Functional Microbial Diversity in Context to Agriculture

Chapter 20

Functional Microbial Diversity in Context to Agriculture Anna Gała˛zka and Karolina Furtak Department of Agricultural Microbiology, Institute of Soil Science and Plant Cultivation—State Research Institute, Puławy, Poland

20.1

INTRODUCTION

The functioning of the soil environment depends mainly on the number and activity of microorganisms, but also on their diversity. Species diversity of microorganisms affects the functional diversity of soil. Despite intensive research development, the number of known microorganisms varies from 0.1% to 10%. The least known group of microorganisms are those that inhabit the soil used for agricultural purposes. This is mainly due to the complexity and dynamics of agroecosystems. The soil microbial composition is influenced by the type of soil, season, weather conditions, the type of plant grown and stage of its growth, and especially the agrotechnics used by human (Torsvik and Øvrea˚s, 2002). In modern research of microbial communities, microorganisms are more and more often used to classify the criterion of their function rather than taxonomic classification. Functional diversity is related to the functions performed by individual species and complete consortia of microorganisms. Knowing it helps to understand the principles of the ecosystem. Diversity can be indicated in a number of ways. One of them is the analysis of expression of genes responsible for particular functions. The enzymatic activity of microorganisms is also determined. An interesting method is to evaluate the consumption of organic substrates by microorganisms. In 1991, Garland and Mills developed a method that uses the ability of microorganisms to consumption different carbon sources—CLPP—Community Level Physiological Profiling. Subsequently, Biolog has established CLPP-based research equipment, which allows to obtain a large amount of data reflecting the metabolic characteristics of microbial consortia in one research.

20.2

HUMAN ACTIVITY AND MICROBIAL DIVERSITY

The soil is a habitat for many different species of microorganisms and is adaptable to changing environmental conditions (Allison and Martiny, 2008). If one or more species are lost, microorganisms that can assume their functions will still be present in the environment (Jurburg and Salles, 2015). However, this ability is limited, and the loss of biodiversity has a negative impact on it. This leads to a disturbance in the stability of the soil environment. Human agricultural activities have a directly impact on the characteristics of the soil, including the composition and functioning of its microbiome. Intensive soil tillage can cause a reduction in the species diversity of soil microorganisms. Some researchers suggest that even a small biodiversity loss (B20%) may have a significant impact on soil functionality, including basic biochemical processes. This is indicated by positive correlations between the biodiversity of microorganisms in soil and the processes related to the cycle of nutrients in soil and respiration (Colombo et al., 2016; Wagg et al., 2014).

20.3

BIOLOG ECOPLATE METHOD

Population profiling with Biolog System is carried out by spectrophotometric measurements of the redox color reaction. It occurs on 96-well plates with different carbon substrates and a crystalline violet pigment, when the microorganisms inoculated into a well are metabolizing the carbon source. The microbial community gives a characteristic response pattern called a metabolic fingerprint (Mulcahy and Edenborn, 2007). This method has advantages and disadvantages, but it contributes to a greater understanding of the cooperation between microorganisms in the environment. Microbial Diversity in the Genomic Era. DOI: https://doi.org/10.1016/B978-0-12-814849-5.00020-4 © 2019 Elsevier Inc. All rights reserved.

347

348

SECTION | IV Functional Microbial Diversity

TABLE 20.1 The Selected Plates Offered by Biolog (According to biolog.com) Type of Biolog Microplates

Application

Plate Content

Example Substances

YT

Identification and characterization of yeast strains

Water as a control Modified tetrazolium dye 35 oxidation tests 59 assimilation tests

Methyl succinate, stachyose, xylitol, adonitol, mannitol, cellobiose, inulin, turanose

FF

Characteristic and identification of filamentous fungi

Modified tetrazolium dye Water as a control 95 carbon sources

Cyclodextrin, maltitol, sucrose, threonine, serine, aspartic acid, arabinose, xylitol, salicin

AN

Identification of anaerobic bacteria

Tetrazolium violet Water as a control 95 carbon sources

Adonitol, amygdalin, dulcitol, glycerol, acetic acid, alaninamide, inosine, glycyl-L-aspartic acid, thymidine

GEN III

Identification of Gram-positive and Gram-negative bacteria without differentiating them

Tetrazolium violet Negative control Positive control 71 carbon sources 23 chemical sensitivity assays

Sucrose, inosine, music acid, glycerol, formic acid, stachyose, myoinositol, citric acid, pectin, gelatin, lactic acid, NaCl

ECO

Microbial community characterization from environmental samples

Tetrazolium violet Water as a control 31 carbon sources

Tween 40, glycogen, putrescine, itaconic acis, serine, threonine, mannitol, xylose, arginine

MT

The plates containing only pigment intended for individual experiments

Tetrazolium violet

(For self-preparation)

20.3.1

The Principle of the Biolog Method

Biolog technique is based on the analysis of colorimetric results of redox reaction. The intensity of the change in the color change of the reducing pigment—tetrazolium violet (Hatzinger et al., 2003)—is measured. As soon as the microorganisms start to consume carbon and release metabolites, the color changes to violet. The spectrophotometric intensity of staining and the variation between the individual wells shall be measured. Several types of Biolog tiles are available to analyze different groups of microorganisms (Table 20.1). Most of the plates include carbon sources with standard lyophilized medium in wells. Depending on the type of plates, these are different sources of carbon. There are also plates with specific sources, e.g., sulfur, nitrogen, or phosphorus, for specialized groups of microorganisms. Biolog along with the plates provide equipment. The main instrument—OmniLog ID—is also used as a plate’s incubator and automatically carries out spectrophotometric measurements. Depending on the settings, the reading can be carried out, e.g., every 15 minutes. This enables detailed monitoring of metabolic changes occurring in wells. It contains at the same time 50 microplates, and the incubator can be set to any temperature. A minor component is the plate reader—MicroStation ID. It can be used to measure for one plate at time. The whole system makes it possible to identify microorganisms and analyze the metabolism of the entire microbial community. The database of Biolog Microbial ID System enables to identify over 2900 species of aerobic and anaerobic bacteria, yeasts, and fungi.

20.3.2

EcoPlate

The EcoPlate is used to create a metabolic profile of whole microbial community occurring in different environments. This method, developed in 1991 by Garland and Mills, was firstly based on an analysis of 95 different carbon sources. Currently, 96-well plates contain 31 different carbon substrates in three repetitions (Insam, 1997). The lyophilized

Functional Microbial Diversity in Context to Agriculture Chapter | 20

349

FIGURE 20.1 Biolog EcoPlate (own elaboration).

substrates are placed in three rows of wells on the plate. In wells number A1, A5, and A9, there is a water as a control sample. All wells contain a tetrazolium violet redox dye (Fig. 20.1). Substrates from EcoPlate can be classified into five biochemical groups: carbohydrates, carboxylic and ketonic acids, amines, polymers, and amino acids (Weber and Legge, 2009). Table 20.2 presents a list of the carbon substrates contained in the EcoPlate and their classification. These are commonly found in the environment, especially in soil (Preston-Mafham et al., 2002). Nine of them are knows as components of exudates of plant roots (Campbell et al., 1997). Carbohydrates are the largest group of substrates on the EcoPate (10). Then there are nine carboxylic and ketone acids. Amino acids are a smaller group (6). There are also four polymers on the plate. The lowest group of substrates are amines represented by phenylethylamine and putrescine.

20.3.2.1 Procedure for EcoPlate Using the Biolog System doesn’t require much preparation, effort, or time. The most commonly used procedure for determining the metabolic profile of microorganisms is included: 1. 2. 3. 4.

Preparation of a suspension containing the tested microorganism community; Transfer of the suspension to the EcoPlate; Incubation of a plate in the OmniLog ID under optimal conditions; Spectrophotometric reading of the color change in wells.

The suspension can be prepared in a few ways. The environmental sample may be suspended in sterile water, 0.9% NaCl, peptone water, phosphate buffer, or Tris buffer (Kelly and Tate, 1998; Hitzl et al., 1997). In some cases, this suspension should be filtered to eliminate the larger particles of, e.g., soil or dilute. Soil samples are prepared as follows: 1 g of fresh soil is suspended in 99-mL sterile solution and shaken for 20 minutes at 15
C, and then incubated at 4
C for 30 minutes (Weber and Legge, 2009). The obtained suspension is inoculated on sterile EcoPlate by 120 μL to each well, being careful not to transfer the medium between the wells. Microplates are incubated in OmniLog ID under optimal conditions. The incubation time and temperature depend on the sample and the expected results. Soil microorganism grows at a temperature of 2527
C. The first changes are visible after only 24 hours of incubation. In order to observe whether a carbon source will be used completely or when the microorganisms have reached maximum growth on a given substrate, incubate should be carried out longer. Some microorganisms grow so slowly that the measurement may take several days.

20.3.2.2 Interpretation of Results When the microorganisms grow in a well and utilized the carbon source, tetrazolium violet is reduced to formazan. This reaction results in a change of color in the well from colorless to purple. The rapidity and range of coloration indicates the dynamics and intensity of microbial metabolism. Spectrophotometric measurement (absorbance or so-called optical density—OD) of color change is carried out at two wavelengths: 560 and 590 nm (Kelly and Tate, 1998). Researchers suggested doing a number of measurements during the incubation and choosing those readings that exhibit

350

SECTION | IV Functional Microbial Diversity

TABLE 20.2 List of Substrates Placed on the Biolog EcoPlate with a Classification to 5 Biochemical Groups (Following Weber and Legge, 2009) Well Number

Substrate

Biochemical Groups

A1

Water (control)



B1

Pyruvic acid methyl ester

Carbohydrates

C1

Tween 40

Polymers

D1

Tween 80

Polymers

E1

α-Cyclodextrin

Polymers

F1

Glycogen

Polymers

G1

D-Cellobiose

Carbohydrates

H1

α-D-Lactose

Carbohydrates

A2

β-Methyl-D-glucoside

Carbohydrates

B2

D-Xylose

Carbohydrates

C2

i-Erythritol

Carbohydrates

D2

D-Mannitol

Carbohydrates

E2

N-Acetyl-D-glucosamine

Carbohydrates

F2

D-Glucosaminic

Carboxylic acids

G2

Glucose-1-phosphate

Carbohydrates

H2

D,L-α-Glycerol

Carbohydrates

A3

D-Galactonic

B3

D-Galacturonic

C3

2-Hydroxy benzoic acid

Carboxylic acids

D3

4-Hydroxy benzoic acid

Carboxylic acids

E3

γ-Hydroxybutyric acid

Carboxylic acids

F3

Itaconic acid

Carboxylic acids

G3

α-Ketobutyric acid

Carboxylic acids

H3

D-Malic

Carboxylic acids

A4

L-Arginine

Amino acids

B4

L-Asparagine

Amino acids

C4

L-Phenylalanine

Amino acids

D4

L-Serine

Amino acids

E4

L-Threonine

Amino acids

F4

Glycyl-L-glutamic acid

Amino acids

G4

Phenylethylamine

Amines

H4

Putrescine

Amines

acid

phosphate

acid γ-lactone acid

acid

Carboxylic acids Carboxylic acids

approximately the same average well color development (AWCD). The AWCD is calculated according to the formula proposed by Garland and Mills (1991): X ð C 2 RÞ AWCD 5 n

Functional Microbial Diversity in Context to Agriculture Chapter | 20

351

where C is the reading from the well OD, R is the reading from the control (water) well OD, and n is the number of substrates on an EcoPlate (31). Kinetic analysis of color changes on plates is also commonly used (Mondini and Insam, 2003). The time from the inoculation of a solution into wells to the beginning of color development, maximum rate of color development and the maximum absorbance in a well can be determined (Campbell et al., 1997). This analysis increases the amount of information that can be obtained about the physiological profile (Garland, 1997). The Shannon index (H0 ) can be used as a diversity of the extent of utilization of particular substrates (substrate evenness) (Zak et al., 1994): H0 5 2

N X   pi lnpi i51

where pi is the proportional color development of the well over total color development of all wells of a plate and N is the number of substrates on an EcoPlate (31). The amount of carbon sources utilized by a sample allows to determine the homogeneity index (E). The results of sample analysis using the Biolog System require multidimensional statistical analyses due to their complexity (Lv et al., 2017).

20.3.2.3 Microbial Community Analysis with EcoPlates—Abilities The EcoPlate is used in various environmental research. The use of these technique allows to determine the availability of microorganisms to catabolized substrates and determine the significance of this metabolism in the population. It is also used to monitor the changes in the communities of microorganisms in soils and other natural environments over time. The EcoPlate can be used to analyze samples from the soil, water, wastewater, sludge, compost, industrial waste, and rhizosphere (Fig. 20.2) (Garland and Mills, 1991). The EcoPlate can be used to determine the effect of soil management practices and plant protection products on the functional diversity of soil microorganisms (Floch et al., 2011), and the effects of the accumulation of antibiotics in soil (Liu et al., 2012). Determination of microbial population metabolism using EcoPlate has many positive aspects. This method also has defects and the results obtained may also be too general for some research (Table 20.3).

20.4 ASSESSMENT OF THE METABOLIC DIVERSITY OF MICROORGANISMS ACCORDING TO ECOPLATE AS AN INDICATORS OF SOIL QUALITY ON EXAMPLE OF REPRESENTATIVE SOILS OF POLAND 20.4.1

Research Aim and Material

Soil quality and its fertility is based on intense microbial activity, including enzymatic activity and metabolic diversity of microorganisms. The choice and elaboration of indicators for evaluation and formation of microbial diversity in soils and soil microorganisms activity in various habitats and management systems is of great importance. Soil enzymes take part in metabolism and catalyze processes connected to matter and energy processing in soil. Any changes in soil properties can lead to the changes in number and activity of soil organisms including their species composition and biodiversity.

FIGURE 20.2 Examples of environmental samples that can be measured using Biolog EcoPlate.

352

SECTION | IV Functional Microbial Diversity

TABLE 20.3 Strengths and Weakness of the Biolog EcoPlate Method (Ross et al., 2008; Malosso et al., 2005) Strengths of the Method

Weakness of the Method

Quickness

Doesn’t include extremophiles microorganisms

Repetitiveness

It is not possible to define the specific properties of microorganisms

Simple procedure Large number of analyzed substrates Excluding the growing and isolation a microbial culture Analysis of the whole microbial population at one time

Determining the metabolism itself may not be enough to determine the environmental impact on microorganisms

Substrates on the plate do not always represent a carbon sources in a specific environmental sample

The measuring instrument that simultaneously incubator The first results after 24 h

The aim of the study was to compare the most commonly used indicators of soil quality on the example of the representative soil of Poland. Soil samples were collected from 17 points of Lubelskie Voivodeship, Poland for analysis of arable soil. They represent areas of quite high degree of intensive tillage located in areas of other than agriculture influence of human activity. Database that characterizes location of soil sampling includes pH, salinity, content of metals, and organic compounds, carbon content, macronutrients content, texture, and others (Table 20.4). Biological diversity was determined in addition to enzymatic activity, glomalins contents and the functional diversity and biodiversity indexes using the Biolog EcoPlate method. More information about soil samples were available in web pages http:// www.gios.gov.pl/chemizm_gleb.

20.4.2

Methods

Metabolic potential of soil communities was evaluated using Biolog EcoPlate (Biolog Inc., Hayward, USA) with 31 carbon sources. Each well of the plate was inoculated with 120 μL of soil inoculum and incubated at 28
C. Absorbance readings were taken every 24 hours for 264 hours at 590 nm with a plate reader Biolog MicroStation. On the basis of data obtained at 120 hours, Richness (S), ShannonWeaver (H0 ), Evenness (E), and AWCD indexes were calculated following Garland and Mills (1991). The glomalin content was determined according to Wright and Upadhyaya methods (Wright et al., 1996). The easily extractable glomalin (EEG), total glomalin (TG) were extracted from soil subsamples. EEG was extracted from 1 g of ground dry-sieved soil with 8 mL of 20-mmol citrate, pH 7.0 at 121
C for 30 minutes. TG was obtained by repeated extraction from 1 g of ground dry-sieved soil with 8 mL of 50 mmol citrate, pH 8.0 at 121
C for 60 minutes. The protein content in the supernatant was determined by the Bradford assay with bovine serum albumin as the standard on 96plate reader (Victor, Perkin Elmer, USA). The dehydrogenases activities (DHA) were determined spectrophotometrical using the triphenyltetrazolium chloride method (Polish Standard, 2011). Statistical analyses were performed using the packet Statistica. PL (version 10.0, StatSoft Inc., Tulsa, USA). Collected data were assessed by a three-way (enzymes activities, microbial populations, biodiversity indices from EcoPlate) analysis of variance for the comparison of means, and significant differences were calculated according to post-hoc Tukey’s HSD (honest significant difference) test at P , 0.05 significant level. Cluster analysis, including grouping of treatments and features, was performed on standardized data from the average absorbance values at 120 hours (Biolog EcoPlate). The dendrogram was prepared with scaled bond distances on the axis (Ward’s method).

Functional Microbial Diversity in Context to Agriculture Chapter | 20

353

TABLE 20.4 Characteristics of Selected Soil Samples Number

Type

Abbreviation

pHH2 O

Corg (%)

Norg (%)

1_Bk

Cambisols

B

5.9

1.21

0.11

2_F

Fluvisols

F

5.7

1.02

0.08

3_Dz

Umbisols

Dz

7.7

0.89

0.10

4_A

Podzols

A

5.9

0.81

0.10

5_Bw

Cambisols

B

6.5

1.18

0.14

6_B

Cambisols

B

7.0

1.22

0.13

7_Bw

Cambisols

B

7.0

0.83

0.10

8_Ar

Combisols (rusty soil)

Ar

5.2

0.97

0.08

9_Bw

Cambisols

B

5.9

1.62

0.94

10_Bw

Cambisols

B

7.4

0.96

0.08

11_Gc

Leptosols

G

7.8

3.18

0.36

12_Bw

Cambisols

B

7.3

1.06

0.11

13_B

Cambisols

B

6.9

0.94

0.11

14_Bw

Cambisols

B

6.6

0.97

0.13

15_B

Cambisols

B

6.5

0.95

0.12

16_Fb

Fluvisols

F

6.4

0.90

0.11

17_C

Charnozems

C

6.7

1.61

0.15

FIGURE 20.3 Dehydrogenases activity of different soil types.

20.4.3

Results

The highest DHA was found in two types of soil: Leptosols and Umbisols (Fig. 20.3). The smallest DHA was in two typed of soil: Cambisols and Charnozems. However, DHA in this samples did not very differ from DHA in other samples. The Leptosols was also characterized the highest content of TG (Fig. 20.4). For Umbisols, both TG and EEG contents were the lowest of all samples (Fig. 20.4). The TG content of all soil was more varied than the EEG content (Fig. 20.4).

354

SECTION | IV Functional Microbial Diversity

FIGURE 20.4 Glomalins contents in different soil types: (A) total glomalin content (TG), (B) EEG content. EEG, easily extractable glomalin.

The heatmaps for the carbon utilization patterns of the substrates located only on the Biolog EcoPlate data incubated for 120 hours from soil samples also confirmed differentiation between types of soil (Fig. 20.5). Differences are also visible between samples from the same soil type (e.g., Cambisols), but from different locations (samples: 1_Bk, 9_Bw, 13_B). This may indicate the sensitivity of the Biolog EcoPlate method. It also illustrates the sensitivity of this method to any changes, including geographical differences in sampling, the effects of atmospheric conditions and cultivation systems, which is reflected in the different levels of use of carbon sources. Based on the principal component analysis (PCA) analysis, as you can see in Fig. 20.6. Three major groups of soils have been indicated: (1) Leptosols and Umbisols, (2) Charnozems and 12 samples from Cambisols, (3) Fluvisols and one sample from Cambisols. Also three clusters have been obtained from the cluster analysis that was performed on the soil quality indicators (Fig. 20.6). The first one incudes the contents for soil glomalins, acid phosphatase activity, and organic nitrogen content. Positive correlations between nitrogen and GRSP fractions in the soil were also obtained by other researchers (Silva et al., 2018). The second group is the percentage of utilization of five biochemical groups of

Functional Microbial Diversity in Context to Agriculture Chapter | 20

355

FIGURE 20.5 Heatmaps for the carbon utilization patterns of the substrates located on the Biolog EcoPlate data incubated for 120 h from soil samples.

substrates and AWCD index derived from the Biolog EcoPlate method. The last group includes microbial biomass carbon content, microbial biomass nitrogen content, DHA, alkaline phosphatase activity, organic carbon content, number of phosphate solubilizing bacteria, number of total bacteria, Azotobacter spp. bacteria, pH. These are indicators that provide information on the transformation processes of soil organic matter. Comparisons of the patterns of substrate utilization and the diversity indexes showed differences in community composition of microorganisms related to different soil types. The principal component of PCA analysis showed the strong correlation between the parameters of soil quality and biodiversity indicators (Table 20.5). Selected indicators of soil microbial diversity explained 87.14% of biological variability in soils.

20.5 G

G G

G

CONCLUSION

The Biolog EcoPlate method is widely accepted as a sensitive tool to indicate viable microbial and provide a fingerprinting for the microbial community composition. The numbers of different groups of microorganisms are also an indicator of changes taking place in the soil environment and soil types. Soil microbial communities including glomalines contents shifted with types of soil. Soil types was important factor affecting soil microbial communities and glomalin content. Further research is needed to determine the soil microbial community composition, to identity a key organisms and their dynamics in differ soil types. It can be assumed that such a factor identifying the quality of soil may be the metabolic potential of soil communities.

356

SECTION | IV Functional Microbial Diversity

FIGURE 20.6 Dendrograms: (A) the bond distances between analyzed soil samples from different soil types; (B) the bond distances between the carbon utilization patterns of the substrates located on the Biolog EcoPlate and microbiological indicators; pHH2 O —soil pH in H2O; pHKCl—soil pH in KCl; Corg—organic carbon content; Norg—organic nitrogen carbon; DHA—dehydrogenases activity; MBC—microbial biomass carbon content; MBN—microbial biomass nitrogen content; C/N—ratio of MBC and MBN; TG—total glomalin content; EEG—easily extractable glomalin; AWCD—average well color development; GRSP—glomalin-related soil protein; PSB—number of phosphate solubilizing bacteria; Azo—Azotobacter spp. bacteria; AlP—alkaline phosphatase activity; F—number of total fungi; AcP—acid phosphatase activity; B—number of total bacteria; five biochemical groups carbon sources from Ecoplate: polymers, carbohydrates, carboxylic and acetic acids, aminoacids, amines, and amides.

Functional Microbial Diversity in Context to Agriculture Chapter | 20

357

TABLE 20.5 Correlation of Carbon Source with the First (PC1) and Second (PC2) Components in Soil: pHH2 O —Soil pH in H2O; pHKCl—Soil pH in KCl; Corg—Organic Carbon Content; Norg—Organic Nitrogen Content; C/N—Ratio Carbon and Nitrogen Contents; DHA—Dehydrogenases Activity; MBC—Microbial Biomass Carbon Content; MBN— Microbial Biomass Nitrogen Content; TG—Total Glomalin Content; EEG—Easily Extractable Glomalin Content; AWCD—Average Well Color Development PC1 (72.44%)

PC2 (14.7%)

pHH2 O

20.805

0.080

pHKCl

20.384

0.816

Corg (%)

20.384

0.816

Norg (%)

0.077

0.290

C/N

0.520

0.287

DHA

20.726

0.478

MBC

20.624

0.719

MBN

20.631

0.657

TG

0.124

0.600

EEG

0.185

20.003

Aminoacids

20.430

20.697

Carboxylic/acetic acids

20.644

20.441

Carbohydrates

20.775

20.310

Polymers

20.433

20.277

AWCD

20.737

20.500

ACKNOWLEDGMENTS The research was conducted within the frames of Tasks 1.4. Multi-Annual Program IUNG—PIB 2016—2020.

REFERENCES Allison, S.D., Martiny, J.B.H., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. U.S.A. 105, 1151211519. Campbell, C.D., Graystons, J., Hirst, D.J., 1997. Use of rhizosphere carbon sources in sole carbon source test to discriminate soil microbial communities. J. Microbiol. Methods 30, 33. Colombo, F., Macdonald, C.A., Jeffries, T.C., Powell, J.R., Singh, B.K., 2016. Impact of forest management practices on soil bacterial diversity and consequences for soil processes. Soil Biol. Biochem. 94, 200210. Floch, C., Chevremont, A.C., Joanico, K., Capowiez, Y., Criquet, S., 2011. Indicators of pesticide contamination: Soil enzyme compared to functional diversity of bacterial communities via Biologs Ecoplates. Eur. J. Soil Biol. 47, 256263. Garland, J.L., 1997. Analysis and interpretation of community-level physiological profiles in microbial ecology. FEMS Microbiol. Ecol. 2, 289300. Garland, J.L., Mills, A., 1991. Classification and characterization of heterotrophic microbial communities on the basis or patterns of community level sole carbon source utilization. Appl. Environ. Microbiol. 57, 23512359. Hatzinger, P.B., Palmer, P., Smith, R.L., Pen˜arrieta, C.T., Yoshinari, T., 2003. Applicability of tetrazolium salts for the measurement of respiratory activity and viability of groundwater bacteria. J. Microbiol. Methods 52, 4758. Hitzl, W., Rangger, A., Sharma, S., Insam, H., 1997. Separation power of 95 substrates of the Biolog system determined in various soils. FEMS Microbiol. Ecol. 22, 167174. http://www.gios.gov.pl/chemizm_gleb. Insam, H., 1997. A new set of substrates proposed for community characterization in environmental samples. In: Insam, H., Rangger, A. (Eds.), Microbial Communities: Functional Versus Structural Approaches (red.). Springer-Verlag KG, Berlin, Germany, pp. 259260. Jurburg, S.D., Salles, J.F., 2015. Functional redundancy and ecosystem function—the soil microbiota as a case study. In: Lo, Y.H., Blanco, J.A., Roy, S. (Eds.), Biodiversity in Ecosystems—Linking Structure and Function (red.). InTech, London, UK, pp. 2949. Kelly, J., Tate, R.L., 1998. Use of Biolog for the analysis of microbial communities from zinc-contaminated soils. J. Environ. Qual. 27, 600608.

358

SECTION | IV Functional Microbial Diversity

Liu, F., Wu, J., Ying, G.G., Luo, Z., Feng, H., 2012. Changes in functional diversity of soil microbial community with addition of antibiotics sulfamethoxazole and chlortetracycline. Appl. Microbiol. Biotechnol. 95, 16151623. Lv, T., Zhang, Y., Carvalho, P.N., Zhang, L., Button, M., Arias, C.A., et al., 2017. Microbial community metabolic function in constructed wetland mesocosms treating the pesticides imazalil and tebuconazole. Ecol. Eng. 98, 378387. Malosso, E., English, L., Hopkins, D.W., O’Donnell, A.G., 2005. Community level physiological profile response to plant residue additions in Antarctic soils. Biol. Fertil. Soils. 42, 6065. Mondini, C., Insam, H., 2003. Community level physiological profiling as a tool to evaluate compost maturity: a kinetic approach. Eur. J. Soil Biol. 39, 141148. Mulcahy M.P. and H.M. Edenborn., Environmental microbial ecology, In: O’Donnell M.A., (Eds.), Tested Studies for Laboratory Teaching (red.), 2007, Vol. 28 Proceedings of the 28th Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 2851. Polish Standard, 2011. PN-EN ISO 23752-1. Preston-Mafham, J., Boddy, L., Randerson, P.F., 2002. Analysis of microbial community functional diversity using sole-carbon-source utilisation profiles—a critique. FEMS Microbiol. Ecol. 42 (1), 114. Ross, M., Goberna, M., Pascual, J.A., Klammer, S., Insam, H., 2008. 16S rDNA analysis reveals low microbial diversity in community level physiological profile assays. J. Microbiol. Methods. 72, 221226. Silva, C.P., Filho, P.F.M., Gomes, V.F.F., Martins, C.M., Cunha, C.S.M., Lobato, M.G.R., 2018. Glomalin-related soil protein content in areas of degraded and revegetated caatinga in the Municipality of Irauc¸uba. J. Agric. Sci. 10 (1), 302312. Torsvik, V., Øvrea˚s, L., 2002. Microbial diversity and function in soil: from genes to ecosystems. Curr. Opin. Microbiol. 5 (3), 240245. Wagg, C., Bender, S.F., Widmer, F., van der Heijden, M.G., 2014. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl. Acad. Sci. U.S.A. 111, 52665270. Weber, K.P., Legge, R.L., 2009. One-dimensional metric for tracking bacterial community divergence using sole carbon source utilization patterns. J. Microbiol. Methods 79, 5561. Wright, S.F., Franke-Synder, M., Morton, J.B., Upadhyaya, A., 1996. Time course study and partial characterization of a protein on hyphae of arbuscular mycorrhizal fungi during active colonization of roots. Plant Soil 181, 193203. Zak, J.C., Willig, M.R., Moorhead, D.L., Wildman, H.G., 1994. Functional diversity of microbial communities: a quantitative approach. Soil Biol. Biochem. 26 (9), 11011108.

FURTHER READING Furtak, K., Gawryjołek, K., Gajda, A.M., Gała˛zka, A., 2017. Comparison of biological activity of soil under maize and winter wheat grown in different cultivation techniques. Plant Soil Environ. 63 (10), 449454. Gała˛zka, A., Grza˛dziel, J., 2018. Fungal genetics and functional diversity of microbial communities in the soil under long-term monoculture of maize using different cultivation techniques. Frontiers in Microbiology, Research Topic: Soil Fungal Biodiversity for Plant and Soil Health. Front. Microbiol. 9, 76. Available from: https://doi.org/10.3389/fmicb.2018.00076. Gała˛zka, A., Gawryjołek, K., Grza˛dziel, J., Fra˛c, M., Ksie˛˙zak, J., 2017a. Microbial community diversity and their interaction of soil under maize growth in different cultivation techniques. Plant Soil Environ. 63 (6), 264270. Gała˛zka, A., Gawryjołek, K., Perzy´nski, A., Gała˛zka, R., Ksie˛˙zak, J., 2017b. Changes of enzymatic activities and microbial communities in soil under long-term maize monoculture and crop rotation. Pol. J. Environ. Stud. 26 (1), 3946. Gała˛zka, A., Gawryjołek, K., Grza˛dziel, J., Ksie˛˙zak, J., 2017c. Effect of different agricultural management practices on soil biological parameters including glomalin fraction. Plant Soil Environ. 63 (7), 300306. Grza˛dziel, J., Gała˛zka, A., 2017. Microplot long-term experiment reveals strong soil type influence on bacteria composition and its functional diversity. Appl. Soil Ecol. Available from: https://doi.org/10.1016/j.apsoil.2017.10.033. Gała˛zka, A., Gawryjołek, K., Gajda, A., Furtak, K., Ksie˛˙zniak, A., Jo´nczyk, K., 2018. Assessment of the glomalins content in the soil under winter wheat in different crop production systems. Plant Soil Environ. 64 (1), 3237. http://www.biolog.com. Jamiołkowska, A., Ksie˛˙zniak, A., Hetman, B., Kopacki, M., Barbara Skwaryło-Bednarz, B., Gała˛zka, A., et al., 2017. Interaction of arbuscular mycorrhizal fungi with plants and soil microflora. Acta Sci. Pol. Hortorum Cultus. 16 (5), 8995. Jamiołkowska, A., Ksie˛˙zniak, A., Gała˛zka, A., Hetman, B., Kopacki, M., Skwaryło-Bednarz, B., 2018. Impact of abiotic factors on development of the community of arbuscular mycorrhizal fungi in the soil: a review. Int. Agrophys. 32, 133140.