Affiliation:
1. Department of Chemistry and Biochemistry University of Montana Missoula Montana USA
2. Department of Land Resources and Environmental Sciences Montana State University Bozeman Montana USA
3. Montana Institute on Ecosystems Montana University System Bozeman Montana USA
4. Flathead Lake Biological Station University of Montana Polson Montana USA
5. Division of Biological Sciences University of Montana Missoula Montana USA
Abstract
AbstractRivers efficiently collect, process, and transport terrestrial‐derived carbon. River ecosystem metabolism is the primary mechanism for processing carbon. Diel cycles of dissolved oxygen (DO) have been used for decades to infer river ecosystem metabolic rates, which are routinely used to predict metabolism of carbon dioxide (CO2) with uncertainties of the assumed stoichiometry ranging by a factor of 4. Dissolved inorganic carbon (DIC) has been less used to directly infer metabolism because it is more difficult to quantify, involves the complexity of inorganic carbon speciation, and as shown in this study, likely requires a two‐station approach. Here, we developed DIC metabolism models using single‐ and two‐station approaches. We compared metabolism estimates based on simultaneous DO and DIC monitoring in the Upper Clark Fork River (USA), which also allowed us to estimate ecosystem‐level photosynthetic and respiratory quotients (PQE and RQE). We observed that metabolism estimates from DIC varied more between single‐ and two‐station approaches than estimates from DO. Due to carbonate buffering, CO2 is slower to equilibrate with the atmosphere compared to DO, likely incorporating a longer distance of upstream heterogeneity. Reach‐averaged PQE ranged from 1.5 to 2.0, while RQE ranged from 0.8 to 1.5. Gross primary production from DO was larger than that from DIC, as was net ecosystem production by . The river was autotrophic based on DO but heterotrophic based on DIC, complicating our understanding of how metabolism regulated CO2 production. We suggest future studies simultaneously model metabolism from DO and DIC to understand carbon processing in rivers.