Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics

Abstract

We present a Bayesian statistical model of diel oxygen dynamics in aquatic ecosystems to simultaneously estimate gross primary production, ecosystem respiration, and oxygen exchange with the atmosphere (and their uncertainties) on the basis of changes in dissolved oxygen concentration, water temperature, irradiance, and, if desired, the 18 O to 16 O ratio (d 18 O-O 2 ). We test this model using simulated data with realistic measurement errors to demonstrate that it accurately estimates the model parameters and that parameter uncertainties correctly scale with error in the observations and number of data points. Application of the model to field data from two productive stream ecosystems with substantial daily dissolved oxygen variation quantified the underlying physical and biological factors that control oxygen dynamics in these ecosystems and provided empirical support for a light saturation model of the photosynthesis-irradiance relationships at the ecosystem scale. Although inclusion of d 18 O-O 2 provides a second oxygen budget, analysis of field data shows that metabolic and reaeration parameters can be accurately estimated by modeling the transient dynamics of dissolved oxygen concentration alone in relation to daily changes in water temperature and light regime. This model is particularly suited to lowgas exchange, high-productivity systems, which have thus far proved challenging to measure ecosystem metabolism accurately. The modeling framework is applicable to single-station, open-system experimental designs and provides a rigorous and generalizable framework for estimating ecosystem metabolism in aquatic ecosystems.

Beyond the urban stream syndrome: organic matter budget for diagnostics and restoration of an impaired urban river

“…Dissolved oxygen was modeled in response to changing light intensity and temperature to simultaneously estimate metabolism and reaeration (Van de Bogert et al 2007;Holtgrieve et al 2010).…”

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