Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.5194/bg-18-3103-2021
Lizenz creative commons licence
Titel (primär) Complex interactions of in-stream dissolved organic matter and nutrient spiralling unravelled by Bayesian regression analysis
Autor Pucher, M.; Flödl, P.; Graeber, D.; Felsenstein, K.; Hein, T.; Weigelhofer, G.
Quelle Biogeosciences
Erscheinungsjahr 2021
Department ASAM
Band/Volume 18
Heft 10
Seite von 3103
Seite bis 3122
Sprache englisch
Topic T5 Future Landscapes
Daten-/Softwarelinks https://doi.org/10.5281/zenodo.4071851
Supplements https://bg.copernicus.org/articles/18/3103/2021/bg-18-3103-2021-supplement.pdf
Abstract

Uptake and release patterns of dissolved organic matter (DOM) compounds and co-transported nutrients are entangled, and the current literature does not provide a consistent picture of the interactions between the retention processes of DOM fractions. We performed plateau addition experiments with five different complex DOM leachates in a small experimental stream impacted by diffuse agricultural pollution. The study used a wide range of DOM qualities by including leachates of cow dung, pig dung, corn leaves, leaves from trees, and whole nettle plants. We measured changes in nutrient and dissolved organic carbon (DOC) concentrations along the stream course and determined DOM fractions by fluorescence measurements and parallel factor (PARAFAC) decomposition. To assess the influences of hydrological transport processes, we used a 1D hydrodynamic model.

We developed a non-linear Bayesian approach based on the nutrient spiralling concept, which we named the “interactions in nutrient spirals using Bayesian regression” (INSBIRE) approach. This approach can disentangle complex interactions of biotic and abiotic drivers of reactive solutes' uptake in multi-component DOM sources. It can show the variability of the uptake velocities and quantify their uncertainty distributions. Furthermore, previous knowledge of nutrient spiralling can be included in the model using prior probability distributions. We used INSBIRE to assess interactions of compound-specific DOM and nutrient spiralling metrics in our experiment.

Bulk DOC uptake varied among sources, showing decreasing uptake velocities in the following order: corn > pig dung > leaves > nettles > cow dung. We found no correlations between bulk DOC uptake and the amounts of protein-like compounds or co-leached soluble reactive phosphorus (SRP). The fastest uptake was observed for SRP and the tryptophan-like component, while the other DOM components' uptake velocities more or less resembled that of the bulk DOC. Almost all DOM components showed a negative relationship between uptake and concentration, known as efficiency loss. Furthermore, we observed a few negative and (weak) positive interactions between the uptake and the concentration of different components, such as a decreased uptake of protein-like compounds at high concentrations of a high-molecular-weight humic-like compound. We also found an influence of the wetted width on the uptake of SRP and a microbially derived humic substance, which indicates the importance of the sediment–water interface for P and humic C cycling in the studied stream.

Overall, we show that bulk DOC is a weak predictor of DOC uptake behaviour for complex DOM leachates. Individual DOM compound uptake, including co-leached nutrients, is controlled by both internal (quality-related) and external (environmental) factors within the same aquatic ecosystem. We conclude that the cycling of different C fractions and their mutual interaction with N and P uptake in streams is a complex, non-linear problem, which can only be assessed with advanced non-linear approaches, such as the presented INSBIRE approach.

dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=24661
Pucher, M., Flödl, P., Graeber, D., Felsenstein, K., Hein, T., Weigelhofer, G. (2021):
Complex interactions of in-stream dissolved organic matter and nutrient spiralling unravelled by Bayesian regression analysis
Biogeosciences 18 (10), 3103 - 3122 10.5194/bg-18-3103-2021