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Isotopic measurements of trace gases such as N2O , CO2 , and CH4 contain valuable information about production and consumption pathways. Quantification of the underlying pathways contributing to variability in isotopic time series can provide answers to key scientific questions, such as the contribution of nitrification and denitrification to N2O emissions under different environmental conditions or the drivers of multiyear variability in atmospheric CH4 growth rate. However, there is currently no data analysis package available to solve isotopic production, mixing, and consumption problems for time series data in a unified manner while accounting for uncertainty in measurements and model parameters as well as temporal autocorrelation between data points and underlying mechanisms. Bayesian hierarchical models combine the use of expert information with measured data and a mathematical mixing model while considering and updating the uncertainties involved, and they are an ideal basis to approach this problem. Here we present the Time-resolved FRactionation And Mixing Evaluation (TimeFRAME) data analysis package. We use four different classes of Bayesian hierarchical models to solve production, mixing, and consumption contributions using multi-isotope time series measurements: (i) independent time step models, (ii) Gaussian process priors on measurements, (iii) Dirichlet–Gaussian process priors, and (iv) generalized linear models with spline bases. We show extensive testing of the four models for the case of N2O production and consumption in different variations. Incorporation of temporal information in approaches (i)–(iv) reduced uncertainty and noise compared to the independent model (i). Dirichlet–Gaussian process prior models have been found to be most reliable, allowing for simultaneous estimation of hyperparameters via Bayesian hierarchical modeling. Generalized linear models with spline bases seem promising as well, especially for fractionation estimation, although the robustness to real datasets is difficult to assess given their high flexibility. Experiments with simulated data for δ15Nbulk and δ15NSP of N2O showed that model performance across all classes could be greatly improved by reducing uncertainty in model input data – particularly isotopic end-members and fractionation factors. The addition of the δ18O additional isotopic dimension yielded a comparatively small benefit for N2O production pathways but improved quantification of the fraction of N2O consumed; however, the addition of isotopic dimensions orthogonal to existing information could strongly improve results, for example, clumped isotopes. The TimeFRAME package can be used to evaluate both static and time series datasets, with flexible choice of the number and type of isotopic end-members and the model setup allowing simple implementation for different trace gases. The package is available in R and is implemented using Stan for parameter estimation, in addition to supplementary functions re-implementing some of the surveyed isotope analysis techniques. [ABSTRACT FROM AUTHOR]

Isotopic measurements of trace gases such as N2O, CO2 and CH4 contain valuable information about production and consumption pathways. Quantification of the underlying pathways contributing to variability in isotopic timeseries can provide answers to key scientific questions, such as the contribution of nitrification and denitrification to N2O emissions under different environmental conditions, or the drivers of multiyear variability in atmospheric CH4 growth rate. However, there is currently no data analysis package available to solve isotopic production, mixing and consumption problems for timeseries data in a unified manner while accounting for uncertainty in measurements and model parameters. Bayesian hierarchical models combine the use of expert information with measured data and a mathematical mixing model while considering and updating the uncertainties involved, and are an ideal basis to approach this problem. Here we present the TimeFRAME data analysis package for 'Time-resolved Fractionation And Mixing Evaluation'. We use four different classes of Bayesian hierarchical model to solve production, mixing and consumption contributions using multi-isotope timeseries measurements: i) independent time step models, ii) Gaussian process priors on measurements, iii) Dirichlet-Gaussian process priors, and iv) generalized linear models with spline bases. All four models have been extensively tested in different variations and for a multitude of scenarios. Dirichlet-Gaussian process prior models have been found to be most reliable, allowing for simultaneous estimation of hyperparameters via Bayesian hierarchical modeling. Generalized linear models with spline bases seem promising as well, especially for fractionation estimation, although the robustness to real datasets is difficult to assess given their high flexibility. Experiments with simulated data for δ15Nbulk and δ15NSP of N2O showed that model performance across all classes could be greatly improved by reducing uncertainty in model input data – particularly isotopic endmembers and fractionation factors. The addition of the δ18O additional isotopic dimension yielded a comparatively small benefit for N2O production pathways but improved quantification of the fraction of N2O consumed. The TimeFRAME package can be used to evaluate both static and timeseries datasets, with flexible choice of the number and type of isotopic endmembers and the model set up allowing simple implementation for different trace gases. The package is available in R, and is implemented using Stan for parameter estimation, in addition to supplementary functions re-implementing some of the surveyed isotope analysis techniques. [ABSTRACT FROM AUTHOR]

Isotopic measurements of trace gases such as N2O, CO2 and CH4 contain valuable information about production and consumption pathways. Quantification of the underlying pathways contributing to variability in isotopic timeseries can provide answers to key scientific questions, such as the contribution of nitrification and denitrification to N2O emissions under different environmental conditions, or the drivers of multiyear variability in atmospheric CH4 growth rate. However, there is currently no data analysis package available to solve isotopic production, mixing and consumption problems for timeseries data in a unified manner while accounting for uncertainty in measurements and model parameters. Bayesian hierarchical models combine the use of expert information with measured data and a mathematical mixing model while considering and updating the uncertainties involved, and are an ideal basis to approach this problem. Here we present the TimeFRAME data analysis package for 'Time-resolved Fractionation And Mixing Evaluation'. We use four different classes of Bayesian hierarchical model to solve production, mixing and consumption contributions using multi-isotope timeseries measurements: i) independent time step models, ii) Gaussian process priors on measurements, iii) Dirichlet-Gaussian process priors, and iv) generalized linear models with spline bases. All four models have been extensively tested in different variations and for a multitude of scenarios. Dirichlet-Gaussian process prior models have been found to be most reliable, allowing for simultaneous estimation of hyperparameters via Bayesian hierarchical modeling. Generalized linear models with spline bases seem promising as well, especially for fractionation estimation, although the robustness to real datasets is difficult to assess given their high flexibility. Experiments with simulated data for δ15Nbulk and δ15NSP of N2O showed that model performance across all classes could be greatly improved by reducing uncertainty in model input data - particularly isotopic endmembers and fractionation factors. The addition of the δ18O additional isotopic dimension yielded a comparatively small benefit for N2O production pathways but improved quantification of the fraction of N2O consumed. The TimeFRAME package can be used to evaluate both static and timeseries datasets, with flexible choice of the number and type of isotopic endmembers and the model set up allowing simple implementation for different trace gases. The package available in R, and is implemented using Stan for parameter estimation, in addition to supplementary functions reimplementing some of the surveyed isotope analysis techniques. [ABSTRACT FROM AUTHOR]

The approaches based on natural abundance N2O stable isotopes are often applied for the estimation of mixing proportions between various N2O -producing pathways as well as for estimation of the extent of N2O reduction to N2. But such applications are associated with numerous uncertainties; hence, their limited accuracy needs to be considered. Here we present the first systematic validation of these methods for laboratory and field studies by applying the 15N gas-flux method as the reference approach. Besides applying dual-isotope plots for interpretation of N2O isotopic data, for the first time we propose a three dimensional N2O isotopocule model based on Bayesian statistics to estimate the N2O mixing proportions and reduction extent based simultaneously on three N2O isotopic signatures (δ15N , δ15N SP , and δ18O). Determination of the mixing proportions of individual pathways with N2O isotopic approaches often appears imprecise, mainly due to imperfect isotopic separation of the particular pathways. Nevertheless, the estimation of N2O reduction is much more robust, when applying an optimal calculation strategy, typically reaching an accuracy of N2O residual fraction determination of about 0.15. [ABSTRACT FROM AUTHOR]

The isotopic composition of nitrous oxide (N2O) provides useful information for evaluating N2O sources and budgets. Due to the co‐occurrence of multiple N2O transformation pathways, it is, however, challenging to use isotopic information to quantify the contribution of distinct processes across variable spatiotemporal scales. Here, we present an overview of recent progress in N2O isotopic studies and provide suggestions for future research, mainly focusing on: analytical techniques; production and consumption processes; and interpretation and modelling approaches. Comparing isotope‐ratio mass spectrometry (IRMS) with laser absorption spectroscopy (LAS), we conclude that IRMS is a precise technique for laboratory analysis of N2O isotopes, while LAS is more suitable for in situ/inline studies and offers advantages for site‐specific analyses. When reviewing the link between the N2O isotopic composition and underlying mechanisms/processes, we find that, at the molecular scale, the specific enzymes and mechanisms involved determine isotopic fractionation effects. In contrast, at plot‐to‐global scales, mixing of N2O derived from different processes and their isotopic variability must be considered. We also find that dual isotope plots are effective for semi‐quantitative attribution of co‐occurring N2O production and reduction processes. More recently, process‐based N2O isotopic models have been developed for natural abundance and 15N‐tracing studies, and have been shown to be effective, particularly for data with adequate temporal resolution. Despite the significant progress made over the last decade, there is still great need and potential for future work, including development of analytical techniques, reference materials and inter‐laboratory comparisons, further exploration of N2O formation and destruction mechanisms, more observations across scales, and design and validation of interpretation and modelling approaches. Synthesizing all these efforts, we are confident that the N2O isotope community will continue to advance our understanding of N2O transformation processes in all spheres of the Earth, and in turn to gain improved constraints on regional and global budgets. [ABSTRACT FROM AUTHOR]

Pure culture studies provide evidence of the ability of soil fungi to produce nitrous oxide (N2O) during denitrification. Soil studies with selective inhibition indicated a possible dominance of fungal compared to bacterial N2O production in soil, which drew more attention to fungal denitrification. Analyzing the isotopic composition of N2O, especially the [sup 15]N site preference of N2O produced (SPN[sub 2]O), showed that N2O of pure bacterial or fungal cultures differed in SPN[sub 2]O values, which might enable the quantification of fungal N2O based on the isotopic endmember signatures of N2O produced by fungi and bacteria. This study aimed to identify the fungal contribution to N2O emissions under anaerobic conditions in incubated repacked soil samples by using different approaches to disentangle sources of N2O. Three soils were incubated under anaerobic conditions to promote denitrification with four treatments of a modified substrate induced respiration with selective inhibition (SIRIN) approach. While one treatment without microbial inhibition served as a control the other three treatments were amended with inhibitors to selectively inhibit bacterial, fungal or bacterial and fungal growth. These treatments were performed in three varieties. In one variety the [sup 15]N tracer technique was used to estimate the effect of N2O reduction on N2O produced, while two other varieties were performed under natural isotopic conditions but with and without acetylene. Three approaches were established to estimate the N2O production by a fungal community in soil: i) A modification of the SIRIN approach was used to calculate N2O evolved from selected organism groups, and ii) SPN2O values from the acetylated treatment were used in the isotope endmember mixing approach (IEM), and iii) the SP/δ 18O mapping approach (SP/δ18O Map) was used to estimate the fungal contribution to N2O production and N2O reduction under anaerobic conditions from the non-acetylated treatment. The three approaches tested revealed a small fungal contribution to N2O fluxes under anaerobic conditions in the soils tested. Quantifying the fungal fraction with modified SIRIN was only possible in one soil and totaled 0.28±0.09. This was higher than the results obtained by IEM and SP/δ18O Map, which accounted zero to 0.20 of N2O produced to the fungal community. To our knowledge, this study was the first attempt to quantify the fungal contribution to anaerobic N2O production by simultaneous application of three approaches, i.e. modified SIRIN, IEM and SP/δ18O Map. While all methods coincided by suggesting a small or missing fungal contribution, further studies under conditions ensuring larger fungal N2O fluxes and including alternative inhibitors are needed to better cross-validate the methods. [ABSTRACT FROM AUTHOR]

The approaches based on natural abundance N2O stable isotopes are often applied for the estimation of mixing proportions between various N2O producing pathways as well as for estimation of the extent of N2O reduction to N2. But such applications are associated with numerous uncertainties and hence their limited accuracy needs to be considered. Here we present the first systematic validation of these methods for laboratory and field studies applying the 15N gas-flux method as the reference approach. Besides applying dual isotope plots for interpretation of N2O isotopic data, for the first time we propose a three dimensional N2O isotopocule model based on Bayesian statistics to estimate the N2O mixing proportions and reduction extent based simultaneously on three N2O isotopic signatures (δ15N, δ15NSP and δ18O). Determination of mixing proportions of individual pathways with N2O isotopic approaches appears often imprecise, mainly due to imperfect isotopic separation of the particular pathways. Nevertheless, the estimation of N2O reduction is much more robust, when applying optimal calculation strategy, reaching typically accuracy of N2O residual fraction determination of about 0.1. [ABSTRACT FROM AUTHOR]

The 15N gas-flux method allows for the quantification of N2 flux and tracing soil N transformations. An important requirement for this method is a homogeneous distribution of the 15N tracer added to soil. This is usually achieved through soil homogenization and admixture of the 15N tracer solution or multipoint injection of tracer solution to intact soil. Both methods may create artefacts. We aimed at comparing the N2 flux determined by the gas-flux method using both tracer distribution approaches. Soil incubation experiments with silt loam soil using (i) intact soil cores injected with 15N label solution, (ii) homogenized soil with injected label solution, and (iii) homogenized soil with admixture of label solution were performed. Intact soil cores with injected 15N tracer solution show a larger variability of the results. Homogenized soil shows better agreement between repetitions, but significant differences in 15N enrichment measured in soil nitrate and in emitted gases were observed. For intact soil, the larger variability of measured values results rather from natural diversity of non-homogenized soil cores than from inhomogeneous label distribution. Generally, comparison of the results of intact cores and homogenized soil did not reveal statistically significant differences in N2 flux determination. In both cases, a pronounced dominance of N2 flux over N2 O flux was noted. It can be concluded that both methods showed close agreement, and homogenized soil is not necessarily characterized by more homogenous 15N label distribution. [ABSTRACT FROM AUTHOR]