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2.5.1

Selective inhibition

In contrast to total microbial inhibition using sterilization methods for soils or growth media (e.g. autoclaving), selective inhibition aims to inhibit just a fraction of the present microbial community. For the separation of a single microbial species or the measurement of contributions to a microbial community’s biomass or metabolic response, several chemicals have already been used in the reported literature based on their inhibitory potential with respect to specific microbial groups (Kreutzer 1963). Since many of these substances can be used for microbial communities in vitro (Tsao 1970), problems may occur with their use in soils due to unknown non-target effects (e.g. fungal inhibitors also inhibiting some archaeal species (Vajrala et al. 2014)). However, within a series of many experiments, Anderson and Domsch developed a method for selective inhibition of fungi and bacteria in soil samples (Anderson and Domsch 1973; Anderson and Domsch 1974; Anderson and Domsch 1978). Within this method, cycloheximide (C15H23NO4) is used as fungal inhibitor to inhibit

the peptidyl transferase activity of the eukaryotic 60 S ribosomal subunit (Schneider-Poetsch et al. 2010) and streptomycin (C21H39N7O12) is used to inhibit the bacterial protein synthesis (Luzzatto et al.

1968). These chemicals are simply applied as a dry powder to the soil and distributed via subsequent mixing of the sample (Anderson and Domsch 1973). Later evaluations of this method revealed a few problems caused by the rapid decomposition of the inhibitory substances in soils and the non- inhibition of a few bacterial groups (Badalucco et al. 1994), however, these chemicals can be assumed to inhibit most fungi (cycloheximide) or bacteria (streptomycin) within 24 – 48 h after application. Other chemicals have also been used for soil microbial selective inhibition, e.g. ethyne (C2H2), Captan (C9H8Cl3NO2S) or oxytetracycline hydrochloride (C22H24N2O9*HCl) (Bailey et al. 2002;

Klemedtsson et al. 1988). However, limited inhibition time and non-target effects are common problems among almost all selective inhibitory substances (Oremland and Capone 1988), and the refined method of Anderson and Domsch (1978) is still regarded as one of the most precise and simplest methods for selective inhibition of bacteria and fungi in soil samples and therefore it is frequently used (e.g., Lin and Brookes (1999), Rex et al. (2015) and Koijman et al. (2016)).

2.5.2

_{N-tracer techniques}

The discovery of 15_{N as a naturally occurring stable isotope of N (Naudé 1929) provided a simple yet}

effective way to mark N within a chemical compound in order to trace back its transformation and distribution over the duration of an experiment (or rather a chemical reaction pathway). The natural abundance of 15_{N in atmospheric N}

2 is only 0.3663% (and so it is in the biosphere), and thus the

percentage within the subsequent reaction products. First techniques were already developed in the 1940s (Rittenberg 1948) and since then 15_{N has been used as tracer element for further investigation}

of the N cycle (Chen et al. 1995; Hauck et al. 1958; Hüser et al. 1960; Jansson 1955; Peterson 1999; Peterson and Fry 1987; Turtschin et al. 1960). Usually the N samples require a certain procedure for purification and transformation into an N gas (e.g., distillation and titration (Bremner and Edwards 1965) or a diffusion method (Brooks et al. 1989; Sorensen and Fresquez 1991)) to be finally analysed using a mass spectrometer. In more recent years, this approach was used for the characterization of chemical reactions forming N2O and N2 (Garber and Hollocher 1981; St John and Hollocher 1977)

where subsequent calculation procedures allow the identification of N pool of origin and (indirectly) the generating pathway (Arah 1997; Boast et al. 1988). Such an approach is used in almost all recent studies focusing on (co)denitrification (Clough et al. 2017; Heil et al. 2015; Laughlin and Stevens 2002; Phillips et al. 2016; Selbie et al. 2015b; Spott et al. 2011).

2.5.3

_N-modelling

Another tool for the estimation of N fluxes/transformations between the different N pools in soil are models which are used to predict gaseous N emissions or to estimate the N turnover in soil. Although recent techniques allow the constant measurement of N fluxes and leaching (e.g. NH3 (Demmers et

al. 1999; Rhoades et al. 2010)), most measured N flux data refer to few points in time and naturally lack information for the intervals in between. Another issue, given limitation of resources, is the lack of simultaneous measurements for all N-pools at the same time.

However, using a 15_{N tracing model data can be acquired for modelling such information. This}

approach started with the pioneering work of Kirkham and Bartholomew (1954), firstly

experimenting with an analytical model based on two N pools. In their first paper they made the assumptions that there would be (i) no preferential use of 15_{N or} 14_{N, (ii) immobilized N would not be}

re-mineralized, and (iii) N transformation rates would follow zero-order kinetics (constant rates). The latter two assumptions were rejected in a following paper (Kirkham and Bartholomew 1955) and as the N transformation rates changed into first-order kinetics. Still, there was no distinction between the NH4+ and NO3- pools, and no distinction was made as to the availability of N within the organic

pool, along with no consideration of possible N losses due to volatilization. So, further work was performed in consideration of these gaps. Subsequently studies have modelled N turnover of the NH4+ and NO3- pools using an analytical approach (Ambus and Christensen 1995; Barraclough 1991;

Davidson et al. 1990; Nishio et al. 1985; Schimel et al. 1989; Tietema and Wessel 1992), while other studies have tried to find a numerical solution (Bjarnason 1988; Myrold and Tiedje 1986; Smith et al. 1994; Wessel and Tietema 1992). However, to find an analytical solution for 3 or more possible N pools is challenging. Most of the analytical models are based on an ‘isotopic dilution’ approach,

meaning that one N pool is initially enriched with 15_{N and becomes ‘diluted’ over time with} 14_{N from}

a less enriched source. In order to determine the dynamics of NH4+ and NO3- for example, a paired

experiment would be necessary with one treatment including 15_NH

4+ enrichment and one for 15NO3-.

Numerical models on the other hand include a description of the modelled system, a numerical resolution of the differential equations and a non-linear optimization procedure (Mary et al. 1998). Thus, the ‘isotopic exchange’ principle, which is able to consider inward- and outward N fluxes from N pools, provides a good model fit to the measured data from an unpaired experiment. Conclusively, with the cost of a minimal loss in precision the numerical approach enables the design of more complex models.

Despite Nishio et al. (1994) using an analytical approach, all following descriptions of the N transformations were based on numerical solutions. Mary et al. (1998) proposed a model named FLUAZ, which included 5 different N pools, 10 possible N transformations (Fig 10) and was based on the Runge-Kutta algorithm (4th _{order, variable time steps) and a non-linear fitting program (based on}

Marquardt’s algorithm). The Runge-Kutta and Marquard algorithms were further used for the ‘15_N

tracing’ model as presented in Müller et al. (2004), this time in combination with the Markow Chain Monte-Carlo (MCMC) method and the Metropolis algorithm (Metropolis et al. 1953) to avoid the optimization procedure being trapped in a local minimum of the misfit function.

This basic version includes 4 mineral and 2 organic soil N pools and 9 possible N transformations ((Müller et al. 2004), model B, (Müller et al. 2007)). This model was later on modified (Inselsbacher et al. 2013; Müller et al. 2014), depending on the raw data provided in the experiment. Another useful novelty was the possible inclusion of a separated NO2--N pool (Müller et al. 2014; Rütting and Müller

2008), which allows the estimation of NO2- and N2O formation via different chemical pathways (Fig.

11). Thus, the 15_{N tracing model group is a highly valuable tool that can be used when methods of}

Figure 10. Compartmental model of N rates considered in FLUAZ (Mary et al. 1998)

Figure 11. 15_{N tracing model to identify pathway specific NO}

2- dynamics (Müller et al. 2014). The

different N-pools are; Nlab = labile soil organic N, Nrec = recalcitrant soil organic N, NH4+

= ammonium, NH4+ads = adsorbed NH4+, NO3- = nitrate, NO2-nit = nitrite of autotrophic

nitrification, NO2-org = nitrite of heterotrophic nitrification, NO2-den = nitrite of

denitrification, Ngas = volatilized NO, N2O and N2. The transformation rates are; A =

adsorption, D = dissimilatory nitrate reduction, H = hydrolyzation, I = immobilization, M = mineralization, R = release, O = oxidation, V = volatilization.