Shifts and recovery of soil microbial communities in a 40-year field trial under mineral fertilization

Pedobiologia - Journal of Soil Ecology 77 (2019) 150575

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Shifts and recovery of soil microbial communities in a 40-year field trial under mineral fertilization

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Jiří Čuhel , Stanislav Malý, Josef Královec ⁎

Central Institute for Supervising and Testing in Agriculture, Hroznová 2, 65606, Brno, Czech Republic

ARTICLE INFO

ABSTRACT

Keywords: Mineral fertilization T-RFLP Microbial community composition Extracellular enzymes Long-term field experiment Resilience

Inorganic fertilizers have been reported to have effects on both microbial activities and soil microbial community structure. However, the published results are often contradictory. To overcome short-term fluctuations in microbial parameters it is necessary to study their changes over the long term. We investigated the impact of a 40-y inorganic fertilization of a grassland field on selected soil microbial enzymatic activities and on the composition of bacterial and fungal communities, assessed by terminal restriction fragment length polymorphism (TRFLP). The following fertilizer treatments were compared: C (control, no fertilization), PK (phosphorus and potassium) fertilizer, 80N (lower nitrogen plus PK fertilizer) and 160 N (higher nitrogen plus PK fertilizer). The field trial also included a NF treatment where fertilization with high nitrogen doses was terminated after 20 y, and the subsequent 20 y were under a non-fertilization regime. Except for arylsulfatase, addition of PK was not sufficient intervention to influence enzyme activities. On the contrary, we observed a significant increase in cellobiosidase, phosphomonoesterase, and β-glucosidase activity in soils fertilized with N, although there was no difference in the enzyme activities between the 80N and 160N treatments. The activities of these enzymes in soils under NF treatment returned to the values of the control soils. Decreased activity of arylsulfatase was detected in treatments with fertilization compared to the control treatment. The shifts in the enzyme activities were accompanied by changes in the composition of whole bacterial and fungal communities, which was also affected by the long-term fertilization. Community composition in fertilized soils clearly differed from the control soils. Contrary to bacteria, 20 y following the cessation of fertilization in the NF treatment was not enough time for complete recovery of the fungal community to that observed in the control treatment. Our findings suggested that soil bacteria are more resilient to chemical fertilization disturbances than fungi.

1. Introduction The application of fertilizers into soil is primarily used to increase nutrient availability to growing plants and therefore to raise the yield of the subsequent harvest; nevertheless, it can also affect soil microorganisms (Marschner et al., 2003). Soil microorganisms have long been recognized for their indispensable role in decomposition of organic residues, cycling of nutrients and production of crops (Shen et al., 2010). Among the most ecologically relevant microbial parameters sensitive to changes in soil management (including fertilization) are microbial enzymatic activities, mainly due to their narrow relationship with the biological status of soil and relatively easy assessment (Moscatelli et al., 2018). Microbial enzymatic activities have been successfully used for assessment of the impact of both mineral and organic fertilizers on the microbial status of different soil types in various

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climatic regions (Debosz et al., 1999; Marinari et al., 2000; Pajares et al., 2009; Reardon and Wuest, 2016; Tamilselvi et al., 2015). Inorganic fertilizers have been reported to have effects on both microbial activities and soil microbial community structure (Böhme et al., 2005; Chen et al., 2014b; Ding et al., 2016; Marschner et al., 2003; Pan et al., 2014). In a literature review by Allison and Martiny (2008), 84% of 38 studies reported that microbial community composition is sensitive to N/P/K fertilization. However, the results are often contradictory, and both stimulating and suppressing effects of fertilizers on activity and composition of microbial communities can be observed. In general, microbial communities are dynamic systems, able to change rapidly in response to a disturbance such as an addition of fertilizer. Despite the sensitivity of microbial parameters to soil management, they are naturally highly variable with regard to different soil types and various physico-chemical soil properties. Field experiments that are managed for a relatively long time (decades) are then a very effective

Corresponding author. E-mail address: [email protected] (J. Čuhel).

https://doi.org/10.1016/j.pedobi.2019.150575 Received 25 February 2019; Received in revised form 25 July 2019; Accepted 28 August 2019 0031-4056/ © 2019 Elsevier GmbH. All rights reserved.

Pedobiologia - Journal of Soil Ecology 77 (2019) 150575

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tool for drawing plausible conclusions about the manipulated parameter, because the other variables remain unchanged. Moreover, longterm field trials, if appropriately arranged, might represent a great opportunity to survey the recovery of microbial communities, whether in terms of their structure or activity, if the manipulation (fertilization) is terminated. As proposed by Stevens (2016), recovery from an environmental perturbation can sometimes be difficult to define, because reversion to a pre-existing state often fails to consider natural developments within the system (e.g. succession). This means that in a permanently changing environment it is not always realistic to expect an individual site to return to a previous state. However, in replicated long-term trials with experimental controls, recovery can be advantageously considered as convergence with control plots (Stevens, 2016). Although, much of work has been done in the field of spontaneous recovery of plant diversity after cessation of fertilization in grasslands (e.g. Královec et al., 2009; Pallett et al., 2016), there are only a very limited number of studies involving terminated inorganic fertilization that are focused on microbial recovery (Chen et al., 2014a; Griffiths et al., 2012; Malý et al., 2009). In general, research on microbial communities after cessation of environmental disturbance is needed to provide a missing insight into their ecological resilience and recovery rate. The present investigation was undertaken (1) to assess the impact of long-term inorganic fertilization of grassland field on soil microbial enzymatic activities and on the composition of soil bacterial and fungal communities, and (2) to analyze possible relationships between microbial community composition and activity. The investigation was carried out in soils of field trial in the Czech Republic, fertilized since 1969. In a previous study situated in the same long-term field fertilization trial (Malý et al., 2009), it was shown that permanent application of mineral fertilizers increased the proportion of r-strategists in soil. However, it was not clear whether the changes in the r/K strategy were linked to changes in the composition of microbial communities or caused by transition between the r/K physiological states. Here we hypothesize that long-term application of mineral fertilizers leads to changes in the functioning of microbial communities, assessed as the activity of extracellular enzymes, and that these changes are coupled with shifts in soil bacterial and fungal community composition. Furthermore, although it is likely in general that repeated additions of fertilizers impact the microbial communities to some degree, it is unknown whether the fertilization produces lasting effects, particularly after the plots return to a non-fertilization regime, and whether the microbial communities are ecologically resilient. Fortunately, the longterm field trial includes a treatment where application of fertilization was stopped after 20 y of fertilization, with the subsequent two decades under a non-fertilization regime. With this treatment, we have a rare opportunity to raise and test the hypothesis that the changes in microbial parameters caused by permanent inorganic fertilizer inputs persist beyond the cessation of the fertilization and can be apparent even 20 y afterward.

different treatments: Control plots (C) have never been fertilized since the establishment of the experiment. Plots PK were annually fertilized with 32 kg P ha−1 as superphosphate and 100 kg K ha−1 as KCl. Plots 80N were annually fertilized with 80 kg N ha−1 as ammonium nitrate with limestone and also with P and K, as with the PK plots. Plots 160N were annually fertilized with 160 kg N ha−1 as ammonium nitrate with limestone and also with P and K, as with the PK plots. Since 1995, the N fertilization has been split into two doses of 80 kg N ha−1 (the first in spring and the second at the first harvest). Plots NF were annually fertilized with 320 kg N ha−1 as ammonium nitrate with limestone and also with P and K, as with the PK plots, but only during the first 20 y period of 1969–1989. After 1989 the plots were no longer fertilized and represent a recovery treatment.

2. Materials and methods

The activities of enzymes involved in the decomposition of biopolymers (α-glucosidase, β-glucosidase, cellobiosidase, chitinase and xylosidase) were measured along with enzymes involved in P (phosphomonoesterase and phosphodiesterase) and S (arylsulfatase) cycles. Artificial substrates containing 4-methylumbelliferone (4-MUF) were used for measurement of enzyme activities using fluorogenic substrates. The estimation of α-glucosidase (E.C. 3.2.1.20), β-glucosidase (E.C. 3.2.1.21), cellobiosidase (E.C. 3.2.1.91), chitinase (E.C. 3.2.1.30), phosphodiesterase (E.C. 3.1.4.1), arylsulfatase (E.C. 3.1.6.1), β-xylosidase (E.C. 3.2.1.37) and phosphomonoesterase (E.C. 3.1.3.1) activity was performed using 4-MUF-α-D-glucopyranoside, 4-MUF-β-D-glucopyranoside, 4-MUF-β-D-cellobiopyranoside, 4-MUF-N-acetyl-β-D-glucosaminide, bis-(4-MUF)-phosphate, 4-MUF-sulfate 4-MUF-β-D-xylopyranoside and 4-MUF-phosphate, respectively, according to ISO/TS 22939 (2010).

2.2. Sample collection and chemical analyses Soil sampling was conducted during two consecutive years. The samples were collected after the second harvest, at the end of August 2009 and 2010, using a soil probe (diameter 4 cm). Ten soil cores with topsoil 0–15 cm were taken from each plot, pooled together to make a composite sample, and transported on ice to the laboratory. The samples were immediately sieved (2 mm) and stored at field moisture content in plastic bags at −20 °C until required. Freezing of soil before measurement of enzyme activities was recommended by Wallenius et al. (2010). They showed that the difference between frozen and fresh mineral soil was within the acceptable limit of 15% for enzymes used in our study. Freezing of soil is also in line with ISO/TS 22939 (2010). For the chemical analyses a portion of soil was dried at room temperature. Soil moisture was determined gravimetrically by drying the soil at 105 °C for 6 h, and all results are expressed per gram of dry soil. Soil pH was measured according to ISO 10390 (1994) using a glass electrode and pH meter in a mixture of 5 g soil and 25 ml 1 M KCl after 5-min shaking and 2-h standing of the mixture. Organic C (Corg) was determined according to ISO 14235 (1998) by wet digestion of a 1 g soil sample with 5 ml 0.27 M K2Cr2O7 solution and 7.5 ml concentrated H2SO4. Extractable C (Cext) in soil samples was analyzed photometrically using the dichromate-oxidation method according to Yakovchenko and Sikora (1998) in 0.5 M K2SO4 extracts of soil and was considered as a labile fraction of soil C. The content of P, K, Ca and Mg was determined after their extraction from the soil with a Mehlich III extraction solution (Mehlich, 2008). An AAnalyst 400 (Perkin-Elmer) spectrophotometer was used for determination of Mg, Ca (atomic absorption spectrometry), and K (atomic emission spectrometry). Phosphorus was determined at 750 nm using a UV-Cary 50 Conc (Varian) spectrophotometer. During the years of soil sampling, harvests were carried out twice a year (in June and August). 2.3. Soil enzyme assays

2.1. Experimental field Soils for the study were sampled from a long-term experiment focused on surveying the effects of fertilization on the production and quality of fodder, as well as on soil quality. The experimental site is located at Závišín near Mariánské Lázně, Czech Republic (49°58′39′′N, 12°45′14′′E, altitude 750 m above sea level) and was established in 1969. The soil is a sandy loam classified as Haplic Cambisol (Dystric) (IUSS Working Group WRB, 2006). Soil characteristics, annual temperature, precipitation, and above all the experimental design of the field experiment is described in detail by Malý et al. (2009). Except for 1990–1994 when no plots were fertilized, the experiment consisted of a completely randomized block design with four replicates and five 2

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J. Čuhel, et al.

Briefly, 1 g of soil weighed in four replicates was homogenized with 100 ml of the 0.5 M acetate buffer (pH 5.5) and the suspension was sonicated for 120 s (150 W). For measurement, 150 μl of substrate solutions (0.67 mM) were mixed with 50 μl of the suspension taken in duplicates in a 96-well microplate. Samples were incubated at 30 °C and fluorescence was recorded after 1 and 3 h using a fluorometer Chameleon II (Hidex, Finland) with a 355 nm excitation filter and 460 nm emission filter. The amount of released MUF was calculated from the calibration curve prepared for each soil to account for quenching of fluorescence by soil particles. The mean was calculated from two results obtained from each suspension, and the median for four replicates was taken for statistical analysis. Substrates stability was checked using measurement of the fluorescence during incubation of two mixtures: sterilized (121 °C, 20 min) soil suspension + buffer + substrate, and buffer + substrate.

of the length of terminal restriction fragments was performed as a custom work by Elisabeth Pharmacon (Czech Republic). Terminal restriction fragments (T-RFs) were analyzed with the GeneMapper® ID Software v3.2 (Applied Biosystems, USA). Fragments with sizes smaller than 50 bp were excluded from further calculations. Fragment peaks were clustered by 1.0 bp and standardized by dividing the single peak by the total peak area of the particular sample. In order to discriminate the peak signal from background noise, all peaks with an area lower than 1% of the total peak area were not considered true fragments. 2.5. Statistical analyses Differences between the treatments with regard to chemical analyses, yield and enzyme activities were tested with analysis of variance (ANOVA) followed by the post-hoc comparison Tukey HSD test (significance level of p < 0.05). Individual data from both years, 2009 and 2010, were taken into the analysis, and the variables “Block” and “Year” were included into the model for the calculation of main effects (Supplementary data 1). The correlation coefficients were computed as Pearson’s product-moment correlations. Statistical computation was done using the R software (R Development Core Team, 2011). T-RFs resulting from digestion by both restriction enzymes were combined together and analyzed with principal component analysis (PCA) on centered data using the CANOCO 5 software (ter Braak and Šmilauer, 2012). The variables “Block” and “Year” were taken as covariates, and physico-chemical soil properties as supplementary variables were projected into ordination plots after analysis. Analyses were done separately for bacterial and fungal communities. Variability in TRFs explained by soil chemical parameters was calculated using redundancy analysis (RDA) followed by the Monte Carlo permutation test with 9999 permutations. One-way analysis of similarity (ANOSIM) was used to determine a significant effect of fertilization treatment on the bacterial and fungal TRFLP profiles using the Bray-Curtis similarity indices at a significance value of p < 0.05. ANOSIM was analyzed with PAST 3.18 software (Hammer et al., 2001) and p-values were adjusted by means of the Holm correction using the R software (R Development Core Team, 2011). The extent of separation of communities from individual plots was based on the ANOSIM R statistics using the scale defined by Ramette (2007) in which 0.75 < R < 1 are well separated, 0.5 < R < 0.75 are separated but overlapping, and R < 0.25 are barely separable. Two samples were excluded from the ordination analyses and ANOSIM of bacteria (NF, 2009; PK, 2010) and two from the analyses of fungi (C, 2010; PK 2010) as outliers.

2.4. DNA extraction and terminal restriction fragment length polymorphism analysis (T-RFLP) For each experimental plot, DNA was extracted in duplicate from 250 mg of soil according to ISO 11063 (2012). Briefly, samples were homogenized in 1 ml of extraction buffer for 30 s at 1600 rev min−1 in a cell disrupter Mikro-Dismembrator S (Sartorius AG, Germany). Soil and cell debris were eliminated by centrifugation (14 000g for 5 min at 4 °C). After precipitation with ice-cold isopropanol and washing with 70% ethanol, the nucleic acids were purified using both polyvinylpolypyrrolidone and sepharose 4B spin columns. The quality and size of DNA fragments were checked by gel electrophoresis. DNA was quantified by fluorescence spectrophotometry in a 96-well microplate using a Fluorescent DNA quantification kit (Bio-Rad, USA) and fluorometer Chameleon II (Hidex, Finland) with 355 nm excitation filter and 460 nm emission filter. The structure of soil microbial communities was analyzed by polymerase chain reaction (PCR) followed by T-RFLP of the bacterial 16S rRNA gene and fungal ITS region. The 30-μl PCR mixture contained 15 μl of FailSafe™ PCR 2X PreMix F (Epicentre Biotechnologies, USA), 1.2 U of Maxima Hot Start Taq DNA Polymerase (Thermo Scientific, USA), 3 μg of BSA (Thermo Scientific, USA), 4 ng of template DNA, 6 pmol of FAM-labeled forward primers and 6 pmol of unlabeled reverse primers (Elisabeth Pharmacon, Czech Republic). The bacterial 16S rRNA gene was amplified using primers 27F (Edwards et al., 1989) and 907R (Lane, 1991) under the following thermocycling: 4 min at 95 °C, followed by 32 cycles of 60 s at 95 °C, 45 s at 58 °C and 60 s at 72 °C, with a final extension for 15 min at 72 °C. The fungal ITS region was amplified using primers ITS1f (Gardes and Bruns, 1993) and ITS4r (White et al., 1990) under the following thermocycling: 4 min at 95 °C, followed by 32 cycles of 60 s at 95 °C, 45 s at 55 °C and 60 s at 72 °C, with a final extension for 15 min at 72 °C. The quality and size of the gained PCR products were checked by gel electrophoresis on 2% agarose, and duplicates (regarding the two DNA extractions per soil sample) were pooled together. PCR products were digested in 10 μl reactions separately with 6 U of restriction enzymes HhaI or MspI (bacteria), and HaeIII or HinfI (fungi) according to the recommendations of the enzymes’ manufacturer (Thermo Scientific, USA). Analysis

3. Results and discussion 3.1. Soil chemical properties and plant aboveground biomass Long-term fertilization of grassland soil led to changes in plant aboveground biomass and almost all assessed soil chemical properties, which is shown in Table 1. Soil reaction (pHKCl) and the content of Ca were significantly higher in plots fertilized with N (80N and 160N). The

Table 1 Chemical properties and plant aboveground biomass (yield) of soils with different fertilization treatment (C, PK, 80N, 160N, NF). Treatment C PK 80N 160N NF † ‡

pHKCl 4.0† 4.0 4.2 4.2 3.9

b‡ b a a b

(0.05) (0.02) (0.05) (0.05) (0.04)

Corg (g kg−1) 28.1 25.8 25.3 25.2 25.2

a ab b b b

(0.76) (0.42) (0.99) (0.76) (0.62)

Cext (mg kg−1) 373 371 327 316 334

a ab bc c bc

(10.1) (5.4) (9.0) (11.3) (8.6)

P (mg kg−1) 31.2 169.7 163.7 121.4 56.4

d a a b c

(3.2) (9.0) (10.1) (5.5) (4.9)

K (mg kg−1) 56.5 137.4 95.5 67.9 52.3

c a b c c

(1.9) (11.9) (5.8) (5.3) (2.6)

Ca (mg kg−1) 997 1115 1391 1376 925

Data represent means and standard errors of mean in parentheses (n = 8). The different letters indicate significant differences between the treatments (Tukey HSD test, p < 0.05). 3

b b a a b

(81.3) (55.9) (52.0) (59.8) (56.6)

Mg (mg kg−1) 95.6 81.6 114.4 99.0 60.1

ab b a ab c

(11.3) (3.6) (9.7) (5.3) (4.7)

Yield (td.w. ha−1) 2.41 3.98 4.63 5.13 3.05

d bc ab a c

(0.31) (0.45) (0.59) (0.61) (0.37)

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increased values of pHKCl and Ca content reflect the fact that ammonium sulphate used as fertilizer in 80N and 160N plots also contained some limestone. In the case of NF plots, terminated fertilization led to the recovery of pHKCl and Ca content, as the values were not significantly different from the control plots (C) with no fertilization. The content of both P and K significantly increased in soils of PK and 80N plots in comparison with C plots. Moreover, the P content of 160N plots was higher than in C plots. However, the values did not correspond with the intensity of fertilization: the P content in 160N plots was lower than in PK and 80N plots, and the K content in 80N and 160N plots was lower in comparison with PK plots. This was probably caused by higher aboveground biomass and increased P and K uptake by plants, which were removed from the field during periodical harvests. The soil K content in the NF plots returned to the values of C plots. The P content of NF plots also moved closer to the values of C plots, although it was still significantly higher. Fertilization with N led to significantly lower soil Corg content in 80N and 160N plots in comparison to C plots. The negative effect of N inputs on soil Corg also persisted in NF plots with cessation of fertilization. This result of Corg decline is not in close accordance with the findings of meta-analysis by Geisseler and Scow (2014), who showed that only 17% of the surveyed published datasets exploring the longterm effect of NPK fertilization on soil microorganisms reported a lower Corg content in fertilized plots. A possible explanation could arise from the fact that in our study plant residues were removed and the only source of organic C by rhizodeposition was not sufficient for microbial demands under NPK fertilization resulting in more intensive mineralization of native soil organic matter.

Detailed results of ANOVA can be found in Supplementary data 1. The activity of cellobiosidase and phosphomonoesterase in soils from the 160 N plots was significantly higher than in soils from the C plots. The activity of β-glucosidase increased in both treatments, where plots were fertilized with N (80N and 160N). Except for arylsulfatase, the activity of other enzymes did not differ between the C and PK treatments, and enzyme activities in soils from plots with cessation of fertilization in 1990 (NF) returned to the values of the C plots. From a functional perspective, the PK inputs did not represent a sufficient intervention to shift microbial activity. Even if fertilization with PK increased the yield of aboveground biomass (Table 1), microbes (here characterized by enzyme activities) were not limited by these nutrients. These findings are consistent with those of Zhang et al. (2015), who reported no difference between the enzymatic activities of unfertilized soil and soil under 20-y PK fertilization. In contrast, a number of studies found that an increased availability of mineral N promoted the production of extracellular hydrolytic enzymes involved in the C and P cycles (Jian et al., 2016; Marklein and Houlton, 2012). This is in line with our results, which showed a significant increase in cellobiosidase, phosphomonoesterase, and β-glucosidase activity in soils fertilized with N (Fig. 1), despite no significant difference in enzyme activities between 80N and 160N treatments, although the activities in 160N plots were slightly higher. As higher production of aboveground plant biomass was reported to stimulate exoenzyme activities (Tate, 2002), we hypothesize that rhizodeposition resulted from plant growth as a response to the application of fertilizer enhanced production of cellobiosidase and βglucosidase. The nutrient stoichiometry was probably important too since the effect of the second dose of N (80N vs 160N), when no additional P and K were applied, was weaker and no significant correlations between the activities and the yield at the second harvest was found. The described differences in enzyme activities may represent not only the instant influence of fertilization on the production of enzymes, but also long-terms effects, since exoenzymes can be stabilized on the surfaces of mineral particles and in complexes with soil organic matter (Nannipieri et al., 2002, 2012). This may explain an apparent contradiction between the enzyme activities and decreasing microbial biomass and basal respiration found in fertilized soils within the previous

3.2. Enzyme activities under different fertilization treatment Five enzyme activities exerted statistically significant differences among plots with different fertilization treatments: namely β-glucosidase, cellobiosidase, arylsulfatase, β-xylosidase and phosphomonoesterase (Fig. 1). Consistency of responses to treatments in two consecutive years was checked visually using bar plots (Supplementary data 1) and no contradiction was detected for the discussed parameters.

Fig. 1. Microbial enzyme activities in soils with different fertilization treatment (C, PK, 80N, 160N, NF). AGL – α-glucosidase, BGL – β-glucosidase, CEL – cellobiosidase, CHI – chitinase, PME – phosphomonoesterase, PDE – phosphodiesterase, SUL – arylsulfatase, XYL – β-xylosidase. Values are means ± standard errors of mean (n = 8). The different letters among the bars indicate significant differences between the treatments (Tukey HSD test, p < 0.05). 4

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the soil from the period of intensive NPK inputs. On the other hand, neither phosphomonoesterase nor phosphodiesterase were inhibited by superphosphate inputs as opposed to the previously reported data (Burns et al., 2013; DeForest et al., 2012; Tabatabai and Dick, 2002). A possible explanation for increasing phosphomonoesterase activity with increasing doses of N (Fig. 1) may consist in the imbalance of the N and P stocks in soil, mainly in the 160N soil where only N but no P was applied at the second fertilization. This hypothesis is supported by lower concentration of P in 160 N plots in comparison with PK and 80N treatments (Table 1). Gianfreda (2015) states that phosphomonoesterases are more abundant in rhizosphere where their activity is stimulated by low molecular weight organic molecules present in root exudates. Previous results from this trial (Malý et al., 2009) showed a shift of microbial communities towards rstrategists, characterized with higher growth rate, in fertilized plots, probably as a result of input of labile organic matter by the root exudates. It resulted in faster decomposition of soil organic matter, which may have been accompanied with higher microbial demand for P acquiring from organic molecules and with increased phosphomonoesterase activity.

Fig. 2. Principal component analysis (PCA) of the relative amount of T-RFs after digestion of amplified bacterial 16S rRNA gene. The analysis included the results of digestion with both restriction enzymes HhaI and MspI. The different symbols indicate the position of plots with different fertilization treatment (C, PK, 80N, 160N, NF) in the ordination space. The letters a and b discriminate samples taken in 2009 and 2010, respectively. Arrows represent chemical parameters which were not used for the analysis and were projected into the plot afterwards as supplementary variables.

3.3. Composition of bacterial and fungal communities under different fertilization treatment Even if long-term N fertilization shifted the activity of selected enzymes to higher values (cellobiosidase, phosphomonoesterase, and βglucosidase) and N/PK inputs repressed arylsulfatase activity, it is not clear whether fertilization influenced the activities directly through changes in soil physical/chemical parameters and kinetics of enzymatic reactions or indirectly through the structure of microbial communities. The same question can arise for NF plots with cessation of fertilization, where the return of enzymatic activities to original values may be connected with the recovery of the community composition of the responsible microorganisms. Therefore, we also surveyed a T-RFLP analysis of the whole bacterial and fungal communities to find a possible linkage between their activity and composition, even though our study design does not permit us to definitively decouple the direct and indirect effects of fertilization on microbial activity. We found that long-term fertilization led to changes in the composition of bacterial communities, which was shown by principal component analysis (PCA) (Fig. 2) and analysis of similarity (ANOSIM) based on the Bray-Curtis distance (Table 2). PCA revealed that first and second axes explained 32.0% of the variability in bacterial community composition in soils under different fertilization treatments, and that the variability was mostly related to Ca, P content and soil pHKCl (Fig. 2). RDA (plot not shown) showed that Ca, pHKCl and P explained 20.3%, 19.4% and 13.6%, respectively, of the variability in the T-RFLP data. It should be mentioned that pHKCl and Ca strongly correlated to each other (0.831, p < 0.001) (Supplementary data 2), and thus the effects of both parameters cannot be distinguished to a large extent. Soil pH was shown to represent a dominance factor in establishing the structure of bacterial communities at both the continental and local field scales (Lauber et al., 2009; Rousk et al., 2010), and although the range of pH values of soils in the present study is relatively narrow in comparison to the cited works (pH 3.5–8.9), our results fit into the this general linkage between soil pH and composition of bacterial communities. In the ordination diagram, unfertilized plots (C and NF) clearly differed from the plots with fertilization. Moreover, groups of PK and 160N plots were formed within the fertilized plots (PK, 80N, 160N), even though the groups were partially overlapping. These results are supported by ANOSIM (Table 2). It showed that PK, 80N and mainly 160N plots significantly differed from the control plots (C), indicating the convincing effect of long-term fertilization on soil bacteria. This is in agreement with Böhme et al. (2005), who recorded changes in bacterial community composition (assessed with PLFA analysis) after long-term (39 and 100 y) NPK fertilization. Similarly, Ramirez et al.

survey, performed on the same field trial (Malý et. al, 2009). Fast depletion of labile C resulted in a decrease in microbial characteristics related to living cells. The activities of cellobiosidase, β-xylosidase and β-glucosidase correlated with soil pHKCl (r = 0.629, 0.505, 0.737, respectively, p < 0.001) and Ca content, indicating soil reaction to be the main driver of changes in microbial activity as a response to fertilizer inputs, even if the range of pH values was relatively narrow among the soil samples (pHKCl 3.8–4.5) (scatter plots displaying relationships between pHKCl and activities of β-glucosidase, cellobiosidase and β-xylosidase, and between Ca concentration and pHKCl, can be found in Supplementary data 2). Soil pH is a master variable because it affects all soil properties, whether chemical, physical, or biological (Brady and Weil, 1999). This is also true for the activity of soil enzymes, which are affected by pH through its controls on microbial enzymatic production, ionization-induced conformational changes of enzymes, and/or availability of substrates and enzymatic co-factors (Tabatabai et al., 1994). Higher activities of enzymes involved in decomposition of biopolymers are in agreement with previous results from the same field trial, when more active microorganisms were found in less acidic soils (Malý et al., 2009). The activity of arylsulfatase showed a different response to fertilization in comparison with the remaining enzymes, since significantly lower arylsulfatase activity was detected in soils under fertilization than in control plots. Similar results were observed by Ai et al. (2012), who determined enzyme activities in soils under 31-y inorganic fertilization and also observed decreasing arylsulfatase activity in both the rhizosphere and bulk soil. The negative effect of fertilization on arylsulfatase activity may be caused by application of superphosphate to provide P inputs into plots PK, 80N and 160N. A non-negligible part of the superphosphate (ca. 5%) is constituted by S in the form of SO42−. Then SO42− is not a limiting component in S cycling and microorganisms are not forced to make SO42− available using arylsulfatase. Indeed, bacterial arylsulfatase activity is induced during S starvation, as Hummerjohann et al. (2000) showed its repression in the presence of SO42− in Pseudomonas aeruginosa, and enhanced arylsulfatase activity by filamentous fungi was found under S-limiting conditions (Fitzgerald, 1976; Marzluf, 1997). The activity of arylsulfatase also remained lower in NF plots with terminated fertilization, indicating sufficient S stock in 5

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Table 2 ANOSIM of the Bray-Curtis dissimilarity indices of bacterial and fungal communities. Pairwise R statistics between the fertilization treatments (C, PK, 80N, 160N, NF) are displayed.

Significance values are indicated as p < 0.05 (*) and p < 0.01 (**).

(2010) observed consistent changes in the structure of the bacterial communities as a response to long-term (8 and 27 y) N addition. Moreover, soils with terminated fertilization (NF plots) were weakly separable from the control plots (R = 0.21, p = 0.025). NF plots were separated from fertilized plots to a similar extent as C plots (Table 2). It seems that 20 y after the cessation of fertilization was enough time for recovery of the bacterial community composition to the condition before initiation of fertilization, if the spontaneous changes in bacterial communities over time are taken into account. Indeed, some changes in soil microbial communities can also be naturally present in control plots without any fertilization, and the state of communities after 40 y is probably different from the state at the beginning of the establishment of the field trial. Differences in fungal community composition among treatments as a response to long-term fertilization were also shown between the plots, although to a lesser extent than in the case of bacteria. The first and second axes of PCA explained 44.3% of the variability in community composition of fungi among soils under different fertilization treatments, and the variability was mainly related to Cext, Ca and pHKCl (Fig. 3). The marginal explained variability in the T-RFLP pattern was 13.9%, 13.1% and 10.5% for Cext, Ca and pHKCl, respectively, as revealed by RDA (plot not shown). Within the ordination diagram, the group of control plots (C) and group of 160 N plots distinctly separated from the rest of the plots with other fertilization treatment. Indeed, according to ANOSIM (Table 2) these two groups (C and 160 N) were well separated from each other (R = 0.84, p = 0.002). A similar trend

was described by Zhou et al. (2016), who suggested that long-term (34 y) N and P fertilizer regimes changed fungal community composition and that the influence of higher fertilizer doses was greater than in the case of lower fertilization rates. Furthermore, a significant separation was also observed between the treatment with cessation of fertilization (NF plots) and the treatment with high NPK inputs (160 N plots) (R = 0.69, p = 0.0004). The plots of NF and C treatments were weakly separated from each other (R = 0.27, p = 0.009), however to a greater extent than in the case of the bacterial community. The fact that the most important soil characteristics linked to fungal T-RFLP patterns was Cext may explain this finding, since Cext differed in treatments C and NF (Table 1). On the contrary, C and NF treatments were indistinguishable with regard to pHKCl and Ca, which were key factors related to bacterial community composition. Nevertheless, it should be mentioned that the determined soil properties can correlate with others, not included in our study. For example, a higher concentration of some organic compounds, preferentially used by soil fungi, may be behind the relationship between Cext and the fungal community composition. These limitations must be considered when relationships between chemical and microbial soil properties are interpreted. Our results show that after termination of fertilization, fungal community composition is also able to move back to the state before fertilization; nevertheless, the return is slower and incomplete in comparison to bacteria. As mentioned above, changes in enzyme activities could be physiological, or they might result from a shift in microbial community composition, although both these alternatives are difficult to distinguish within field experiments. The activity of cellobiosidase and βglucosidase, as well as the composition of the bacterial community, exerted a similar trend among the fertilization treatments (compare Fig. 1 and 2). Both the activity of these enzymes and the composition of the bacterial community in the 80N and 160N plots clearly separated from the C plots; at the same time, the 80N and 160N plots did not differ from each other, and the NF plots returned to the values of the C plots. Furthermore, the composition of the fungal community (Fig. 3) reflected arylsulfatase activity: neither the activity nor the community composition in the NF plots returned to the state of the C plots. Even if changes in enzyme activities under our study could be at least partly explained by the shifts in microbial community composition, further experimental testing of causal relationships is needed. 3.4. Recovery of microbial communities after cessation of fertilization From an ecological point of view, the shifts and recovery of microbial communities reflect the concepts of resistance and resilience, fundamental parts of the biological stability of disturbed ecosystems (Griffiths and Philippot, 2013). Mineral fertilization in our study can also be considered an ecological disturbance, whose duration is relatively long (a so-called press disturbance). Thus, the obtained results of the shifted activity and composition of bacterial and fungal communities as a response to mineral fertilization are in agreement with previous findings of Allison and Martiny (2008), who suggested that most

Fig. 3. Principal component analysis (PCA) of the relative amount of T-RFs after digestion of amplified fungal ITS region. The analysis included the results of digestion with both restriction enzymes HaeIII and HinfI. The different symbols indicate the position of plots with different fertilization treatment (C, PK, 80N, 160N, NF) in the ordination space. The letters a and b discriminate samples taken in 2009 and 2010, respectively. Arrows represent chemical parameters which were not used for the analysis and were projected into the plot afterwards as supplementary variables. 6

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microbial communities are sensitive to disturbances. Our experimental design involved unique treatment of terminated fertilization (NF), which enabled studying microbial resilience after a recovery time of 20 y. According to our knowledge, there is no other microbiological study working with a similarly long or longer time period following the end of a long-term disturbance. The recovery treatment with shorter time (ca. 10 y) can be found in studies by Griffiths et al. (2012), who surveyed C:N:P stoichiometry and nutrient limitation of the microbial biomass in a grazed grassland soil under long-term different inorganic P fertilization, and also by Chen et al. (2014a), who analyzed composition of microbial communities in soils of the same field trial. We should note, that in our study the consequences of mineral fertilization also persisted in soils of the NF treatment 20 y after the cessation of fertilization. These consequences became apparent through lower values of Corg and Cext and higher values of P content and yield compared to the control treatment (C). Thus in a certain sense, the disturbance may be considered to have continued after ceasing fertilization. Despite this fact, we were able to demonstrate the recovery of both principal ecological characteristics of the microbial community, i.e. function and composition. From a functional perspective, activities of all assessed enzymes excluding arylsulfatase, shifted by long-term mineral fertilization, returned to their pre-perturbation states; from the viewpoint of microbial community composition, we recorded the recovery of the whole bacterial community. On the other hand, 20 y of interrupted fertilization was not enough time for complete regeneration of the composition of the whole fungal community, which demonstrated a slower recovery rate in comparison with bacteria. More generally, soil bacteria seem to be more resilient than fungi to chemical fertilization disturbances. Our observations are supported by a hypothesis pronounced by de Vries and Shade (2013). According to the hypothesis, the increased fungal/bacterial ratio of a soil microbial community may be indicative of the prevalence of K-strategists in that community, as fungi are typically considered to be slower growing than bacteria. And, pursuant to this, with the increasing relative abundance of fungi, the resistance of the microbial community structure will increase, whereas the resilience will decrease. Nevertheless, additional research of long-term trials using more specific phylogenetic information on soil bacterial and fungal community structure or techniques that describe communities at finer levels of taxonomic resolution are needed to satisfactorily bridge the gap between both functional and compositional parameters of resistance and resilience of microbial communities in soil.

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