Sequential Abiotic‐Biotic Processes Drive Organic Carbon Transformation in Peat Bogs

Abstract

Peatlands, which store one third of the terrestrial carbon (C), are subject to large disturbances under a changing climate. It is crucial to understand how microbial and physiochemical factors affect the vulnerability of these large C stores to predict climate‐induced greenhouse gas fluxes. Here, we used a combination of mass spectrometry and spectroscopy techniques, to understand sequential biotic and abiotic degradation pathways of Sphagnum fallax leachate in an anaerobic incubation experiment, in the presence and absence of microorganisms. Removal of microorganisms was carried out by passing aqueous samples through 0.2‐µm filters. Our results revealed that S. fallax leachate degradation by abiotic reactions is a significant contributor to CO2 production. Further, abiotic factors, such as low pH, are responsible for partial dissolved organic carbon (DOC) degradation that produces bioavailable compounds that shift microbial metabolic pathways and stimulate respiration in peat bogs. Acid‐catalyzed hydrolysis of Sphagnum‐ produced glycosides can provide the microbial communities with glucose and stimulate microbial respiration of DOC to CO2. These results, while unique to peatlands, demonstrate the importance and underscore the complexity of sequential abiotic and biotic processes on C cycling in peat bogs. It is therefore crucial to incorporate abiotic degradation and sequential below‐ground biotic and abiotic interactions into climate models for a better prediction of the influence of climate change on DOC stability in peatlands. These findings might not be representative of other ecosystems with different environmental conditions including mineral‐rich peatlands and plant matter in surface peat horizons that comprise discrete microbial populations, and DOC composition.

Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

“…This approach is possible because of the ultra‐high mass accuracy of FTICR–MS. These possible transformations were used to create networks of connectivity based on potential decomposition pathways for each plant tissue sample using Cytoscape 3.8.1 and the MetaNetter 2 plug‐in (Shannon et al, 2003 ) as previously described in (AminiTabrizi et al, 2020 ; Fudyma, Chu, et al, 2021 ; Fudyma, Toyoda, et al, 2021 ). Network analysis was performed to calculate the network heterogeneity and the clustering coefficient of each sample.…”

“…However, nutrient content alone cannot predict the decomposability of plant litter, particularly where microbial decomposition is limited by factors such as oxygen and electron acceptor availability – as it is in wetlands. A more detailed chemical analysis of the decomposability of organic material is possible using high‐resolution mass spectrometry such as FT‐ICR MS which has been used previously to show differences in the composition of soil organic matter with permafrost thaw (Fudyma, Toyoda, et al, 2021 ; Hodgkins et al, 2014 ). Spectrometric techniques have great potential for understanding the decomposability of plant litter because they allow investigators to identify differences in bioavailability of material based on factors such as chemical complexity and the energetic favorability of decomposition for particular compounds (Fudyma, Toyoda, et al, 2021 ).…”

“…A more detailed chemical analysis of the decomposability of organic material is possible using high‐resolution mass spectrometry such as FT‐ICR MS which has been used previously to show differences in the composition of soil organic matter with permafrost thaw (Fudyma, Toyoda, et al, 2021 ; Hodgkins et al, 2014 ). Spectrometric techniques have great potential for understanding the decomposability of plant litter because they allow investigators to identify differences in bioavailability of material based on factors such as chemical complexity and the energetic favorability of decomposition for particular compounds (Fudyma, Toyoda, et al, 2021 ). For example, the nominal oxidation state of carbon (NOSC) of each compound in a sample can be used to indicate the potential thermodynamic energy yield on oxidation of the C of each molecule (Keiluweit et al, 2016 ; Wilson & Tfaily, 2018 ).…”

MetaboDirect: An Analytical Pipeline for the processing of FTICR-MS-based Metabolomics Data

“…The first metabolomics dataset was generated from an established, ecologically relevant marine phage-host model system [34][35][36] designed to study new virus-hostnutrient interactions and its impact on the composition of bacterial metabolites [37]. The second metabolomics dataset was obtained from a study that aimed at elucidating plant leachate in particular Sphagnum fallax leachate degradation pathways and biochemical transformation in the presence and absence of microorganisms [12].…”

“…The quantity and the quality of OM in a given ecosystem are dependent on its microbiome composition and the environmental conditions present in that system. They are also dependent on several microbial regulatory processes, such as transcription, translation, protein interactions, and their interactions with the biotic and abiotic components of the system [10][11][12][13][14]. Characterizing OM molecular composition is therefore vital for understanding the role that microorganisms play in all major element biogeochemical cycles and can constitute an important predictor of the response of the biological systems to environmental perturbations [8,15].…”