N2O source partitioning and reduction progress quantified with isotopocule datasets – a review of the modelling strategies
Abstract
Nitrous oxide (N2O) isotope signatures provide crucial information for partitioning microbial production pathways, including bacterial, fungal, and nitrifier denitrification, chemo-denitrification and nitrification, as well as for estimating the degree of N2O reduction to N2. However, interpreting the three-dimensional isotope data is highly challenging due to numerous production pathways and simultaneous occurrence of production and consumption. Here we comprehensively review the application of the Fractionation And Mixing Evaluation (FRAME) model, a Bayesian Markov-Chain Monte Carlo (MCMC) tool explicitly developed to resolve complex, underdetermined isotope mixing and fractionation dynamics. The FRAME model shows widespread applicability across diverse global ecosystems, spanning agricultural soils, peatlands, groundwaters, rivers of Europe, America, Asia, and Africa, as well as marine environments. To address common interpretative challenges, standardized guidelines and best practices for configuring the model is established, and an overview of output quality checks and problem-solving ideas is provided. Most recent methodological enhancements of the model performance, including low-level 15N labelling, integration of natural abundance results with 15N tracing, and utilizing model extension for high-resolution time-series datasets are discussed. Furthermore, we introduce FRAME v2.0, a major software update that features improved statistical treatment of source uncertainty and extended auxiliary variable priors, while maintaining full backward compatibility. The model's performance is discussed in the context of various cases studies and independent reference methods, like the 15N gas flux approach and microbial functional gene analysis. This review presents unified standardised and robust interpretation strategies supported by an enhanced computational tool to quantify N2O production pathways and reduction across ecosystems.
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BibTeX
@misc{lewickaszczebak2026source,
title = {{N2O source partitioning and reduction progress quantified with isotopocule datasets – a review of the modelling strategies}},
author = {Dominka Lewicka-Szczebak and Maciej Piotr Lewicki and Caroline Buchen-Tschiskale and Reinhard Well},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x52f9x},
url = {https://eartharxiv.org/repository/view/15723/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - N2O source partitioning and reduction progress quantified with isotopocule datasets – a review of the modelling strategies
AU - Dominka Lewicka-Szczebak
AU - Maciej Piotr Lewicki
AU - Caroline Buchen-Tschiskale
AU - Reinhard Well
PY - 2026
DA - 2026/10/23
PB - EarthArXiv
DO - 10.31223/x52f9x
UR - https://eartharxiv.org/repository/view/15723/
AB - Nitrous oxide (N2O) isotope signatures provide crucial information for partitioning microbial production pathways, including bacterial, fungal, and nitrifier denitrification, chemo-denitrification and nitrification, as well as for estimating the degree of N2O reduction to N2. However, interpreting the three-dimensional isotope data is highly challenging due to numerous production pathways and simultaneous occurrence of production and consumption. Here we comprehensively review the application of the Fractionation And Mixing Evaluation (FRAME) model, a Bayesian Markov-Chain Monte Carlo (MCMC) tool explicitly developed to resolve complex, underdetermined isotope mixing and fractionation dynamics. The FRAME model shows widespread applicability across diverse global ecosystems, spanning agricultural soils, peatlands, groundwaters, rivers of Europe, America, Asia, and Africa, as well as marine environments. To address common interpretative challenges, standardized guidelines and best practices for configuring the model is established, and an overview of output quality checks and problem-solving ideas is provided. Most recent methodological enhancements of the model performance, including low-level 15N labelling, integration of natural abundance results with 15N tracing, and utilizing model extension for high-resolution time-series datasets are discussed. Furthermore, we introduce FRAME v2.0, a major software update that features improved statistical treatment of source uncertainty and extended auxiliary variable priors, while maintaining full backward compatibility. The model's performance is discussed in the context of various cases studies and independent reference methods, like the 15N gas flux approach and microbial functional gene analysis. This review presents unified standardised and robust interpretation strategies supported by an enhanced computational tool to quantify N2O production pathways and reduction across ecosystems.
ER -