Fungicides Affects Soil Microbial Communities Biology Essay


The aims of this study were to investigate the effects of long-term application of Cu-based fungicides on soil microbial communities using community level physiological profiles (CLPPs) and profiling of phospholipid fatty acids (PLFAs). A principal component analysis (PCA) of CLPPs gave a first principal component (PC1) which was strongly correlated to soil pH (r = 0.85, p < 0.05), indicating that the microbial community metabolic functions in these orchard soils were to a large extent determined by soil pH. Stepwise regression analysis between PC1 and PC2 scores for PLFA data and soil properties showed that the change of PC2 was mainly due to the available soil K (r = 0.84, p < 0.05), which means that soil fertilization could reduce the negative effects of Cu in the soils. Moreover, the ratio of Gram-/Gram+, individual fatty acids i15:0(2OH) and 11Me18:1?7c can be suggested to be the most sensitive indicators for Cu pollution in the orchards.

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Key words: Apple orchard soil; Copper fungicides; Community level physiological profiles; Profiling of phospholipid fatty acids


Pollution of agricultural soils by heavy metals from industrial and agricultural activities is a major environmental problem. Heavy metal pollution cannot only result in adverse effects on various parameters relating to plant quality and yield, but also could cause changes in the size, composition and activity of the soil microbial communities (Giller et al., 1998). The most commonly used indicators for microbial community status include gross microbial indices such as microbial biomass, respiration rate, metabolic quotient, and enzyme activities. However, these indices give only limited information on the structure of soil microbial community (Nikli?ska et al., 2005). When considering soil quality, an important question must be addressed concerning the relationship between the structure and function of microbial communities which are affected by heavy metals (Suhadolc et al., 2004). Studies have already shown that metal contamination could cause a shift within the soil microbial communities from sensitive to less sensitive microorganisms (Maliszewska et al., 1985; Van Beelen and Doelman, 1997). Therefore, the methods mentioned above may do not reveal more subtle changes in microbial community structure, and more additional analytical techniques giving insight into the structure of soil microbial communities may be required for a comprehensive environmental risk assessment (Abaye et al., 2005; Nikli?ska et al., 2005).

Phospholipid fatty acid (PLFA) analysis and the community-level physiological profile (CLPP) method are commonly used to indicate community structure (Pennanen et al., 1996; B''th et al., 2005; Schmitt et al., 2005, 2006). Each of these approaches offers a focus on specific aspects of soil microbiological characteristics and represents an independent analysis of differences or changes in soil microbial community structure and functions (Widmer et al., 2001). Phospholipid fatty acids are major constitutes of the membranes of all living cells, and different groups of microorganisms could be characterized by the changes of specific PLFA profiles in soil microbial composition (B''th and Anderson, 2003; Puglisi et al., 2005). Biolog analysis is based on the premise that microorganisms vary in the pattern and the rate at which they utilize carbon sources. Therefore, carbon-based substrate utilization patterns have also been widely utilized for describing community-level physiological profiles using Biolog plates (e.g., Garland and Mills, 1991; Larkin, 2003), and they could provide valuable information on the functional aspects of the targeted communities (Pennanen, 2001; Shishido et al., 2008).

Copper-based fungicides have been recognized for more than two centuries (Pietrzak and McPhail, 2004), and foliar application of these fungicides has led to a significant input of Cu to soil, through direct application, drift, or dripping of excess sprays from leaf surfaces (Chaignon et al., 2003). As fungicides are applied to control fungal diseases, they will also affect soil beneficial fungi and other soil organisms. Therefore, in many regions of the world there are increasing concerns that Cu may reach the concentrations in orchard soils toxic to soil organisms, microbial activities, enzyme activities, or even phytotoxicity (Brun et al., 2001; Li et al., 2005; Viti et al., 2008; Wang et al., 2009). However, limited studies have been done to study the changes of soil microbial community due to the long-term application of Cu-based fungicides. In addition, quantifying the long-term responses of microbial populations to Cu-based fungicides is important because of relatively large-scale of orchards in many countries.

The objective of this study was to evaluate the changes of soil microbial community using a set of complementary techniques that included PLFA profiling to evaluate changes in structural composition and an analysis of CLPPs in Biolog EcoPlates to evaluate physiological changes, due to the long-term application of Cu fungicides in orchards. It is perceived that such assessment is likely to yield a deep understanding of the effects of Cu-based fungicides on the structure and functioning of soil microbial communities and in turn on ecosystem processes.

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Study sites and experimental design

The sampling sites (35'40.539? to 35'40.864? N, 118'55.524? to 118'55.951?E) were conducted in Rizhao City, which locates in the southeast of Shandong Province, China. The annual average temperature and precipitation of this region are 12.7'C and 1146.7 mm, respectively. The soil type was brown soils (Udic Luvisols), and the clay content (< 2 ?m) of the area studied varied from 128 to 131 g/kg. Because the orchards studied are collective properties belonging to a village, so they were received the same amounts of copper-based fungicides (16 kg/hare copper applications per year, and the ratio of CuSO4 : CaO : H2O is 1 : 2: 250, m/m/m) and fertilizer (poultry and swine manure, about 100 kg per tree per year). The Bordeaux mixture was applied right from the beginning of the apple trees being planted for 4-5 times per year. An adjacent forest which had not received any artificial input of Cu (native soil) about 1 km away from the orchards was chosen to collect a reference soil.

The soil samples were collected from six spots in each orchard (each spot was a composite of six sub-samples). The soil sub-samples were collected at a distance of 1 m from tree trunk at a depth of 0-20 cm (after removing the litter layer). Part of each soil sample was air-dried at ambient temperature, crushed, and sieved to pass through a 2 mm sieve to analyze soil pH and other physicochemical properties. The remaining soil was kept moist in the dark at 4'C to determine PLFAs and CLPPs.

Determination of soil physicochemical properties

The soil samples were ground to pass through a 100-mesh screen (? 0.149 mm), and then digested with HF'HNO3'HClO4 for determination of total soil metal (Cu, Cd, Cr, Mn, Ni, Pb, and Zn) concentrations by Inductively Coupled Plasma (ICP). Soil pH was measured by a pH meter with a ratio of 1:2.5 soil to water (m/m). Soil water holding capacity was determined according to Hillel (1998). Soil total organic carbon (Corg) was determined by oxidizing a soil solution with K2Cr2O7 and concentrated H2SO4 at 170'185'C, and then titrating the solution with FeSO4. Available soil N, P, and K concentrations were determined according to Nelson and Bremner (1972), Bray and Kurtz (1945), and Jones (1973), respectively. Soil solution free Cu ions were determined using a Cu ion-selective electrode (Cu-ISE: Orion-94-29 vs Thermo Electron Ag/AgCl Orion 90-02, Thermo Electron Corp.) after shaking 10 g of soil with 20 ml of 0.01 mol/L KNO3 solution in an end'over'end shaker for 16 hr, centrifuging the solution for 15 min at 5000 ' g, and filtering it through 45-'m cellulosic membranes.

BIOLOG community-level physiological profiling

The community-level physiological profile (CLPP) was determined using 31 different simple organic substrates contained in 96-well microtiter plates (Biolog ECO plates). Briefly, 10 g of fresh soil was suspended in 100 ml sterile saline solution (0.85%, m/v) with 5 g of 3 mm glass beads on a rotary shaker at 300 r/min for 10 min at 25'C. Suspensions were allowed to settle for 10 min before diluting 100-fold. Ten milliliter aliquots of the dilutions were added to 90 ml sterile saline solution (0.85%, m/v) containing different concentrations of the contaminants. After mixing, 150 ml of the suspension was added to each well of a Biolog ECO plate. Absorbance of the wells at 590 nm was read using a Biolog automated BIOLOG Microplate Reader (Biolog, Hayward, CA, USA) and the data were collected by Microlog 4.01 software (Biolog, Hayward, CA, USA). The plates were then sealed inside a plastic bag and incubated at 25'C in the dark, and read every 12 hr over 7 days, during which no contamination of control wells (only water) was found. The readings at 96 hr incubation were used for subsequent analysis. Average well color development (AWCD), which was calculated as the average optical density across all wells per plate, was used as an indicator of general microbial activity.

Phospholipid fatty acid analysis

The composition of PLFAs was analysed in different soil samples to determine the phenotypic microbial diversity. PLFAs were extracted from freeze-dried soil with chloroform/methanol/citrate buffer (0.15 mol/L, pH 4.0) 1:2:0.8 (v/v/v). Pooled supernatants (two repeated extractions) were split into two phases by the addition of chloroform and the above extracting buffer. The lipid-containing phase was transferred to burned glass tubes, dried under N2, dissolved in 600 ml of chloroform, and transferred to a silica gel cartridge (500 mg, 3 ml; Supelco, Bellofonte, PA, USA). Following the elution of neutral lipids and glycolipids with 10 ml chloroform and 10 ml acetone, respectively, phospholipids were eluted with 8 ml methanol and dried under N2. Methyl myristate fatty acid (19:0) was added as internal standard, and PLFAs were subsequently derivatized by mild-alkali methanolysis. The resulting fatty acid methyl esters were then separated and identified by Agilent 6890N gas chromatography (Agilent, Wilmington, DE, USA) fitted with a MIDI Sherlock' microbial identification system (Version 4.5, MIDI, Newark, NJ, USA). Individual fatty acids were designated in terms of total number of carbon atoms: number of double bonds, followed by the position (?) of the double bond from the methyl end of the molecule. The prefixes ''a'' and ''i'' indicate anteiso- and iso-branching, respectively, and ''10Me'' describes a methyl group on the tenth carbon atom from the carboxyl end of the molecule, and ''cy'' represents a cyclopropane fatty acid. Gram-positive bacteria were identified by the PLFAs: i15:0, a15:0, i16:0, i17:0, a17:0 and 10Me17:0 and Gram-negative bacteria were represented by the PLFAs: 16:1?7c, 16:1?5c, cy17:0, 18:1?7c and cy19:0 (Frosteg'rd et al., 1993a, b). The quantity of the fatty acid 18:2?6,9c was used as an indicator of fungal biomass since it is suggested to be mainly of fungal origin in soil (Olsson, 1999). Ratios of Gram-/Gram+ PLFA and fungi/bacteria PLFA were used as indicators for changes in the relative abundance of these microbial groups.

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Statistical analysis

All statistical analyses were carried out with the program SPSS 11.5 for Windows. Six replicate samples were run for the five orchard soils and the reference soil. Simple correlation procedures were also used to study the relationship between each individual PLFA and the soil physicochemical properties. The average well color development (AWCD) value of Biolog data was calculated for each sample at each time point by dividing the sum of the optical density data by 31 (number of substrates) to avoid bias between samples with different numbers of culturable organisms, and then the data were analyzed by principal component analysis (PCA). PLFAs were also analyzed initially by PCA to reduce the dimensionality. Based on cumulative percentage of the variability, the first two principal components (PC1s and PC2s) were subjected to a stepwise regression analysis to determine the relationship between soil physicochemical parameters and PLFA or CLPP profiles. Statistical significance is accepted when the probability of the result assuming the null hypothesis (p) is less than 0.05.


Soil physical and chemical properties

Soil physicochemical properties were shown in Table 1. No difference was found between the orchard soils and the control soil for soil Cd, Cr, Mn, Ni, Pb, and Zn except soil Cu. The soil pH ranged from 4.28 to 5.17 was generally acidic for both the fungicide treated and the reference soils. Soil Corg varied from 5.29 to 6.22 g/kg, and the soil water holding capacity ranged from 25.2 to 31.6%. The mean available soil N, P, and K varied considerably among soil samples in the orchards with values ranging from 69.1 to 260 mg/kg for available soil N (with 13.1 mg/kg for reference soil), 158 to 424 mg/kg for available soil P (with 38.8 mg/kg for reference soil), and 202 to 449 mg/kg for available soil K (with 49.0 mg/kg for reference soil), respectively. The total Cu concentration in the reference soil was low (12.5 mg/kg), while the average concentration of soil Cu differed markedly with the orchard ages, being 21.8 (5 years), 17.8 (15 years), 41.9 (20 years), 103 (30 years), and 141 mg/kg (45 years), respectively. The mean soil solution free Cu2+ concentrations varied in the range of 3.13 ' 10-8 and 4.08 ' 10-6 mol/L, being the lowest in the reference soil and highest in the orchard aged 45 years. The detail information about the soil physicochemical characteristics could be found in Wang et al. (2009).

Table 1

Mean soil physicochemical properties and indicators of PLFAs for apple orchard and reference soils (n = 6).


Substrate utilization patterns of microbial population

The soil community-level physiological profiles of the bacterial communities were determined by examining the microbial utilization of 31 different carbon sources. The average utilization (AWCD) of the C sources for the soil samples using the Biolog plates generally followed the same pattern with increasing incubation time (Fig. 1). Of the soil samples, the AWCD did not vary in a consistent manner with the soil Cu concentrations. The sample from the orchard aged 5 years had the highest AWCD values for all C sources while the soil sample from orchard aged 45 years showed the lowest activities (Fig. 1). The AWCD standardized data after 96 hr incubation in the Ecoplates were used for the principal components analysis (Fig. 2). The first principal component (PC1) of the Biolog data accounted for 33.8% of the variance, and the second (PC2) accounted for 25.8% of the variation, while the remaining PCs accounted for only a few percent of the variance and were excluded from further analyses. No significant relationship was found between the principal component scores and total soil Cu or free Cu2+ concentration. The scores of PC1 appeared to increase with the increase of soil pH (r = 0.85, p < 0.05), while the separation of CLPP profiles on the PC2 axis did not correlate with the physicochemical properties measured. Correlation analysis of the loadings of the most influential C sources on the first component indicated that the separation of sites along the PC1 axis could be explained by a number of substrates utilized, especially the carbohydrates (D-Xylose, ?-D-Lactose), carboxylic acids (?-Hydroxybutyric Acid), and miscellaneous (Glucose-1-Phosphate).

Fig. 1

Fig. 1 Average well color development (AWCD) over time of all and different groups of carbon sources in the apple orchard and reference soils.

Fig. 2

Fig. 2 Score plots of the two first principal components (PCs) in a principal component analysis of CLPP data after 96 h incubation in Biolog Ecoplates for the apple orchard and reference soils. S1, reference soil; S2 to S6 represented the soil sampled from orchard aged 5 years, 15 years, 20 years, 30 years, and 45 years, respectively.

Phospholipid fatty acid profile

Shifts in the microbial community structures following the Cu-based fungicide treatment were demonstrated by the PLFA analyses. The average total PLFA, Gram-positive bacteria, Gram-negative bacteria, and total fungal PLFA contents for the sampling sites were shown in Table 1. The average total PLFA content in the reference soil was 23.7 nmol/g dry soil, while the values for the orchard soils were 19.3 (5 years), 27.3 (15 years), 26.3 (20 years), 39.6 (30 years), and 23.7 (45 years) nmol/g dry soil, respectively. The ratios of fungi/bacteria PLFA and Gram-/Gram+ PLFA varied from 0.04 to 0.11 and 0.51 to 0.92, respectively (Table 1). No significant relationship was found between the total PLFA, Gram-positive bacteria, Gram-negative bacteria, total fungal PLFA contents, or ratio of fungi/bacteria PLFA and the soil physicochemical properties. However, the ratio of Gram-/Gram+ was significantly related to soil total Cu concentration (r = 0.88, p < 0.05), soil solution free Cu2+ (r = 0.84, p < 0.05), available soil N (r = 0.97, p < 0.05), and available soil K (r = 0.94, p < 0.05), respectively. The data of the 24 most common PLFAs were subjected to a principal component analysis (Fig. 3). The first principal component (PC1) explained about 61.2% of the variation in the data set, while the second, PC2, explained 24.0%. Together PC1 and PC2 explained 85.2% of the total variance in the first two dimensions of the plot, suggesting that they could be used as an integrated index of the PLFAs to determine the relationship among the soils. Stepwise regression analysis between PC1 and PC2 scores for PLFA data and soil properties showed that none of metal variables or the other soil physicochemical properties measured was included in the regression equation as independent variables for PC1, while the change of PC2 was mainly due to the available soil K (r = 0.84, p < 0.05). The individual PLFAs mostly responsible for the changes along PC2 were i15:0, i16:0, i16:0(H), 10Me16:0, 10Me17:0, and 11Me18:1?7c (Fig. 4). Simple correlations were done between fatty acid concentrations and soil basic physicochemical characteristics. It is interesting to note that two of the fatty acids (i15:0(2OH) and 11Me18:1?7c) were significantly related to the soil free Cu2+ (r = 0.85 and -0.86, p < 0.05, respectively).

Fig. 3

Fig. 3 Score plots of the two first PCs in a principal component analysis of the PLFA pattern for the apple orchard and reference soils (standard error were not shown). S1, reference soil; S2 to S6 represented the soil sampled from orchard aged 5 years, 15 years, 20 years, 30 years, and 45 years, respectively.

Fig. 4

Fig. 4 Score plot of principal component analysis showing the separation of the study plots along PC1 and PC2 using PLFA data and the corresponding loading values for the PLFAs.


Studies about soil microorganism in orchards are mainly focusing on the soil microbial biomass carbon, C mineralization, and specific respiration rate (Wang et al., 2009). However, such measurements only consider the microbial community as a whole, and questions remain as to how metal contamination affects specific populations within soil microbial communities (Kelly et al., 2003). Multivariate analysis of responses to carbon substrates or phospholipid fatty acid profiles are frequently used to distinguish the microbial community structure between different sites or treatments (Pennanen et al., 1996; Pennanen, 2001), and these two methods were used to assess the microbial community characteristics in soil samples obtained from apple orchards differing in age, and from a neighbouring forest.

The community-level physiological profiles have been used to provide insights into the effects of disturbance on soil microbial communities (Kong et al., 2006), and the AWCD calculated from the average well color development is an indicator of the overall metabolic activity of the microbial community (Bossio et al., 2005). The AWCD did not vary in a consistent manner of soil Cu concentration, and no significant relationship was found between them. The PCA analysis of CLPPs also indicated that the metabolic functions of microbial communities at the sampling sites have not been affected by the Cu inputs. This was supported by other authors, who have already found that the AWCD values from different sites did not correlate with heavy metal contents, and this was possibly due to the changes of soil pH, organic matter content, or adaptation of soil microorganisms to the high levels of soil heavy metals (B''th et al., 1998a; Pennanen, 2001; Yao et al., 2003; Schipper and Lee, 2004; Zhang et al., 2007a). In this study, we also found that the systematic change in substrate utilization pattern was associated with different pH values, and a number of substrates utilized were also influenced by soil pH. In fact, Pennanen (2001) has reported that Biolog was not able to detect any change in physiological profiles of forest floor microbial communities polluted with heavy metals, and soil pH was the strongest factor linked to soil catabolic function. Some other authors have also found that the most important effect on CLPPs was from pH (Pennanen et al., 1998; Nikli?ska et al., 2005; Wakelin et al., 2008), and this method may be useful for studying the effects of soil acidification or liming on soil microbial communities (Nikli?ska et al., 2005). In fact, the Biolog method suffers from several disadvantages. It relies on the growth (and/or substrate utilization) of the bacterial community, thus only a small percentage of the total community is assessed. Due to high concentrations of C sources in Biolog plates, some bacterial species which can use these C sources, will grow and reproduce quickly, while some other bacteria give very few reactions on the plates, and the number of inoculating microorganisms may not be the major factor on AWCD if the samples have a similar community structure (Smalla et al., 1998; Preston-Mafham et al., 2002). Another potential drawback of the Biolog method includes the fact that the C sources tested are not necessarily those found in soil (Campbell et al., 1997). Many organisms present in soil will not grow in the wells and, conversely, organisms growing in the wells may not have been active in the soil. Therefore, CLPPs probably lose sensitivity due to a bias toward under representing metabolic diversity, and the CLPPs method may do not necessarily reflect the original microbial community (Smalla et al., 1998). In a word, the CLPP results in this study should be carefully interpreted and accompanied by complementary techniques (Ros et al., 2008).

In contaminated soils, a population shift in favor of resistant microorganisms occurs due to the elimination of sensitive organisms, and such population shifts can lead to changes in phospholipid fatty acid patterns of the soil. Changes in microbial community structure, as determined by changes in PLFA profiles, have been reported in Cu-, Ni-, Cr-, and Zn-contaminated soils (Frosteg'rd et al., 1993b; Pennanen et al., 1996; Kelly et al., 1999; Kamaludeen et al., 2003; Frey et al., 2006). Therefore, the PLFA profiles were also used to investigate the effect of Cu-based fungicides on soil microbial community structure in this study. The mean total Cu and free Cu2+ concentrations of orchard soils had a tendency to increase with the increase of orchard years that the Cu-containing fungicides (Bordeaux mixture) were used. In theory, the increasing Cu loads might have imposed selective pressure and osmotic stress on soil microbial community. However, no significant relationship was found between the soil total PLFA, Gram-positive bacteria, Gram-negative bacteria, total fungal PLFA contents, or ratio of fungi/bacteria PLFA and soil total Cu concentration or soil solution free Cu2+ in this study. Instead, when the principal component analysis was used, we found that the changes of PLFAs in this study were mainly due to available soil K, and the ratio of Gram-/Gram+ was also significantly related to available soil N and available soil K. A reason for the changes in abundances of signature PLFA did not consistently change with heavy metal contents may be due to that the soil samples used in this study were selected from the filed directly. In laboratory studies in which soils were contaminated artificially with different metals, the PLFA patterns were often drastically affected by the metal addition. The discrepancy between field and laboratory studies could be due to roots, since in the laboratory experiments with incubated soils no plants were present (Pennanen et al., 1996). Moreover, B''th et al. (1998b) suggested that the disadvantage of PLFA analysis as a monitoring tool in studying environmental pollution is that the PLFA pattern is often affected by other environmental factors such as soil moisture, pH and temperature, etc, which was also convinced by this study. The soil fertilization is possibilities for reducing the negative effects of heavy metals in the environment since nutrients would improve the low nutritional status of the polluted soil (Pennanen, 2001). Zhang et al. (2006) have found that some PLFA ratios, such as fungi/bacteria, were positively related to organic C, total N, total P, NO3'N, available P, pH and porosity instead of soil metal contents in the revegetated site. Zhang et al. (2007b) have also found that most of the variation in PLFA patterns in the soils was affected by fertilizer application, and different fertilizer levels have an impact on the community structure of specific microbial groups (Zelles, 1999). Therefore, although laboratory studies can give indications of the direct effects of metals, they can never simulate field conditions. Another reason for the unaffected general PLFA pattern might be the very low levels of heavy metals in the present study, lower than e.g. the levels where the signs of changed PLFA patterns were found. It is interesting to note that the ratio of Gram-/Gram+ PLFA and two fatty acids (i15:0(2OH) and 11Me18:1?7c) showed significant correlations with soil Cu content, and it has been already convinced by other authors that the fatty acids i15:0(2OH) and 11Me18:1?7c are sensitive indicators for increasing Cu loading (Hinojosa et al., 2005; Sun et al., 2007). Thus, the ratio of Gram-/Gram+, individual fatty acids i15:0(2OH) and 11Me18:1?7c can also be suggested to be the most sensitive indicators for Cu pollution in the orchards, and the PLFA pattern would be affected only by considerably higher levels of metals.


Two community-level profiling techniques (PLFA profiling and CLPPs in Biolog EcoPlates) were used to evaluate the changes in soil microbial structural composition due to long-term application of Cu fungicides in the orchards. The metabolic functions (determined using the Biolog plates) and structure of microbial communities (as determined by changes in PLFA profiles) at the sampling sites have not been significantly affected by the Cu inputs. Meanwhile, the systematic change in substrate utilization pattern was mainly associated with different pH values, and the different fertilizer practices have an impact on the community structure of specific microbial groups. In addition, the fatty acids i15:0(2OH) and 11Me18:1?7c appears to be particularly responsive to environmental changes and may be a good indicator of changes in the microbial community structure. However, although the changes in the microbial variables of the orchard soils were relatively small, more attention should be paid due to continuous application of Cu fungicides.