2

The Soil Ecosystem: Biological Participants

Soils may contain a variety of easily visible and identifiable life forms, including plant roots, insects, and earthworms. If these macroorganisms are removed, the remaining bulk soil may appear to the casual observer to be a lifeless mass. But this seemingly lifeless mass of clay, sand, and silt, which easily slips between our fingers, is home to an incredibly diverse and complex community of organisms that are too small to be seen with the unaided eye, including bacteria, archaea, fungi, protozoa, and viruses. This microbial community accounts for less than 0.5% (w/w) of the soil mass yet, these microorganisms have a major impact on soil properties and processes. Indeed, the integrity of the total ecosystem, both above and below ground, rests on the stability, resilience, and function of the soil microbial community. Destruction of the soil microbiota through mismanagement or environmental pollution can result in decline or even death of the aboveground plant and animal populations. Thus, development of an understanding of soil microbes, their properties, and the nature of their interactions with and within their environment is essential.

The study of soil microbial communities presents some unique challenges, due to factors such as their small size, incredible diversity, and heterogeneous distribution, as well as the heterogeneous nature of soils themselves (as discussed in Chapter 1). The objective of this chapter is to provide an overview of some key characteristics of soil microbial communities and their relationship to total ecosystem function. This discussion of soil microbes will include a presentation of some commonly used analytical procedures available for quantifying soil microbial populations.

2.1 The Living Soil Component

Historically, research on soil microbial communities has come from three main perspectives: (i) identification of the organisms present and elucidation of their relationships with other members of the soil community, i.e. basic community ecology studies; (ii) assessment of the activity of microbes participating in processes of agricultural importance, such as nitrogen fixation, nitrogen mineralization, denitrification, or pesticide decomposition, in order to inform agricultural management practices; and (iii) isolation of organisms capable of producing anthropogenically important products, e.g. antibiotics. More recently, with the observation that human activities have caused a decline in the quality of soil resources, considerable effort has been expended in the study of soil microbial processes associated with maintaining ecosystem stability or renovation of damaged sites. For example, there has been great interest in determining the identity and physiological properties of microbes involved in the degradation of a variety of toxic compounds, with the goal of developing bioremediation strategies.

Since soil bacterial and fungal communities are prime participants in each of these areas, most research on soil microbiology has focused on these two groups of organisms. Thus, much of what is described in this chapter relates to these two groups, yet it must be remembered that interactions with protozoa, nematodes, and other higher life forms help to maintain the vigor and productivity of the bacterial and fungal communities. Chapter 6 provides a more detailed discussion of these interactions. In addition, there is growing evidence that archaea, a group of prokaryotes once thought to be mainly extremophiles, are more numerous in soils and play a more significant role in soil processes than was previously recognized. For example, the recent discovery of ammonia‐oxidizing archaea (Könneke et al. 2005) and the demonstration that these organisms outnumber ammonia oxidizing bacteria in a variety of soils (Leininger et al. 2006) suggest that archaea may be significant contributors to ammonia oxidation in soil, a process that was previously assigned exclusively to the bacteria.

2.1.1 Biological and Genetic Implications of Occurrence of Living Cells in Soil

To gain a full appreciation of the consequences of biological activities in soil, several unique properties of the living component of soil must be examined in greater detail. Characteristics of soil microorganisms that confer special properties to soil processes include that (i) they possess a diverse metabolic capacity encoded in their genomes that is not necessarily expressed phenotypically at any particular point in time, (ii) they replicate, (iii) they have a definite cellular structure, (iv) they form resting structures, (v) they catalyze reactions that change the soil, and (vi) they are a link between aboveground and belowground processes.

2.1.1.1 Gene Pool Potential

A unique property of the soil microbiota is that the biochemical potential contained therein is significantly greater than that which is expressed at any particular point in time. There are several reasons for this phenomenon. First, many of the genes within the genomes of active soil microbes are not expressed under a given set of environmental conditions. Second, many microorganisms in soil are dormant; that is, they are present in an inactive resting state, and essentially none of the products of the genes contained in these inactive microbes is produced (Lennon and Jones 2011). Finally, advances in sequencing technology have revealed that soil microbial communities include a vast number of taxa that are present at very low abundance, a pool of organisms that has recently been termed the “rare biosphere” (Sogin et al. 2006).

Research on the rare biosphere is still in the early stages, and one question that remains unresolved is whether these rare taxa are dormant or active but present in very low abundance, perhaps due to very slow metabolic rates. Recent studies have indicated that it is possible for some rare taxa to multiply and become dominant under appropriate conditions, a phenomenon termed “conditionally rare taxa” (Shade et al. 2014). Therefore, it can be concluded that three major pools of potential but inactive metabolic processes found in soil are (i) active microbial cells with noninduced enzymatic capability, (ii) cells present in an inactive or dormant state, or (iii) cells present with insufficient population densities to produce measurable effects on soil metabolic activity, i.e. the rare biosphere. Based on these scenarios, we can conclude that genotypic traits exist within the soil microbial biomass which when they are induced could result in a biological community with entirely different properties than currently exist.

Induction of metabolic activities in soils: Synthesis of requisite enzymes for previously unexpressed metabolic activities is induced primarily by changes in the physical or chemical properties of the soil. Physical modification of the soil properties can be exemplified by the development of anoxic conditions. Imposition of this oxygen‐free status commonly occurs as the result of flooding where the excess soil moisture is retained under reasonably static conditions. The lack of water movement within and overlying the soil limits the transport of oxygen to soil microsites. The only source of molecular oxygen becomes diffusion from the atmosphere through the water into soil pores – an extremely slow process. This situation coupled with an actively respiring aerobic microbial community results in rapid depletion of free oxygen in water‐saturated soils. Prior to exhaustion of the free oxygen supplies, aerobic metabolism predominates. Without a continued oxygen supply, aerobic respiration cannot continue and enzymes associated with anaerobic respiration are induced.

One example of a metabolic activity stimulated by the development of anoxic conditions is denitrification, and an examination of the kinetics of induction of this activity can serve as a model of changes in other biologically catalyzed processes. The process of denitrification converts nitrogen oxides (nitrate and nitrite) to nitrous oxide and dinitrogen at a low level in most soils. Following soil pore‐saturating rainfall, should all other requirements for denitrification be available, nitrogen oxides are reduced to dinitrogen and/or nitrous oxide at rates several orders of magnitude greater than occurred prior to flooding of the soil. This stimulation of denitrification enzyme activity results from induction of denitrification enzymes under the oxygen‐limited conditions imposed by water saturation of a soil. Smith and Tiedje (1979) found that upon oxygen depletion, two distinct phases of denitrification were observed: phase 1, in which nitrogen oxide reduction proceeded at rates supported by the quantities of previously existing denitrification enzymes, and phase 2, in which higher rates resulted from increased expression of genes encoding the key enzymes in the denitrification pathway. In their study, phase 2 began after 4–8 hours of anoxic conditions and was not dependent on significant microbial growth. Finally, should conditions conducive for denitrification persist, eventually the soil denitrification capacity would be enhanced by increases in population densities of the responsible bacteria. A variety of studies have shown field responses of denitrification activity reflective of these laboratory observations (Christensen et al. 1991a; Flühler et al. 1976; Focht 1974; Parsons et al. 1991; Sexton et al. 1985).

Changes in the chemical properties of soils can also result in the induction of previously unexpressed metabolic activities. For example, enzyme synthesis can be stimulated by various soil amendments – including naturally occurring inputs of plant or animal tissues or anthropogenic inputs such as fertilizers, sludge (i.e. sewage biosolids), compost or xenobiotic compounds, such as pesticides. As with denitrification, a lag in enzyme activity of a few hours following soil amendment is typically observed before a rapid increase in the enzyme activity is detected.

In summary, enzyme induction provides a mechanism for a rapid, short‐term response of the microbial community to ecosystem perturbation. New enzymatic activity can be expressed in existing populations in as short a timeframe as 30 minutes to a few hours. After removal of the stimulus, enzymatic activities could return to preinduced levels as quickly as they were induced. The longevity of any soil enzymatic activity depends upon the stability of the proteins involved, the protein synthesis rate, and the rate at which the protein is decomposed by cellular or extracellular proteolytic activity. Therefore, enzyme induction could be viewed as a rapid biochemical response for maintenance of ecosystem homeostasis.

2.1.1.2 Cellular Replication and Soil Properties

Several characteristics of soil microbial communities must be considered when assessing the importance of cellular proliferation to the dynamics of metabolic function in any soil.

1. Soil microbial biomass is composed of a mixture of actively growing and dormant cells.

2. A wide range of population densities exist within soil microbial communities. Populations of individual species making up the microbial community vary over several orders of magnitude – from as little as 100 or fewer individuals of some rare species per gram of soil to perhaps a million or more organisms of other species per gram.

3. The relative densities of various populations are determined by their ability to compete with neighboring organisms.

4. At least the minimal chemical, physical, and biological requirements of an individual microbial species must be met before growth and replication of that species can occur.

5. The chemical, physical, and biological properties of any soil site are constantly in a state of flux. As these properties change or are altered, the activities and nature of the microbial community are also modified.

In contrast to the relatively rapid response associated with induction of key enzymes discussed above, cellular proliferation is a slow adaptation to ecosystem modification but its impact on the ecosystem may be much more lasting. Enzymes or enzyme systems may be synthesized at rates that result in detectable levels of activity in a matter of hours. In contrast, changes in a metabolic rate occurring as a result of cellular proliferation may require several days or months to become detectable. The time limitations involved with cellular replication are shown when a bacterially catalyzed process is considered. Bacterial cells reproduce by binary fission with growth rates in soil of from less than one to about two or three cell divisions per day. Thus, since production of populations of several thousand cells may be necessary to yield a detectable change in metabolic activity, several days may be required for development of a sufficient cell mass to have a measurable impact on the soil community. Indeed, it may take several weeks of incubation in the presence of the growth‐inducing substance before population densities of a few hundred individuals develop in situations where only a few cells with the requisite metabolic capacity existed prior to soil amendment. However, once the microbial populations have developed, they may survive as resting cells or spores for decades, or longer, even though the conditions causing their development no longer exist. Thus, a large population of inactive or resting cells may persist in a soil in which they are no longer capable of growth. Long‐term survival is more commonly the exception (i.e. most nonfunctional, nonspore‐forming biomass will be decomposed in periods of a few months to a year), but even this level of persistence of a phenotype in soil exceeds that resulting from enzyme induction and subsequent repression of the activity.

Relationship of enzyme induction and cellular proliferation to the concept of steady‐state conditions: A discussion of replication of microbial cells and induction of nonexpressed activities requires a consideration of the concept of steady‐state conditions in soil. For most scientists, an understanding of steadystate conditions is based on experience with chemical reactions. In the hypothetical reaction:

an equilibrium state, or steady‐state situation, is reached; that is, the forward and back reactions will reach equilibrium. For a given chemical reaction, the steady state is dependent upon the reaction constants for the forward and reverse reaction and the concentrations of the reactants and the products.

Within the reasonably defined situation of the test tube, predictable levels of all chemical species can be reached. Such is not the situation in a soil ecosystem. The concentrations of reactants, products, and levels of the enzymes involved are highly variable. Parameters affecting reaction conditions change with time, temperature, and certainly season. Therefore, if we wish to apply the steady‐state concept to soils, we must develop a definition of steady‐state that is applicable to soil biological populations.

One option would be to define steady state by the growth status of the microbial populations (i.e. population densities are unchanging). In axenic culture, this situation could be represented by stationary growth phase (see Chapter 4) but this condition is never really achieved in soil by actively respiring microbes. Growth of microbial cells is encouraged by consumption of microbial biomass by predator populations, augmentation of availability of carbon and energy supplies induced by diffusion of fixed carbon substances to the microbial cells, by mass transfer of nutrient laden water through the soil pores, or through root growth and death associated with plant community growth and decline, as well as by variation in soil physical properties, such as temperature and moisture. Furthermore, cell longevity may be reduced by the stress of coping with the harsh conditions of their environment. Thus, the microbial biomass could be envisioned as being in a constant state of flux.

Since both the microbial populations and their environment are steadily, albeit at times very slowly, changing, a chronological dimension to the definition of steady state must be applied. The fact that a variety of soil properties that control microbial population densities vary regularly on a daily and seasonal level suggests steady‐state conditions may be determined by consideration of changes in mean population densities with time. For example, temperature cycles daily as well as seasonally. Nutrient levels available to microbial populations frequently relate to plant growing season since photosynthetically fixed carbon is the primary carbon and energy source for most soil microbes. To accommodate this regular cycling or fluctuation of soil properties controlling microbial activity, a steady state could be said to exist in a soil ecosystem when the cycles of microbial activity or proliferation are replicated on a regular (time) basis (e.g. seasonally, according to plant growth status, or annually).

Based on even this periodic variation of soil microbial properties and activity, a question could be raised regarding whether steady‐state conditions really ever occur in soils. In reality, a true steady‐state condition is rarely, or perhaps never, achieved in the highly variable soil environment, but many of our concepts and models of soil systems are based on an assumption of steady state. Perhaps a more scientifically defensible expression of the status of the soil microbial community would be to modify our concept of steady state by designation of the occurrence of a quasi steady state rather than an absolute steady state condition for truly, a constant level of biological activity is never reached in any soil system, even on an annual basis. It would be rare to observe a field situation where exactly the same physical and chemical conditions impinging on microbial activity occur repeatedly year after year.

Similarly, considerations must be taken into account when evaluating the microbial activity at the field level at any single point in time. Again, as indicated in Chapter 1, the physical properties are extremely variable across a field surface, dependent upon such ecosystem‐related properties as the distribution and nature of the plant community. Variation in microbial activity becomes even more acute as the level of the microsite is approached. This heterogeneity in levels of activity of a microbial process in soil has been clearly demonstrated (Christensen et al. 1991b; Parkin 1987; Parkin et al. 1987). For example, denitrification requires anoxic conditions plus metabolizable carbon sources and nitrate to occur. These necessities vary spatially and chronologically in soil. Parkin et al. (1987) found “hot spots” of high specific denitrification in soil, which were linked to microsites with elevated particulate organic carbon. In a soil core where a high denitrification rate was measured, 85% of the denitrification capacity of a 98 g sample was found in an 0.08 g subsample of the core. Thus, it is easy to postulate that depending upon the distribution of the microbial colonies and the substrates necessary for their function, activity could vary from essentially zero (no microbial cells present) to extremely high levels (large colonies in the vicinity of active plant roots). Again, the mean value of the activity in question over the field of study could be defined as the steady‐state level of activity for the duration of the study. The “hot” or “cold” spots of activity may vary in intensity and location with time but the overall mean of metabolic activity in the field could remain reasonably constant.

In conclusion, a clear picture of the nature of steady state in a soil ecosystem could be likened to the daily conditions of any large city. When taken as a whole, the activity of the population appears to differ little on a daily basis (i.e. quasi steady state); cars and buses are streaming into the city and the sidewalks are clogged with people rushing to and from their offices. On any given weekday, the numbers of individuals associated with any given location appear to be reasonably constant. Yet, at the vantage point of a street corner, the situation appears to be nearly chaotic. Such is the activity of field soil as exemplified by the reactions occurring along a transect of any field site. Biological activities may be extremely variable along the transect (varying over several orders of magnitude), but an average value representative of the overall soil ecosystem along the transect can be calculated. Furthermore, the activity may be highly variable during the growing season but again, a value representative of the process rates for the season may be estimated. In our model of the city, this variability could be likened to that involved with comparing weekdays with weekends. Operationally, the highly variable soil systems could be defined as being at a quasi steady‐state level. An apparently chaotic system can be reduced to some semblance of reproducible order when integrated along a long transect or over the time frame of a growing or annual season. It is this potential for predictability and representation that makes possible the description of an ecosystem whose whole existence is based on processes occurring in highly variable microsites.

2.1.1.3 Cell Structure and Biochemical Stability in Soil

Amendment of soil with essentially any biodecomposable substrate, assuming conditions are appropriate for microbial metabolism to occur, results in a reasonably rapid depletion of the added material. In apparent contradiction to that observation is the fact that a wide variety of decomposable organic compounds contained in living cells are readily isolated from soil. That is, an apparent stability of labile substances is observed. These contrasting observations result from the definite cellular structure of microbial biomass. Readily decomposable organic compounds are protected within the cellular structure. Hence longevity of the biochemicals is not determined totally by the capacity of the microbial community to synthesize the enzymes necessary for their catabolism, but rather decomposition kinetics are determined, at least in part, by the capacity of the microbial biomass to breach the protective cellular barriers. In situations where the carbonaceous substrates are encased within protective cell walls, microbial populations may be starving in a soil containing large reserves of carbon and energy supplies that can support microbial cellular growth locked within protective cell walls.

2.1.1.4 Resting Structures and Soil Respiration

Another unique property of the soil biological system is that the cells may enter a metabolic state that allows them to survive under adverse conditions. Two levels of nonreplicating states of viable cells exist in soil. In the absence of appropriate energy supplies, microbes may enter a dormant or reduced metabolic state, or they may actually form resistant inactive structures (e.g. spores, endospores, sclerotia).

Whereas the impact of true resting structures, such as spores, on soil is minimal until they outgrow and form active vegetative cells, those cells in a resting (low activity) state have a continuous but low‐level effect on the conditions of the microsite. These latter microbes must continue to consume energy to maintain their cellular structure. Thus, by oxidizing internal energy sources, they continue to consume free oxygen (respire) and producing carbon dioxide. At least a minimal localized effect of the carbon dioxide generated from respiration associated with cellular maintenance would be expected to occur. Generation of this gas alters the atmosphere of the microsite where the resting microbial cell resides and increases the acidity of the interstitial soil water. For example, nitrifiers in the absence of their energy source, ammonium or nitrite, or any heterotroph existing in the absence of fixed carbon substrates may enter this situation where the only metabolic processes occurring in the cell are those associated with maintaining the cell structure. This form of existence could be pictured as a type of starvation where ultimate survival depends upon an eventual influx of an energy‐supplying nutrient. Interestingly, microbes may exist for extended periods of time (decades to centuries for Arthrobacter sp.) under these conditions. For these microbes, cellular metabolic rates are reduced to those levels necessary to maintain a minimal cell structure (i.e. the level of enzyme synthesis machinery and metabolic tools necessary to synthesize the enzymes and cellular products necessary to take advantage of proper growth conditions when they develop). Considering the chronological as well as physical heterogeneity of organic carbon distribution within soil, it is logical to conclude that the majority of soil microbes are found either in this reduced metabolic state or in resting structures.

2.1.1.5 Microbial Activity and Soil Properties

Microbes are also unique in soil in that they can alter the solubility of soil mineral components, chemically reduce organic compounds to essentially undetectable levels, modify soil structure, oxidize inorganic compounds, and use a variety of soil components as electron acceptors. Merely through growth and metabolism, soil microbes alter their environment. For example, soil structure is enhanced by production of polysaccharides, which may link soil particles into macroaggregates (Chapter 1). In contrast, soil structure may be lost through oxidation of the colloidal organic matter supporting soil aggregate structure by the same microbes. A less obvious but nonetheless important aspect of the impact of microbial metabolism on soil particulates is the dissolution of soil minerals resulting from organic or inorganic acid production. A variety of organic acids, including acetic acid, citric acid, and lactic acid, carbonic acid (carbon dioxide) and mineral acids (sulfuric acid and nitric acid), are produced by the soil microbial biomass. These acids are direct contributors to the weathering of soil minerals.

Soil organic and inorganic components may also be oxidized or reduced as energy sources or electron acceptors for the microbial community. All nonphototrophic microorganisms must oxidize growth substrates for energy. Common reactions are organic carbon oxidation to carbon dioxide, oxidation of ammonium to nitrite and nitrite to nitrate, oxidation of ferrous to ferric iron, and elemental sulfur conversion to sulfuric acid (see Chapter 4).

The impact of these oxidative processes on soil is magnified by the nature of the final acceptor of the electrons produced by oxidation of the growth supporting substances. A basic principle of biochemistry is that all oxidative reactions must be balanced by comparable reductive processes. Thus, some environmental substances must be reduced as a result of microbial growth. Aerobic catabolism of organic carbon most commonly involves reduction of oxygen to water. Denitrifiers oxidize organic carbon compounds while reducing nitrate or nitrite to nitrous oxide and dinitrogen. Furthermore, carbon oxidation may result also in the reduction of sulfur oxides to sulfide or elemental sulfur, or the reduction of ferric to ferrous iron.

Practical implications of this basic biological principle become apparent when it is considered that both oxidative and reductive processes have major implications in reclamation of contaminated soils. Whereas aerobic, heterotrophic metabolism can be used to purify soils contaminated by organic compounds (e.g. hydrocarbons such as petroleum), reductive processes may be exploited for remediation of metal‐contaminated and acid‐impacted sites. For example, sulfate reduction to hydrogen sulfide may be facilitated in renovation of acid mine drainage‐impacted sites (Mills 1985). Similarly, hydrogen sulfide production may be used to encourage removal of heavy metals from waters or immobilization of these metals in soil systems through precipitation as metal sulfides (see Wildung and Garland 1985). Frequently, a wetland‐type system is developed to accomplish these tasks. Passage of metal‐bearing water via overland flow through swampy ecosystems where hydrogen sulfide is generated results in precipitation of the metal sulfides.

2.1.1.6 Microbial Links to Aboveground Communities

It is relatively easy to forget that the processes occurring in soil cannot occur at maximum rates in the absence of fixed carbon inputs from the aboveground plant community. Some microbial activity is supported by catabolism of colloidal soil organic matter and more slowly decomposed biological components (complex polysaccharides, lignin, and lignin products), but maximal biomass productivity is derived from catabolism of more easily oxidized substrates, such as simple polysaccharides and proteins. Since the primary, natural source of such compounds in soil is plant biomass, a nearly essential dependency develops between the autotrophic aboveground community and soil microscopic life.

Similarly, the aboveground community is dependent upon the decomposer population in soil to mineralize macro‐ and micronutrients contained within dead plant biomass. For example, the organic nitrogen in the plant biomass must be converted to ammonium and nitrate before it can again be incorporated into new plant biomass. The facilitators of this cyclic process are the soil microbial community. (See Chapter 12 for further discussion of these processes.)

2.1.2 Implications of Microbial Properties for Handling of Soil Samples

An appreciation of the unique properties of living organisms in soil is mandatory for study and characterization of the versatile and rather fragile material called soil. A key point is that soil samples to be used to characterize native processes must not be treated in a manner destructive to the living systems contained therein. Of first consideration when collecting soil samples is remembering that the prime mover in the system is biological. Thus, the sample should be maintained so that biotic components are preserved and induction of major changes in their composition is avoided.

A primary example of a commonly used procedure in soil sample collection that necessarily results in alteration of soil biological activity is provided by an evaluation of the problems encountered by air drying soil samples. Historically, soil scientists have preserved soil samples by air drying. With this procedure, moist soil samples are incubated in the laboratory under conditions that allow soil moisture to approach ambient atmospheric levels. The final level of moisture retained in the soil may be considerably below that normally occurring under field conditions in that periodic influxes of water from rainfall or dew formation are precluded. This procedure is appropriate for preservation of many chemical properties of soil, but it is disastrous for evaluation of most soil microbial processes.

Air drying of soil creates a new ecosystem that may be quite different from that existing in the field from which the soil was collected. For soil microbes to function, there must be a coating of water on the soil particles (see Chapter 5). When soils are dried, sensitive microbes may die or be induced to enter into a resting stage. The fact that major changes in soil properties occur during soil desiccation is suggested by the observation that a burst of microbial respiration occurs when air‐dried soils are remoistened. This increased respiration results from the oxidation of organic carbon liberated from soil aggregates disrupted by soil drying and from catabolism of the dead microbial biomass. This results in changes in the levels and nature of soil enzymatic activities, modification of soil organic components, and changes in the composition of the microbial community.

Thus, modification of the physical condition of soil can have a dramatic effect on the nature of the active microbial populations. This situation is particularly acute if these are the microbial communities of interest. For example, strictly anaerobic organisms are killed by contact with molecular oxygen. A gradient of sensitivity of such populations occurs, but in studies where quantification of such populations or their activities is desired (especially with anoxic soil samples such as swampy material), oxygenation of the soil must be avoided. Modification of the aeration status of soils from that existing in situ under oxygen‐free or oxygen‐limited conditions may also cause increased population densities of aerobic microbes. In addition, a pool of partially decomposed fixed carbon substrates accumulates under oxygen‐limited conditions. These substances provide carbon and energy for the aerobic microbes once free oxygen is introduced into the system, thereby resulting in augmentation of their population densities.

2.2 Measurement of Soil Microbial Biomass

Microbial biomass is a primary catalyst of biogeochemical processes in soil as well as an energy and nutrient reservoir. Its significance is exemplified not only by the infinite array of biochemical transformations catalyzed in soil but also by the quantities of fixed nitrogen it contains. Anderson and Domsch (1980) found that in 26 agricultural soils, nitrogen contained in the microbial biomass ranged form 0.5% to 15.3% of total soil nitrogen, with an average of approximately 5%. This nitrogen becomes available to the aboveground community upon death and decay of the microbial cells. In soils not receiving exogenously supplied fixed nitrogen (for example nonagriculture soils), this nitrogen pool is sufficiently large that its stability is a prime factor in controlling the flux of fixed nitrogen through the ecosystem. In disturbed soils, such as when grassland soils are initially cultivated, a decline in soil microbial biomass results in a release of large quantities of fixed nitrogen. The fixed nitrogen not incorporated into newly synthesized microbial cells or aboveground biomass is lost from the ecosystem through leaching to groundwater or through runoff. Hence, it is frequently useful to estimate the size of the soil microbial biomass and its stability.

Microbial biomass is readily assessed in simple ecosystems, such as the growth flask, but its measurement in soil is complicated by the complexity of the system and the fact that these cells comprise a small portion of the total soil mass. One could propose to estimate soil microbial biomass by counting the number of individual living units, e.g. cells for bacteria or mass of mycelium for fungi, in a soil sample using a microscope. These types of direct counts are frequently done, but it is essentially impossible to isolate all of the microbial cells from soil. In addition, direct microbial counts do not enable us to distinguish viable from dormant or nonviable cells, thus limiting the relevance of these counts. Alternative methods for estimating soil microbial biomass utilize various surrogates as indicators of the quantity of living cells present in a soil sample. Such a surrogate should be a reasonably easily quantified cellular component that can be extracted from soil. An ideal indicator of microbial biomass should be:

· present in all microbial cells

· found in all microbial cells at the same concentration, regardless of species designation

· present in the cells at the same concentration, independent of growth status

· rapidly decomposed upon death of the cell

· quantitatively extractable from soil

· easily assayed.

The last trait on this list, ease of analysis, is necessitated by the large number of samples that are generally processed and by the fact that the biomass may change during storage of the samples. A rapid, easily conducted procedure can allow avoidance of long storage times between sample collection and analysis. A variety of candidate substances for estimating soil microbial biomass have been used, but none meets all of the above criteria. Soil microbial biomass has been commonly estimated through:

· direct counting of microbes (Lundgren 1981; Paul and Johnson 1977; Rosser 1980; Sonderstrom 1977)

· analysis of specific cellular components, such as adenosine triphosphate (ATP), phospholipids, or muramic acid (Ausmus 1971; Fazio et al. 1979; Findlay et al. 1989; Jenkinson et al. 1979; King and White 1977; Paul and Johnson 1977; Verstraete et al. 1983)

· measurement of specific microbial processes, such as nitrogen mineralization (Alef et al. 1988) and reduction of dimethylsulfoxide to dimethylsulfide (Alef and Kleiner 1989)

· measurement of respiration rates (Anderson and Domsch 1973)

· direct analysis of cellular components solubilized by chloroform fumigation (Brookes et al. 1982; Sparling and West 1988; Tate et al. 1988; Vance et al. 1987a) or the carbon dioxide produced by respiration of these products (chloroform fumigation‐incubation method) (Jenkinson 1976; Jenkinson and Powlson 1980).

Of these, the most commonly encountered procedures are direct counts, ATP analysis, respiration methods, and variations on the chloroform fumigation procedure. As noted below, these procedures are commonly used singly, but there are examples of studies where comparable results were achieved using multiple methods in parallel on the same samples (e.g. Fritze et al. 1996). It must be noted, however, that comparable results should not always be anticipated since the various procedures measure different parameters of the microbial biomass and therefore should not be anticipated to vary in concert. For example, the aerobic respiration methods commonly assess glucose metabolism whereas the chloroform fumigation‐incubation approach relates to the quantities of cellular carbon released by the chloroform and subsequently mineralized by the surviving microbial community. We will discuss several of the most commonly used methods for estimating microbial biomass below, with an emphasis on the strengths and problems associated with each method. A more detailed presentation of these methods can be found in Parkinson and Paul (1982).

2.2.1 Direct Counting Methods

Soil bacterial and fungal biomass may be viewed directly by microscopic observation of preparations of soil samples. Typically, small quantities of soil are suspended and spread in a thin layer on a microscope slide or, alternatively, the liquid used to suspend the soil is extracted and the microbes contained therein collected on an appropriate filter medium. The slide or filter is dried and a portion of the preparation is examined under the microscope. One challenge with this approach is that the refraction index of microbial cells is not sufficiently different from that of some soil particulate organic components, making it difficult to differentiate microbial cells from nonliving soil components. A variety of methods have been developed to enhance microscopic resolution of microbial cells in soil. For example, fluoresecent DNA binding stains such as acridine orange (e.g. Mills and Bell 1986), diaminopimelic acid (DAPI) (e.g. Bottomley and Dughri 1989) and the SYBR dyes (e.g. Weinbauer et al. 1998) can be very effective for discriminating cells from other soil particles, and these stains have been widely used in estimating bacterial numbers in soils. Drawbacks of these stains include an inability to distinguish viable from dormant or nonviable cells and a lack of specificity, as these stains will bind to any double‐stranded DNA they come into contact with.

Alternative approaches have been developed to overcome the shortcomings of these direct counting techniques. For example, metabolically active bacteria may be detected by treating soil samples with tetrazolium salts (water‐insoluble formazan accumulates within metabolically active cells), labeling cells with radioactive substrates (autoradiography) (see Roszak and Colwell 1987) or by counting metabolically active bacteria. With the latter procedure, soil samples are incubated with naladixic acid, an inhibitor of DNA synthesis. The antibiotic amendment causes an elongation of metabolically active cells. The active cells can grow, but they cannot divide. These microbial cells may be detected in the treated soil preparations with acridine orange stain and epifluorescence microscopy. Direct microscopic counts can also be limited to specific microbial groups (species in some cases) through the use of fluorescently labeled antibodies (e.g. Demezas and Bottomley 1986) or fluorescently labeled oligonucleotide probes (e.g. Christensen et al. 1999).

Once the microbial cells have been quantified in several microscopic fields, the values are converted to biomass by multiplying the number of cells per unit of soil by their volume times the cell density. Calculation of the number of cells per unit of soil requires knowledge of the area of the microscope field and dilution of soil used in preparing the slide. Biovolume may be calculated from cellular dimensions estimated during their enumeration. Difficulties are encountered in converting the cell volume to cell mass (Bakken and Olsen 1983; Bratbak 1985; Bratbak and Dundas 1984; Van Veen and Paul 1979). Such conversion requires assumption of the impact of moisture on changes in cell dimension during sample preparation.

Once the counts of cells existent on the prepared slides are determined, along with the challenge of converting biovolume to biomass, problems arise regarding the validity of extrapolation of data collected from the limited area of the microscope slide to field‐scale dimensions. These procedures generally use small quantities of soil (perhaps less than a gram), so some concern about extrapolation of the data to represent field or even ecosystem values is appropriate. Great care must be exercised with extrapolation of the direct count data since very small soil samples are generally used.

2.2.2 ATP Measure of Soil Microbial Biomass

For these estimates, soil is homogenized in a buffer solution to extract the ATP (Figure 2.1). Generally an acidic buffer is used to minimize solubilization of soil humic substances, which interfere with measurement of the ATP. The extractant is removed from the soil suspension by centrifugation or filtration and the ATP is quantified by measuring light production with the firefly luciferin‐luciferase system. Soil microbial biomass carbon is calculated by multiplying the measured ATP concentrations by a constant that is proportional to the amount of ATP contained per unit of microbial biomass as determined using a standard bacterial cell culture cultivated in laboratory media. The procedure is dependent upon the extraction efficiency of ATP from the soil sample and selection of an appropriate constant for final calculations.

Figure 2.1 Outline of a typical ATP analysis procedure for estimating soil microbial biomass (see also Parkinson and Paul 1982).

An assumption underlying this procedure is that there is negligible variation in the ATP contents of microbial cells. The validity of this assumption is questionable since cellular ATP contents are highly dependent upon the metabolic status of the cell. Actively metabolizing microbial cells contain considerably more ATP than is found in resting cells. Recall that a major portion of the bacterial cells in surface soils exist in a resting or inactive stage. Furthermore, while the proportion of the cells that are active is unknown, it is reasonable to assume that this value varies between soils collected from different ecosystem types and within soils from the same ecosystem, depending on such soil properties as energy inputs and moisture levels. For further information on the use of ATP for microbial biomass measurements, see Ausmus (1971), Jenkinson et al. (1979), Ahmed and Oades (1984), Paul and Johnson (1977), and Verstraete et al. (1983).

2.2.3 Soil Aerobic Respiration Measurements

The use of soil respiration as an indicator of soil microbial biomass is based on the principle that the rate at which fixed carbon substrates are oxidized to carbon dioxide in a soil sample is proportional to the quantities of organisms mediating the reaction (Anderson and Domsch 1978) (Figure 2.2). With this procedure, soil samples must be collected and any plant roots or macroscopic biomass removed to negate carbon dioxide contributions from these sources. With the soil respiration procedure, an easily metabolizable carbon source, such as glucose, is added to the soil sample and carbon dioxide evolution measured with infrared analysis, gas chromatography, or should sufficiently large quantities of carbon dioxide be produced, by titration of the gas collected in alkaline solutions. Alternatively, ¹⁴C‐labeled substrates (e.g. ¹⁴C‐glucose) may be added to the soil sample and the ¹⁴C‐labeled carbon dioxide produced collected and quantified.

This procedure may be used to differentiate fungal and bacterial respiration in soil by amending the samples with population specific antibiotics (Anderson and Domsch 1973, 1975). Streptomycin is generally used to inhibit bacterial respiration and cycloheximide for eukaryotic respiration. Each antibiotic should be used singly and in combination since a proportion of the microbial population is resistant to both antibiotics. Furthermore, several concentrations of antibiotic must be tested with each soil studied to assure use of optimal antibiotic levels. Inhibition of microbial activity is proportional to the quantity of antibiotic dissolved in the soil interstitial water. Antibiotics sorbed onto soil organic matter or clays are inactivated. Thus, the effective concentration of the antibiotic in soil may be significantly lower than the total quantity added. Higher antibiotic concentrations are required for inhibition of microbial populations in soils with high clay or colloidal organic matter contents than would be necessitated in a sandy soil, for example. Similarly, too much antibiotic must not be added to the soil since excessive antibiotic concentrations may inhibit the activity of nontarget microbes.

Figure 2.2 Outline of aerobic respiration method for estimating soil microbial biomass (see also Parkinson and Paul 1982).

A major consideration in use of any technique involving measurement of carbon dioxide from soil is the soil pH (e.g. see Martens 1987). Microbial biomass may be underestimated by failure to recover all the carbon dioxide produced during the incubation period. In neutral or alkaline soils, carbon dioxide is retained in soil as bicarbonate or carbonate due to the following equilibrium:

2.2.4 Chloroform Fumigation (Extraction and Incubation) Technique

Because of the general simplicity of this technique and the fact that it is relatively inexpensive, chloroform fumigation procedures are among the most commonly employed methods for estimation of soil microbial biomass. Thus, the principles, applicability, and problems associated with the technique must be examined in detail.

Chloroform fumigation procedures (Figure 2.3) are based on (i) the disruption of cellular membranes by chloroform and (ii) quantification of some cellular constituent (e.g. carbon, nitrogen, phosphorus) contained in the biomass of the cells killed by the chloroform. Generally, chloroform vapors are used. Following fumigation, soils may be extracted directly and soluble carbon (e.g. Deluca 1998; Sparling and West 1988; Tate et al. 1988; Vance et al. 1987a), nitrogen compounds (e.g. Azam et al. 1988; Dahlin and Witter 1998; Gunapala and Scow 1998), or phosphate (Brookes et al. 1982, 1984; Hedley and Stewart 1982) measured (estimates of microbial biomass carbon, nitrogen, or phosphorus, respectively). Alternatively, for determination of microbial biomass carbon, the soils may be incubated to allow biological conversion of the dead microbial biomass to carbon dioxide to occur. This procedure is based on the assumption that the biomass carbon of the cells killed by the chloroform vapors is used by the surviving soil microbial biomass as a carbon and energy source. (Note that aerobic incubation conditions are required. Anaerobic conditions result in incomplete oxidation of the cellular carbon of the dead cells.) To assure existence of adequate microbial populations during the incubation period, the fumigated and control soil samples are usually inoculated by amendment of the chloroform‐treated soil with small quantities of nonfumigated soil (Chapman 1987). For assessment of carbon dioxide production, soils are sealed in respiration chambers. Carbon dioxide is either collected in alkaline solutions and titrated or assayed with gas chromatographic procedures.

Figure 2.3 Outline of the chloroform fumigation‐incubation procedure for estimation of soil microbial biomass (see also Parkinson and Paul 1982).

For each of the procedures – incubation or direct extraction methods – assay of nonfumigated soil serves as a control. The total biomass present is calculated by subtracting quantities of carbon dioxide produced in nonfumigated soil samples (controls) from that yielded in the fumigated soil samples and dividing by a constant. The constant is a value representative of the killing efficiency of the fumigation procedure. Most frequently, for assessment of microbial biomass carbon, killing efficiency is considered to be about 40% (i.e. a constant of 0.4). Different values for the constant are used when quantifying microbial biomass nitrogen (Azam et al. 1988) and subsurface soils (e.g. Dictor et al. 1998; Tessier et al. 1998). Since the killing efficacy of the chloroform varies with such soil properties as moisture and type, it is imperative that the formula used to convert the raw data to biomass carbon be stated so that the data may be recalculated should it be necessary to compare them with values from other studies where alternative calculation procedures were used.

Some variation in incubation period of the soil samples following chloroform fumigation has been used. The treatment and control samples may be incubated concurrently for 10 days and total carbon dioxide yield assayed, or the control sample could be incubated for 20 days with carbon dioxide evolved between days 10 and 20 being used in the calculations. In this situation, for the calculations, the carbon dioxide produced in control samples during the last 10 days of a 20‐day incubation period is subtracted from that yielded in the fumigated samples during the first 10 days of incubation. This procedure avoids inclusion of the commonly observed burst of microbial activity in disturbed soils samples (as would be anticipated to occur with control soils) in the final calculation. Failure to compensate for this soil disturbance‐induced carbon dioxide production may result in an underestimation of total soil microbial biomass.

Aside from providing a reasonable estimation of the quantities of cellular components (carbon, nitrogen, and phosphorus) contained in microbial biomass, chloroform fumigation of soils is a valuable tool for demonstration of assimilation of soil amendments by the soil microbial community. For example, Kassim et al. (1982) quantified the incorporation of glucose, acetate, pyruvate, uracil, uridine, amino acids and a variety of polysaccharides into microbial biomass. To accomplish this, ¹⁴C‐labeled substrates were added to soil samples and then the amended soil samples were incubated for varying time periods to allow their incorporation into various soil organic matter fractions, including microbial biomass. Following incubation, the nonreacted ¹⁴C‐labeled amendment was washed from the soil. For analysis of the microbial biomass‐incorporated materials, the soil samples were fumigated with chloroform. Assessment of the excess ¹⁴C‐labeled carbon dioxide produced during the incubation period in the fumigated samples provided an estimation of the quantity of the metabolized carbon incorporated into microbial cellular components.

The chloroform fumigation procedure is an effective means for estimating microbial biomass in most soils, but difficulties have been encountered with application of the technique to acidic soils (Coûteaux et al. 1989; Vance et al. 1987a, 1987b), wet soils (Ross 1987, 1988) and in plant biomass‐amended soils (Martens 1985). A common trait among these situations is that the capacity of the microbial community to recover following fumigation and subsequent catabolism of the dead microbial cell material has been affected. Large quantities of available carbon preexisting in the soil samples may result in minimal differences between quantities of carbon dioxide produced by control and fumigated soils (i.e. the carbon dioxide production from the killed biomass oxidation is not significantly greater than the large quantities produced from catabolism of native soil organic matter). In extremely acidic soils, data suggest that the nature of the population (fungal vs bacterial contributions to carbon metabolism) may differ between fumigated and control soils (Tate 1991). In either case, modification of the incubation procedure and/or method of calculation of the microbial biomass may be necessary (Vance et al. 1987c).

2.2.5 Limitations of Microbial Biomass Measurements

A variety of inaccuracies are intrinsically associated with estimation of soil microbial biomass. (Note that in every case, the word “estimate” not “measure” is used in conjunction with the value produced from the microbial biomass assessments.) In selecting procedures, determining constants for calculation of results and interpreting the data, basic properties of the soil must be considered. These include the soil moisture level, pH, and organic matter contents referred to above as well as soil texture. Extraction efficiency of charged materials may be reduced in soils with high clay contents. Furthermore, high levels of colloidal soil organic matter may sorb products of interest or interfere with analysis (ATP‐luciferin‐luciferase assay).

It should also be noted that the calculation for each of the procedures described above requires use of a constant. Selection of the control value assumes a specific effect of soil drying on biovolume for direct counting procedures, a concentration of ATP in cells of soil microbes for the ATP‐based assay, or a killing efficiency for chloroform fumigation. All such assumptions have a leveling effect on the data for they may be true for some samples or cells and not for others.

The above difficulties must be considered by the investigator in data analysis but are to a certain degree beyond experimental control. Other procedures that are within design control involve laboratory and field procedural variables. Nearly all procedures require some degree of sample storage prior to analysis. Once soil samples are collected, creation of a new ecosystem commences. Soil properties limiting microbial activity in the field site are removed (e.g. perhaps temperature, soil structural occlusion of organic matter, moisture variation) and new delimiters of microbial population densities and activities are created. Such changes must be minimized. The most reasonable means of reducing artifacts due to sample preparation procedures is to assay the microbial biomass as quickly after collection as possible (preferably within minutes to a few hours). Should this not be possible, storage at 4 °C and avoidance of direct sunlight are mandatory. Some studies suggest that soil samples can be stored at −20 °C for extended periods without major changes in some measures of microbial biomass and activity (e.g. Stenberg et al. 1998). It is imperative that control studies be conducted to validate storage procedures since utility of a particular storage method may vary with soil or ecosystem source as well as with assay method. Good laboratory practice requires documentation of all sample preparation procedures so that any compromising of the results can be determined.

Finally, the researcher must ask the question, “What is being measured by the procedure used?” Some methods provide an estimation of the total microbial populations present (e.g. chloroform fumigation procedures or ATP analysis), whereas other methods only quantify the microbes capable of a particular reaction. For example, utilization of glucose in soil aerobic respiration measurements provides a good estimate of the biomass of organisms capable of oxidizing glucose as an energy source. In the former situation (total biomass quantification), both active and inactive microbial populations are quantified whereas in the latter (soil aerobic respiration assessment), only those capable of catabolizing a particular biochemical process are enumerated.

2.3 The Nature of Soil Inhabitants

All seven major groups of microorganisms plus higher animals interact in soil to catalyze the biogeochemical processes occurring therein. Soil bacteria, archaea, fungi, actinomycetes, protozoa, algae, and viruses, as well as nematodes and mite populations, contribute to development of the total community. The arrangement of these classes of organisms may be evaluated in a hierarchical manner. This association provides the basis for fundamental viewpoints in examining native ecosystems. The bottom of this arrangement is the individual organism. The summation of the individuals within a given taxonomic or functional group is a population. The totality of all populations of different organisms in soil constitutes the community, and the interaction of all biological components with the abiotic portions of the environment constitutes the ecosystem.

Soil ecological research can be divided by the portion of this hierarchy stressed in the study. Such research may have an autecological or synecological basis. An examination of the behavior of a single species in an ecosystem, including a study of the effect of chemical, biological, and physical aspects of the ecosystem on the population is an autecological study. In contrast, soil may be examined as a whole. In this situation, soil is being treated as if it were a single “tissue.” Thus, the relationships between the environment and community of organisms (synecology) is being stressed. For synecological research, processes are of more concern than the identity of the individual microbes involved. For example, denitrification kinetics and its variation between soil types may be studied (synecology) or a researcher may concentrate on the identity of the individual denitrifying species existent in a soil site (autecology).

2.4 Autecology and Soil Microbiology

Quantification of microbial biomass is useful for expressing ecosystem potential (e.g. nutrient or energy cycling, or even general biological capacity), but most evaluations of soil community activity require elucidation of specific biological or biochemical processes and their kinetics. The processes themselves may be the center of the experiments (synecology) (see Section 2.5) or elucidation of the specific microbes and the system properties controlling their activity may be the pivotal point of the research (autecology). The latter aspect of ecosystem evaluation is the topic of this section.

The starting point for discussion of autecological research is a determination of what constitutes a bacterial species. An autecological study by strict definition involves assessment of the presence or activity of individual species. This is reasonably readily accomplished with higher organisms where species designations are more easily assayed, but the fluidity of bacterial species complicates such research for the soil microbiologist. This difficulty arises from the reasonably easy exchange of genetic material that occurs between bacteria via horizontal (or lateral) gene transfer. The definition of a bacterial species resides in the genetic material contained therein and its expression as it impacts cellular morphology, colonial structure and metabolic capacity. Should the genetic material resulting in distinctive properties that define one of the bacterial species be readily transferred into another species, the distinction between the two groups of bacteria becomes questionable at best. Such problems with speciation of soil bacteria are reasonably common.

Questionable or difficult separation of bacterial isolates into species has led to other more easily derived groupings of bacteria. Thus, autecological research may involve the study of bacterial groupings separated by phenotypic traits (functional or even colonial groupings, i.e. guilds) rather than by species designation (Mills and Bell 1986). Alternatively, an autecological study, in principle, may involve analysis of DNA sequences with a selected percent similarity as the basis for species designation (see Chapter 3.). Thus, the discussion of autecological research that follows is based on the expanded definition of autecology, which allows for the study of guilds as well as scientifically accepted species designations.

2.4.1 Limitations to Autecological Research

It must be noted that the compromise involved in delineation of bacterial species incorporates a limitation into the value of the data derived from such studies. A primary objective of any ecological study is not only to understand the particular system of study, but to develop principles applicable to a variety of situations. Taxonomic designation of the living organisms studied has provided a common link between research projects. The phrase, “a lion is a lion no matter where it is encountered” can be readily accepted. To some degree, such a statement could be made for many of the historically studied bacterial species, but a generalization of this type regarding bacteria is questionable. Thus, use of the less restrictively derived guilds limits intersystem and interlaboratory comparisons.

A second difficulty associated with autecological research results from the minute dimensions of soil microbes. Autecological research provides meaningful data for understanding biological processes, but extrapolation of such research to an ecosystem scale is limited. Data are collected clearly describing the interactions of microbial colonies and their environment, but the area impacted by an individual microbial colony in soil encompasses a few cubic microns at best. The inherently small size of the microsite impacted by the individual microbe necessitates collection of large quantities of data before a clear picture of the effect of the totality of soil microbial colonies emerges (Tate 1986). Development of automated microbiological procedures for species identification, expanded use of fluorescent antibodies (including monoclonal antibodies), and application of DNA‐based methods to detection of individual microbial species and quantification of their density in soils should result in an expansion of autecological soil microbiological research.

2.4.2 Autecological Methods

2.4.2.1 Viable Counts/Enrichment Cultures

Prior to the development of methods for detecting nucleic acid sequences specific for selected microbes and the application of these methods to physically and chemically complex systems like soil, the initial step in any autecological study of soil involved growth of the microbes of interest in axenic culture. Indeed, this remains the initiation point – and in many cases the primary limitation – for most such research today. Exceptions to this observation are exemplified by studies in which the experimenter's interest resides simply with detecting selected microbes and estimating their population density. In that situation, indicators such as species‐specific activities or DNA‐based techniques eliminate the necessity of some cultural studies.

Microbes may be isolated from soil using nonselective techniques, such as growth on soil extract agar (perhaps this could better be termed “less selective” since growth in any laboratory medium allows growth of only a portion of the microbial population), or selective techniques, such as selective growth and enrichment culture. With the latter two procedures, microbes with certain desired physiological properties are directly sought. With enrichment cultures, increases in microbes existing in low population densities are favored prior to isolation of the axenic cultures, whereas with selective growth an extract of soil may be cultured directly on selective media.

The first concern in enumerating specific soil microbes, especially bacteria, results from their population density. Typically bacteria exist in soil at densities of 10⁸–10⁹ colony forming units per gram dry soil. These are associated with 10⁷–10⁹ actinomycete propagules per gram dry soil and about 10⁶ fungal and protozoan propagules each per gram dry soil. Direct plating on a nonselective agar medium would clearly be unfruitful. Soil samples must therefore be diluted to reduce colonial growth on agar plates to 20–300 colonies per plate (Figure 2.4). Consideration of the dilution factor plus the quantity of diluent added to the growth medium allows calculation of viable microbes in the original soil sample. A result of this necessity to dilute soil samples is that the data from such studies are highly variable. A slight variation in dilution precision – especially at the lower dilutions – causes large differences in estimated propagule densities. Variations as large as plus or minus 100% are not uncommon.

With viable counts of microbes from any environmental sample, a qualification must be made regarding the meaning of “viable.” Those bacteria classified as viable for any specific experiment in reality only include the propagules capable of growth on the media used and only under the particular incubation conditions employed. Comparison of data from direct microscopic analysis of soil samples with results from viable plate counts reveals that in most cases only about 0.1–10% of the observed bacteria grow in culture, a phenomenon known as the “great plate count anomaly” (Staley and Konopka 1985).

A further complication associated with microbiological analyses of individual species in soil samples results from the variation in microbial growth rates. Typically, those organisms that form visible colonies during the most commonly used incubation times are those with the shortest generation times. Thus, many organisms that are important in situ are overgrown. This difficulty may be overcome by using selective media (for example, media amended with antibiotics to which the desired population is resistant but most soil bacteria or fungi are sensitive or media containing selective growth substrates) or enrichment cultures, again based on some unique property of the microbe(s) of interest.

Figure 2.4 Outline of procedure for estimation of microbial biomass using direct observation of fluorescein isothiocyanate (FITC) label. See Schmidt and Paul (1982) for details of procedure.

Viable plate counts are also used for enumeration of soil fungal populations. Estimates of fungal population densities in soil is confounded by their prolific spore production. Immense quantities of spores may exist in soil with few or none of the organisms existent as hyphae. Generally, fungal population densities are best estimated by direct microscopic observation.

2.4.2.2 Most Probable Number Procedures

A variation of the viable count procedure is the most probable number estimation of microbial populations. Many microbial populations (e.g. a variety of protozoa, nitrifiers, and sulfur‐reducing bacteria) are difficult to grow in defined media. Also, when all potential contributors to a given metabolic function, perhaps nitrification, are to be quantified, it must be realized that some individual species contributing to the activity may have been grown in axenic culture, but the probability frequently exists that other nonculturable strains occur in soil. Most probable number procedures may provide a measure of the density of such populations (Figure 2.5).

Most probable number procedures are based on determination of the dilution of the soil sample beyond which no propagule of the population to be quantified can be detected, i.e. the extinction point. Therefore, with this method soil must be diluted until no further propagules of the organisms of interest are present in the highest dilution prepared and multiple tubes of culture media (e.g. five per dilution) are inoculated from each dilution. After an appropriate incubation period, a trait common to all of the organisms to be quantified is measured (visual observation of protozoan cells, nitrate or nitrite production for nitrifiers, or black color produced by precipitation of iron sulfide, for the examples listed above). Population densities are determined using prepared tables relating to the statistical probability of the presence of the organisms of interest to the number of positive samples for each dilution (e.g. Alexander 1982). This procedure retains the problems of selectivity of media and incubation conditions. Furthermore, due to the nature of the calculation procedure and the range of dilutions used, confidence limits are frequently as great as plus and minus 300%.

Figure 2.5 Outline of viable plate count method for estimating numbers of organisms per gram of soil.

2.4.2.3 Sources of Error in Viable Count Procedures

Each step in any viable count procedure (soil sample collection and storage, preparation of dilutions, colony development) contains major impediments for production of an accurate population census. The most fundamental of these is the necessity of collecting site representative soil samples. Once attained, the soil must be stored in a manner that precludes major changes in the population density. Fortunately, considering the intrinsic variation of the dilution procedure itself, some change in the microbial populations can occur before statistically significant effects are detected. That is, with reasonable sample storage procedures, changes in microbial population densities are usually less than the inherent variation of plate count or most probable number data. For most studies, storage of the soil samples for limited times at 4 °C results in minimal changes in the data.

Two sources of data variation incurred during preparation of soil dilutions may be controlled by careful attention to proper laboratory procedures and experimental protocol (these are variability due to dilution procedure precision and the potential for microbial growth during dilution preparation). A third source of error is basic to the procedure – association of microbial cells with soil particles – and likely must be considered during data interpretation, but little can be done to prevent the problem. Association of microbial propagules with soil particulates may result in an underestimation of their number. Typically, microbial colonies in soil consist of a few cells adsorbed to a soil particle or linked through production of a polysaccharide slime. Incomplete mixing of the soil in the diluent could result in data that are an estimate of soil particle‐bound colonies rather than separated microbial propagules. This data compromise can be minimized by thoroughly suspending and mixing the soil sample in the diluent, but realistically it cannot be eliminated.

A problem frequently encountered even when extreme caution is used in preparing the dilutions is microbial growth in the dilution media. Microorganisms generally have generation times of several hours in native soil samples. Thus, it might be considered that time lapse from preparation of the initial dilution to plating of the sample has little effect on the colonies developing on the plate. Unfortunately, this assumption is not true. Mixing of the soil sample in the dilution medium liberates metabolizable organic material from the soil. The solubolized organic carbon may precipitate a few rapid divisions of the soil heterotrophic microbes under the less restrictive conditions of the dilution tube. This problem is particularly acute when the diluent contains mineralizable organic carbon. For example, it is not unusual to prepare dilutions in 0.1% (w/v) amino acids or 0.1 strength of a growth medium such as trypticase soy broth to minimize the impact of dilution on fragile microbial cells. Hence, it is desirable to minimize the time lapse between preparation of the initial dilution and inoculation of the growth medium.

2.4.2.4 Interpretation of Viable Count Data

Interpretation of viable count data must be based on the knowledge that (i) not all soil microbes are capable of growth on the medium and (ii) those organism detected are a mixture of organisms that were active and those which were inactive in situ. As indicated above, the number of colonies developing on the growth media represents only the microbes capable of growth on the selected medium under the prevailing incubation conditions. This is particularly true when selective media are used. These media are necessarily more stringent than those selected for estimation of total microbial populations (e.g. bacterial or fungal population densities). For example, it is not unusual to attempt to isolate organisms capable of using a particular pesticide as a carbon and energy source. To do this, a medium is prepared containing nitrogen, phosphorus, sulfur, and trace minerals necessary for growth and buffered at an appropriate pH for population development. A concentration of pesticide is added to the salts medium that is sufficient for colonial development, but not toxic. This medium appears to be ideal for the study, yet no microbes grow or only a few species are isolated from a soil sample in which it is known that a diverse population capable of catabolizing the pesticide exists. A limiting factor associated with the medium is that all microbes that require vitamins or amino acids are precluded from growth (see Figure 2.6).

A further caveat regarding interpretation of viable count data relates to the nature of the state of the microbial populations in soil. Production of a colony on the test medium indicates that the particular organism was viable in the soil of interest, but nothing is indicated regarding the activity of the microbe in the native soil sample. Actively growing cells, nongrowing cells, and resting cells will develop colonies under appropriate conditions. Thus, isolation of a particular organism in axenic culture solely means that the organism was present in the soil sample, not that it contributed to the metabolic activity expressed in the soil at the time of sampling.

2.4.3 PCR for Quantification of Soil Microbes (see Figure 2.7)

Soil microorganisms can also be quantified using a modification of the standard polymerase chain reaction (PCR) known as real‐time PCR. Standard PCR makes copies of a targeted DNA sequence through repeated cycles of in vitro DNA synthesis. PCR depends on some knowledge of the DNA sequence to be copied so that primers complementary to regions flanking the targeted DNA sequence can be designed and employed in the assay. PCR also depends on the use of a heat‐stable DNA polymerase, e.g. DNA polymerase isolated or cloned from Thermus aquaticus, a thermophilic bacterium, because thermal cycling is used to achieve repeated rounds of DNA denaturing, primer annealing and DNA synthesis. PCR results in exponential amplification of the target DNA sequence, resulting in thousands to millions of copies of the sequence. The application of PCR‐based assays to soil microbiology has revolutionized our understanding of soil microbial communities and has enabled a variety of approaches for the exploration of soil microbial diversity. Chapter 3 will discuss these approaches in detail.

Figure 2.6 Outline of most probable number method for estimating number of organisms per gram of soil.

A modification of the standard PCR assay can be used for quantification of soil microorganisms. This modification, known as real‐time PCR, enables the progress of the PCR reaction to be assessed after each round of amplification. As explained below, this real‐time monitoring enables the user to determine the copy number of the target DNA sequence in an unknown sample, thus making the assay quantitative, leading to the term quantitative PCR (qPCR). There are two methods that can be used to accomplish real‐time qPCR, SYBR‐based assays and Taq‐man assays. Both of these approaches depend on the monitoring of fluorescent reporters as indicators of the progress of the reaction, and so both require a specialized thermal cycler with the capability of monitoring fluorescence in each reaction tube after each round of replication.

Figure 2.7 Outline of polymerase chain reaction (PCR) process for amplifying specific DNA sequences.

2.4.3.1 SYBR Method for Real‐Time PCR

SYBR dyes bind to double‐stranded DNA and fluoresce only when bound. In a SYBR‐based real‐time PCR assay, a SYBR dye such as SYBR Green is included in the PCR mixture and fluorescence in each of the PCR reaction tubes (or wells if run in a microwell plate) is measured after each round of replication using a specialized thermal cycler (as mentioned above). As the reaction proceeds and more copies of the target DNA are produced, this will increase the amount of double‐stranded DNA in the reaction tube, which will increase the fluorescent signal from that reaction due to more binding of SYBR Green molecules. For a given PCR reaction, plotting the fluorescent signal against cycle number will produce an S‐shaped curve, with an exponential increase followed by a plateau. It is possible to define a threshold level of fluorescence, generally near the inflection point at which fluorescence begins to increase most rapidly, such that the number of cycles required to cross this threshold (the C_T value) can be correlated with the number of copies of the target DNA sequence present in the PCR reaction at the start of the assay (i.e. cycle 0). To quantify the target DNA in a sample with an unknown concentration of the target (e.g. DNA extracted from a soil sample), a series of DNA standards of known copy number are run in parallel with the unknown sample. These standards are generally a dilution series of genomic DNA from a pure culture of an organism known to contain the target DNA sequence, but in some cases clones of the target DNA sequence can also be used as standards. After running the qPCR reaction, the standards are used to generate a standard curve of copy number vs C_T value. This standard curve can be used to determine the copy numbers of the target in the unknown samples based on their measured CT values.

One significant limitation of SYBR‐based qPCR is the lack of specificity of the SYBR dyes. These dyes will bind to any double‐stranded DNA, so any nonspecific amplification or primer‐dimer formation that occurs during the PCR reaction will contribute to the measured fluorescent signal. It is thus critically important that primers and cycling parameters (especially the annealing temperature) be chosen carefully and validated experimentally. To validate reaction specificity, most SYBR‐based qPCR reactions include the determination of a melting profile at the conclusion of the amplification cycles to verify the production of only one product. Most qPCR thermal cyclers can be programmed to determine a melting profile immediately at the conclusion of the PCR cycles. Melting profiles are produced by running the products of the PCR reactions through a range of temperatures, for example from 50 to 95 °C, and reading the fluorescent signal at set increments, for example after every 1 °C increase. This increase in temperature will cause the PCR amplicons to denature, resulting in a decrease in double‐stranded DNA and thus a decrease in the fluorescence produced by bound SYBR dye molecules. For a single PCR product, the melting curve should show a rapid decrease with a steep slope at the melting temperature of that specific DNA fragment.

The melting temperature of a given DNA fragment depends on the length and the guanine‐cytosine (GC) content, and to a lesser degree on the specific base sequence, so if a PCR reaction produces more than one product (due to nonspecific amplification or the formation of primer‐dimers) these products will have different melting temperatures and the melting curve will show more than one inflection point. The melting profile is thus an extremely useful way to verify the production of only one product in a qPCR reaction. Additionally, the products of the qPCR reactions are usually analyzed by gel electrophoresis to verify the production of a single product of the expected size.

2.4.3.2 Taq‐Man Method for Real‐Time PCR

The Taq‐Man approach to qPCR is similar to the SYBR approach, but differs in the fluorescent reporter used. The TaqMan approach includes a probe, which is a short DNA fragment (~20 bases) that is complementary to a region within the target DNA sequence. This probe is synthesized with a fluorophore attached to the 5′ end and a quencher at the 3′ end. The quencher blocks the fluorescence of the fluorophore when both are in close physical proximity, e.g. if both are attached to the probe. In the Taq‐Man approach many copies of this labeled probe are included in the qPCR reaction and during each annealing step the primers, which flank the target DNA sequence, and the probe molecules, which target a region within the target sequence, will bind to their complementary regions. When DNA polymerase copies the target sequence by extending one of the primers it eventually runs into the probe that is hybridized to the template strand, and the 5′ to 3′ exonuclease activity of the polymerase degrades the probe so that it can continue its DNA synthesis. Degradation of the probe releases the fluorophore from the 5′ end of the probe, freeing the fluorophore from close proximity to the quencher, resulting in fluorescence of the fluorophore. Therefore, in the Taq‐Man qPCR assay, the fluorescent signal is proportional to the number of copies of the target gene. As with the SYBR‐based qPCR assay discussed above, the copy number of a target DNA sequence in an unknown sample can be determined by generating a standard curve of fluorescence vs cycle number using a series of DNA standards of known copy number. This standard curve can be then used to determine the copy numbers of the target DNA sequence within the unknown samples based on their measured C_T values.

The Taq‐Man assay has a much higher specificity than the SYBR‐based assay because with the Taq‐Man approach fluorescence is only produced when the probe binds to the specific target DNA sequence and is subsequently degraded. Due to the nature of the fluorescent reporter system, the Taq‐Man approach does not enable the determination of melting profiles, but the higher specificity of the Taq‐Man assay reduces the need for this analysis. The specificity of the Taq‐Man approach is a significant advantage over to the SYBR‐based approach. However, the design of the internal probe for the Taq‐Man approach does require more knowledge of the target sequence and it requires that a region of sequence conservation exist within the target DNA sequence. It can be difficult to meet these requirements for some qPCR applications and for some target DNAs, making the SYBR approach a more appealing option in some cases.

2.4.3.3 Applications of Quantitative Real‐Time PCR to Soil Microbiology

Quantitative PCR can be used to quantify any target DNA sequence as long as suitable primers can be designed, although qPCR does work best for relatively short amplicons (500 bases or less). For quantification of soil bacteria primers targeting the 16S rRNA gene can be used (e.g. Fierer et al. 2005; Okano et al. 2004) as this gene is present in all bacteria and it contains regions that are sufficiently well conserved to allow for the design of primers that will target all bacterial species, so called universal primers. It is also possible to use qPCR to target more specific taxonomic groups of bacteria by designing probes to target 16S rRNA gene sequences that are specific for the taxonomic group of interest. It is also possible to use qPCR to target copy numbers of genes encoding enzymes involved in specific metabolic pathways, also known as functional genes. The use of functional gene primers allows quantification of microorganisms involved in specific soil processes. For example, ammonia‐oxidizing bacteria and archaea have been quantified in soil using primers targeting their respective amoA genes, which encode the alpha subunit of the ammonia monoxygenase enzyme (Leininger et al. 2006). Denitrifying bacteria have also been quantified in soil using primers targeting several genes involved in the denitrification pathway, including nirS, nirK and nosZ (Wallenstein and Vilgalys 2005). Finally, qPCR approaches have been used to quantify fungi in soils based on primers targeting eukaryotic rRNA genes (Bates and Garcia‐Pichel 2009) or the rRNA intergenic transcribed spacer (ITS) region (Fierer et al. 2005).

2.4.3.4 Limitations of qPCR Approaches

Quantitative PCR is a powerful tool for the quantification of a wide array of gene targets in soil, but it has significant limitations that must be considered when assessing data produced with this approach. For example, qPCR can be used to determine copy numbers of target DNA sequences within soil samples, and gene copy numbers can be used as a proxy for the population size of the organisms containing the target gene. However, it is not possible to convert gene copy numbers to cell numbers because there is not a consistent relationship between copy numbers of various genes and cell numbers. For example, bacterial genomes have been found to have as few as one and as many as 13 copies of the rRNA operon (Klappenbach et al. 2000). Functional genes can also appear multiple times within bacterial genomes; for example the amoA gene is found two to three times per genome in beta‐proteobacterial ammonia oxidizers but in single copies in the genomes of gamma‐proteobacterial ammonia oxidizers (Norton et al. 2002).

Another limitation to consider is the fact that all DNA‐based approaches, including qPCR, are dependent on DNA extraction from soil, which has the potential to introduce biases into the results. For example, all soil DNA extraction protocols lyse cells using processes such as enzymatic lysis, freeze–thaw, or bead beating. However, bacterial cells are not all lysed with the same efficiency by these various processes (Miller et al. 1999). So quantification of organisms using DNA‐based approaches may be biased toward organisms that are lysed more easily.

Finally, for all DNA‐based approaches it should be recognized that DNA can persist in soil outside of a cell for significant periods, from months to years (Demaneche et al. 2001). For example, free extracellular DNA can bind to soil particles including clays and this binding can protect DNA from degradation by nucleases (Blum et al. 1997). Significant amounts of this extracellular DNA can be detected in soils (Demaneche et al. 2001) and can be coextracted with DNA from cells lysed by the DNA isolation procedure (Frotegard et al. 1999). Thus, when working with environmental samples, it is possible for PCR to amplify DNA from cells that are no longer living, which would be a confounding factor in enumeration of bacterial populations.

2.4.4 Expression of Population Density per Unit of Soil

The ultimate goal for most ecological studies is to compare data from divergent soil ecosystems and to evaluate the impact of differences in physical, chemical, or biological properties between the sites on the activities. Traditionally, soil population densities, enzymatic activities, as well as chemical concentrations have been expressed on a per gram of dry soil basis. This method of data presentation may be reasonable with some systems, but with the diversity of soils encountered in field studies, consideration of the bulk density and the volumes of soil represented is important. This conclusion is based on the observation that, in reality, soil processes occur in a three dimensional system, i.e. a volume of soil. The quantity of soil contained within that volume is highly variable. The bulk densities of most mineral soils range from about 0.9 to 1.2. Thus, activity per gram dry soil is approximately the activity per cubic cm of soil. In contrast, bulk densities of soils containing high levels of organic matter or organic soils may be quite low. For example, organic soils frequently have bulk densities of 0.1 g soil cm⁻³ or less. Assuming a bulk density of 0.1, any activity expressed per gram of dry soil would represent 10 cm³ of soil within the native environment. Thus, if results for the low bulk density organic soil were compared on a dry weight basis with those similarly expressed for an average mineral soil, a 10‐fold error in conceptualization of the field situation would result from the different field volumes represented. Hence, it is important when evaluating published data, or preparing one's own data for publication, to include a study of the site soil bulk density so that activities or populations in comparable quantities of soil can be evaluated.

2.4.5 Products of Soil Autecological Research

Provision of a list of most frequently encountered bacterial or fungal species, historically the most commonly studied soil populations, can only be criticized for its omissions than for its inclusiveness. For not only is there a vast array of species naturally present and functioning within any soil ecosystem, but propagules gain entry by transport through air, water, or sediment transport. Further, a variety of anthropogenic activities (such as those associated with waste disposal) augment the range of microbial species present. Commonly encountered soil bacterial species include representatives from the genera Acinetobacter, Agrobacterium, Alcaligenes, Arthrobacter, Bacillus, Brevibacterium, Caulobacter, Cellulomonas, Clostridium, Corynebacterium, Flavobacterium, Hyphomycrobium, Metallogenium, Micrococcus, Mycobacterium, Pseudomonas, Sarcina, Streptococcus, and Xanthomonas. These heterotrophic bacterial genera are augmented in soil by autotrophic and mixotrophic representatives, including a variety of nitrifiers, Thiobacillus species, and iron bacteria. Actinomycete populations include the commonly encountered Nocardia sp. and Streptomyces sp. Fungal populations are composed of a variety of slow‐ and fast‐growing species. Perhaps the most commonly encountered are Penicillium and Aspergillus species plus representatives of the Zygomycetes and the mycorrhizae associated ascomycetes and basidiomycetes.

The objectives of experiments requiring identification of soil populations range from isolation of individuals with traits of interest to elucidation of the diversity of soil microbes by identification of tens to hundreds of different microbial species. The latter studies include evaluation of the interactions between the diverse populations present in soil as well as a determination of the effects of external stimuli, including anthropogenic impacts such as clear‐cutting of forests or industrial pollution, on microbial diversity. Diversity studies of soil microbes have been limited in the past due to the difficulty of determining function in situ and the vast quantities of data necessary to identify the species present or to group the organisms into guilds based on structural or physiological similarity. Development of automated techniques for culture characterization and data analysis has made such studies more practical (see Mills and Bell 1986; Holder‐Franklin 1986; Holder Franklin and Tate 1986; Russek‐Cohen and Colwell 1986). These experiments have provided an understanding of the genetic potential within a given ecosystem and an indication of the resilience of the system to external perturbation, i.e. the capacity for homeostatic stability. Some systems with low species diversity, such as soils receiving thermal hot spring outflows, appear to be reasonably stable, but the classic maxim has been that the greater the species diversity, the higher the probability that the community has the capacity to overcome intrusions such as influx of contaminants or pH or temperature alterations. Much of this stability is derived from latent genotypic capability. Methods for assessing soil microbial diversity will be discussed in more detail in Chapter 3.

2.5 Principles and Products of Synecological Research

Although the sciences of microbiology and ecology have traditionally involved association of a genus and species name with the living creatures studied, as indicated above, such activities have more limited value in soil ecosystem characterization. This is due in part to problems defining bacterial species and the capacity of soil organisms to exist in active, inactive, or resting states. Thus, the presence of an organism does not necessarily prove its current participation in ecosystem driving or defining activities. Synecological studies are therefore of growing importance in defining soil activity. Synecological research involves assessments of metabolic activities, enzymatic activities, as well as the properties of the soil ecosystem resulting from and controlling these activities.

As with the evaluation of specific microbial populations discussed above, a prime concern in examining soil as a “tissue” is collection of a representative soil sample. Since soil enzyme analysis or respiration measurements generally require larger samples than are necessary for detection of individual microbial species (grams of soil for a synecological study vs. perhaps milligrams or less for an autecological analysis), sample heterogeneity difficulties are reduced. Composited soil samples collected in a manner to average system variability become more practical; for example, a composited soil sample from a corn field may contain equal quantities of soil from between rows and within rows. Considerations with soil collection may reside more with concern for aboveground plant distribution affect or soil horizon influences than is possible with autecological studies.

2.6 Interphase Between Study of Individual and Community Microbiology

Neither the study of individual species present in soil and their metabolic capability nor consideration of soil metabolic activities solely provides the conceptual basis upon which to construct the science of soil microbiology. Axenic cultures of soil microorganisms provide a detailed understanding of the nuances of the metabolic intricacy of the “players” in the soil ecosystem. Evaluation of enzymatic and respiratory activities of isolated soil samples demonstrate site‐specific and perhaps even chronologically limited system traits, but without an understanding of the organisms present little is revealed regarding the resilience of the community. That is, both the axenic culture and the soil sample are informative for the soil microbiologist.

This conclusion is demonstrated quite clearly when the impact of soil heterogeneity on growth factor limitations and microbial interactions are considered. Microorganisms require growth factors (i.e. vitamins and amino acids) for growth. A greater proportion of soil bacteria require these factors than are capable of synthesizing their metabolic needs de novo. The effect of this observation can be seen by examining a hypothetical reaction catalyzed by a microorganism requiring a vitamin. Assume that in the test tube the following reaction sequence is catalyzed by a single microbe:

Let us hypothesize that for this reaction to go to completion a growth factor is required for conversion of product B to product C. This may not be considered in laboratory experimentation because the growth media typically used are supplemented with yeast extract or comparable vitamin and amino acid sources to provide all nondefined growth needs. Thus, experimental results are published with the conclusion that the bacterial isolate, perhaps one commonly occurring in soil, is perfectly capable of mineralizing the compound of concern, yielding carbon dioxide and water. That is, the compound of interest is concluded to be biodegradable. Unfortunately, the requisite growth factor may be missing from the soil microsite. Therefore, instead of observing complete mineralization as the laboratory data would have predicted, product B accumulates. Thus, a substance that laboratory data indicated is biodegradable yields intermediates which accumulate in the ecosystem.

Laboratory data could also predict environmental stability of a product when complete decomposition is observed in native soils. This situation occurs in ecosystems where no single microbial species possess the enzymatic capacity to convert the substance to carbon dioxide and water, but a consortia exists which can catalyze the conversion. Again, consider the above generic reaction sequence:

In this situation, substance A may be converted to product B by a one microbial species. A second or even a third microorganism could be postulated to be required for transformation of products B–C and C–D. Thus, data from axenic culture indicates environmental stability of substance A whereas amendment of soil with substance A results in complete conversion of substance A to product D.

2.7 Concluding Comments

This analysis of the soil biological community provides justification for reaching a seemingly heretical conclusion that the total potential of a soil ecosystem exceeds the sum of its parts. Conceptually, soil is readily separated into a mass of mineral and chemical components as discussed in Chapter 1 plus a variety of living organisms. It is these latter participants in the system that transform the soil mineral matrix into the ever changing, life supporting substance essential for our terrestrial existence. Although a vast array of biological processes occur in a soil sample, a much greater potential is contained within the unexpressed microbial metagenome. Thus, development of an understanding of the intricacies of the interaction of the biological dimension of soil with its physical and chemical aspects is essential for proper stewardship of our ecosystem. This conceptual understanding is based on the results of a variety of autecological and synecological studies of soil organisms and processes. As introduced in the brief overview of some methods available for analysis of soil microbes, validity and interpretation of the data collected in these ecological studies must be predicated on a firm appreciation of the methodological problems associated with soil microbiological research. Even with their inherent limitations, such studies provide the foundation for further incursions into evaluation of the ever‐changing world of the soil microbe.

References

1. Ahmed, M. and Oades, J.M. (1984). Distribution of organic matter and adenosine‐triphosphate after fractionation of soils by physical procedures. Soil Biol. Biochem. 16: 465–470.

2. Alef, K. and Kleiner, D. (1989). Rapid and sensitive determination of microbial activity in soils and in soil aggregates by dimethylsulfoxide reduction. Biol. Fertil. Soils 8: 349–355.

3. Alef, K., Beck, T., Zelles, L., and Kleiner, D. (1988). A comparison of methods to estimate microbial biomass and N‐mineralization in agricultural and grassland soils. Soil Biol. Biochem. 20: 561–565.

4. Alexander, M. (1982). Most probable number method for microbial populations. In: Methods of Soil Analysis, Part 2 (ed. A.L. Page), 815–820. Madison, WI: Agronomy Society of America, Inc.

5. Anderson, J.P.E. and Domsch, K.H. (1973). Quantification of bacterial and fungal contributions to soil respiration. Arch. Mikrobiol. 93: 113–127.

6. Anderson, J.P.E. and Domsch, K.H. (1975). Measurement of bacterial and fungal contributions to respiration of selected agricultural and forest soils. Can. J. Microbiol. 21: 314–322.

7. Anderson, J.P.E. and Domsch, K.H. (1978). A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10: 215–221.

8. Anderson, J.P.E. and Domsch, K.H. (1980). Quantities of plant nutrients in the microbial biomass of selected soils. Soil Sci. 130: 211–216.

9. Ausmus, B.S. (1971). Adenosine triphosphate – a measure of active microbial biomass. Biologie du Sol 14: 8–9.

10. Azam, F., Mulvaney, R.L., and Stevenson, F.J. (1988). Determination of in situ K_N by the chloroform fumigation‐incubation method and mineralization of biomass N under anaerobic conditions. Plant and Soil 111: 87–93.

11. Bakken, L.R. and Olsen, R.A. (1983). Buoyant densities and dry‐matter contents of microorganisms: conversion of a measured biovolume into biomass. Appl. Environ. Microbiol. 45: 1188–1195.

12. Bates, S.T. and Garcia‐Pichel, F. (2009). A culture‐independent study of free‐living fungi in biological soil crusts of the Colorado Plateau: their diversity and relative contribution to microbial biomass. Environ. Microbiol. 11: 56–67.

13. Blum, S.A.E., Lorenz, M.G., and Wackernagel, W. (1997). Mechanism of retarded DNA degradation and prokaryotic origin of DNases in nonsterile soils. Syst. Appl. Microbiol. 20: 513–521.

14. Bottomley, P.J. and Dughri, M.H. (1989). Population size and distribution of Rhizobium leguminosarum bv. trifolii in relation to total soil bacteria and soil depth. Appl. Environ. Microbiol. 55: 959–964.

15. Bratbak, G. (1985). Bacterial biovolume and biomass estimations. Appl. Environ. Microbiol. 49: 1488–1493.

16. Bratbak, G. and Dundas, I. (1984). Bacterial dry matter content and biomass estimations. Appl. Environ. Microbiol. 48: 755–757.

17. Brookes, P.C., Powlson, D.S., and Jenkinson, D.S. (1982). Measurement of microbial biomass phosphorus in soil. Soil Biol. Biochem. 14: 319–329.

18. Brookes, P.C., Powlson, D.S., and Jenkinson, D.S. (1984). Phosphorus in the soil microbial biomass. Soil Biol. Biochem. 16: 169–175.

19. Chapman, S.J. (1987). Inoculum in the fumigation method for soil biomass determination. Soil Biol. Biochem. 19: 83–87.

20. Christensen, S., Simkins, S., and Tiedje, J.M. (1991a). Temporal patterns of soil denitrification: their stability and causes. Soil Sci. Soc. Am. J. 54: 1614–1618.

21. Christensen, S., Simkins, S., and Tiedje, J.M. (1991b). Spatial variation in denitrification: dependency of active centers on the soil environment. Soil Sci. Soc. Am. J.: 1608–1613.

22. Christensen, H., Hansen, M., and Sørensen, J. (1999). Counting and size classification of active soil bacteria by fluorescence in situ hybridization with an rRNS oligonucleotide probe. Appl. Environ. Microbiol. 65: 1753–1761.

23. Coûteaux, M.M., Henkinet, R., Pitta, P. et al. (1989). Native carbon mineralization of an acid organic soil after use of the chloroform‐fumigation method to estimate microbial biomass. Biol. Fertil. Soils 8: 172–177.

24. Dahlin, S. and Witter, E. (1998). Can the low microbial biomass C‐to‐organic‐C ration in an acid and a metal contaminated soil be explained by differences in the substrate utilization efficiency and maintenance requirements. Soil Biol. Biochem. 30: 633–641.

25. Deluca, T.H. (1998). Relationship of 0.5 M K₂SO₄ extractable anthrone‐reactive carbon to indices of microbial activity in forest soils. Soil Biol. Biochem. 10: 1293–1299.

26. Demaneche, S., Jocteur‐Monrozier, L., Quiquampoix, H., and Simonet, P. (2001). Evaluation of biological and physical protection against nuclease degradation of clay‐bound plasmid DNA. Appl. Environ. Microbiol. 67: 293–299.

27. Demezas, D.H. and Bottomley, P.J. (1986). Autecology in rhizospheres and nodulating behavior of indigenous Rhizobium trifolii. Appl. Environ. Microbiol. 52: 1014–1019.

28. Dictor, M.C., Tessier, L., and Soulas, G. (1998). Reassessment of the K‐ec coefficient of the fumigation‐extraction method in a soil profile. Soil Biol. Biochem. 30: 119–127.

29. Fazio, S.D., Mayberry, W.R., and White, D.C. (1979). Muramic acid assay in sediments. Appl. Environ. Microbiol. 38: 349–350.

30. Fierer, N., Jackson, J.A., Vilgalys, R., and Jackson, R.B. (2005). Assessment of soil microbial community structure by use of taxon‐specific quantitative PCR assays. Appl. Environ. Microbiol. 71: 4117–4120.

31. Findlay, R.H., King, G.M., and Watling, L. (1989). Efficacy of phospholipid analysis in determining microbial biomass in sediments. Appl. Environ. Microbiol. 55: 2888–2893.

32. Flühler, H., Ardakani, M.S., Szuszkiewicz, T.E., and Stolzy, L.H. (1976). Field‐measured nitrous oxide concentrations, redox potentials, oxygen diffusion rates, and oxygen partial pressures in relation to denitrification. Soil Sci. 122: 107–114.

33. Focht, D.D. (1974). The effect of temperature, pH, and aeration on the production of nitrous oxide and gaseous nitrogen – a zero‐order kinetic model. Soil Sci. 118: 173–179.

34. Fritze, H., Vanhala, P., Pietkäinen, J., and Mälkönen, E. (1996). Vitality fertilization of Scots pine stands growing along a gradient of heavy metal pollution: sort‐term effects on microbial biomass and respiration rate of the humus layer. Fresenius J. Anal. Chem. 354: 750–755.

35. Gunapala, N. and Scow, K.M. (1998). Dynamics of soil microbial biomass and activity in conventional and organic farming systems. Soil Biol. Biochem. 30: 805–816.

36. Hedley, M.J. and Stewart, J.W.B. (1982). Method to measure microbial biomass phosphorus in soils. Soil Biol. Biochem. 14: 337–385.

37. Holder Franklin, M.A. and Tate, R.L. III (1986). Introduction of the computer into autecological studies. In: Microbial Autecology: A Method for Environmental Studies (ed. R.L. Tate III), 76–91. New York: Wiley.

38. Holder‐Franklin, M.A. (1986). Ecological relationships of microbiota in water and soil as revealed by diversity measurements. In: Microbial Autecology: A Method for Environmental Studies (ed. R.L. Tate III), 93–132. New York: Wiley.

39. Jenkinson, D.S. (1976). The effects of biocidal treatments on metabolism in soil. IV. The decomposition of fumigated organisms in soil. Soil Biol. Biochem. 8: 203–208.

40. Jenkinson, D.S. and Powlson, D.S. (1980). Measurement of microbial biomass in intact soil cores and sieved soil. Soil Biol. Biochem. 12: 579–581.

41. Jenkinson, D.S., Davidson, S.A., and Powlson, D.S. (1979). Adenosine triphosphate and microbial biomass in soil. Soil Biol. Biochem. 11: 521–527.

42. Kassim, G., Martin, J.P., and Haider, K. (1982). Incorporation of a wide variety of organic substrate carbons into soil biomass as estimated by the fumigation procedure. Soil Sci. Soc. Am. J. 45: 1106–1112.

43. King, J.D. and White, D.C. (1977). Muramic acid as a measure of microbial biomass in estuarine and marine samples. Appl. Environ. Microbiol. 33: 777–783.

44. Klappenbach, J.A., Dunbar, J.M., and Schmidt, T.M. (2000). rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66: 1328–1333.

45. Könneke, M., Bernhard, A.E., de la Torre, J.R. et al. (2005). Isolation of an autotrophic ammonia‐oxidizing marine archaeon. Nature 437: 543–546.

46. Leininger, S., Urich, T., Schloter, M. et al. (2006). Archaea predominate among ammonia‐oxidizing prokaryotes in soils. Nature 442: 806–809.

47. Lennon, J.T. and Jones, S.E. (2011). Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9 (2): 119–130.

48. Lundgren, B. (1981). Fluorescein diacetate as a stain of metabolically active bacteria in soil. Oikos 36: 17–22.

49. Martens, R. (1985). Limitations in the application of the fumigation technique for biomass estimations in amended soils. Soil Biol. Biochem. 17: 57–63.

50. Martens, R. (1987). Estimation of microbial biomass in soil by the respiration method: importance of soil pH and flushing methods for the measurement of respired CO₂. Soil Biol. Biochem. 19: 77–81.

51. Miller, D.N., Bryant, J.E., Madsen, E.L., and Ghiorse, W.C. (1999). Evaluation and optimization of DNA extraction and purification procedures for soil and sediment samples. Appl. Environ. Microbiol. 65: 4715–4724.

52. Mills, A.L. (1985). Acid mine drainage: microbial impact on the recovery of soil and water ecosystems. In: Soil Reclamation Processes: Microbiological Analyses and Applications (eds. R.L. Tate III and D.A. Klein), 35–81. New York: Marcel Dekker.

53. Mills, A.L. and Bell, P.E. (1986). Determination of individual organisms and their activities in situ. In: Microbial Autecology: A Method for Environmental Studies (ed. R.L. Tate III), 27–60. New York: Wiley.

54. Norton, J., Alzerreca, J., Suwa, Y., and Klotz, M. (2002). Diversity of ammonia monooxygenase operon in autotrophic ammonia‐oxidizing bacteria. Arch. Microbiol. 177: 139–149.

55. Okano, Y., Hristova, K.R., Leutenegger, C.M. et al. (2004). Application of real‐time PCR to study effects of ammonium on population size of ammonia‐oxidizing bacteria in soil. Appl. Environ. Microbiol. 70: 1008–1016.

56. Parkin, T.B. (1987). Soil microsites as a source of denitrification variability. Soil Sci. Soc. Am. J. 51: 1194–1199.

57. Parkin, T.B., Starr, J.L., and Meisinger, J.J. (1987). Influence of sample size on measurement of soil denitrification. Soil Sci. Soc. Am. J. 51: 1492–1507.

58. Parkinson, D. and Paul, E.A. (1982). Microbial biomass. In: Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (ed. A.L. Page), 821–830. Madison, WI: American Society of Agronomy.

59. Parsons, L.L., Murray, R.E., and Smith, M.S. (1991). Soil denitrification dynamics: spatial and temporal variations of enzyme activity, populations, and nitrogen gas loss. Soil Sci. Soc. Am. J. 55: 90–95.

60. Paul, E.A. and Johnson, R.L. (1977). Microscopic counting and adenosine 5′‐triphosphate measurement in determining microbial growth in soils. Appl. Environ. Microbiol. 34: 263–269.

61. Ross, R.J. (1987). Soil microbial biomass estimated by the fumigation‐incubation procedure: seasonal fluctuations and influences of soil moisture content. Soil Biol. Biochem. 19: 397–404.

62. Ross, D.J. (1988). Modifications to the fumigation procedure to measure microbial biomass C in wet soils under pasture: influence on estimates on seasonal fluctuations in the soil biomass. Soil Biol. Biochem. 20: 3777–3383.

63. Rosser, D.J. (1980). Ethidium bromide a general purpose fluorescent stain for nucleic acid in bacteria and eukaryotes and its use in microbial ecology studies. Soil Biol. Biochem. 12: 329–336.

64. Roszak, D.B. and Colwell, R.R. (1987). Metabolic activity of bacterial cells enumerated by direct viable count. Appl. Environ. Microbiol. 53: 2889–2983.

65. Russek‐Cohen, R. and Colwell, R.R. (1986). Application of numerical taxonomy procedures in microbial ecology. In: Microbial Autecology: A Method for Environmental Studies (ed. R.L. Tate III), 133–146. New York: Wiley.

66. Schmidt, E.L. and Paul, E.A. (1982). Microscopic methods for soil microorganisms. In: Methods of Soil Analysis, Part 2 (ed. A.L. Page), 803–814. Madison, WI: Agronomy Society of America, Inc.

67. Sexton, A.J., Parkin, T.B., and Tiedje, J.M. (1985). Temporal response of soil denitrification rates to rainfall and irrigation. Soil Sci. Soc. Am. J. 49: 99–103.

68. Shade, A., Jones, S.E., Caporaso, J.G. et al. (2014). Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. MBio 5 (4): e01371–e01314.

69. Smith, M.S. and Tiedje, J.M. (1979). Phases of denitrification following oxygen depletion in soil. Soil Biol. Biochem. 11: 261–267.

70. Sogin, M.L., Morrison, H.G., Huber, J.A. et al. (2006). Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. U. S. A. 103: 12115–12120.

71. Sonderstrom, B.E. (1977). Vital staining of fungi in pure cultures and in soil with fluorescein diacetate. Soil Biol. Biochem. 9: 59–63.

72. Sparling, G.P. and West, A.W. (1988). A direct extraction method to estimate soil microbial C: calibration in situ using microbial respiration and ¹⁴C labelled cells. Soil Biol. Biochem. 20: 337–343.

73. Staley, J.T. and Konopka, A. (1985). Measurements of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Annu. Rev. Microbiol. 39: 321–346.

74. Stenberg, B., Johansson, M., Pell, M. et al. (1998). Microbial biomass and activities in soil as affected by frozen and cold storage. Soil Biol. Biochem. 30: 393–402.

75. Tate, R.L. (ed.) (1986). Importance of autecology in microbial ecology. In: Microbial Autecology. A Method for Environmental Studies, 1–26. New York: Wiley Interscience.

76. Tate, R.L. III (1991). Microbial biomass measurement in acidic soils: effect of fungal:bacterial activity ratios and soil amendment. Soil Sci. 152: 220–225.

77. Tate, K.R., Ross, D.J., and Feltham, C.W. (1988). A direct extraction method to estimate soil microbial C: effects of experimental variables and some different calibration procedures. Soil Biol. Biochem. 20: 329–335.

78. Tessier, L., Gregorich, E.G., and Topp, E. (1998). Spatial variability of soil microbial biomass measured by the fumigation extraction method, and K‐ec as affected by depth and manure application. Soil Biol. Biochem. 30: 1369–1377.

79. Van Veen, J.A. and Paul, E.A. (1979). Conversion of biovolume measurements of soil microorganisms, grown under various moisture tensions, to biomass and their nutrient content. Appl. Environ. Microbiol. 37: 686–692.

80. Vance, E.D., Brookes, P.C., and Jenkinson, D.S. (1987a). An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19: 703–707.

81. Vance, E.D., Brookes, P.C., and Jenkinson, D.S. (1987b). Microbial biomass measurements in forest soils: the use of the chloroform fumigation‐incubation method in strongly acid soils. Soil Biol. Biochem. 19: 697–702.

82. Vance, E.D., Brookes, P.C., and Jenkinson, D.S. (1987c). Microbial biomass measurements in forest soils: determination of K_C values and tests of hypotheses to explain the failure of the chloroform fumigation‐incubation method in acid soils. Soil Biol. Biochem. 19: 689–696.

83. Verstraete, W., van de Werf, H., Kucnirowicz, K., and Ilaiwi, M. (1983). Specific measurement of soil microbial ATP. Soil Biol. Biochem. 15: 391–396.

84. Wallenstein, M.D. and Vilgalys, R.J. (2005). Quantitative analysis of nitrogen cycling genes in soils. Pedobiologia 49: 665–672.

85. Weinbauer, M.G., Beckmann, C., and Höfle, M.G. (1998). Utility of green fluorescent nucleic acid dyes and aluminum oxide membrane filters for rapid epifluorescence enumeration of soil and sediment bacteria. Appl. Environ. Microbiol. 64: 5000–5003.

86. Wildung, R.E. and Garland, T.R. (1985). Microbial development on oil shale waste: influence on geochemistry. In: Soil Reclamation Processes: Microbiological Analyses and Applications (eds. R.L. Tate III and D.A. Klein), 107–139. New York: Marcel Dekker.