown–Forsythe test, respectively. Since these assumptions proved not to be justified, the data were transformed with the Box–Cox transformation, that met ANOVA assumptions. The results presented here, however, consider the untransformed values. All statistical tests were performed at the standard 5% significance level, To simplify the interpretation of the results a multivariate analysis, considering all the elements in study, was carried out with a Principal Component Analysis (PCA), using a varimax rotation based on the correlation matrix (with the Box–Cox transformed data), in order to show the relationships between variables and observe potential associations between variables in each ash colour. All statistical analyses were carried out with Statistica 7.0 for windows (Statsoft, Tusla, USA). 3. Results 3.1. CaCO3 and pH The CaCO3 concentration (ANOVA: F = 160.48; p b 0.001) and pH (ANOVA: F = 18.81; p b 0.001) varied significanly among the five ash colour classes (Fig. 2A and B). In CaCO3 the mean values range from 1.54% (very dark brown) to 32.46% (light grey) and the pH from 7.34 to 8.18. In both variables the values increased with fire severity. 3.2. Total Carbon (TC), Total Nitrogen (TN) and C/N ratio

2.3. Laboratory analyses Ash colour analyses were carried out by pulverizing 1 g of ash in the Frich Pulverizate 23 for about 2 min in order to homogenize the sample (Úbeda et al., 2009). The colour was determined according to the following classes of the Munsell colour chart (Munsell, 1975): 10 YR 2/2, very dark brown, 10 YR 2/1, black, 10 YR 3/1, very dark grey, 10 YR 4/1, dark grey, and 10 YR 6/1 and 10 YR 7/2, light gray. This was done by a single person and under identical artificial light conditions. CaCO3 content was analysed, with a Bernard's calcimeter calibrated with 0.2 g of pure CaCO3 using a 1:2 hydrochloric acid solution (50% HCl and 50% deionised water) Subsamples of ash weighing 0.2 g were mixed with the 1:2 solution. The CaCO3 was estimated by calculating the difference between the volume of CO2 before and after introducing each sample (Úbeda et al., 2009). TC and TN contents were also analysed with the pulverised samples using the process of combustion–reduction by gas chromatography with a thermic conductivity detector EA Flash Series 112 (Thermo-Fisher Scientific, Milan). The data acquisition and the respective calculations were effectuated with the software Eafer 300 (Thermo-Fisher Scientific, Milan) (Pereira et al., 2010). Ash pH was analysed by creating an ash slurry for each sample by mixing 6 g of ash and 36 ml of deionised water for 2 h on a Thermo Scientific Variomag Poly inductive-drive stirrer. The solution produced was filtered through a 4.7 cm diameter Whatman QMA quartz fibre filtre, using a Millipore 220/240 Volt, 50 Hz pump. pH was measured with a Crisol GLP 22 pH meter (Úbeda et al., 2009). Waterextractable ions were determined using an ash slurry 1 g of ash and 40 ml of deionised water, which was mixed for 24 h and then filtered though a Whatman QMA quartz fibre filtre. The solution was analyzed by inductively coupled plasma mass spectrometry (ICP-MS) with a Perkin Elmer, model Elan-6000 Spectrometer, and by optical emission spectrometry (OES) with the PerkinElmer Optima 3200 RL Spectrometer (Pereira et al., 2011).

We identified significant differences in TC (ANOVA: F = 11.39; p b 0.001), TN (ANOVA: F = 5.09; p b 0.001) and C/N ratio (ANOVA: F = 5.21; p b 0.001) concentration among ash colours (ANOVA: F = 11.39; p b 0.001) (Fig. 3A, B and C). The highest concentration was observed in very dark brown ash with 46.04%, and lower in light grey ash, 17.82%). The concentrations of ash TN followed the same pattern of the ash TC, thus the highest amounts were observed in very dark brown ash (1.67%) with lower quantities in, light grey ash (0.87%). The high C/N ratio was identified in very dark brown ash (32.64), and lower in light gray (19.47). Ash TC, ash TN, and C/N ratio decrease with fire severity. 3.3. Water-extractable elements We identified significant differences in Ca (ANOVA: F = 3.50; p b 0.01), Mg (ANOVA: F = 3.09; p b 0.01), Na (ANOVA: F = 3.19; p b 0.01) and K (ANOVA: F = 2.56; p b 0.05) among all five ash colours (Fig. 4A, B, C and D). The higher quantities of Ca were observed in black ash extracts, with 5553.32 mg/l, and lower in, dark grey ash extracts, with 4014.85 mg/l. Higher amounts of Mg were also found in black ash extracts (1418.61 mg/l), and lower in dark grey ash (1075.39 mg/l). Relative to Na, the concentration among ash colours, was higher in very dark grey ash extracts (1920.62 mg/l) and lower in dark brown ash extracts (1226.76 mg/l). The major concentrations of K were found in black ash extracts (3609.42 mg/l), and lower in very dark brown ash (2521.45 mg/l). Overall, the ash extracts contained more Ca, followed by K, Na and Mg. For these water soluble elements no relationship with fire severity was found. TP (ANOVA: F = 6.53; p b 0.001), TS (ANOVA: F = 3.69; p b 0.01) and Si (ANOVA: F = 2.45; p b 0.05) showed significant differences among ash colours classes (Fig. 5A, B and C). However, TP and TS reveal contrasting tendencies of increasing and decreasing, respectively, mean values with increasing fire severity from very dark brown to light grey ashes. Silica (Si), on the other hand, revealed significantly higher values

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Fig. 2. Mean and standard error of the A) CaCO3 concentrations and B) pH values for the five ash colour classes in order of increasing fire severity from very dark brown to light grey. Colour classes that differ significantly at alpha= 0.05 (Fischer test) are indicated with distinct letters.Summary of ANOVA results and Fisher LSD test for A) CaCO3 and B) ash pH according ash colour. The data are presented as mean ± SE (error bars). Different letters mean significant differences at a p b 0.05. (N = 102), CaCO3 data in %.

for the black and very dark grey ashes than not only the lighter grey ashes but also the dark brown ashes, i.e. for ashes produced at lower as well as higher fire severities. 3.4. Multivariate analysis A multivariate analysis was carried out with a PCA in order to find relationships between the elements analysed. The PCA identified three factors that explain at least one of the studied variables. The first three PCA axes together explain 71% of the total variation, with the first and second axis explaining 36 and 22%, respectively (Fig. 6A). The PCA classified the studied variables into three groups, the first included the elements that had higher values in very dark brown and black ash: TC, TN, C/N ratio and the water-extractable K, TP, Ca and Mg. The second group is composed of water-extractable Na and Si and these nutrients showed higher values in very dark grey and dark grey ash. The third and last group aggregated the elements with higher quantities in light grey ash, which included CaCO3, pH and water-extractable TS. Fig. 6B shows the relation between factor 1 and factor 2, using all cases analysed. This allowed us to classify the differences between all cases and ash colours. Very dark brown and black ash appear on the right side of the graphic,

Fig. 3. Mean and standard error of the A) Total Carbon, B) Total Nitrogen concentrations and C) C/N ratio for the five ash colour classes in order of increasing fire severity from very dark brown to light grey. Colour classes that differ significantly at alpha = 0.05 (Fischer test) are indicated with distinct letters.Summary of ANOVA results and Fisher LSD test for a) ash TC, b) ash TN and c) ash C/N ratio, according ash colour. The data are presented as mean ± SE (error bars). Different letters mean significant differences at a p b 0.05. (N = 102), data in %.

and this indicates that there were not substantial differences in the analyses between the two ash colours. Some differences are identified between these colours and very dark grey and dark grey ash (Fig. 6B).

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Fig. 4. Mean and standard error of the A) Calcium, B) Magnesium, C) Sodium and D) Potassium concentrations in ash extracts for the five ash colour classes in order of increasing fire severity from very dark brown to light grey. Colour classes that differ significantly at alpha = 0.05 (Fischer test) are indicated with distinct letters.Summary of ANOVA results and Fisher LSD test for a) ash Ca, b) ash Mg, c) ash Na, and D) ash K, according ash colour. The data are presented as mean ± SE (error bars). Different letters mean significant differences at a p b 0.05. (N = 102), data in %.

The dynamic of the studied elements is very similar between these ash colours. On the left side of Fig. 6B, the light grey ash cases are grouped, which as expected differ substantially from very dark brown ash, but also (to a lesser degree) from very dark grey and dark grey ash. Overall, there is an increase of the fire severity from the right to the left in Fig. 6A and B, as shown by the arrow. The PCA also allowed us to observe that fire severity has important implications for the amount of nutrients and water-extractable elements present in the ash. 4. Discussion 4.1. CaCO3 and pH Our results showed that ash pH increases with fire severity as does ash CaCO3. The lower values were identified in very brown ash and the highest in light grey ash. There is a strong relation between ash lightness, pH and CaCO3 (Goforth et al., 2005; Soto and Díaz-Fierros, 1993; Úbeda et al., 2009). Several studies identified wildfire ash with high pH, especially white ash. Ulery et al. (1993) observed a pH of 12 in Quercus ash, Hageman et al. (2008) pH 13 and Goforth et al. (2005) pH 12 from ash collected in a burned mixed conifer forest. In our study the ash produced at higher severity was light grey rather than white, thus it was expected that the ash pH level would not reach those high values. For our study the average pH was 8.18,

despite the presence of CaCO3 that increases pH significantly (Pereira et al., 2010). Also Goforth et al. (2005) observed a pH of 8.8 in white ash where CaCO3 was abundant. Prior studies simulating fire effects in the laboratory have established that CaCO3 concentration increases with fire severity (Iglesias et al., 1997; Liodakis et al., 2007; Quintana et al., 2007; Raison and McGarity, 1980; Soto and Díaz-Fierros, 1993; Úbeda et al., 2009) and in the field (Goforth et al., 2005; Ulery et al., 1993; Woods and Balfour, 2010). The combustion of wood causes the mineralization of basic elements, creating oxides that are converted to carbonates and hydroxides with exposure to the environment, compounds that are rich in alkaline metals such as Ca, (Etiegni and Campbell, 1991). The CaCO3 is created as a result of the transformation of calcium oxalate with the loss of carbon dioxide due to temperature, observed at temperatures between 350 and 400 °C (Pereira et al., 2010; Quintana et al., 2007). However, this depends on the burned plant species. Pereira (2010) observed this transformation at 350 °C for Pinus pinaster and at 400 °C for Quercus suber ash created in laboratory. First, this shows that species can respond differently to the same temperature. Even the same species located in different environments have a different response (Úbeda et al., 2009) and species flammability can be determined by the ecosystem as observed by Mutch (1970). Second, it is very likely that the temperatures at which the ash was produced were, for the most part, higher than 350 °C.

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Úbeda et al. (2009). Small amounts of CaCO3 were found in very dark brown ash and black ash, and that amount increases rapidly when the ash becomes grey and especially light gray. The presence of CaCO3 is responsible for the ash brightness that increases with fire severity. Goforth et al. (2005) and Ulery et al. (1993) found that this mineral was the major component of white ash. In this study we did not identify white ash, thus we expected the amounts of CaCO3 to be less than that identified in the previous studies. Ulery et al. (1993) observed that light-coloured ash collected after a high severity wildfire had between 34 and 95% of CaCO3 and darkcoloured ash 12%. In our study light grey ash had on average 32.46% of CaCO3 and dark-coloured ash (e.g. very dark grey ash and black ash) had 11.08–5.58% of CaCO3, which is similar to the observations in the previous study. Our results showed that ash pH increases with fire severity as does ash CaCO3 and there is a high correlation between them, r = 0.74, p b 0.0001 (see also Fig. 2A and B, and Fig. 6A). The lower values were identified in very brown ash and the highest in light grey ash. There is a strong relation between ash chroma value, pH and CaCO3 (Goforth et al., 2005; Soto and Díaz-Fierros, 1993; Úbeda et al., 2009). In our study the ash produced at higher severity was light grey, and on average, the pH value was 8.18, lower than the pH value observed in white ash of 12 and 9 by Ulery et al. (1993), 11.7–10.1 by Goforth et al. (2005) and 13 by Hageman et al. (2008). Goforth et al. (2005) observed a pH of 8.8 in white ash where CaCO3 was abundant. In our work, dark ash pH values ranged from 7.44 to 7.79 in very dark grey and black ash, respectively. Ulery et al. (1993) identified pH values of 7.8 in darker ashes and Goforth et al. (2005) around 8 and this showed that the results obtained are similar, especially in relation to the Ulery study. It is widely reported in the literature that fire ash is alkaline (Hageman et al., 2008; Raison and McGarity, 1980; Ulery et al., 1993) and this increases with the temperature of combustion and severity (Henig-Sever et al., 2001; Qian et al., 2009a; Úbeda et al., 2009).

4.2. Total Carbon (TC), Total Nitrogen (TN) and C/N ratio

Fig. 5. Mean and standard error of the A) Total Phosphorous, B) Total Sulphur and C) Silica concentrations in ash extracts for the five ash colour classes in order of increasing fire severity from very dark brown to light grey. Colour classes that differ significantly at alpha = 0.05 (Fischer test) are indicated with distinct letters. Summary of ANOVA results and Fisher LSD test for A) ash water-extractable TP, B) ash water-extractable TS and C) ash water-extractable Si, according ash colour. The data are presented as mean ± SE (error bars). Different letters mean significant differences at a p b 0.05. (N = 102), data in mg/l.

In our study using ash collected from real fires we observed that the amount of ash CaCO3 increased with the severity of the fire, which is in accordance with the laboratory studies carried out by

In our study, there was a decrease of TC with fire severity. The higher amounts were identified in very dark brown ash and lower in light gray ash, and this agrees with the results of other studies. Goforth et al. (2005) and Khanna et al. (1994) found that carbon decreased with the degree of combustion and black ash contains higher amounts of carbon than grey or white ash. Ash colour lightness gives an indirect estimation of the degree of carbon combustion and shows if the organic matter combustion is complete. Goforth et al. (2005) identified an inverse relationship between ash colour and organic carbon. Nevertheless, even in high severity combustion, some amounts of carbon remain in ash, as we observed in this study and elsewhere (Bodi et al., 2011; Goforth et al., 2005; Raison and McGarity, 1980). Wildfires strongly reduce ash TC (Murphy et al., 2006) and the TC concentration depends on the fire severity (Khanna et al., 1994). Carbon is lost from the ecosystem especially by volatilization that begins around 200 °C and increases with the temperature of exposure (Neary et al., 2005; Pereira, 2010; Qian et al., 2009a,b). Thus ash TC was a good indirect estimator of fire severity. Our results showed that TN decreases with fire severity are gradual, but evident in light ash colour where concentrations are below 1% (Fig. 3B). The concentration is higher in darker ash than in lighter ash as identified elsewhere (Khanna et al., 1994). Goforth et al. (2005) also identified higher concentrations of TN in dark ash than in white ash and an inverse relationship between ash lightness and as TN content. Due to the difference of methods applied and ecosystems affected, we did not compare our results with those obtained in the previous studies.

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A

111

B

Fig. 6. Relation between factor 1 and factor 2 A) variables and B) cases. Very Dark Brown (VDB), Black (B), Very Dark Grey (VDG), Dark Brown (DG) and Light Gray (LG). Bold lines divide the groups of nutrients and cases according to ash colour. Colour circumferences show the three groups identified. The arrow in both figures shows an increase in fire severity increase.

Some studies showed that after wildfires, great amounts of N are lost through volatilization, which depends on fire severity. As with carbon, nitrogen is very sensitive to fire temperatures and starts to volatilize around 200 °C. Ash TN losses increase with the temperature. The loss of nitrogen is proportional to the quantity of organic matter combusted and it is a good predictor of fire severity (DeBano and Conrad, 1978; Gray and Dighton, 2006; Murphy et al., 2006; Neary et al., 2005; Ponder et al., 2009; Qian et al., 2009a). The decrease of TC is higher than TN, and we expected a reduction of C/N ratio as observed in Quintana et al. (2007), which and is attributed to the quick volatilization of carbon dioxide to the atmosphere. Also Fernandez et al. (1997) observed in burned soils a higher decrease of carbon than nitrogen, leading to a decrease of C/N ratio. The organic matter mineralization is more intense for light ash than for dark ash (Goforth et al., 2005). Similar to TC and TN, the highest C/N ratio is identified in very dark brown ash produced at lower severity and the lowest in light grey ash produced at higher severity. 4.3. Extractable elements The water-extractable Ca, Mg and K exhibited higher quantities in black ash and lower amounts in dark grey ash. In the case of waterextractable K, the lowest concentrations were observed in very dark brown ash. Water-extractable Na showed higher concentrations in very dark ash and lower in very dark brown ash. This means that the major availability of water-extractable cations was observed when ash is black and very dark grey – at medium fire severity – as observed in laboratory studies elsewhere (Pereira, 2010). Ca and Mg are more soluble in the pH range of 7 and 8 (Holden, 2005), which is in the range observed in all types of ash colour identified in our study with the exception of light gray. Na and K show a high solubility at pH above 6, but especially above 7.5 (Neary et al., 2005; Troeh and Thompson, 2005), values characteristic of all ash types identified in this work. The high concentration of water-extractable Ca in very dark brown and black ash extracts in comparison with grey-coloured ash can be explained by the presence of CaCO3 in ash. When ash is composed of CaCO3, this can significantly influence the type and amount of elements in solution. CaCO3 has a low solubility, especially at high pH and in the interaction with other metals in solution. The solubility of this mineral increases with a decrease in pH (Morse et al., 2007; Ruiz-Agudo et al., 2009; Steenari et al., 1999; Ulery et al., 1993; Wolthers et al., 2008). Liodakis et al. (2009) observed that

CaCO3 was the major mineral component of Pinus halepensis and Quercus cocciferae, after 5 leaching tests. CaCO3 surfaces when in contact with solutions with pH between 7 and 10.8, become negatively charged (Wolthers et al., 2004) and this change in surface charge attracts the cations in solution. This capacity to attract cations occurs by processes of sorption, adsorption, precipitation and co-precipitation and is favoured at high pH (Brady et al., 1999; Ettler et al., 2006; Menadakis et al., 2007; Zachara et al., 1990). However, the capacity of CaCO3 to sorb elements depends strongly on the ionic strength of the solution and on the ionic radii of each ion, and the metals with high valence bind more easily to CaCO3 surfaces (Brady et al., 1999; Ragavan and Adams, 2009). Ions with smaller ionic radii than Ca2+ are more efficiently incorporated onto CaCO3 surfaces through substitution for this element (Comans and Middelburg, 1987; Ettler et al., 2006; Menadakis et al., 2007). The ionic radii of Mg2+ is 0.72 Å, smaller than the Ca2+ ionic radii, 0.99 Å, and the incorporation of the smaller cation onto CaCO3 surfaces is facilitated as observed by Astilleros et al. (2006) and Menadakis et al. (2007). This mechanism explains the reduction of water-extractable Mg in the ash slurries with high CaCO3. Na + and K + have less valence than the previous cations and bigger ion radii than Ca 2+, 1.02 Å and 1.38 Å, and do not change position directly with Ca on CaCO3 surfaces (Busenberg and Plummer, 1985). Thus, the conditions for the incorporation of the monovalent cations are less favourable. However under some conditions Na and K can be captured onto CaCO3 surfaces, especially in the presence of crystals formed by other ions incorporated previously. The incorporation of monovalent cations increases with the crystal growth (Busenberg and Plummer, 1985; Ishikawa and Ichikuni, 1984). According to Jung et al. (2005), the presence of Na and K in solution significantly changes the crystal morphology of CaCO3 surfaces. In addition, if these ions are at high concentrations in solution they can compete with Ca and Mg for the available places (Brady et al., 1999). This mechanism explains the reduction of water-extractable Na and K in the ash with important amounts of CaCO3. The water extracts of ash were composed mainly of Ca and K as observed by Blank and Zamudio (1998), Quintana et al. (2007) and Ulery et al. (1993) and this is because ash is very rich in both of these elements as reported elsewhere ( Khanna et al., 1994; Liodakis et al., 2005, 2009). These elements are easily soluble, especially at favourable pH values and in black ash with a low % of CaCO3. The mineralization imposed by fire temperatures increases the availability of nutrients to be leached (Mataix-Solera and Cerdà, 2009). However, ash nutrient availability depends on pH and ash

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mineral composition (Demeyer et al. 2001; Liodakis et al., 2009; Pereira et al., 2010; Pereira et al., 2011). The ash water-extractable cations did not have a straightforward relationship with fire severity, thus cannot be used as a predictor of the fire effects on ash properties as also observed in a laboratory environment (Úbeda et al., 2009). Water-extractable TP showed a decrease in concentration with fire severity (higher in very dark brown and black ash and lower in light gray ash) contrary to water-extractable TS (higher concentrations in light gray ash and lower in very dark brown), and waterextractable Si (higher concentrations in black and very dark grey ash and lower in light gray ash), nor did it show any relationship as observed for the cations. Thus, water extractable TP and TS can be considered good predictors of fire severity as we identified for ash TC, TN and C/N ratio. The dynamic of water-extractable TP is strongly related to the pH, type and amount of nutrients in solution, and ash mineral composition (Pereira et al., 2010). The range of pH solubility for Phosphorous is very narrow, from 6.5 to 7.5 (Varennes, 2003). The pH levels in the very dark brown ash range from 6.93 and 7.51, thus within the window of major solubility of Phosphorous. In relation to the other ash colours, this range increases in the ash produced at high severity, thus it was expected that TP extracts would be lower. At lower pH, TP precipitates, strongly with Aluminium, Manganese and Iron and at a higher pH, it precipitates strongly with Ca and Mg. It can substitute for Ca sites on CaCO3 surfaces as a result of the smaller ionic radii (0.38 Å) as discussed in several studies (Carreira and Lajtha, 1997; Ishikawa and Ichikuni, 1981; Pereira et al., 2010; So et al., 2011) and this could also explain the decrease of this element in the extracts from ash produced at higher severity. TS it is easy leached from ash (Khanna et al., 1994) and it is an element very sensitive to pH changes (Nodvin et al., 1986). Its solubility increases in the pH range between 6 and 8 (Troeh and Thompson, 2005). Thus it was expected that TS concentrations would be higher in the ash produced at high severity, where the pH levels range from 7.82 to 8.64. Contrary to other water-extractable elements, we did not observe a reduction of TS in the light ash, with a considerable % of CaCO3. Maree et al. (2004) observed that Sulphur is only captured from solution when in contact with CaCO3 surfaces when the pH is equal to or higher than 12. In this study no sample reached this value, thus it was predictable that water-extractable TS increased with fire severity. Water-extractable Si showed a similar behaviour to waterextractable Na. This element showed a high solubility at pH equal to or greater than 7 (Steenari et al., 1999). In this study, pH levels in black and very dark grey ash slurries were higher than 6.70, thus facilitating the extraction of Si. However, we observed a decrease of water-extractable Si in the ash produced at higher severity, especially in light grey ash. Little is known about the effects of CaCO3 surfaces on Si dynamics, however it is known that CaCO3 has the ability to capture Si ions in suspension (Hager, 1980), and also other anions (Goldberg and Glaubig, 1988; Zachara et al., 1990). One logical reason for this capture is the ionic radii of Si (0.40 Å), which is smaller than Ca (0.99 Å).

and light grey ash showed higher concentrations of CaCO3, pH values and water-extractable TS. The presence of ash with CaCO3 induces a great complexity on the type and amount of ions in solution. Metals with ionic radii smaller than Ca 2+ are more easily captured by CaCO3 surfaces. In addition, ions compete for available places to bind. This selectivity and competition between ions will have important implications for the type and amount of elements in solution, especially in the ash produced at higher severity with high pH, and thus for the nutrients available for plants (Balaz et al., 2005; Karageorgiou et al., 2007). After fire, ash is a valuable soil protection against erosion agents, and this ash-bed depends on fire severity as observed elsewhere (Cerdà and Doerr, 2008; Pereira et al., 2010; Zavala et al., 2009). The influence of ash on soil chemistry will be different according to the type of ash produced and it is probable that different types and amounts of nutrients are leached from the ash produced at different severities and as such ash will have different impacts on plant recuperation across the landscape. In addition, previous studies have shown that ash colour and, by extension, ash chemistry and waterextractable elements vary strongly even over small distances (Pereira, 2010). Thus the nutrients available to plants are highly heterogeneous across the landscape, inducing a great complexity on the spatial pattern of vegetation recovery, normally in patches. It is known that vegetation recovers especially in the areas where ash accumulates (Cammeraat and Imeson, 1999; Chambers and Attiwill, 1994; Close and Wilson, 2002), as a result of the high nutrient availability (Clarke et al., 2005; Kutiel and Naveh, 1987) and we observed also this in our study areas 9 months after the fire (Fig. 7).

5. Conclusions The principal conclusions of this study about the chemical properties of ashes produced in wildfire in Pinus and Quercus forests in south Portugal are the following: 1) The five colour classes revealed that ash chemical and waterextractable elements were different according to fire severity. 2) There is a decrease of ash TC, TN, C/N ratio, water-extractable TP with fire severity and an increase of ash pH, CaCO3 and waterextractable TS. In the remaining elements no relation with fire severity was observed. 3) Ash pH and CaCO3 have complex implications on the type and amount of water soluble nutrients, especially when produced at high severity

4.4. Overall discussion We observed that ash chemistry is different among ash colours and the type and amount of water-extractable nutrients are different according to fire severity. Three groups of elements were identified with the PCA that show clearly the differences in the ash chemical composition and water-extractable nutrients. This means that ash colour is an important parameter to identify the type and amount of nutrients available for vegetation recovery after a wildfire. In this case very dark brown and black ashes are associated with high TC, TN and C/N ratio and water-extractable Ca, Mg, K and TP. Ash extracts rich in Na and Si were observed in very dark grey and dark grey ash

Fig. 7. Vegetation recovery 9 months after the fire in the Casal do Sapo plot.

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Acknowledgements This study was supported by the Spanish Ministry of Science and Technology, project: CGL2006-11107-C02-02/BOS “Evaluation of the quality of Mediterranean soils affected by fire in a middle and large term” and European Regional Development Fund (FEDER) Funds, FUEGORED (Spanish Network of Forest Fire Effects on Soils http:// grupo.us.es/fuegored/) and to Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya. The first author acknowledges the support of the Lithuanian Scientific Research Council, project: “Fire impacts in Lithuanian soils and ecosystems”, contract: Nr. MIP11387. We are also thankful to Serveis Cientıfico-Tecnics from the University of Barcelona.

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