Data-limited models to predict river temperatures for aquatic species at risk1

Abstract

In data-poor regions, modeled river temperatures are essential for predicting potential stressors for species at risk. With limited data from the Grand, Thames, and Sydenham rivers in southern Ontario, Canada, we evaluated simple mixed-effect regression models to predict water temperature using air temperature from nearby weather stations. Model performance was assessed for periods relevant to the fitness of the black redhorse (Moxostoma duquesni): June to August, when heat events may be likely; and May, when spawning occurs. All of the models performed better when trained on data from these periods, compared with using data from the entire growing season. The best model was a linear regression using 5 days of lagged air temperature. This model had a root mean square error for summer means of 1.5 °C. The differences in prediction error at different times of year highlight the importance of considering species ecology in model interpretation. However, the improvement in model fit when using only data from the relevant time of year suggests that relatively simple models can be used effectively in a management arena when applied appropriately.