IHSS&IWA26 / BRNO / CZECHIA / 23–28 August 2026 BOOK OF ABSTRACTS 34 Analysis and Characterization Monday, 24 August 2026 / Hall B+C SL11 Reframing Natural Organic Matter Research through Compositional Data Analysis Morimaru Kida1, Julian Merder2, Thorsten Dittmar3,4, Vera Pawlowsky-Glahn5, Juan José Egozcue6 1 Kobe University, Graduate School of Agricultural Science, Soil Science Laboratory, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, JAPAN, morimaru.kida@people.kobe-u.ac.jp 2 Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States 3 Institute for Chemistry and Biology of the Marine Environment (ICBM), School of Mathematics and Science, University of Oldenburg, Oldenburg, Germany 4 Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany 5 Department of Computer Sciences, Applied Mathematics, and Statistics, Universitat de Girona, Girona, Spain 6 Independent researcher Compositional data (CoDa) are ubiquitous in environmental research. They describe parts of a whole, including percentages, proportions, and relative or absolute abundances. Such data consist of positive values, where the essential information is contained in the ratios among components. Conventional statistical methods developed for real random variables often produce spurious results when applied to CoDa and are therefore inappropriate. This stems from the unique geometric property of CoDa, namely, their sample space is confined to a simplex rather than the real Euclidean space (e.g., CoDa with three parts can be represented inside a triangle) [1,2]. Notably, the simplex has one fewer dimension than the number of components. CoDa analysis has gained broad recognition across disciplines, from geosciences to social sciences, and has seen a recent rapid increase in use in microbial genomics. In contrast, its application in natural organic matter (NOM) research remains limited, even though data generated by major analytical techniques—such as mass spectrometry, fluorescence spectroscopy, and nuclear magnetic resonance spectroscopy—are inherently compositional [3]. Appropriate handling of CoDa is necessary for valid statistical inference when investigating factors affecting NOM dynamics. Given the structural similarity between NOM and high-throughput sequencing data, for which CoDa analysis has been successfully adopted, we argue that CoDa analysis should also be consistently integrated into NOM research to prevent analytical pitfalls and misleading inferences. A few pioneering studies have applied CoDa analysis to NOM data, and a wide array of useful open-source tools are already available. In this talk, we will walk through the application of CoDa analysis to NOM
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