Meta-analytical insights into organic matter enrichment in the surface microlayer.
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| Title: | Meta-analytical insights into organic matter enrichment in the surface microlayer. |
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| Authors: | Silva, Amavi1 (AUTHOR) asilva@geomar.de, Nikzad, Surandokht2,3 (AUTHOR), Barthelmeß, Theresa1 (AUTHOR), Engel, Anja1,4 (AUTHOR), Herrmann, Hartmut5 (AUTHOR), van Pinxteren, Manuela5 (AUTHOR), Wirtz, Kai2,4 (AUTHOR), Wurl, Oliver6 (AUTHOR), Schartau, Markus1 (AUTHOR) mschartau@geomar.de |
| Source: | Biogeosciences. 2026, Vol. 23 Issue 4, p1697-1718. 22p. |
| Subject Terms: | *Sea surface microlayer, *Organic compounds, *Research methodology, *Fatty acids, *Nitrogen compounds, *Amino acids, *Colloidal carbon, *Biogeochemistry |
| Abstract: | The surface microlayer (SML), the uppermost ∼ 1 mm water layer at the air-water interface, plays a critical role in mediating Earth system processes, yet current knowledge of its composition and organic matter enrichment remains scattered across disciplines. Here, we present the first known meta-analysis of SML studies that quantitatively assesses the distributional characteristics of selected organic compounds, including organic carbon and nitrogen, amino acids, fatty acids, transparent exopolymer particles, carbohydrates, lipids and proteins, through probability density estimates, central tendency metrics and correlation analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, while also highlighting the significance of surfactant-specific factors that govern selective enrichment in the SML. We find that enrichment patterns can vary systematically with environmental and methodological conditions, underscoring the need to account for such influences when interpreting observations and developing SML-based models. We provide the full range of typical EF values for the studied compounds, offering a clear reference for assessing whether new measurements are typical or extreme. While delving into the ability of EFs to reflect organic matter partitioning in the SML, we also critically examine their limitations in capturing trophic variability and suggest that EF-based assessments be complemented with metrics that remove background variability from underlying water concentrations, enabling more accurate interpretations of true SML enrichment and informing future modelling efforts. Additionally, our meta-analysis demonstrates that logarithmic data transformations and robust central tendency estimates outperform traditional linear-scale approaches, providing more accurate and reliable SML enrichment estimates. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | The surface microlayer (SML), the uppermost ∼ 1 mm water layer at the air-water interface, plays a critical role in mediating Earth system processes, yet current knowledge of its composition and organic matter enrichment remains scattered across disciplines. Here, we present the first known meta-analysis of SML studies that quantitatively assesses the distributional characteristics of selected organic compounds, including organic carbon and nitrogen, amino acids, fatty acids, transparent exopolymer particles, carbohydrates, lipids and proteins, through probability density estimates, central tendency metrics and correlation analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, while also highlighting the significance of surfactant-specific factors that govern selective enrichment in the SML. We find that enrichment patterns can vary systematically with environmental and methodological conditions, underscoring the need to account for such influences when interpreting observations and developing SML-based models. We provide the full range of typical EF values for the studied compounds, offering a clear reference for assessing whether new measurements are typical or extreme. While delving into the ability of EFs to reflect organic matter partitioning in the SML, we also critically examine their limitations in capturing trophic variability and suggest that EF-based assessments be complemented with metrics that remove background variability from underlying water concentrations, enabling more accurate interpretations of true SML enrichment and informing future modelling efforts. Additionally, our meta-analysis demonstrates that logarithmic data transformations and robust central tendency estimates outperform traditional linear-scale approaches, providing more accurate and reliable SML enrichment estimates. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 17264170 |
| DOI: | 10.5194/bg-23-1697-2026 |
