The Effect of Goldfish (Carassius auratus) on Water Quality in Horse Stock Tanks.

Goldfish (Carassius auratus) have been reported as a method to keep water tanks clean; however, little information exists on this approach. The objectives were to evaluate the efficacy of goldfish on maintaining water quality in tanks and to evaluate the frequency that this method is used. The first objective was completed during June through October 2017 in St. Paul, MN, using plastic and metal 379 L stock tanks, each with and without goldfish in a drylot that housed six adult horses. The stocking rate was 5 goldfish per tank. Daily readings of total dissolved solids (TDS) and water turbidity (NTU), and weekly samples to measure chlorophyll a were taken. At the end of each 28-day period, tanks were cleaned and rotated. Plastic tanks had lower TDS than metal tanks (P < .001); however, metal tanks had lower NTU and chlorophyll a (P ≤ .008). Adding goldfish resulted in lower TDS (P < .001); however, there was no effect on NTU or chlorophyll a (P ≥ .097). No parameters had an impact on horse preference (P ≥ .108). The second objective was completed using an online survey that was open from October 31 until December 15, 2018. Of the 672 completed surveys, 56% had not tried using goldfish in water tanks, 26% had utilized goldfish in the past, and 18% currently used goldfish. The inclusion of goldfish in water tanks did not affect all water quality parameters; however, 44% of survey respondents had tried, or were currently using, this management method.

Three-dimensional lake water quality modeling: sensitivity and uncertainty analyses.

Two sensitivity and uncertainty analysis methods are applied to a three-dimensional coupled hydrodynamic-ecological model (ELCOM-CAEDYM) of a morphologically complex lake. The primary goals of the analyses are to increase confidence in the model predictions, identify influential model parameters, quantify the uncertainty of model prediction, and explore the spatial and temporal variabilities of model predictions. The influence of model parameters on four model-predicted variables (model output) and the contributions of each of the model-predicted variables to the total variations in model output are presented. The contributions of predicted water temperature, dissolved oxygen, total phosphorus, and algal biomass contributed 3, 13, 26, and 58% of total model output variance, respectively. The fraction of variance resulting from model parameter uncertainty was calculated by two methods and used for evaluation and ranking of the most influential model parameters. Nine out of the top 10 parameters identified by each method agreed, but their ranks were different. Spatial and temporal changes of model uncertainty were investigated and visualized. Model uncertainty appeared to be concentrated around specific water depths and dates that corresponded to significant storm events. The results suggest that spatial and temporal variations in the predicted water quality variables are sensitive to the hydrodynamics of physical perturbations such as those caused by stream inflows generated by storm events. The sensitivity and uncertainty analyses identified the mineralization of dissolved organic carbon, sediment phosphorus release rate, algal metabolic loss rate, internal phosphorus concentration, and phosphorus uptake rate as the most influential model parameters.