the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Meta-metabolome ecology reveals that geochemistry and microbial functional potential are linked to organic matter development across seven rivers
Abstract. Rivers receive substantial dissolved organic matter (DOM) input from the land and transport it to the ocean. As DOM travels through watersheds, it undergoes biotic and abiotic transformations that impact biogeochemical cycles and any subsequent CO2 release into the atmosphere. While recent research has increased our mechanistic knowledge of DOM composition within watersheds, DOM development across broad spatial distances and within divergent biomes is under investigated. Here, we combined DOM characterization, geochemical analyses, and shotgun metagenomics to analyze samples from seven rivers ranging from the U.S. Pacific Northwest to Berlin, Germany. Initial analyses revealed that many DOM properties were distinguished by river type (e.g., wastewater, headwater) and that geochemistry often explained variation across rivers. At a global scale, analyses rooted in meta-metabolome ecology indicated that DOM was structured overwhelmingly by deterministic selection. When controlling for scale, however, analyses indicated that ecological assembly dynamics were again partially structured by river type. Finally, microbial analyses revealed that many riverine microbes from our systems shared core metabolic functional potential while differing in peripheral capabilities in across the rivers. Further analysis of the carbon degradation potential for recovered metagenomically assembled genomes indicated that the sampled rivers had strong taxonomically conserved niche differentiation and that carbon degradation potential diversity was significantly related to organic matter diversity. Together, these results help us uncover interconnections between the development of DOM, riverine geochemistry, and microbial functional potential.
Status: open (extended)
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RC1: 'Comment on egusphere-2024-3899', Anonymous Referee #1, 08 Apr 2025
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The manuscript is well written, and the data analysis is very thorough and state of the art. The authors attempted to connect geochemical characteristics, DOM molecule formulas, and metagenomics based on 7 rivers across a large spatial scale. They basically concluded that DOM properties can be distinguished by river type and geochemistry of the rivers differed. They also showed that the microbes of the rivers shared core functional potential. While I appreciate the data and particularly the excellent data analysis, I feel that the conclusions are not that novel and I also have some concerns that the authors need to address.
The several rivers seemed to be randomly selected without any justification. It needs to be clearly stated why these rivers are selected and how representative they are in terms of the world’s rivers. It is also stated the river types, wastewater vs headwater, but more concrete data or logic connections are needed. What are the nutrient data? Other than the RDA in Figure 2, there is no nutrient data reported in the manuscript. Also, how exactly can you connect DOM to the wastewater, directly input of DOM or through nutrient-inspired algal blooms? In general, statistical analysis is fancy but there is little or no mechanistical connection. I feel that this shortcoming is throughout the manuscript, such as the connection between DOM and metagenomics.
A set of geochemical parameters were selected for the work, including Cl, Mg, TN F, Fe etc., but why? For example, why not Chla and why not dissolved oxygen? Chla would be very straightforward to connect to DOM and microbes. I am not saying you have to include Chla, but need to justify why you chose this specific set of parameters among the numerous choices.
The authors used metabolome for the DOM characterization. I am not very sure this is an accurate definition as it is assumed all the formulas obtained from FTICR-MS are metabolites. I don’t think this is true because there could be contribution from abiotic reactions or selective preservation.
FTICR-MS is a non-quantitative technique; thus, it is great that the authors chose to use ‘presence or absence” to process the data. But this is still tricky if you don’t inject the same amount of carbon (or DOC) in samples when you are trying to compare them. In other words, the absence or presence of a specific molecule may depend on the matrix or DOC concentration. Some QA/QC on this aspect needs to be added.
Citation: https://doi.org/10.5194/egusphere-2024-3899-RC1
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