MICROBIOLOGICAL PROPERTIES IN ACIDIC FOREST SOILS WITHSPECIAL CONSIDERATION OF KCl EXTRACTABLE Al

ILLMER PAUL∗, OBERTEGGER ULRIKE and SCHINNER FRANZInstitute of Microbiology, University of Innsbruck, A-6020 Innsbruck, Austria

(∗ author for correspondance, e-mail: Paul.Illmer@uibk.ac.at; fax: +43/(0)512/507/2928)

(Received 8 July 2002; accepted 17 March 2003)

Abstract. To determine the importance of Al-availability for soil micro-organisms 95 forest soilsfrom Tyrol/Austria with comparable topography, vegetation, climatic conditions, soil type and withlow soil pH (median = 3.9) were investigated for their physical (percentage of stable aggregates,water holding capacity), chemical (pH, electrical conductivity, contents of organic matter, concentra-tions of easily extractable aluminium, calcium, potassium, magnesium and phosphorus) and microbi-ological characteristics (microbial biomass and respiration, metabolic quotient, content of ATP, activ-ities of protease and CM-cellulase, cfu-values of total and Al-tolerant bacteria and fungi). A highlysignificant negative correlation was detected between concentrations of KCl-extractable aluminiumand soil pH. By the application of multivariate statistical methods, the effect of the concentration ofKCl-extractable aluminium on abundance and activities of soil micro-organisms could be revealed.Al turned out to be of great importance for micro-organisms and often outmatched the significance ofother well known soil properties like organic matter, pH or water holding capacity. However, due tovery healthy trees at the sites under investigation no effect of Al or soil acidification on forest declinecould be detected.

Keywords: Al toxicity, aluminium, exchangeable Al, forest soil, microbial biomass, pH, soil acidity

1. Introduction

With 8.13% Aluminium is the third most abundant element in the earth’s crust,the most abundant metal and a key constituent in most common rocks exceptlimestone and sandstone. Nevertheless, besides Sr, Pb and Sb it is one of the veryfew elements not essential for biological functions, which is probably due to thevery strong toxicity towards all forms of life including micro-organisms (Pina andCervantes, 1996). Research into Al-toxicity increased after the findings of Ulrich etal. (1980), who were able to show that acid deposition leads to a reduction of soilpH and thus to a mobilization of potential toxic Al species. This in turn was recog-nized to be of great importance for the symptoms classed under the general term‘forest decline’. Meanwhile numerous toxic effects of Al have been demonstratedin a great number of investigations (Cronan and Grigal, 1995; Pina and Cervantes,1996; Hovmand and Bille-Hansen, 2001), but non-symbiotic soil micro-organismshave mostly been ignored. In a former investigation we were able to show that anincreased Al-availability in soil led to a distinct reduction in microbial biomassand microbial activities (Illmer et al., 1995). To verify both these results and the

Water, Air, and Soil Pollution 148: 3–14, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

4 P. ILLMER ET AL.

working hypothesis that Al-availability controls microbial properties and processesin forest soils the present empiric study was carried out using a greater varietyof samples of different soils. We investigated direct effects of KCl-extractable Aland also tried to detect concealed effects of Al by the application of multivariatestatistical methods.

2. Material and Methods

2.1. SAMPLING SITES

Tyrol is a province in Austria located in the Central Alps. In 1988 a grid (4 × 4km) was laid over the whole of the Tyrol, and the vegetation and soil of each of the658 resulting locations were analyzed. The data were collected in the BZI (Boden-zustandsinventur – soil state inventary) and WZI (Waldzustandsinventur – foreststate inventary) data bases (Anonymous, 1989) and enabled the selection of sitesfor the present investigation. The prerequisites were: low soil pH, similar altitude(between 1000 and 1400 m above sea level), rainfall (1000 mm) and vegetation(mainly spruce) and good accessibility. Soil types belonged to the podzol referencesoil group (mainly leptic podzols and dystric cambisols). Out of 263 possible forestcovered sites, 95 were selected for the present investigation.

Samples were taken from the Ahe and B horizons during autumn. The soilswere sieved (2 mm) and stored at 4 ◦C for further use. Analyses were performedwithen few days (microbial investigations) and several weeks (physical and somechemical analysis) after sampling.

2.2. CHEMICAL CHARACTERIZATION

The contents of moisture and of organic matter (loss of ignition) were determinedgravimetrically at 105 and 430 ◦C respectively. The actual and potential aciditieswere determined in H2O and 0.01M CaCl2 respectively. Electrical conductivity wasdetermined electrochemically after the suspension of soil in distilled water. Thedetermination of aggregate stability and of the water holding capacity followedmethods described in Schinner et al. (1996). Following the standard method ofSSSA, exchangeable Al was determined by atomic absorption spectroscopy (AAS)in 1 M KCl extracts which are known to be well suited for investigations dealingwith exchangeable Al and soil acidification processes (Bertsch and Bloom, 1996).Exchangeable Ca, Mg and K were extracted with 1M CH3COONH4 (Suarez, 1996)and also determined according to standard methods for AAS. The extraction anddetermination of easily exchangeable P followed the method described by Illmer(1996). For their ecological significance (Cronan and Grigal, 1995; de Wit et al.,2001) molar quotients were determined from elemental concentrations.

AL-TOXICITY AND SOIL MICRO-ORGANISMS 5

2.3. MICROBIOLOGICAL INVESTIGATIONS

The microbial biomass (SIR – Substrate Induced Respiration) was determinedby measuring the CO2 release from soil samples after an application of glucose(Anderson and Domsch, 1978), whereas basal respiration was determined withoutsubstrate application. The ATP extraction procedure followed the method of Le-htokari et al. (1983), using the luciferin/luciferase system (LKB no. 1243–102).For the determination of colony forming units (cfu) of bacteria and fungi, serialdilutions of soils in NaCl solution (0.85%) were incubated at 20 ◦C on soil extractagar for bacteria (Illmer and Schinner, 1997) and on malt extract agar (Merck no.5391) containing lactate and chloramphenicol for fungi. To determine the percent-age of Al- (and acid-) tolerant micro-organisms, filter-sterile solutions of AlCl3 ·6H2O were added to the autoclaved medium (Illmer and Schinner, 1997). The finalconcentration of 2mM Al resulted in a pH-reduction down to about 4.0. However,Al-tolerant micro-organisms have to be acid tolerant as well, because the simultan-eous pH reduction cannot be avoided. Neutral pH-values (e.g. after the applicationof buffers) decrease Al-solubility, bioavailability and thus toxicity (Driscoll andSchecher, 1988) and are therefore not suited for investigating Al-toxicity.

2.4. STATISTICS

After performing descriptive statistics, the data were analyzed with (M)ANOVA orRank-ANOVA according to the presence or lack of normal distribution (KS-Test).Multivariate methods (partial correlation, multiple regression, factor analysis, etc.)were used to analyze concealed relationships. Plots of predicted values of multipleregression versus raw residuals were used to test the assumption of linearity regard-ing the relationship between dependent and independent variables. Several levelsof significance were discriminated: P ≥ 0.05 n.s.; P < 0.05∗; P < 0.01∗∗ and P <0.001∗∗∗. Positive and negative correlations are represented by + and – respectively(e.g. ∗∗-).

3. Results and Discussion

Chemical and microbiological properties of the soils under investigation (togetherwith abbreviations used throughout the text) are given in Table I and are well withinthe range of those normally found in acidic forest soils (Cronan and Grigal, 1995;Hovmand and Bille-Hansen, 1999; Jentschke et al., 2001).

3.1. ABIOTIC SOIL PROPERTIES

Exchangeable Al is often represented by the KCl-extractable fraction and wasshown to be well suited for investigations dealing with soil acidification processes(Bertsch and Bloom, 1996). A highly significant correlation between exchangeable

6 P. ILLMER ET AL.

TABLE I

Parameters under investigation, abbreviation, units and measured values; n = 95

Parameter Abbreviation Unit Mean Median Minimum Maximum Quart. range

Dry matter DM g g−1 0.645 0.688 0.299 0.901 0.204

Organic matter OM g g−1 0.519 0.488 0.098 0.949 0.285

Water-holding capacity WHC g g−1 1.198 1.038 0.060 4.831 0.974

pH (CaCl2, 0.01M) pH –log(H+) 4.169 3.905 2.760 7.335 1.390pH (H2O) pHH2O –log(H+) 4.670 4.400 3.005 7.415 1.025

Electrical conductivity EC µS cm−1 85.49 78.30 25.05 169.30 46.20Percentage of stable aggregates AGG % stable Aggregates 61.36 61.54 6.80 95.29 29.34

Al-concentration AL µmol g−1 DM 22.25 14.94 0.12 105.91 25.49

Ca-concentration CA µmol g−1 DM 52.23 44.33 4.37 321.12 52.87Molar ratio of Ca to Al CA/AL ratio 28.75 2.44 0.05 607.27 15.87

K-concentration K µmol g−1 DM 6.457 4.276 0.804 63.110 4.805

Molar ratio of K to Al K/AL ratio 2.207 0.345 0.019 35.512 1.470

Mg-concentration MG µmol g−1 DM 13.82 11.13 1.62 107.01 12.00

Molar ratio of Mg to Al MG/AL ratio 6.276 0.667 0.015 104.068 5.389

P-concentration P µmol g−1 DM 0.146 0.071 0.008 2.202 0.093

Molar ratio of P to Al P/AL ratio 0.039 0.007 0.000 0.558 0.035

Microbial biomass (SIR) BIO mg C-biomass 100g−1 DM 1.382 1.169 –0.075 4.115 1.280

Microbial respiration RES mg CO2 g-1 DM 24h−1 0.575 0.460 –0.420 2.770 0.470

Metabolic quotient QCO2 ratio (RES/BIO) 0.465 0.425 –2.400 4.047 0.262

ATP-concentration ATP µmol g−1 DM 0.0201 0.0139 0.0026 0.0960 0.0180

cfu of bacteria BAC cfu g−1 DM 7.6E+06 3.9E+06 3.0E+05 4.5E+07 6.9E+06

cfu of Al-tolerant bacteria BAC-AL cfu g−1 DM 7.7E+05 4.8E+05 1.8E+04 5.1E+06 8.2E+05

Portion of Al-tolerant bacteria BAC-RATIO ratio (BAC-AL/BAC) 0.18 0.11 0.00 1.30 0.21

cfu of fungi FUN cfu g−1 DM 5.9E+05 3.0E+05 3.7E+03 4.1E+06 7.3E+05

cfu of Al-tolerant fungi (AF) FUN-AL cfu g−1 DM 5.2E+05 2.1E+05 3.7E+03 7.2E+06 4.6E+05

Portion of Al-tolerant fungi FUN-RATIO ratio (FUN-AL/FUN) 0.79 0.68 0.12 4.22 0.51Ratio of bacteria to fungi BAC/FUN ratio (BAC/FUN) 35.90 12.55 1.06 775.00 19.16

Ratio of BAC-Al to FUN-Al TOL-RATIO ratio (BAC-AL/FUN-AL) 4.08 2.28 0.12 53.40 2.55

Activity of protease PROT µg tyrosine equivalents 1120 1009 132 3375 738

g−1 DM 2h−1

Activity of CM-Cellulase CMC µg glucose equivalents 1035 969 59 2845 650

g−1 DM 24h−1

Damage classification of trees SCHAD Austrian category of damage 1.30 1.27 1.00 2.43 0.34

Al and soil pH is known from the literature (Driscoll and Schecher 1988; van Heeset al., 2001) and was confirmed in the present investigation (Figure 1).

The data of Figure 1 demonstrate that, at a soil pH below 4 (about half of theinvestigated soils), the Al availability distinctly increased. However, despite verylow pH-values (minimum = 2.7, the lowest 15% of the values were beneath 3.2)the concentrations of extractable Al rarely exceeded 100 µmol g−1 DM, whereasdistinctly higher concentrations have been reported elsewhere (Cronan and Grigal,1995; Drohan and Sharpe, 1997). Although the close relationship between acidityand Al-availability is widely accepted (Hovmand and Bille-Hansen, 1999) the com-plexity of this relationship is sometimes underestimated, and a separate analysis ofsoil horizons is often necessary to arrive at conclusive results (van Hees et al.,2001).

The concentration of KCl-extractable Al and the content of organic matter (OM)were highly significantly correlated (P < 0.01∗∗+) - a connection which is also

AL-TOXICITY AND SOIL MICRO-ORGANISMS 7

Figure 1. Relationship between pH (CaCl2) and concentration of KCl-extractable aluminium.

known from literature (Mulder et al., 2001). Several reasons for this connectionare conceivable: (i) Mobilized Al is re-adsorbed onto insoluble organic matter,from where it can be desorbed again by the extraction solution (KCl) (Bertsch andBloom, 1996). (ii) Al forms soluble complexes with low molecular weight organiccompounds (e.g. citrate), resulting in an accumulation of organic Al complexes(van Hees and Lundström, 2000; Mulder et al., 2001). (iii) A pseudo-correlationbetween Al and OM via the pH seems at least possible (Illmer et al., 1995) asthere is also a correlation between pH and OM (P < 0.01∗∗-). By means of a partialcorrelation analysis, which provides some elucidation of such complex interactionsystems, the negative linear correlation between the content of pH and OM wasverified (pH-OM: P < 0.01∗∗-). Contrary to that, the connection between Al andOM no longer remained significant, which points to the risk of overestimatingconnections established on the basis of linear regression analysis.

The rate of decomposition of organic matter and therefore its content in soilmight change according to soil acidification and Al-availability (Miltner and Zech,1998; Mulder et al., 2001). Multiple regression is a tool to simultaneously analyzethe relationship between several predictor variables and one dependent variable.Thus, we conducted such a multiple regression analysis with OM defined as thedependent variable, and all other abiotic parameters as the independent variables.This analysis led to a highly significant model (R2 = 0.834; P < 0.001∗∗∗) whichincluded moisture content, pH, concentration of extractable Ca, stable aggregatesand electrical conductivity. All other parameters, including concentration of Al,were rejected because of their too low F-values. The exclusion of Al in contrast to

8 P. ILLMER ET AL.

the inclusion of pH confirmed results of Bottner et al. (1998), and also the aboveview that the concentration of extractable Al itself has no significant influence onthe content of organic matter whereas it is significantly influenced by acidity.

3.2. MICROBIOLOGICAL PROPERTIES

Biotic parameters determined in the present investigation show a highly significantcorrelation with abiotic parameters, indicated by a canonical correlation coefficientof Rcan = 0.911 (P < 0.001∗∗∗). Yet, this correlation simply indicates a correlationbetween two sets of variables and does not give information about details such asthe direction of influence.

Linear regression analysis indicates that parameters describing microbial bio-mass and microbial activities (BIO, RES, QCO2, ATP, CMC, PRO) are signific-antly correlated with typical key factors of soil such as OM or WHC (Figure 2),which confirms data from literature (Paul and Clarke, 1996; Vanlauwe et al., 1999)and also points to usual conditions found in the soils.

However, not a single correlation (linear or nonlinear) of these biotic variableswith Al could be detected. At first glance, exchangeable Al seems be of minorimportance in connection with soil microbial properties, which would contradictresults of former investigations (Illmer et al., 1995; Raubuch and Beese, 1995;Miltner and Zech, 1998). However, restricting the data to the more acidic half ofthe samples (pH < median = 3.9) led to significant linear correlations of Al withthe data representing microbial biomass i.e. BIO (P < 0.05∗-), RES (P < 0.05∗-) and ATP (P < 0.05∗-), whereas no correlations of these parameters were foundin connection with soil pH. Thus, the influence of exchangeable Al on microbialbiomass in soils seems to increase along with decreasing soil pH and increasingAl-extractability.

Contrary to physiological biomass parameters, the complete data describing theabundance of micro-organisms (cfu-values of bacteria and fungi) showed distinctcorrelations with the concentrations of extractable Al. While the total number ofbacteria was greatly reduced by high levels of exchangeable Al, the number ofAl- and acid-tolerant bacteria remained quite constant, thus leading to a highlysignificant increase of the ratio of tolerant vs. total bacterial counts (P < 0.001∗∗∗+).In contrast to this, both counts of fungi (total and tolerant) slightly decreased withincreasing extractability of Al, and the ratio of tolerant vs. total fungal countsdecreased significantly (P < 0.001∗∗∗–). This different behavior possibly points todifferent abilities of pro- and eukaryotes to cope with high levels of aluminium(Pina and Cervantes, 1996). Although fungi were sometimes shown to exhibit ahigher tolerance towards Al than bacteria (Kanazawa and Kunito, 1996 and Kawaiet al., 2000), other studies could not confirm this (Illmer et al., 1995). However, thetotal percentage of tolerant microorganisms (i.e. 79 and 18% for fungi and bacteria;see Table I) may lead to different conclusions than the Al-dependent change ofthese percentages. Furthermore there also was a strong correlation of cfu-values

AL-TOXICITY AND SOIL MICRO-ORGANISMS 9

Figure 2. Linear correlations between water-holding capacity, WHC and microbial biomass, BIO (A)and content of organic matter, OM and activity of CM-cellulase, CMC (B).

10 P. ILLMER ET AL.

TABLE II

Standardized regression coefficients (β) derived from multiple regression analysis. Biotic parameters(e.g. BIO) were defined as dependent, abiotic variables (DM, OM etc.) as independent variables. Allcalculated models were highly significant (P < 0.001∗∗∗). Further explanation see text

BIO RES ATP BAC FUN PRO CMC

DM n.s. –0.380 *** –0.146 * –0.298 *** n.s. –0.368 *** n.s.

OM 0.449 *** 0.181 ** 0.175 * n.s. 0.597 *** n.s. 0.494 ***

WHC 0.297 *** n.s. 0.342 *** –0.166 * –0.475 *** 0.114 * n.s.

pH 0.351 *** 0.194 *** n.s. 0.512 *** 0.293 *** n.s. n.s.

EC –0.251 *** n.s. –0.071 * 0.218 *** 0.221 *** –0.090 * 0.213 ***

AGG –0.251 *** –0.086 ** –0.150 *** n.s. –0.114 ** –0.249 *** –0.202 ***

AL –0.153 *** –0.209 *** –0.289 *** –0.202 *** –0.182 *** –0.195 *** 0.077 *

CA n.s. n.s. n.s. –0.263 ** n.s. n.s. n.s.

CA/AL 0.145 * n.s. n.s. n.s. n.s. n.s. –0.174 *

K n.s. n.s. n.s. n.s. n.s. –0.191 *** n.s.

K/AL n.s. n.s. n.s. 0.195 ** 0.179 ** –0.137 * n.s.

MG n.s. 0.210 *** n.s. n.s. –0.193 * n.s. n.s.

MG/AL –0.199 ** –0.146 * –0.252 ** –0.498 *** –0.540 *** n.s. –0.135 *

P 0.164 *** 0.398 *** n.s. n.s. n.s. n.s. 0.078 *

P/AL n.s. n.s. 0.248 *** n.s. n.s. n.s. 0.265 ***

with pH, and even a partial correlation analysis was not able to throw light on thecomplex interactions between exchangeable Al, pH and cfu-values. Neither candata from literature, which point to a close connection between Al-availability andthe occurrence of Al-tolerant micro-organisms, rule out this pH-effect (Kawai etal., 2000). Generally, a clear discrimination between the effects of Al ions and H+has been shown to be very difficult, if not impossible, to determine (Parent andCampbell, 1994; Illmer and Schinner, 1997).

Multiple regression analysis was again applied to investigate the influence ofexchangeable Al on soil biota. Each biotic parameter was defined as a dependentvariable and put against all abiotic parameters under investigation. This enabledthe development of models described on the basis of their respective β-values(standardized regression coefficients), as summarized in Table II.

β-values indicate that, e.g. microbial biomass (i.e. BIO) is well described in themultivariate linear model: BIO = 0.449OM + 0.297WHC + 0.351pH – 0.251EC –0.251AGG – 0.153AL + 0.145CA/AL – 0.199MG/AL + 0.164P; (P < 0.001∗∗∗). Acomprehensive view of the results confirms the content of organic matter, the pH,the electrical conductivity and the aggregate stability to belong to the key factors ofsoil regarding their impact on biotic parameters (Paul and Clarke, 1996). All thesefactors occur in many of the models and are characterized by high positive or neg-

AL-TOXICITY AND SOIL MICRO-ORGANISMS 11

ative loads for β indicating their influence within the respective model. However,Al-concentration is the only abiotic factor occurring in each model, thus pointing tothe relevance of exchangeable Al for biotic parameters in acidic soils (Bertsch andBloom, 1996).The significance of Al for soil microflora was additionally under-lined by the high β-values in connection with elemental quotients (Ca/Al, Mg/Al,K/Al, P/Al). Molar quotients of Ca/Al and Mg/Al were also shown to be excellentstress indicators in acidic forest ecosystems (Cronan and Grigal, 1995; de Wit etal., 2001).

3.3. FACTOR ANALYSIS

Factor analysis detects structures within extensive data, and can also reduce thenumber of variables with minimal loss of information. Three biotic and three abi-otic factors (bioPC1 to bioPC3 and abioPC1 to abioPC3) were extracted, whichrepresent about 60 and 70% of the total variance of biotic and abiotic paramet-ers respectively. Biotic factors were well suited for detecting structures withinthe data. bioPC1 (29% of total variance) represents the significant physiologicalbiomass data (loadings of more than 0.7): microbial biomass, concentration ofATP, microbial respiration, activities of protease and CMC-cellulase. In contrastbioPC2 is characterized by high loadings for total counts of bacteria and fungiand counts of Al-tolerant fungi, thus representing data describing the abundance ofmicro-organisms in soils (Figure 3).

By the combination of the factor analysis tool with a multiple regression ana-lysis we were able to investigate which abiotic parameter (independent variables)significantly influenced the condensed biotic information represented by the prin-cipal components (dependent variables). Several methods of modeling (very highF-values of > 30 were the prerequisite for inclusion in the model and for avoid-ing redundancy) were again used, which all led to comparable results. Using thestepwise forward selection of variables resulted in a highly significant multipleregression model (P < 0.001∗∗∗) describing (physiological) bioPC1 with only in-cluding WHC, AGG, OM and AL. The model with bioPC2, (representing theabundance of micro-organisms) as a dependent variable only included Mg/AL, pH,WHC, and EC (P < 0.001∗∗∗).

While parameters like OM, pH, WHC and AGG are well known for their keyfunctions in most soils (Raubuch and Beese, 1995 and Paul and Clarke, 1996) ithas been virtually impossible to establish the central significance of Al for soilmicro-flora in a great number of acidic soils in the same distinctive way.

3.4. FOREST DECLINE

The average classification of tree health at the investigated sites (1.3) lies wellbeneath 1.5, which is defined as the threshold value for undisturbed trees in theAustrian classification system (Anonymous, 1989). Only 2 sites out of a total of 95had to be classified higher than 2 and are therefore assumed to be damaged. Due

12 P. ILLMER ET AL.

Figure 3. Factor loadings for the first two biotic principal components extracted from the data.Abbreviations see Table I.

to the narrow distribution of the data and because of this surprisingly good health– indicated by needle loss – there was no detectable linear, non-linear or multiplerelationship, neither to abiotic nor to biotic parameters.

4. Conclusion

Univariate regression analysis did not prove to be a good means of investigatinginterrelationships between soil parameters and micro-flora as long as it is an onlyand unreflected tool. Supporting evidence had to be gained from other analysesfor the hypothesis that KCl-extractable Al distinctly influences microbial biomassand activities in acidic forest soils. Due to the great ecological importance of boththe symbiotic and the free living soil micro-organisms, more attention should bepaid to Al availability, even though less sensitive plants like spruce do not showapparent stress symptoms just as yet.

AL-TOXICITY AND SOIL MICRO-ORGANISMS 13

Acknowledgment

This study was supported by the Austrian Science Fund (FWF), Project No. P11371-BIO. We thank Robert Seitz of the County Forestry Commission for his help inselecting sampling sites.

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