Impact of land use on soil function and bacterial community in the Brazilian savanna

LENKA BOBUL’SKÁ SUÉLLEN P. ESPÍNDOLA MICHELLE A. COELHO ADÃO S. FERREIRA About the authors

Abstract

Land use systems have a great impact on soil function and microbial diversity in tropical soils. Our study aimed to evaluate soil biochemical indicators and community composition and to assess the relationship between soil biochemical and microbial indicators and bacterial diversity of three agroecosystems (pine forest, soya and sugarcane) and native Cerrado forest in the Brazilian savanna. Soil biochemical indicators (soil organic matter and enzymes) and high-throughput sequencing of 16S rDNA were performed in two topsoil depths (0-5 cm and 5-10 cm). Soil microbial and enzyme activity showed that agricultural soil usage has a negative impact on soil function compared to native and pine forests. Results also revealed higher enzyme activities in 0-5 cm depth compared to 5-10 cm depth, but enzymatic activities depend on land use systems. Soil bacterial community was affected by land use systems and depth, revealing changes in structure and abundance of bacterial composition. Alpha-diversity indexes were higher in the agricultural systems than in the forests, however they showed a significant negative correlation with most of the studied soil microbial and biochemical indicators. Our research had brought new relevant information about the relationship between the soil biochemical indicators and the bacterial diversity in the Brazilian Cerrado.

Key words
soil quality indicators; bacterial diversity; depths; native and pine forest; soya and sugarcane agroecosystems

INTRODUCTION

Land use systems have caused several impacts on soil function, soil chemical, physical and biological properties (Gil-Sotres et al. 2005, Thomson et al. 2015, Manoharan et al. 2017, Vinhal-Freitas et al. 2017). Soil functions are essential for the biosphere and include nutrient cycling, C storage and turnover, water maintenance, soil structure arrangement, regulation of soil biota diversity, biotic regulation, buffering, etc. (Arnold 2016). The impacts on soil functions and soil properties result mainly in the loss of soil organic matter (SOM) and soil microbial properties, such as microbial activity and microbial biomass (Vinhal-Freitas et al. 2013, 2017). Soil enzyme activities, which are associated with carbon transformations and nutrient cycling, are also affected by land use systems. Hydrolytic enzymes have an extracellular activity and are mainly produced by soil microorganisms. Such indicators are important for assessing the intensity of soil degradation among different use ecosystems. However, the effects of land use on soil function are also determined by agricultural practices, soil type and environmental conditions (Gil-Sotres et al. 2005, Wallenius et al. 2011). Soil microbial community seems to be highly responsive to all or any soil physical, chemical and biological changes, as well as environmental conditions. Therefore, studies with different long-term agroecosystems are very important for assessing the changes of soil quality indicators, soil function and soil microbial community (Fernandez et al. 2016, Vinhal-Freitas et al. 2017). The relationship between soil quality indicators (biochemical and microbial attributes) and soil bacterial composition can be useful for better understanding the changes of microbial community and soil function under land use systems.

Bacterial community has paramount importance in several soil ecological processes and plays a key role in soil function. However, soil bacterial community structure, including abundance, richness and diversity depends on several and integrate abiotic factors such as soil pH, nutrient content, moisture and temperature (Nemergut et al. 2011, Zhalmina et al. 2015, Fernandez et al. 2016). Particularly, studies that determine diversity through pyrosequencing of the 16s rRNA gene have shown that soil pH has a strong effect on bacterial diversity, indicating that higher bacterial diversity is usually found in pH from 6.0 to 7.0 (Nemergut et al. 2011, Zhalmina et al. 2015, van der Bom et al. 2018, Liu et al. 2019). Soil microbial diversity is also affected by nitrogen addition, but such changes still depend on N source, soil type and management practices (Zhalmina et al. 2015). However, soil pH and moisture appear to be the major drivers of microbial community composition in agroecosystems (Lauber et al. 2008, Fernandez et al. 2016, Liu et al. 2019, Byers et al. 2020). Moreover, these abiotic factors in the soil surface layer are strongly altered by land use type (i.e. plant cover), altering the microbial community structure and affecting soil function.

The shallow depth of topsoil has a role of utmost importance in the productivity of agroecosystems due to higher nutrient stocks than subsoil. Soil depth are strongly influenced by the deposition litter as well as environmental conditions, which depend on daily and seasonal fluctuations. In topsoil, the structure and composition of soil microbial community might sensitively be changed within a shallow depth, and this might govern many soil ecological processes, such as decomposition and mineralization of nutrients the in topsoil (Paul 2007). However, most studies have commonly been performed on a wide layer of surface topsoils (Bobuľská et al. 2015, Engelhardt et al. 2018, Sarto et al. 2020), decreasing the microbial activity indices and microbial community composition were showed.

The Brazilian Cerrado, as one of the most humid savanna region of the world, occupies over 200 million hectares and is equivalent to 22% of the Brazilian territory. It is also the second largest biome in the country and moreover, the region is a global biodiversity hotspot (Batlle-Bayer et al. 2010, Carranza et al. 2014). The majority of soils are old, highly weathered (such as Oxisols and Ultisols), rich in iron and aluminum oxides, acidic and poor in nutrients (Vinhal-Freitas et al. 2013). In the recent years, the ongoing conversion of the native Cerrado ecosystems into agricultural lands is of high concern. Several authors have reported that changes in the use and managements of Cerrado soils have promoted significant changes in physical and biochemical indices (Lobato et al. 2018, Costa et al. 2020). In the Brazilian Cerrado, surveys of shallow depths (0-5 and 5-10 cm layers) on topsoil have been recently reported for microbial and biochemical indicators (Vinhal-Freitas et al. 2013, 2017), as well as soil bacterial community composition using of next-generation sequencing (Rampelotto et al. 2013, Catão et al. 2014). However, the relationship between soil function and bacterial community composition is still unknown. This relationship can help us better understanding the transformation of nutrients of soil under land use systems in the Brazilian savanna. Therefore, the present study aimed (i) to compare the changes in soil microbial and biochemical properties in the native Cerrado forest and different long-term agricultural agroecosystems, (ii) to evaluate the soil bacterial community composition and diversity under different land use conditions, and (iii) to determine the relationship between soil function and bacterial community composition in three agroecosystems of the Brazilian Savanna and the native Cerrado system.

MATERIALS AND METHODS

Sites

The study was performed in soil samples collected in the native Cerrado forest, pine forest, soya field (~ 17 years old with the crop rotation using corn every 4 years) and sugarcane field (~ 18 years old with new cycles every 5 years) in the region of the Uberlândia city (Minas Gerais State), in the south-eastern of Brazil (Fig. 1). The dominant plant species of Cerrado ecosystem is composed of a wide range of species such as Qualea grandifolia Mart. (pau-terra), Bowdichia virgilioides Kunth (sucupira-preta), Pterodon pubescens (Benth.) (sucupira), Caryocar brasiliense Cambess. (pequi), Vatairea macrocarpa (Benth.) Ducke (angelim do cerrado), Astronium fraxinifolium Schott (Gonçalo-alves), Eugenia dysenterica DC. (cagaita), Hymenaea stigonocarpas Mart. (jatobá) and others with no anthropogenic alteration (Vinhal-Freitas et al. 2013Vinhal-Freitas IC, Ferreira AS, Corrêa GF & Wendling B. 2013. Land use impact on microbial and biochemical indicators in agroecosystems of the Brazilian Cerrado. Vadose Zone J 12: 1-8.). The pine forest was under a dense planted forest represented by species Pinus caribaea Morelet var. hondurensis (Sénéclauze). The pine forest was fertilized only once when the seedlings were planted in 1976 and there was a 10-cm layer of litter on the soil surface consisting of needles, cones and woodchips. The sites are in the same climatic zone, which was classified as Cwa according to the Köppen’s classification. The sites are in areas with the same soil type, classified as Oxisols (Soil Taxonomy, USA, 1992). More information on management of the sites was described in the previous reports (Vinhal-Freitas et al. 2013Vinhal-Freitas IC, Ferreira AS, Corrêa GF & Wendling B. 2013. Land use impact on microbial and biochemical indicators in agroecosystems of the Brazilian Cerrado. Vadose Zone J 12: 1-8., Leite et al. 2018LEITE MVM, BOBUL`SKÁ L, ESPÍNDULA SP, CAMPOS MR, AZEVEDO LCB & FERRIERA AS. 2018. Modeling of soil phosphatase activity in land use ecosystems and topsoil layers in the Brazilian Cerrado. Ecol Model 385:182-188.). In April 2016, four soil samples per site were taken in an area of 600 cm2 (20 cm x 30 cm) and two depths (0-5 and 5-10 cm), accounting 32 soil samples. Within each site, the soil samples were approximately spaced in 100 meters from each other. Four subsamples were collected and subsequently well mixed for making the soil samples. After the samples were transferred into the laboratory, they were sieved (3 mm) and stored in the plastic bags at 4oC until analysed.

Figure 1
Experimental sites (Pine forest, native Cerrado forest, soya and sugarcane systems) of the studied area.

Soil physicochemical analyses

A portion of soil samples was air-dried for 3 days and completely crushed in a porcelain crucible. This sample was used to determine the sand, silt and clay content according to the pipette method (Gee & Bauder 1986Gee GW & Bauder JW. 1986. Particle-size analysis. In: Klute A et al. (Eds), Methods of soil analysis: Part 1. SSSA Book Ser. 5. SSSA and ASA, Madison, WI.), which were used to find the soil textural class of each site (Table I). Soil pH was determined in water (1:2.5 soil/water). Soil organic carbon (SOC) was analysed in acid solution containing potassium dichromate (Yeamans & Bremner 1988) and total nitrogen (NT) was evaluated by the Kjeldahl method (Black 1965Black CA. 1965. Methods of Soil Analysis, Part I and II. American Society Inc. Publishing, Madison, U.S.A, p. 770–779.).

Table I
Sites and soil characterization of different land use types.

P, K+, Ca2+, Mg2+, and Al3+, were determined according to Tedesco et al. (1995)Tedesco MJ, Bohnem C, Gianello CA, Bissani CA & Volkweiss SJ. 1995. Análise de Solo, Plantas e outros Materiais, 2nd ed. Universidade Federal do Rio Grande do Sul, Porto Alegre, 174 p. (in brasil)., after the samples that had been dried, sieved (< 2 mm) and crushed in a porcelain crucible. All the analyses of soil physicochemical characterizations are shown in Table II.

Table II
Physical and chemical properties (values a) of soils investigated in two depths and different land use systems in the Brazilian Savanna.

Soil microbial and biochemical analyses

Soil microbial respiration (SMR) was measured by CO2 gas emissions from 100 g field moist soil in sealed bottles (500 mL) using the standard method (Stotzky 1965Stotzky G. 1965. Microbial respiration. In: Black, editor, Methods of soil analysis, part 2. ASA, Madison, WI. p. 1550-1570.) for 21 days at 25oC. Microbial biomass carbon (MBC) was determined by the extraction method in the solution of potassium sulphate (0.5 mol L-1) as described by Vance et al. (1987)Vance ED, Brookes PC & Jenkinson DS. 1987. An extraction method for measuring soil microbial biomass-C. Soil Biol Biochem 19: 703-707.. In the same extract, N concentration was quantified for assessing microbial biomass nitrogen (MBN) (Brookes et al. 1985Brookes PC, Landman A, Pruden G & Jenkinson DS. 1985. Chloroform fumigation and release of soil N: a rapid direct extraction method to measure microbial biomass N in soil. Soil Biol Biochem 17: 837–42.). Metabolic quotient (qCO2) of the soil was calculated using SMR to MBC ratio (Anderson & Domsch 1993Anderson JPE & Domsch KH. 1993. The metabolic quotient (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biol Biochem 25: 393-395.). Enzymatic activity assays, beta-glucosidase (GLU), urease (URE), fluorescein diacetate (FDA), dehydrogenase (DHA), phosphatase (PHO) and arylsulphatase (ARY), were determined using field-moist soil samples using specific substrates of each enzyme (Sigma). All the essay conditions are shown in Table III previous published (Vinhal-Freitas et al. 2017Vinhal-Freitas IC, Corrêa GF, Wendling B, Bobuľská L & Ferreira AS. 2017. Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol Indic 74: 182-190.).

Table III
Incubation conditions of enzymes used with biochemical indicators.

DNA extraction, 16S rRNA amplification and pyrosequencing

Genomic DNA was extracted from 0.250 g of soil sample using the PowerSoil DNA Isolation Kit (MoBio, Qiagen). The genomic DNA concentration was determined by using the Qubit Fluorometer Kit (Invitrogen, Carlsbad, CA) following the manufacturer’s recommendations. The V4-V5 region of the 16S rRNA gene was amplified using archaeal/bacterial primers 515F and 806R (Caporaso et al. 2012Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G & Knight R. 2012. Ultra-high throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621-1624.) and amplicons sequenced in the PGM Ion Torrent (Life Technologies). To distinguish each sample, a unique barcode sequence was inserted into the forward primer. The forward and reverse primers were tagged with adapter, pad and linker sequences. Each used 25 ul of the PCR mixture consisted of 2.5 ul of 10 x PCR Buffer (Invitrogen), 1.5 ul of MgCl2 (50 mM), 5 ul of dNTP mix (0.01 nM), 0.5 ul of each primer (10 uM 515 F and 806R), 0.5 ul of PlatinunTM Taq DNA Polymerase (Invitrogen), 100 ng of genomic DNA and 5 – 10 ul of sterile ultrapure water. The PCR conditions were 94oC for 2 min, 30 cycles of 94oC per 45 s denaturation; 55oC per 45 s annealing and 72oC per 1 min extension; followed by 72oC per 6 min. The triplicate amplicons were pooled and purified with the Agencourt® AMPure® XP Reagent (Beckman Coulter, USA) and magnetic rack. The final concentration of the amplified DNA was estimated by using the Qubit Fluorometer Kit (Invitrogen, Carlsbad, CA). Equimolar concentrations of amplicons from all samples were mixed. This composite sample was used for library preparation with the Ion OneTouchTM 2 System with the IonPGMTM Template OT2 400 Kit Template. Sequencing 400 on Ion PGMTM System using Ion 314TM Chip v2 (Thermo Fischer Scientific, Waltham, USA).

Statistical analyses

Statistical analyses were carried out in R using the Vegan and Phyloseq packages (Oksanen et al. 2007Oksanen J, Kindt R, Legendre P, O´Hara B, Stevens MHH & Oksanen MJ. 2007. “The vegan package.” Community ecology package 10., R Core Team 2012R Core Team. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.ISBN 3-900051-07-0, URL http://www.R-project.org/.
http://www.R-project.org/...
, McMurdie & Holmes 2013McMurdie PJ & Holmes S. 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS One 8: e61217.). Soil microbial and biochemical properties were compared with box-plots showing differences among land use changes. Constrained Analysis of Principal Coordinates (CAP) was used to partition the UniFrac distance matrices of variation among samples using soil physicochemical, microbial and biochemical attributes (Anderson & Willis 2003Anderson MJ & Willis TJ. 2003. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84: 511-525.). Beta-diversity was evaluated using Weighted UniFrac distances to assess phylogenetic differences between samples (Anderson & Willis 2003Anderson MJ & Willis TJ. 2003. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84: 511-525.). Shannon diversity index (H’) was determined in each replicate, depth, and ecosystem within taxonomic groups (phylum, class, order, family, genus and species). Person’s correlation (r) was performed to assess the relationship between the bacterial compositions and soil quality indicators, which was tested at 5 % significance level according to the Student’s test.

RESULTS

Soil microbial and biochemical indicators

The values of soil microbial indicators varied among different land use and two soil depths (Fig. 2). The greatest values of MBC, SMR, MBN, and FDA were found in native Cerrado forest, but soil DHA was higher under pine system in 0-5 cm depth compared to other ecosystems. SMR had a lower change among ecosystems (pine, soya and sugarcane), but high values were observed under native Cerrado forest (Fig. 2b). Overall, the values of microbial indicators were higher in 0-5 cm depth than in 5-10 cm. In contrast to microbial indicators, soil metabolic quotient (qCO2) values were higher in agricultural systems than in forest systems (Fig. 2d).

Figure 2
Soil microbial indicators in different land-uses and soil depths in the Brazilian Cerrado. (a), microbial biomass carbon (ug C g-1 soil dry); (b), soil microbial respiration (ug CO2-C g-1 soil dry day-1); (c), microbial biomass nitrogen (ug N g-1 soil dry); (d), metabolic coefficient (ug CO2-C mg-1 MBC h-1); (e), fluorescein diacetate activity (ug FDA g-1 soil h-1); (f), dehydrogenase activity (ug INTF g-1 soil h-1).

Soil biochemical indicators also presented changes with land use systems and depths in topsoil. Beta-glucosidase activity (0-5 cm depth) was higher in agroecosystems (soya and sugarcane) than in the native Cerrado and pine forest (Fig. 3a), and the differences in activity between both depths were accentuated with lower values in 5-10 cm depth than in the 0-5 cm depth. Urease and phosphatase activities had the same response pattern in relation to land-use systems and depths with the highest values in native Cerrado forest and lowest values in the sugarcane field (Fig. 3b and 3c). ARYL activity was higher in native Cerrado forest than in other ecosystems (Fig. 3d).

Figure 3
Soil biochemical indicators in different land- uses and soil depths in the Brazilian Cerrado. (a), β-glucosidase activity (ug p-NP g-1 soil h-1); (b), urease activity (ug NH3 g-1 soil h-1); (c), phosphatase activity (ug p-NP g-1 soil h-1); (d), arylsulphatase activity (ug p-NP g-1 soil h-1). Result of each indicator within land use and depth is indicated by box-plots analysis.

The results showed that differences in soil microbial and biochemical indicators depend on the ecosystem type and soil depth. Results also showed a low variability of indicators in each land use and depth, which can be very important to study the correlation between soil biochemical indicators and microbial community composition in topsoil.

Microbial community composition

A high number of 1.19 million high-quality 16S rRNA gene reads were obtained through high-throughput sequencing, which was classified in 463 592 OTUs at 97% sequence similarity. A total of OTUs reads, only 1.4% were identified as unassigned OTUs. The bacterial community composition was mainly dominated by Proteobacteria (28%), Acidobacteria (27%), Actinobacteria (14%), Verrucomicrobia (6%), Bacteroidetes (4.9%), Chloroflexi (4.5%), and AD3 (3.2%). However, the composition of the bacterial community was strongly affected by land use systems (Fig. 4a). Acidobacteria phylum was negatively impacted on the number of OTUs when the land use was altered from the native Cerrado forest to pine forest (39% reduction), soya and sugarcane (both with 68% reduction). An increase of Actinobacteria was observed in soya and sugarcane agroecosystems. Decrease of WPS-2 phylum was also observed in soya and sugarcane agroecosystems. In general, the results showed that the top at 5-10 cm depth had a higher abundance of OTUs within phyla compared to the top at 0-5 cm depth, except for Actinobacteria and Bacteroidetes (Fig. 4a). In pine system, there was also a greater relative abundance of Acidobacteria at 0-5 cm depth. Fig. 5b shows that phyla under 2% relative abundance are also affected by land use agroecosystems and those specific phyla (Armatimonadetes, Crenarchaeota, Firmicutes, and Nitrospirae) also depend on the specified depth.

Figure 4
Taxonomic composition of soil bacterial communities in different land use ecosystems and two depths. (a) relative abundance of phylum greater than 2 %. (b) relative abundance of rare functional groups smaller than 2 %. Phylogenetic analysis was performed of high-throughput sequencing of 16S rDNA gene at 97% similarly level.
Figure 5
FWeighted UniFrac distances (a and b) and Shannon diversity indexes (c) in land use ecosystems and two depths. The analysis of diversity was performed of high-throughput sequencing of 16S rDNA gene at 97% similarly level.

Microbial diversity

Beta- and alpha-diversity of the bacterial community were characterized using weighted UniFrac distances and Shannon’s Diversity index (H’), respectively. Principal coordinate analysis (PCoA) of weighted UniFrac distances showed the similarity and differences of the bacterial community composition among ecosystems (Fig. 5a). The pine forest has a greater similarity with the native Cerrado forest, but there is a difference between two ecosystems. Principal coordinate 3 shows agroecosystems clustering by soil depth, while the native Cerrado and pine forest do not (Fig. 5b). Such results are reliable due to the high values obtained in weighted UniFrac analysis accounting higher than 72% of the variance observed. Shannon diversity index indicated a greater alpha diversity in agroecosystems than in the native Cerrado and pine forest (Fig. 5c). The H’ indices increased from higher to lower taxonomic levels and were similar between two depths within the same land use. In Fig. 6, it is shown comparative alpha-diversity indices determined by four indexes (Simpson, Cha1, ACE and Shannon). All of the indexes confirmed a higher alpha-diversity in the agroecosystems than in the native and pine forests.

Figure 6
Comparative soil diversity indexes among land use ecosystems. The analysis of violin plot shows alpha-diversity determined by Simpson, Chao1, ACE and Shannon indices of soil bacterial species.

Correlation between soil bacterial community and quality indicators

A total of 15 bacterial taxonomic groups (including Shannon index) and 14 soil quality indicators were correlated using Person’s correlation. Soil quality indicators, such as pH, MBC, MBN, qCO2, FDA, DHA, URE, and PHOP, showed significant correlations with bacterial taxonomic groups and Shannon Index (Table IV), which were associated with more than 6 bacterial taxonomic groups (including Shannon index). TOC, SMR, BGL, and ARYL showed significant correlations with less than 5 bacterial taxonomic groups. C:N rate indicator did not show any correlation with bacterial taxonomic groups.

Table IV
Person’s correlation (r) between abundance of taxonomic groups and soil quality indicators in two depths of topsoil in Cerrado agroecosystems (n=32). Minimal significance value of ¨r¨ was estimated in 0.35 or – 0.35 (p< 0.05) according to Student’s test.

The bacterial taxonomic groups, such as Acidobacteria (11/14), Archaebacteria (12/14), Bacteroidetes (8/14), Gemmatimodadetes (10/14), Planctomycetes (7/14), Gamma-proteobacteria (8/14), Firmicutes (7/14) and Shannon index (11/14) were significantly correlated with several soil quality indicators (Table IV). Proteobacteria (alpha-, beta- and gamma-) did not show any correlation with soil quality indicators studied in this survey. Actinobacteria and AD3 showed correlations with a few soil quality indicators, e.g., Actinobacteria and pH (r=0.55), AD3 and pH (r=-0.46) and AD3 and TN (r=-0.46).

DISCUSSION

Agricultural soils are constantly changing due to the use of different agricultural practices, such as tillage, fertilization, pesticide application, as well as transit of heavy machines in the crops management. Such practices modify the physical, chemical and biological properties and consequently alter the soil function and quality of ecosystems (Tilman et al. 2006Tilman D, Reich PB & Knops JM. 2006. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441: 629-632., Tiemann et al. 2015Tiemann LK, Grandy AS, Atkinson EE, Marin-Spiotta E & McDaniel MD. 2015. Crop rotational diversity enhances belowground communities and function in an agroecosystem. Ecol Lett 18: 761-771.). Microbial indicators, such as MBC, MBN, FDA, and DHA decreased in soya and sugarcane agroecosystems. These indicators are very important for ecosystem functions, because they measure a general response of soil microorganisms to environmental disturbances, indicating the efficiency of microorganisms in exploring soil resources. Generally, values of these indicators decreased in subsurface layers compared to surface layers, suggesting that microbial metabolism in the surface is more intense than in the subsurface. The major soil metabolic activity in the surface is positively correlated with a greater SOM concentration and nutrient availability in long-term agroecosystems (Coonan et al. 2020Coonan EC, Kirkby CA, Kirkegaard JA, Amidy MR, Strong CL & Richardson AE. 2020. Microorganisms and nutrient stoichiometry as mediators of soil organic matter dynamics. Nutr Cycling Agroecosyst 117: 273-298.). Soil microbial respiration (CO2 released from the soil) is an indicator used to assess the transformation and mineralization of SOM, and it is positively associated with MBC. When there is some disequilibrium between MBC and SMR, it shows that soil microorganisms have difficulty assessing SOM and SMR increases in relation to MBC (Anderson & Domsch 1993Anderson JPE & Domsch KH. 1993. The metabolic quotient (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biol Biochem 25: 393-395., Vinhal-Freitas et al. 2017Vinhal-Freitas IC, Corrêa GF, Wendling B, Bobuľská L & Ferreira AS. 2017. Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol Indic 74: 182-190.). Thus, the metabolic quotient (qCO2), the ratio between SMR and MBC, is an index used to measure soil disturbances and inefficiency of carbon use by soil microorganisms (Anderson & Domsch 1993Anderson JPE & Domsch KH. 1993. The metabolic quotient (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biol Biochem 25: 393-395.). Our results show that the native Cerrado forest has a high soil microbial respiration, but metabolic coefficient (qCO2) is lower than in pine forest and much lower than in sugarcane and soya agroecosystems.

Our results showed greater beta-glucosidase activity in agroecosystems compared to the native Cerrado forest. The increase in beta-glucosidase activity is usually linked with soil microbial respiration, because soil microorganisms depend on glucose production from the cellobiose-substrate reaction by beta-glucosidase (Bailey et al. 2013Bailey VL, Fansier SJ, Stegen JC & McCue LA. 2013. Linking microbial community structure to β-glucosidic function in soil aggregates. ISME J 7: 2044-2053., Moreno et al. 2013Moreno B, Cañizares R, Nuñez R & Benitez E. 2013. Genetic diversity of bacterial β-glucosidase-encoding genes as a function of soil management. Biol Fertil Soils 49: 735-745.). Nevertheless, the synthesis of beta-glucosidase may be influenced by bacterial community composition (Bailey et al. 2013Bailey VL, Fansier SJ, Stegen JC & McCue LA. 2013. Linking microbial community structure to β-glucosidic function in soil aggregates. ISME J 7: 2044-2053., Moreno et al. 2013Moreno B, Cañizares R, Nuñez R & Benitez E. 2013. Genetic diversity of bacterial β-glucosidase-encoding genes as a function of soil management. Biol Fertil Soils 49: 735-745.). In addition, our results reveal that beta-glucosidase activity can also be strongly linked to the relative abundance of Actinobacteria phylum. The study of Zang et al. (2017)Zang X, Liu M, Wang H, Fan Y, Zhang H, Liu J, Xing E, Xu X & Li H. 2017. The distribution of active ß-glucosidase-producing microbial communities in composting. Can J Microbiol 63: 998-1008. showed that the major reservoirs of beta-glucosidase genes were the bacterial phyla Actinobacteria, as well as Firmicutes, Proteobacteria and Deinococcus-Thermus. On the other hand, other hydrolytic enzymes related to soil nitrogen (urease), phosphorus (phosphatase) and sulphur (arylsulphatase) cycling were negatively impacted by land use changes, showing the loss of soil quality and ecological soil function in agricultural systems. Except for arylsulphatase in the soya agroecosystem, a higher biochemical activity always occurs in surface layers rather than in subsurface layers. Particularly, our study does not only show comparative changes among soil microbial and biochemical properties, but also reveals the soil functional degradation by land use agroecosystems in the Brazilian Cerrado.

Assessing microbial community composition is of fundamental importance to understanding ecological processes and the functional role of microbiota in terrestrial ecosystems. Our results showed that bacterial community composition significantly altered with the land uses and soil depths. Proteobacteria, Acidobacteria, and Actinobacteria were the phyla with the greatest relative abundance OTUs in sites. The dominance of these phyla in the Brazilian Cerrado has been shown in other surveys (Rampelotto et al. 2013Rampelotto PH, Ferreira AS, Barboza ADM & Roesch LFW. 2013. Changes in diversity, abundance, and structure of soil bacterial communities in Brazilian savanna under different Land Use systems. Microb Ecol 66: 593-607., Catão et al. 2014Catão ECP, Lopes FAC, Araújo JF, Castro AP, Barreto CC, Bustamante MMC, Quirino BF & Krüger RH. 2014. Soil Acidobacteria 16S rRNA gene sequences reveal subgroup level differences between Savanna-like Cerrado and Atlantic Forest Brazilian Biomes. Int J Microbiol 2014: 1-12.). Kielak et al. (2016)Kielak AM, Barreto CC, Kowalchuk GA, van Veen JA & Kuramae EE. 2016. The ecology of Acidobacteria: Moving beyond genes and genomes. Front Microbiol 7: 1-16. reported that Acidobacteria represents a highly diverse phylum resident to a wide range of environments around the Earth, but there is still relatively little information about the ecological role of this phylum. Acidobacteria phylum has been considered as an oligotrophic group of microorganisms in soil due to a slower growth rate and ability to metabolize nutrient-poor and recalcitrant C substrates (Davis et al. 2011Davis K, Sangwan P & Janssen PH. 2011. Acidobacteria, Rubrobacteriadae and Chloroflexi are abundant among very slow-growing and mini-colony-forming soil bacteria. Environ Microbiol 13: 798-805., Fierer et al. 2012Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA & Knight R. 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6: 1007-1017.). In particular, our studies show that native Cerrado forest has a high microbial activity and SOM content. In addition, the native Cerrado forest presents a high plant diversity that may release different metabolic compounds such as amino acids, sugars, and organic acids. These soluble compounds are readily available to soil microorganisms. In contrast, the native Cerrado forest has a lower availability of nutrients, like nitrogen, phosphorus, calcium, and magnesium, because the majority of nutrients are bonded to SOM. Studies have also revealed that the acidobacterial community has different metabolic profiles that may metabolize many carbon sources, reduce nitrate and nitrite and resist watering stress (Catão et al. 2014Catão ECP, Lopes FAC, Araújo JF, Castro AP, Barreto CC, Bustamante MMC, Quirino BF & Krüger RH. 2014. Soil Acidobacteria 16S rRNA gene sequences reveal subgroup level differences between Savanna-like Cerrado and Atlantic Forest Brazilian Biomes. Int J Microbiol 2014: 1-12., Kielak et al. 2016Kielak AM, Barreto CC, Kowalchuk GA, van Veen JA & Kuramae EE. 2016. The ecology of Acidobacteria: Moving beyond genes and genomes. Front Microbiol 7: 1-16.). In this study, the results show that the acidobacterial community is sensitive to land use changes. Although the land use causes many alterations in soil physiochemical properties, the increase of soil pH might be a primary factor in reducing of the acidobacterial community. Increasing soil pH can directly affect the metabolic function of the acidobacterial community and indirectly favour the growth of other microbial groups. In general, these effects may decrease the competitive capacity of the acidobacterial community in soil environments.

Proteobacteria and Actinobacteria are ecological groups of soil microorganisms with the fundamental roles in ecosystem processes due to their diversity, abundance and metabolic profiles. Our results showed that the proteobacterial community hardly changed in the community composition with the highest relative abundance detected in the sugarcane ecosystem. In general, there was a lower abundance of this phylum in subsurface layers than in surface layers, except for the soya system, which showed a similar pattern. These results reveal that the proteobacterial community may have a lower dependence on pH due to a similar pattern observed among land use systems. The distribution of this phylum in each studied ecosystem can be linked to the C source available in soil (Fierer et al. 2012Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA & Knight R. 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6: 1007-1017.). On the other hand, the abundance of Actinobacteria increased in agricultural soils (soya and sugarcane agroecosystems) in relation to those observed in the native Cerrado and pine forest. These results can be associated with pH-dependent values, as Actinobacteria have a better growth in neutral pH conditions. Fierer et al. (2012)Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA & Knight R. 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6: 1007-1017. also reported that N fertilization increased the abundance of the actinobacterial community, suggesting a positive effect of this nutrient on ecological distribution of Actinobacteria in soil. Concerning soil function, Proteobacteria and Actinobacteria have been putatively identified as being copiotrophic taxa, which have high growth rates in conditions of elevated C availability (Eilers et al. 2012Eilers KG, Debenport S, Anderson S & Fierer N. 2012. Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem 50: 58-65., Fierer et al. 2012Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA & Knight R. 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6: 1007-1017.).

Soil microbial diversity is considered to be critical to the integrity, function, and long-term sustainability of soil ecosystems. Many studies have shown that microbial diversity in soil ecosystems decreases with the land use intensification, such as nutrient availability (van der Heijden et al. 2008van der Heijden MGA, Bardgett RD & van Straalen NM. 2008. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11: 296-310., Tiemann et al. 2015Tiemann LK, Grandy AS, Atkinson EE, Marin-Spiotta E & McDaniel MD. 2015. Crop rotational diversity enhances belowground communities and function in an agroecosystem. Ecol Lett 18: 761-771., Zhalmina et al. 2015), nitrogen deposition and chemical contamination (Gans et al. 2005Gans J, Woilinsky M & Dunbar J. 2005. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science 309: 1387-1390., Li et al. 2016Li H, Xu Z, Yang S, Li X, Top EM, Wang R, Zhang Y, Cai J, Yao F, Han X & Jiang Y. 2016. Responses of soil bacterial communities to nitrogen deposition and precipitation increment are closely linked with aboveground community variation. Microb Ecol 71: 974-989.). In the present work, weighted UniFrac distances were used to measure beta-diversity of soil microbial communities. Beta-diversity is defined as the variation in the community composition and measurement of pair-wise dissimilarity between plots (Prober et al. 2015Prober SM ET AL. 2015. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol Lett 18: 85-95.). Studies have shown that the β-diversity of soil microbes has a positive correlation with plant β-diversity in many environments (Prober et al. 2015Prober SM ET AL. 2015. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol Lett 18: 85-95.). It is also shown that pine forest, as a monoculture system, is quite similar to the native Cerrado forest in relation to soil bacterial composition. Pine forest is a stable environment with the few anthropogenic disturbances and nutrient poor, containing a thick layer of litter aboveground, as well as eco-mycorrhizal fungi associations with roots are observed in this ecosystem. Such characteristics of pine forest can be determinant on bacterial communities and soil quality, but more studies are needed in order to better understand the bacterial diversity in the pine forests of tropical soils.

The values of Shannon diversity index (H’) were revealed to be higher in agricultural soils compared to native Cerrado forest and pine forest soil. The Shannon index (H’) has been used to assess the α-diversity and is considered a sensitive indicator to evaluate the anthropogenic perturbations such as nitrogen fertilizers, pH effects and heavy metal stresses (Jangid et al. 2008Jangid K, Willians MA, Franzluebbers AJ, Sanderlin JS, Reeves JH, Jenkins MB, Endale DM, Coleman DC & Whitman WB. 2008. Relative impacts of land-use, management intensity and fertilization upon soil microbial community structure in agricultural systems. Soil Biol Biochem 40: 2843-2853., Zhalmina et al. 2015, Liu et al. 2019Liu C, Jin, Y, Hu Y, Tang J, Xiong Q, Xu M, Bibi F & Beng KC. 2019. Drivers of soil bacterial community structure and diversity in tropical agroforestry systems. Agric Ecosyst Environ 278: 24-34.). Nevertheless, Prober et al. (2015)Prober SM ET AL. 2015. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol Lett 18: 85-95. reported that microbial a-diversity had a weak correlation with plant diversity aboveground in biogeographic scales. In our study, the high values of a-diversity of the bacterial community in soya and sugarcane agroecosystems can be a result of soil chemical properties (soil reaction, nutrients content, SOM) elapsed by land use changes. Changes in soil pH in these agroecosystems can be the main abiotic factor that drives the alpha-diversity of bacterial communities. These findings correspond with the previous research showing a strong positive relationship when assessed between bacterial a-diversity and soil pH (Lauber et al. 2008Lauber C, Strickland M, Knight R & Fierer N. 2008. The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol Biochem 40: 2407-2415., Zhalmina et al. 2015, Li et al. 2016Li H, Xu Z, Yang S, Li X, Top EM, Wang R, Zhang Y, Cai J, Yao F, Han X & Jiang Y. 2016. Responses of soil bacterial communities to nitrogen deposition and precipitation increment are closely linked with aboveground community variation. Microb Ecol 71: 974-989., van der Bom et al. 2018van der Bom F, Nunes I, Raymond NS, Hansen V, Bonnichse L, Magida J, Nybroe O & Jensen LS. 2018. Long-term fertilisation form, level and duration affect the diversity, structure and functioning of soil microbial communities in the field. Soil Biol Biochem 122: 91-103.). Although there were differences in soil pH between two soil depths and within the same system, the present results did not indicate differences of bacterial alpha-diversity between the depths. Changes in the Shannon diversity indexes might have been influenced by the great abundance of rare bacterial communities (i.e., Armatimonadetes, Firmicutes, Nitrospirae, and WS3) in agroecosystems, as the Shannon index is affected by both the number of species and their equitability or evenness. It is also believed that a rise in copiotrophic microorganisms can increase bacterial a-diversity values in soils. The present work shows that a high a-diversity of bacterial communities might indicate a lower use efficiency of soil resources as shown above by soil quality indicators.

Some studies have separately shown the effects of land use on soil microbial diversity (Rampelotto et al. 2013Rampelotto PH, Ferreira AS, Barboza ADM & Roesch LFW. 2013. Changes in diversity, abundance, and structure of soil bacterial communities in Brazilian savanna under different Land Use systems. Microb Ecol 66: 593-607., Manoharan et al. 2017Manoharan L, Kushwaha SK, Ahrén D & Hedlund K. 2017. Agricultural land use determines functional genetic diversity of soil microbial communities. Soil Biol Biochem 115: 423-432.) and soil quality indicators (Vinhal-Freitas et al. 2013Vinhal-Freitas IC, Ferreira AS, Corrêa GF & Wendling B. 2013. Land use impact on microbial and biochemical indicators in agroecosystems of the Brazilian Cerrado. Vadose Zone J 12: 1-8., Vinhal-Freitas et al. 2017Vinhal-Freitas IC, Corrêa GF, Wendling B, Bobuľská L & Ferreira AS. 2017. Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol Indic 74: 182-190.). To the best of our knowledge, there is no information about the relationship between soil quality indicators and soil bacterial community composition in tropical ecosystems. In this study, our results showed that soil pH had a negative correlation with Acidobacteria, AD3, and Gamma-proteobacteria, but it had a positive correlation with Actinobacteria, Archaebacteria, Bacteroidetes, Gemmatimonadetes, Planctomycetes and Firmicutes groups, as well as Shannon Index. Soil TOC was positively correlated with Acidobacteria and negatively correlated with three other groups (Archaebacteria, Bacteriadetes, and Gemmatimonadestes), including Shannon index. Soil TN had a negative correlation with AD3 and Archaebacteria, and C:N rate did not interference in soil bacterial composition. These results show that soil pH and TOC are important chemical properties with the impact on soil bacterial compositions under land use systems, indicating that both properties have an opposite effect on soil bacterial composition. Some studies have reported that soil pH has a key role in governing soil microbial community structure (Ling et al. 2016Ling N, Zhu C, Xue C, Chen H, Duan Y, Peng C, Guo S & Shen Q. 2016. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by networks analysis. Soil Biol Biochem 99: 137-149., Zhalmina et al. 2015), but it appears that soil pH is more important when there were comparisons in the same site with different fertilization levels.

Our results show that the increasing Shannon diversity index is negatively correlated with most of soil quality indicators and soil function properties. A positive correlation of Shannon index is only observed with qCO2 and beta-glucosidase activity. These results show that soil bacterial community used to determinate Shannon indices may have a low contribution on soil quality indicator and soil function. Thus, assessing Shannon indices in land use agroecosystems could not be representative for comparing the soil function, but it could be important to evaluate differences in management practices within the same land use system (Nemergut et al. 2011NEMERGUT DR ET AL. 2011. Global patterns in the biogeography of bacterial taxa. Environ Microbiol 13: 135–144., Zhalmina et al. 2015).

CONCLUSIONS

This work showed ecological effects of land use change on soil function and microbial composition in the Brazilian Cerrado. Soil microbial and biochemical indicators showed that agricultural soil usage has a negative impact on soil function, indicating a lower use-efficiency of soil resources by microorganisms in agroecosystems. The effects of land use systems on soil quality indicators were higher in 0-5 cm depth than in 5-10 cm depth. Agricultural soil usage tends to increase the relative abundance of copiotrophic bacteria (Proteobacteria and Actinobacteria) and rare groups of some taxa (Nitrospirae, Firmicutes and Armatimonadetes) and decrease the relative abundance of oligotrophic bacteria (Acidobacteria). Soil pH was the soil variable that most affected bacterial community composition, but its relationship depends on the taxonomic group. Shannon diversity (H’) index had a negative correlation with most of soil microbial and biochemical indicators assessed but it had a positive correlation with soil pH, qCO2 and beta-glucosidase activity. Our survey over land use systems in the Brazilian savanna (native forest, pine forest, soya and sugarcane agroecosystems) showed that soil function might be more dependent on the microbial abundance than on the microbial diversity.

ACKNOWLEDGMENTS

The authors are thankful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil) for scholarship support of the last author. The work was also supported by the Agency of Ministry of Education, Science, Research and Sport of the Slovak Republic ITMS: 26110230119.

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Publication Dates

  • Publication in this collection
    20 Sept 2021
  • Date of issue
    2021

History

  • Received
    10 Dec 2020
  • Accepted
    20 Mar 2021
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