A SENSITIVITY ANALYSIS OF INFERENTIAL MODELLING TOOLS THAT USE BOTH DISSOLVED OXYGEN AND INORGANIC CARBON MEASUREMENTS TO ESTIMATE WHOLE-STREAM METABOLISM
Aquatic ecosystem primary production, aerobic respiration, and gas exchange with the atmosphere are often inferred from temporal variation in dissolved oxygen (DO) concentrations. New sensor technologies continue to increase the number of metabolite concentrations that can be measured concurrently in streams. Our objective is to design and test inferential modelling tools that use dissolved inorganic carbon (DIC) time-series measurements to provide additional information about stream metabolic rates. Using Monte Carlo analyses with these modeling tools, we demonstrate how DIC data may help differentiate between the effects of respiration and gas exchange on DO dynamics. Furthermore, we use multivariate Bayesian algorithms to understand how multiple metabolites (i.e. DO and DIC) are weighted in their contribution to estimates of metabolic rates. This work contributes to the modeling tool kit necessary to use new metabolite sensors for inference of stream ecosystem metabolism and demonstrates the analyses necessary to build confidence in the inferential integrity of those tools. Ultimately, the goal is to improve the efficiency and effectiveness with which we can use new and future data streams to better understand the controls on stream metabolic regimes.
Elfrida Isaksen-Swensen (Primary Presenter/Author), Montana State University, email@example.com;
Robert Payn (Co-Presenter/Co-Author), Montana State University, Montana Institute on Ecosystems, firstname.lastname@example.org;
Meryl Storb (Co-Presenter/Co-Author), Montana State University, email@example.com;
Todd Schlotfeldt (Co-Presenter/Co-Author), Montana State University, firstname.lastname@example.org;