Organic Matter Transformations are Disconnected Between Surface Water and the Hyporheic Zone

Stegen, James C.; Fansler, Sarah; Tfaily, Malak M.; Garayburu‐Caruso, Vanessa; Goldman, Amy; Danczak, Robert E.; Chu, Rosalie K.; Renteria, Lupita; Tagestad, Jerry; Toyoda, Jason

doi:10.5194/bg-2022-19

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…In brief, the transformation analysis allows researchers to identify potential reactions by first looking at the mass difference between all peaks within a sample, and then comparing these differences to a list of common and known reactions. Recent research has demonstrated that results from this analysis are significantly related to ecosystem biogeochemistry (Graham et al, 2018;Buser-Young et al, 2023) and provide insight into both biotic and abiotic processes (Fudyma et al, 2021;Stegen et al, 2022). Given that these transformations are inherently tied to DOM composition changes, we suspect that the number of transformations within a sample should be related to the functional diversity of that sample (e.g., functional diversity calculated using H/C measures degradation state while transformations also partially estimate degradation may be significantly related).…”

Section: Introductionmentioning

confidence: 98%

Riverine organic matter functional diversity increases with catchment size

et al. 2023

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A large amount of dissolved organic matter (DOM) is transported to the ocean from terrestrial inputs each year (~0.95 Pg C per year) and undergoes a series of abiotic and biotic reactions, causing a significant release of CO2. Combined, these reactions result in variable DOM characteristics (e.g., nominal oxidation state of carbon, double-bond equivalents, chemodiversity) which have demonstrated impacts on biogeochemistry and ecosystem function. Despite this importance, however, comparatively few studies focus on the drivers for DOM chemodiversity along a riverine continuum. Here, we characterized DOM within samples collected from a stream network in the Yakima River Basin using ultrahigh-resolution mass spectrometry (i.e., FTICR-MS). To link DOM chemistry to potential function, we identified putative biochemical transformations within each sample. We also used various molecular characteristics (e.g., thermodynamic favorability, degradability) to calculate a series of functional diversity metrics. We observed that the diversity of biochemical transformations increased with increasing upstream catchment area and landcover. This increase was also connected to expanding functional diversity of the molecular formula. This pattern suggests that as molecular formulas become more diverse in thermodynamics or degradability, there is increased opportunity for biochemical transformations, potentially creating a self-reinforcing cycle where transformations in turn increase diversity and diversity increase transformations. We also observed that these patterns are, in part, connected to landcover whereby the occurrence of many landcover types (e.g., agriculture, urban, forest, shrub) could expand DOM functional diversity. For example, we observed that a novel functional diversity metric measuring similarity to common freshwater molecular formulas (i.e., carboxyl-rich alicyclic molecules) was significantly related to urban coverage. These results show that DOM diversity does not decrease along stream networks, as predicted by a common conceptual model known as the River Continuum Concept, but rather are influenced by the thermodynamic and degradation potential of molecular formula within the DOM, as well as landcover patterns.

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Section: Introductionmentioning

confidence: 98%

Riverine organic matter functional diversity increases with catchment size

et al. 2023

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“…To conduct this study, we analyzed previously published data from the WHONDRS Summer 2019 Sampling (S19S) campaign (Stegen et al, 2018) using unsupervised and supervised ML approaches to identify the environmental parameters influencing the diversity of DOM clusters (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…The samples we analyzed were collected and processed in 2019 as part of the WHONDRS consortium (Stegen et al, 2018), and the data were retrieved from publicly available data packages Toyoda et al, 2020). Full details on sample and metadata collection are provided in Garayburu-Caruso et al (2020).…”

Section: Data Sourcesmentioning

confidence: 99%

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Müller,

D’Andrilli,

Silverman

et al. 2024

Front. Water

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Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes (2H and 18O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

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Organic matter transformations are disconnected between surface water and the hyporheic zone

et al. 2022

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Abstract. Biochemical transformations of organic matter (OM) are a primary driver of river corridor biogeochemistry, thereby modulating ecosystem processes at local to global scales. OM transformations are driven by diverse biotic and abiotic processes, but we lack knowledge of how the diversity of those processes varies across river corridors and across surface and subsurface components of river corridors. To fill this gap we quantified the number of putative biotic and abiotic transformations of organic molecules across diverse river corridors using ultra-high-resolution mass spectrometry. The number of unique transformations is used here as a proxy for the diversity of biochemical processes underlying observed profiles of organic molecules. For this, we use public data spanning the contiguous United States (ConUS) from the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our results show that surface water OM had more biotic and abiotic transformations than OM from shallow hyporheic zone sediments (1–3 cm depth). We observed substantially more biotic than abiotic transformations, and the numbers of biotic and abiotic transformations were highly correlated with each other. We found no relationship between the number of transformations in surface water and sediments and no meaningful relationships with latitude, longitude, or climate. We also found that the composition of transformations in sediments was not linked with transformation composition in adjacent surface waters. We infer that OM transformations represented in surface water are an integrated signal of diverse processes occurring throughout the upstream catchment. In contrast, OM transformations in sediments likely reflect a narrower range of processes within the sampled volume. This indicates decoupling between the processes influencing surface water and sediment OM, despite the potential for hydrologic exchange to homogenize OM. We infer that the processes influencing OM transformations and the scales at which they operate diverge between surface water and sediments.

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Organic Matter Transformations are Disconnected Between Surface Water and the Hyporheic Zone

Cited by 3 publications

References 42 publications

Riverine organic matter functional diversity increases with catchment size

Riverine organic matter functional diversity increases with catchment size

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Organic matter transformations are disconnected between surface water and the hyporheic zone

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