Fast processing of diel oxygen curves: Estimating stream metabolism with base (BAyesian single-station estimation)

The measurement of stream metabolism (gross primary production and respiration) has become more feasible with the availability of more reliable dissolved oxygen (DO) probes. Such metabolic measurements offer important opportunities in fundamental and applied research, especially in relating stream metabolic responses to human and other pressures. The accurate determination of the reaeration coefficient is one challenge for making reliable ecological inferences from DO measurements made over many diel periods (i.e., months or years). We outline three methods for calculating atmospheric reaeration but concentrate on the use of statistical estimation to simultaneously estimate reaeration and metabolic rates using Bayesian model fitting. While there are existing programs (ModelMaker and Bayesian Metabolic Model [BaMM]), these are either slow or unable to be used easily for fitting multiple days of metabolic data (one to many months). Our implementation, BAyesian Single-station Estimation (BASE), uses freely available software (R and OpenBUGS), includes a batch mode that can fit data for many days, and provides visual and statistical measures of “goodness-of-fit.” We compare the results of the BASE, ModelMaker, and BaMM programs.