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High rates of daytime river metabolism are an underestimated component of carbon cycling

Environmental Studies and Forestry

High rates of daytime river metabolism are an underestimated component of carbon cycling

F. Tromboni, E. R. Hotchkiss, et al.

This study reveals that river ecosystem respiration rates are faster than previously thought, based on innovative measurements using stable oxygen isotope signatures. Conducted by Flavia Tromboni and colleagues, the research highlights significant differences in metabolism estimations in rivers across diverse biomes.

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Playback language: English
Introduction
Understanding carbon cycling in aquatic ecosystems is hampered by the challenge of accurately estimating carbon fixation (gross primary production, GPP) and respiration (ecosystem respiration, ER). Traditional methods, primarily relying on diel changes in dissolved oxygen (DO) concentrations, often underestimate these crucial processes. These methods typically assume constant daytime and nighttime ecosystem respiration rates, neglecting the potential for diel variability. This oversight can significantly affect the accuracy of carbon cycling estimates. The current study addresses this limitation by integrating stable oxygen isotope signatures (δ¹⁸O₂) with DO measurements to provide a more comprehensive assessment of river metabolism. The integration of δ¹⁸O₂ data allows for resolving diel changes in ecosystem respiration, thereby providing a more complete picture of carbon fluxes in rivers. The overarching goal of this research is to improve our understanding of carbon cycling in rivers by incorporating a more accurate and detailed estimation of ecosystem respiration, using both DO and δ¹⁸O₂ data. The results have broad implications for assessing the role of rivers in the global carbon cycle and predicting their responses to environmental change. Accurate measurement of GPP and ER is paramount for understanding the functioning of aquatic ecosystems and their contribution to global biogeochemical cycles. The importance of this research stems from the fact that rivers play a significant role in carbon sequestration and emission, and an accurate accounting of these processes is crucial for developing effective carbon management strategies. The study aims to refine existing models of river metabolism to better reflect the complex interplay between photosynthetic and respiratory processes throughout the day. This will contribute to a more accurate representation of riverine carbon cycling at both local and global scales.
Literature Review
Previous research has highlighted the importance of riverine carbon cycling in the global carbon budget. Studies have shown that inland waters, including rivers, are significant sources and sinks of carbon dioxide (CO₂). However, existing methodologies for assessing river metabolism, often based solely on diel DO measurements, have been criticized for their limitations. The study by Cole et al. (2007) emphasized the need for incorporating inland waters into terrestrial carbon budget calculations. Battin et al. (2008) further investigated the biophysical controls on organic carbon fluxes in river networks. Raymond et al. (2013) provided a global estimate of CO₂ emissions from inland waters. These studies laid the groundwork for this research by demonstrating the significance of riverine carbon cycling and the need for improved measurement techniques. Hotchkiss and Hall (2014) previously demonstrated the significant impact of diel variations in ER in temperate streams by using a coupled O₂ and δ¹⁸O₂ model, providing initial evidence for the approach used in the current study. The current study expands on this by evaluating a broader range of rivers across various biomes and exploring the underlying mechanisms driving these diel patterns. Existing studies have typically relied on DO concentration data alone, which provides an incomplete representation of river metabolism, particularly concerning ER. The current research builds upon this existing literature by utilizing a coupled oxygen isotope and concentration approach which is more robust and offers a more complete picture. The cited studies show various estimations of riverine metabolism with contrasting results. The current study adds to the body of literature by using a novel approach, improving accuracy and addressing the limitations of previous studies.
Methodology
This study utilized a coupled modeling approach, combining high-frequency dissolved oxygen (DO) concentration data with stable oxygen isotope (δ¹⁸O₂) measurements collected at 14 river sites across three biomes (tropical, temperate, subarctic) and various ecoregions (mountain steppe, desert). The sites included a diverse range of physical and biological characteristics (Table 1), providing a robust test of the methodology. Traditional DO-based metabolism assessments were conducted at each site to provide a baseline for comparison. Changes in DO concentrations and temperature were measured every 10 minutes over at least 24 hours using MiniDOT loggers, calibrated before and after deployment to adjust for drift. δ¹⁸O₂ samples were collected every 2 hours during the same 24-hour period, preserved with HgCl₂, and analyzed using a Micromass Isoprime stable isotope ratio mass spectrometer. Physical characteristics such as conductivity, slope, flow velocity, depth, and photosynthetically active radiation (PAR) were also measured. Biofilm ash-free dry mass (AFDM) was determined from rock samples to assess the contribution of benthic communities to metabolism. Land-use data were collected for each watershed using satellite imagery and national inventories. Oxygen isotope fractionation during respiration was assessed using sealed Plexiglas recirculating chambers containing rocks, sediment, and/or macrophytes. These chambers allowed measurements of isotope fractionation factor (αr) under controlled conditions. Ecosystem metabolism was modeled using two approaches: (1) an open-channel metabolism model based solely on DO concentrations, and (2) a coupled O₂ and δ¹⁸O₂ model that incorporates diel changes in both DO and δ¹⁸O₂. Bayesian inverse modeling was employed to estimate the parameters of both models. The coupled model uses the DO-only model results as prior estimates for GPPO₂ and ERO₂. The resulting daily rates of ecosystem metabolism (GPP and ER) were then temperature-normalized to 20°C for better comparability across sites. Multiple linear regression and ANOVA were used to investigate the factors influencing the magnitude of diel ER and the differences between sites. The R statistical software was used for all analyses. The coupled O₂ and δ¹⁸O₂ model provided estimates of diel GPP and ER that accounted for the diel variation in ER, a factor neglected by the DO-only model. The models were run for 200,000 iterations, burn-in was removed, and quality of model fits was assessed. The αr value that produced the best model fit was selected for each site. This rigorous methodology involved a multi-faceted, highly detailed approach of integrating multiple data streams and utilizing Bayesian statistical techniques that are used to extract meaningful data from complex field measurements. The use of multiple sites across diverse biomes ensures the wide applicability of the findings.
Key Findings
The study's key findings revolve around the significant underestimation of daytime ecosystem respiration (ER) and gross primary production (GPP) in rivers when using traditional methods based solely on dissolved oxygen (DO) concentrations. The coupled O₂-δ¹⁸O₂ model consistently revealed that isotopically derived ecosystem respiration was greater during the day than at night across all 14 sites. This diel variation in ER, ranging from a minimum of 1 g O₂ m⁻² d⁻¹ to a maximum of 113 g O₂ m⁻² d⁻¹, was overlooked by the DO-only model. The temperature (20°C) normalized rates of ER and GPP derived from oxygen isotope data were substantially higher than those obtained from DO data alone; specifically, ER was 1.1 to 87 times higher and GPP was 1.5 to 22 times higher. The difference between ER estimated by the DO-only model (ER₀₂) and the coupled model (δ¹⁸O₂-ER) was particularly striking in tropical (up to 1700% higher) and desert rivers (up to 2200% higher). Temperature normalization did not significantly alter the main results, indicating that photosynthesis, rather than temperature alone, was the primary driver of increased daytime ER. The strong positive relationship between δ¹⁸O₂-ER and δ¹⁸O₂-GPP suggests that increased carbon availability to heterotrophs through photosynthesis-derived dissolved organic carbon (DOC) exudation and/or photochemical changes in DOC availability likely fuels the higher daytime ER. These results were observed consistently across all biomes and sites, highlighting the wide applicability of this observation. The differences between the traditional method and the coupled model indicate that we have significantly underestimated both GPP and ER. The magnitude of differences between the coupled model and the traditional method underscores the considerable underestimation of both GPP and ER in rivers worldwide. This improved measurement technique suggests significantly faster rates of carbon cycling than previously estimated. The high daily rates of GPP and ER highlight the important contribution of rivers to global carbon cycles, which was previously underestimated by traditional methods. The coupled method provides more accurate estimations of GPP and ER, thus contributing to better calculations of net ecosystem production (NEP). This shows that photosynthesis is more influential than previously thought within riverine food webs. The results suggest that river metabolism is significantly faster than traditional models predict, which leads to significant implications for our understanding of carbon cycling and food webs.
Discussion
The findings of this study significantly advance our understanding of riverine carbon cycling by demonstrating the substantial underestimation of daytime ecosystem respiration and gross primary production using traditional DO-only methods. The coupled O₂-δ¹⁸O₂ model offers a more accurate and nuanced representation of river metabolism, revealing a previously unrecognized level of diel variation in respiration driven primarily by photosynthesis. The increased daytime respiration likely results from the rapid utilization of photosynthate-derived DOC by heterotrophic microorganisms. This suggests a more dynamic and tightly coupled relationship between primary producers and consumers in river ecosystems than previously assumed. The study’s implications extend beyond a refined understanding of river metabolism; it underscores the limitations of relying solely on DO concentration data for studying carbon cycling in aquatic systems. These results have implications for the broader field of ecosystem ecology, impacting our interpretation of metabolic processes in other benthic biofilm-dominated environments. Future research could further investigate the relative contributions of DOC exudation and photochemical processes to daytime respiration, possibly using pulse-chase isotope experiments. Improving the temporal resolution of δ¹⁸O₂ data collection, through the use of high-frequency δ¹⁸O₂ analyzers, would improve the accuracy of model estimations. The findings necessitate a re-evaluation of riverine carbon budgets and their role in global carbon dynamics. Accurate estimations of river metabolism are essential for understanding the responses of rivers to climate change and human impacts. The study suggests future research should focus on refining the diel metabolism models to incorporate additional parameters and high-frequency measurements. The presented study refines our understanding of the mechanisms driving river carbon cycling and offers a more robust methodology for assessing river metabolism, thereby improving our ability to predict river response to environmental change.
Conclusion
This study demonstrates that traditional methods of measuring river metabolism, relying solely on diel dissolved oxygen concentrations, significantly underestimate the rates of both gross primary production and ecosystem respiration. The integration of stable oxygen isotopes (δ¹⁸O₂) into a coupled model provides a more accurate representation, revealing a dynamic diel pattern driven by photosynthetic activity. The results highlight the importance of daytime respiration and the rapid cycling of carbon within riverine ecosystems. Future research should focus on improving model sophistication, increasing temporal resolution of data collection, and further exploring the underlying mechanisms driving this diel pattern. The findings necessitate a reevaluation of the role of rivers in global carbon cycling and their responses to environmental change.
Limitations
While the coupled O₂-δ¹⁸O₂ model provides a significant improvement over traditional DO-only methods, some limitations remain. The accuracy of the model is dependent on the accuracy of the oxygen isotope fractionation factor (αr) during respiration, which can vary among sites. The relatively low temporal resolution of δ¹⁸O₂ samples (every 2 hours) compared to DO data (every 10 minutes) may introduce some uncertainty, although the findings are robust. The study focuses on a specific set of rivers, although selected to span a range of biomes and conditions, limiting the complete generalizability to all rivers globally. Lastly, the potential influence of other processes not directly addressed by the model, such as photorespiration, remains a topic for further research.
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