Microbial biomass, enzyme and mineralization activity in relation to ...

Soil Biology & Biochemistry 41 (2009) 969–977

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Microbial biomass, enzyme and mineralization activity in relation to soil organic C, N and P turnover influenced by acid metal stress Yong-Tao Li a, b, Corrine Rouland c, Marc Benedetti b, Fang-bai Li d, Anne Pando c, Patrick Lavelle c, Jun Dai a, * a

College of Natural Resources and Environment, South China Agricultural University, 510642 Guangzhou, China Laboratoire de Geóchimie des Eaux, Universite´ Paris-Diderot, IPGP, case 7052, 5 rue Thomas Mann 75205 Paris Cedex 13, France Laboratoire d’Ecologie des Sols Tropicaux, Institut de Recherche pour le De´veloppement, 93143 Bondy Cedex, France d Guangdong Key Laboratory of Agricultural Environment Pollution Integrated Control, Guangdong Institute of Eco-Environment and Soil Sciences, Guangzhou 510650, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 April 2008 Received in revised form 13 January 2009 Accepted 25 January 2009 Available online 14 February 2009

This study focused on the potential of using soil microbial biomass, enzyme and mineralization activities involved in organic C, N and P turnover, to evaluate the quality of a subtropical agricultural soil affected by long-term acid metal stress. Fractions of C, N and P involved in soil organic matter, microbial biomass and mineralization processes were estimated. Total enzyme activity (FDA) and eight hydrolase activities (xylanase, amylase, b-glucosidase, invertase, N-acteyl-glucosaminidase, urease, alkaline and acid phosphatases) in different decomposition stages of organic C, N and P were selected to characterize the soil functional diversity. These biological datasets were compared with soil metal variables (total contents and free and ligand-complexed ions of Cu, Pb, Zn, Cd, Al and Mn), using principal component analyses, co-inertia and discriminant analyses. The multiple statistics indicate that the metal variables were significantly related with not only general biological factors, but also respective datasets of biomass, enzyme activities and mineralization rates (all P < 0.001). In general, metal variables were inversely related to parameters and indices of microbial biomass C, N and P, FDA and C-related polysaccharidase and heterosidase activities, and P mineralization. As comparison, metal variables exhibited positive relationships with parameters and indices of N-related N-acteyl-glucosaminidase, urease, ammonification, total N mineralization and metabolic quotient, compared with inhibited nitrification. Specifically, free and complexed metal cations showed higher bioavailability than total contents in most cases. Cu, Pb, Al and Mn had different ecotoxicological impacts than Cd and Zn did. Stepwise regression models demonstrated that metal variables are key stress factors, but most of them excluded soil pH. Furthermore, spatial distribution in land uses and of sampling sites clearly separated the soil samples in these models (P < 0.001). We conclude that such a statistical analysis of microbiological and biochemical indices can provide a reliable and comprehensive indication of changes in soil quality and organic nutrient cycling, after exposure to long-term acid metal stress. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Ammonification Co-inertia analysis Enzyme Metal ions Mineralization Nitrification

1. Introduction Soil microorganisms and enzymes are the primary mediators of soil biological processes, including organic matter degradation, mineralization and nutrient recycling. They play an important role in maintaining soil ecosystem quality and functional diversity

* Corresponding author at: College of Natural Resources and Environment, South China Agricultural University, Room No. 1014, Wushan Road, Guangzhou 510650, PR China. Tel./fax: þ86 20 8528 1812. E-mail address: daijun@pub.guangzhou.gd.cn (J. Dai). 0038-0717/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2009.01.021

(Tabatabai, 1994; Kandeler et al., 1996). Structural polysaccharides include cellulose, xylane, chitin and polyphenol, while starch is the fundamental storage polysaccharide in plants. Once being incorporated into soil, these polysaccharides are hydrolyzed to oligosaccharides by polysaccharidases, e.g. xylanase for xylane and hemicellulose, and amylase for starch. They are further degraded to monosaccharides by heterosidases, i.e. b-glucosidase for cellobiose, invertase for sucrose, N-acetyl-b-D-glucosaminidase for chitooligosaccharides. Low molecular-weight sugars are mineralized as energy sources by soil microbes. Organic N and P are simultaneously mineralized in the process of decomposition by other hydrolases, such as urease and phosphatase. Accordingly, the

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Y.-T. Li et al. / Soil Biology & Biochemistry 41 (2009) 969–977

activities of enzymes involved in soil organic C, N and P cycles are considered to be useful indices (Pankhurst, 1994; Miller and Dick, 1995), or representing modification of microbial communities, because this community composition determines the potential for soil enzyme syntheses (Dick et al., 1996; Badiane et al., 2001). In the recent decades, the mining industry produced a large amount of acid metal-rich wastes, which can pose serious risks to the soil environment (Sheoran and Sheoran, 2006). The nature and extent of this contamination is highly variable, depending on the composition of mine ore body, its associated geological strata, and climate (Johnson and Hallberg, 2005). Thus, it is important to accurately assess ecotoxicity of metals under strongly acidic soil conditions. A number of general and specific biochemical parameters were used as indicators to estimate soil degradation caused by heavy metal stress (He et al., 2003). However, the limitations of individual biochemical properties or simple indices have been criticized because such ecotoxicological studies remain few and somewhat contradictory (Giller et al., 1998; Vig et al., 2003). These disparities may result from contaminant chemistries, microbial community structures, or methodological differences (Gil-Sotres et al., 2005). Some studies have determined the total heavy metals concentrations, which is known to overestimate biological toxicity. However, soil quality depends on bioavailable heavy metals, since only these fractions can affect soil microbes (Hattori, 1992). Moreover, microbial species may differ in susceptibility, and particular enzymes may only catalyze single reactions in the soil. Organic C turnover bioassays are commonly used, but cannot be taken as indices of more complex functional changes such as total microbial activity or soil quality (TrasarCepeda et al., 2000; Gil-Sotres et al., 2005). Many studies focused on short-term laboratory additions or field incubations of heavy metals to obtain detailed ecotoxicological information in controlled soil conditions. However, the acute toxicity of metals cannot necessarily be extrapolated to represent chronic and gradual metal stress on microbes in contaminated soils (Renella et al., 2002). Gil-Sotres et al. (2005) pointed out that a promising solution to better reflect the complexity of soil system is to use complex indices expressed by a set of biochemical parameters, such as the cascade of enzyme activities approach proposed by Nannipieri et al. (2002), and to use statistical techniques, such as principal component analysis. We hypothesized that comprehensive set of microbiological indices and their relationships may allow us to elucidate explicitly the combined stress of metal and acidity, and explain the pattern of scattered responses of single parameters involved in specific soil processes. Therefore, this study was designed to characterize long-term and comprehensive effects of metal fractions on a set of microbiological and biochemical indices involved in organic C, N and P turnover in subtropical agricultural soils polluted by acid mine drainage. We aimed to discern the bio-toxicity of various metal species, including free and ligand-complexed cations in soil solution, and total metal contents in soils. Some microbiological ratios were employed to reduce bias from the variability in soil organic and microbial substrates.

2. Material and methods 2.1. Site descriptions and soil sampling The study sites are located in Yanghe valley of Dabaoshan Mountain in the Guangdong province of Southern China. A cluster opencast mines and smelters of copper pyrites, limonites, siderite and Pb/Zn mines distributed on top and on slopes of Dabaoshan Mountain have been active during the last 40 years. Downstream,

the Yanghe River has been polluted by large quantities of Cu, Pb, Cd, Zn mine wastes. Soil samples were taken from agricultural fields at two sites. Site A lies at Village Liang Qiao, 3 km from the mine cluster, and site B is located at Village Shang Ba, 6 km from the mine cluster. In each site, three paddy fields (labeled by P1, P2 and P3 for site A; P4, P5 and P6 for site B) were chosen on the basis of their distance to trunk canal (10 m, 100 m, 200 m). Near each paddy field, a non-flooded field was selected, which was grassland in site A (labeled by N1, N2 and N3) and sugarcane in site B (N4, N5 and N6). In each field plot, three soils were separately sampled at the irrigation input site, middle and output site, respectively. Three sediment samples were collected beside each rice field in site A (labeled by S). These 39 samples were collected from surface horizon (0–15 cm depth) and were air-dried at ambient temperature. Fractions of the soil samples were crushed and sieved to 0.15 mm for organic C, N, P and total metal analyses. Other fractions were ground to 2 mm for metal speciation and bioassay measurements. Soil physiochemical characteristics and metal fractions in soil and soil solution were determined as previously (Li et al., 2009). Soil pH averaged 4.0 and ranged from 3.0 to 6.0. Most of the soils were silt loam or loam. Soil CEC averaged 6.6 cmol kg1. Total soil heavy metals averaged 283 mg Cu kg1 (range 107–665 mg kg1), 393 mg Pb g1 (range 130–1107 mg kg1), 296 mg Zn kg1 (range 135–644 mg kg1), and 0.67 mg Cd kg1 (range 0.08–2.50 mg kg1). In addition, free ions and metal-ligand complexes of metals in soil solution averaged 0.84 and 0.41 mg Cu l1, 0.32 and 0.08 mg Pb l1, 2.30 and 1.42 mg Zn l1, 22.7 and 6.01 mg Cd l1, 11.9 and 7.5 mg Al l1, 9.10 and 5.22 mg Mn l1, respectively. 2.2. Soil organic C, N and P Soil organic carbon (Corg) was determined by dichromate digestion, total nitrogen (Ntot) by Kjeldahl digestion, and total phosphorus (Ptot) by colorimetric determination with ammonium molybdate after acid digestion. 2.3. Soil microbial biomass C, N and P Microbial biomass C (Cmic), N (Nmic) and P (Pmic) were measured by the chloroform fumigation extraction method (Vance et al., ¨ hlinger, 1996). Thirty grams (dry weight equivalent) of soil 1987; O for each sample was fumigated with ethanol-free chloroform for 24 h at 28 C after 7 days incubation at 60% of water holding capacity (WHC). Simultaneously, another subsample was incubated under the same conditions without fumigation. After complete removal of CHCl3, organic C and N from fumigated and non-fumigated soil samples were extracted with 0.5 M K2SO4 with a soil: extractant ratio of 1:5 (w/v), and inorganic P was extracted with 0.5 M NaHCO3 (adjust the pH to 8.5 with 1 M NaOH) with a soil: extractant ratio of 1:20 (w/v) for 30 min on a rotating shaker. The soil suspensions were filtered after shaking and centrifugation at 5100 g for 10 min. Extractable organic C in soil extracts was analyzed by the dichromate digestion (ISO, 1997). Extractable total N was converted into NO 3 -N by alkaline persulfate oxidation (Cabrera and Beare, 1993), followed by colorimetric analysis. Inorganic P was analyzed in neutralized aliquots of soil extracts by the ammonium molybdate–ascorbic acid method (Murphy and Riley, 1962). Cmic, Nmic and Pmic were calculated from: Cmic ¼ Ec/0.38 (Vance et al., 1987), and Nmic ¼ En/0.45 (Jenkinson, 1988), and Pmic ¼ Ep/0.4 ¨ hlinger, 1996), where Ec, En and Ep are the differences between (O extractable C, N and P from fumigated and non-fumigated samples.


2.4. Enzyme assay The criteria for selecting enzyme bioassays were their importance in nutrient cycling and the simplicity of the assays. Enzyme assays were made on soil samples adjusted to 60% WHC and then pre-incubated at 25 C for 7 days to reactivate the microflora. Incubations were in plastic containers with perforated plastic covers to restrict evaporation and permit gas exchange. Moisture levels were adjusted gravimetrically every 2 days with deionized water. The activities of acid (AcdP; EC 3.1.3.2) and alkaline (AlkP; EC 3.1.3.1) phosphatases (Margesin, 1997), b-glucosidase (Glu; EC 3.2.1.21) (Eivazi and Tabatabai, 1990) and N-acetyl-b-D-glucosaminidase (NAGase; EC 3.2.1.30) (Parham and Deng, 2000) were assayed on the basis of p-nitrophenol (pNP) release after cleavage of enzyme-specific synthetic substrates at natural soil average pH. The specific substrates (Sigma, St. Louis USA) were p-nitrophenol phosphate (10 M) for AcdP and AlkP, 4-nitrophenyl-b-D-glucopyanoside for Glu, p-nitrophenyl N-acetyl-b-D-glucosaminide for NAGase. For assay of phosphatases and NAGase, 100 mg soil sample (dry weight) was mixed with 400 ml Marc ILVAIN buffered solutions (pH 4), except for pH 9 for AlkP, and 100 ml of desired substrate (10 M). Matching soils were used as controls with 400 l buffer but no enzyme substrate. The soil was vortexed briefly and then incubated for 1 h at 37 C on an orbital shaker. Then, reactions were stopped by the addition of 100 ml of 0.5 M CaCl2, in order to prevent dissolution of humic substances. Same volume of substrates was added to controls as those to samples. The pNP released was extracted with 400 ml of 0.5 M NaOH. The suspensions were shaked briefly and were centrifuged at 15,000 rev min1 and 20 C for 5 min. Color intensity of supernatant pNP was immediately determined colorimetrically at 400 nm. b-glucosidase was measured as described as Badiane et al. (2001) and Mora et al. (2005). One hundred milligrams of soil was incubated at 37 C for 2 h, with 400 ml of a citrate phosphate buffer at pH 4 and 400 ml 10 M of substrates. After incubation, the samples were centrifuged at 14,000 g for 3 min and 200 ml of the supernatant were used to determine pNP released. Three milliliters of 2% Na2CO3 solution (w/v) was added to the supernatant to stop the reaction and the absorbance measured at 410 nm. Controls were used simultaneously for each sample, but substrate was added to controls after incubation. The enzymatic activity is expressed as released pNP per unit dry soil weight and incubation time. Xylanase (EC 3.2.1.8), amylase (EC 3.2.1.1) and invertase (EC 3.2.1.26) activities were assayed by determination of reducing sugars released according to the Somog–Nelson (molybdenum blue) method described by Deng and Tabatabai (1994) and Badiane et al. (2001). Soil (300 mg, dry weight) was mixed with 400 ml of citrate phosphate buffer at pH 4. Then, 400 ml of 2.5 M starch (Sigma) autoclaved at 100 C for 20 min was added as substrates for amylase assay or 400 ml of 10 M xylane (Sigma) was added as substrate for xylanase assay, or 400 ml of 8% sucrose as substrate for invertase assay. After incubation at 37 C for 2 h, the samples were centrifuged at 15,000 rev min1 and 4 C for 3 min. Then, supernatant (250 ml) was mixed with equal volumes of Somogyi reagent and distilled water in a 10-ml glass tube crammed with cotton fibers and then boiled for exactly 20 min. After cooling with current water, 250 ml Nilson reagent and certain volume of distilled water based on reducing sugar concentration were added to glass tubes, then they were agitated for 10–30 min. The absorbance was measured at 650 nm. Controls were performed simultaneously for each sample. Amylase, xylanase and invertase activities are expressed as glucose equivalents (GE) per unit dry soil weight and incubation time.

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Urease (EC 3.5.1.5) activity were measured by modified Berthelot reaction (Kandeler, 1996). Soil sample (100 mg, dry weight) was mixed with 400 ml Marc ILVAIN buffered solutions (pH 4) and 100 ml of 79.9 mM urea substrate. Simultaneous controls for each soil sample contained only 0.4 ml buffered solutions. The soils were vortexed briefly and then incubated for 2 h at 37 C on an orbital shaker. Controls were added with 100 ml substrate, and both samples and controls were added with 1.0-ml 2 M KCl solutions. After 30 min of shaking and 5 min of centrifugation, released ammoniums were extracted and determined based on the reaction of sodium salicylate with NH3 in the presence of sodium dichloroisocyanurate under alkaline conditions. The absorbance was measured at 660 nm. Urease activity was expressed as ammonium (NHþ 4 -N) released per unit soil weight and incubation time. Total microbial activity potential was measured through fluorescein diacetate (FDA) hydrolysis assay, which hydrolyze colorless FDA to release a colored end product fluorescein (Green et al., 2006). Soil samples and controls (300 mg air-dried) were preincubated in 30-ml glass tube. Samples were mixed with 10 ml Marc ILVAIN buffered solutions (pH 6.0) and 100 ml 2 M FDA (Sigma– Aldrich) substrate solution, while controls received only the buffer. The soils were swirled briefly and then incubated for 2 h at 37 C on an orbital shaker. Then, 100 ml FDA substrate solutions were added to controls, and 400 ml of acetones were added to all suspensions to terminate FDA hydrolysis. Soil suspensions (2 ml) were centrifuged at 15,000 rev min1 and 4 C for 3 min. Absorbance of the supernatants were measured on a spectrophotometer at 490 nm.

2.5. Soil mineralization of organic C, N and P Organic carbon mineralization (Cmin) was evaluated by measurement of soil CO2 respiration (Zibilsk, 1994). Fifty grams of each sample was moistened to 60% of WHC and pre-incubated at 28 C for 7 days, then was placed into 1-l Mason jars that contained a 50-ml vial containing 20 ml 0.2 M NaOH, and was capped tightly for 14 days incubations at 28 C. Controls were performed without soil samples. The vials containing NaOH were added with excess 1 M BaCl2 to precipitate CO2 3 and remaining NaOH was titrated with standardized 0.1 M HCl to the phenolphthalein endpoint. Cmin was expressed as mg C-CO2 released per kg soil weight and incubation time. Nitrogen mineralization was determined by measuring the production of mineral N (NHþ 4 and NO3 ) during incubation. Incubations were carried out with 50 g (dry weight equivalent) of soil moistened with ultra-pure distilled water to 60% of WHC in an oven at 28 C for 21 days. NHþ 4 and NO3 contents were measured by the Nessler and phenoldisulfonic methods, respectively. For the measurement of NHþ 4 , a 10 g soil sample (dry weight equivalent) was shaken with 50 ml of KCl (2.0 M) for 30 min. Filtration was performed after centrifugation for 10 min at 5100 g. The NHþ 4 was measured with a spectro-colorimeter after adding two drops of stabilizer–disperser and 0.4 ml of Nessler reagent per 10 ml of filtrate (Dai et al., 2004). For the measurement of NO 3 , a 10 g soil sample was shaken with 50 ml of CuSO4 (0.01 M) for 30 min. Then filtration was performed after addition of 0.2 g of Ca(OH)2 and MgCO3 powder to the suspension. Two milliliters of filtrate was evaporated at 80 C to dryness and then 2 ml of phenoldisulfonic acid, 20 ml of ultra-pure distilled water and 10 ml of concentrated NH4OH, were added (Bremner, 1965). The color produced by phenoldisulfonic acid was measured with a spectro-colorimeter. The net ammonification and nitrification rate was calculated as the difference of N-NHþ 4 and N-NO3 contents before and after incubation. The mineralization rate of total organic N was estimated by sum of ammonification and nitrification rate.

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Phosphorus mineralization was determined by similar incubation procedure as nitrogen mineralization. Mineralized inorganic P was extracted with 0.5 M NaHCO3 and was analyzed by the ammonium molybdate–ascorbic acid method described in biomass P measurement. 2.6. PCA, co-inertia analyses and stepwise regression analyses The variables were grouped into five classes: (i) 16 metal variables including total contents, free and ligand-complexed ions of Cu, Pb, Zn, Cd; free and complexed Al and Mn; (ii) soil organic matter variables including total organic C, N and P; and (iii) microbial biomass variables quantifying biomass C, N and P, and their ratios to corresponding unit soil organic C, N or P; (iv) enzyme variables including FDA, b-glucosidase, urease, NAGase, xylanase, AlkP, AcdP, invertase, amylase and (v) mineralization variables including mineralization of C, N (NHþ 4 -N and NO3 -N) and P, and their ratios to corresponding soil organic compounds and microbial biomass, respectively. For each group of variables a PCA and a discriminant analysis were run. The PCA reduced the data and constructed linear combinations (principal components) of the variables that explain a large part of the total original variability. The discriminant analysis compares the mathematical distances among the soil samples. This analysis uses a test of permutations that calculates the total interclass inertia for each distribution of the individuals within the groups, thereby optimizing the power to discriminate among them. Afterwards, co-inertia analyses (CIA) (Velasquez et al., 2005) were done to test the PCA correlations between two datasets: (i) metal vs biomass variable sets; (ii) metal vs enzyme variable sets; (iii) metal vs mineralization variable sets; (iv) metal vs general microbial indices. CIA is a multivariate method that identifies trends or co-relationships for multiple datasets, especially between a fauna or flora and its environmental factors (Culhane et al., 2003). Only the ratios based on unit organic and microbial biomass C, N and P were involved in the CIA in order to exclude the disturbance of soil organic and microbial substrates. In order to differentiate metal effect from acid stress, stepwise regression was used to remove the noisy and indirect variables, determine the set of environmental variables that most closely influence the microbial variable, and include them in the multiple regression equation, accomplished by the repetition of a variable picking algorithm (Burkholder and Lieber, 1996).

Mineralization rate of soil organic C, N and P (Table 1) markedly varied, ranging from 18.6 to 33.1 mg CO2-C kg1 d1, 0.26 to 1 1 1 1 d , 0.01 to 0.20 mg NO d , and 0.45 1.75 mg NHþ 4 -N kg 3 -N kg to 6.36 mg P kg1 d1. Soil nitrification (mineralized NO 3 -N) was notably low with average of 0.71 mg kg1 d1, compared to with mean of ammonification (mineralized NHþ 4 -N) 5.21 mg kg1 d1. Mineralized soil organic C, N, P consumed 0.18% of Corg, 0.15% of Ntot and 1.17% of Ptot per day, on average. Average ratios of mineralized soil organic C, N and P per day to corresponding microbial biomass C, N and P were 0.20, 0.06 and 0.39. Mineralized C had significantly positive relationships with Corg, Ntot, Cmic, Nmic and mineralized P, whereas mineralized N and P was not closely related to organic and microbial substrates in most cases. 3.2. Soil enzyme activity Soil hydrolase activity was low according to the nine enzyme bioassays in soil natural pH condition (Table 2). The mean of FDA expressed as total enzyme activity was 0.9 mg fluorescein g1 soil h1. Average values of polysaccharidase activity were 11.8 mg GE g1 soil h1 for amylase and 3.9 mg GE g1 soil h1 for xylanase. Means of the heterosidase activity, b-glucosidase and invertase, were 47.8 mg pNP g1 soil h1 and 0.4 mg GE soil g1 h1, respectively. NAGase and urease activity in relation to organic N metabolism were 20.5 mg pNP g1 soil h1 and 2.9 mg NHþ 4N g1 soil h1. Acid and alkaline phosphatases averaged 54.7 and 20.0 mg pNP g1 soil h1. 3.3. Co-inertia analysis between soil metal and microbial biomass datasets The co-inertia analysis (Fig. 1) showed that soil metal dataset (16 variables) had a highly significant correlation (P < 0.001) with the microbial biomass dataset (6 variables). The first two PCA factors explained 94.3% of total data variability. Free Al3þ and Mn2þ were strongly negatively correlated to Cmic, Cmic/Corg, Pmic and Pmic/Ptot, while dissolved ligand complexes of Al and Mn were negatively related to Cmic, Cmic/Corg, Nmic and Nmic/Ntot (P < 0.01 in most cases). Free and complexed ions and total contents of Cu and Pb showed negative relationships (P < 0.05 for all cases) with only Pmic and Pmic/Ptot, with exception of additional effect of Cu2þ on biomass C indices. In contrast, various fractions of Cd and Zn did not showed negative relationship with biomass indices, except for total Cd.

3. Results 3.4. Co-inertia analysis between soil metal and enzyme datasets 3.1. Fractions of C, N and P in soil organic matter, microbial biomass and mineralization The means of organic carbon (Table 1) were 14.7 g kg1, total N 1.27 g kg1 and total P 0.56 g kg1. The average soil C/N ratio was 11.6, and C/P ratio was 27.3. Microbial biomass C (Table 1) in the soils had a mean of 140.5 mg kg1 and ranged from 73.2 to 264.6 mg kg1, accounting for 0.98% of total Corg (Microbial quotient) on an average. Microbial biomass N (Table 1) varied from 9.9 to 95.3 mg kg1 with average 43.0 mg kg1, accounting for average 3.29% of Ntot in the soils. Microbial biomass P (Table 1) had a mean of 32.2 mg kg1 and ranged from 4.4 to 65.3 mg kg1, which contributed to 5.7% of Ptot in the soils. Microbial biomass C/N ratios varied from 1.5 to 14.1 with mean of 4.4, while biomass C/P ratios were from 1.9 to 17.72 with average of 6.29. Cmic had significant positive correlations with both Nmic and Pmic (P < 0.01). Microbial biomass C, N and P were significantly and positively related to soil organic C (P < 0.01), N (P < 0.05) and P (P < 0.01), respectively.

A highly significant (P < 0.001) correlation was observed between the soil enzyme dataset (9 variables) and metal dataset (16 variables) according to co-inertia analysis (Fig. 2). The first two factors explained 90.0% of the total variability. NAGase and urease (involved in soil organic N cycles) as well as amylase dominated in factor 2. They exhibited highly positive correlation with metal contents, especially with labile fractions (free and complexed ions) of Cd and Zn (P < 0.05). These enzymes had no significant correlation with Al and Mn. In contrast, FDA, invertase, AcdP and b-glucosidase had important contribution to positive direction of factor 1, which had negative relationships (P < 0.05) with total and free Cu ions, free and complexed Mn ions, and total Pb and total Cd, but had no significant correlation with Zn fractions. In comparison, xylanase and AlkP were not significantly associated with any metal variables. Enzyme indices expressed per unit organic matter or microbial biomass showed similar correlations with environmental variables as pure enzyme activities expressed per unit soil weight.


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Table 1 Fractions of C, N and P involved in organic matters, microbial biomass and mineralization (Mean SD, n ¼ 3). Fields

C

N

P

Ntot (g kg1)

Nmic (mg kg1)

NHþ 4 -Nmin (mg kg1 d1)

NO 3 -Nmin (mg kg1 d1)

Total-Nmin (mg kg1 d1)

Ptot (g kg1)

Sediment samples beside irrigation channel in site A S 10.22 0.49 87.4 9.4 19.6 0.7

1.33 0.09

22.6 4.3

0.92 0.02

0.07 0.02

0.99 0.03

0.41 0.06

9.8 2.9

0.58 0.15

Paddy soils in site A P1 15.48 0.66 P2 16.45 1.54 P3 11.50 1.88

Corg (g kg1)

Cmic (mg kg1)

Cmin (mg kg1 d1)

Pmic (mg kg1)

Pmin (mg kg1 d1)

120.8 15.7 198.6 31.9 100.6 4.8

25.7 0.4 25.0 0.3 25.1 1.3

1.15 0.04 1.44 0.07 1.46 0.09

71.5 9.6 81.9 12.0 39.3 4.8

2.71 1.59 1.46 0.88 1.80 0.69

0.23 0.27 0.11 0.01 0.44 0.36

2.94 1.78 1.56 0.89 2.24 0.70

0.35 0.06 0.56 0.04 0.72 0.04

13.8 4.4 35.0 5.6 48.1 1.9

3.99 2.42 3.42 2.99 6.40 2.25

Grassland soil in site A N1 12.77 0.93 133.6 50.3 N2 11.57 1.65 120.8 15.7 N3 15.70 1.26 84.8 17.2

20.8 1.5 19.1 0.6 20.1 0.5

0.93 0.08 1.02 0.10 1.30 0.19

24.2 1.1 14.6 1.5 35.5 4.1

1.96 1.51 1.22 0.33 0.80 0.33

0.11 0.08 0.14 0.07 0.41 0.19

2.06 1.58 1.36 0.26 1.21 0.20

0.62 0.03 0.72 0.04 0.52 0.10

28.1 22.2 36.9 0.4 6.2 1.8

3.73 0.52 17.67 0.22 17.24 3.26

Paddy soils in site B P4 14.95 1.41 P5 17.32 1.21 P6 15.56 0.81

152.2 22.1 127.8 17.2 162.6 10.1

33.0 0.1 33.0 0.1 32.8 0.1

1.45 0.02 1.36 0.02 1.47 0.02

30.3 9.2 15.8 5.7 20.0 1.1

4.62 0.61 1.75 0.72 0.78 0.07

0.11 0.03 0.06 0.04 0.17 0.09

4.73 0.62 1.81 0.71 0.95 0.12

0.57 0.17 0.66 0.05 0.45 0.19

21.5 1.1 55.5 22.3 40.5 24.8

2.85 0.26 4.62 0.78 5.31 0.70

Sugarcane soils in site B N4 16.64 1.48 143.1 17.6 N5 16.99 1.60 236.0 25.0 N6 15.58 0.48 158.6 17.7

30.5 0.2 30.8 0.3 30.9 0.2

1.71 0.14 1.42 0.08 1.83 0.04

69.9 2.5 77.5 10.6 56.2 10.5

2.55 0.45 1.10 0.84 1.02 0.42

0.38 0.11 0.09 0.04 0.24 0.14

2.92 0.41 1.19 0.80 1.27 0.55

0.58 0.03 0.74 0.04 0.45 0.11

42.7 12.4 43.3 20.5 47.4 9.9

7.11 0.43 3.45 1.29 6.35 2.09

Corg, Ntot, Ptot: total organic C, N and P; Cmic, Nmic, Pmic: microbial C, N and P; Cmin, Total-Nmin, NHþ 4 -Nmin, NO3 -Nmin, Pmin: organic C mineralization rate, total N mineralization rate, mineralized NHþ 4 -N, mineralized NO3 -N, organic P mineralization rate.

3.5. Co-inertia analysis between soil mineralization and metal datasets

3.6. Co-inertia analysis between total biological indices and metal datasets

The co-inertia analysis (Fig. 3) showed that soil mineralization dataset (15 variables) were significantly correlated (P < 0.001) to the metal dataset (16 variables). The first two PCA factors explained 84.9% of total data variability. The first factor (66.7%) was dominated by N and P mineralization indices and labile Cd and Zn fractions. High values of total organic N mineralization, mineralized NHþ 4 -N, and their ratios to organic N and microbial biomass N, were positively correlated to free and complexed Cd and Zn (P < 0.01, except for complexed Zn), and to complexed Cu and Pb (P < 0.05). In comparison, NO 3 -N mineralization indices were negatively related to metal variables, but the correlations were not statistically significant. Regarding P mineralization indices, they were negatively correlated with labile fractions of Cd and Zn. The second factor of the PCA was dominated mostly by organic C mineralization indices. Metabolic quotient (Cmin/Cmic) was highly and positively associated with free ions of Cd, Al and Mn, whereas Cmin and Cmin/Corg had contradictory (positive, negative and no significant) relationship with different metal variables.

Soil metal dataset was significantly correlated (P < 0.001, Fig. 4) to the general microbiological dataset (22 variables) including biomass, mineralization and enzyme variables. All biological variables involved in co-inertia analysis were expressed per unit of corresponding soil organic and microbial substrates. The first two principal components explained 80.2% of data variances. All metal variables were on the negative side of F1 axes (60.3%), and were obviously distinguished from most biological indices in the opposite direction, except for some of N-related indices. These variables were clearly classified into two types. In correlation circle of coinertia analysis (Fig. 4a), the variables in relation to N turnover distributed in Zone B and opposite Zone D (Fig. 4a), while variables involved in organic C circle were exhibited in Zone A and opposite C. In the Zone B, Zn and Cd fractions were significantly and positively associated with indices of ammonification and total N mineralization, and N-related enzymes (NAGase and urease), whereas these heavy metals were negatively related to nitrification indices located in the opposite Zone C. In the Zone A, Cu, Pb, Al and

Table 2 Soil enzymes involved in soil C, N and P turnover in acid mine drainage polluted soils (Mean SD, n ¼ 3). C related N related P related Fields FDA mg fluorescein b-Glucosidase Amylase Xylanase Invertase Urease NAGase Acid phosphatases Alkaline phosphatases 1 1 (g h ) 1 1 (mg pNP g1 h1) (mg GE g1 h1) (mg GE g1 h1) (mg GE g1 h1) (mg NHþ h ) (mg pNP g1 h1) (mg pNP g1 h1) (mg pNP g1 h1) 4 -N g S P1 P2 P3 N1 N2 N3 P4 P5 P6 N4 N5 N6

0.42 0.23 0.72 0.13 0.83 0.13 0.73 0.07 0.55 0.06 0.86 0.45 0.68 0.11 1.14 0.37 1.15 0.12 1.30 0.23 1.36 0.23 1.34 0.09 0.99 0.39

61.8 18.1 33.9 30.4 47.3 4.7 47.2 31.9 18.5 11.5 30.6 11.9 43.2 27.9 45.5 20.1 39.3 3.9 46.7 14.5 50.4 28.5 85.5 28.7 71.6 4.7

2.0 0.8 10.9 9.5 11.0 9.2 26.7 7.8 11.8 9.2 1.0 0.7 5.1 7.0 19.9 19.6 29.1 21.7 16.0 18.2 5.4 6.7 13.9 21.4 1.1 0.7

pNP: p-nitrophenol; GE: glucose equivalents.

5.37 6.57 2.47 1.18 3.07 1.58 4.40 5.68 1.63 1.26 2.96 1.81 2.61 1.14 3.45 0.62 6.77 8.36 4.36 0.45 3.86 2.83 7.44 4.85 2.60 2.09

0.18 0.03 0.17 0.02 0.15 0.01 0.24 0.01 0.34 0.33 0.18 0.04 0.35 0.10 0.19 0.01 0.26 0.03 0.51 0.01 0.63 0.26 1.17 0.26 0.79 0.09

0.89 0.79 3.67 1.73 1.69 1.12 2.78 2.88 1.48 1.13 0.84 0.74 1.51 0.47 4.23 3.18 4.86 4.91 6.72 3.18 4.75 2.79 1.30 0.79 3.15 4.69

11.1 4.8 23.8 12.5 21.7 12.8 32.4 1.5 28.8 15.5 26.1 8.2 2.4 0.0 49.5 39.4 16.0 20.8 10.7 7.8 8.8 2.9 12.7 0.0 23.1 18.3

46.1 20.5 26.9 14.3 56.2 9.3 43.0 12.2 17.9 9.4 43.6 14.9 28.1 6.0 59.5 7.7 65.6 4.7 88.8 9.7 43.1 34.0 107.6 16.4 85.3 2.2

42.2 28.0 9.5 6.7 21.8 10.0 26.9 3.1 6.0 4.0 25.2 15.2 10.8 7.2 23.4 6.3 11.1 5.9 26.0 13.9 21.3 12.9 7.7 5.3 27.6 24.0

974


Co-inertia=8.5 RV coefficient: 0.28 P