Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
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.

中文翻译：

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用于预测处于危险中的水生物种的河流温度的数据有限模型

加拿大渔业和水产科学杂志，提前印刷。
在数据匮乏的地区，模拟河流温度对于预测面临风险的物种的潜在压力至关重要。由于来自加拿大安大略省南部的格兰德河、泰晤士河和西德纳姆河的数据有限，我们评估了简单的混合效应回归模型，以使用附近气象站的气温来预测水温。针对与黑红马 (Moxostoma duquesni) 的适应性相关的时期评估模型性能：6 月至 8 月，可能发生高温事件；和 5 月，产卵发生时。与使用整个生长季节的数据相比，所有模型在使用这些时期的数据进行训练时都表现得更好。最好的模型是使用 5 天滞后气温的线性回归。该模型的夏季平均值的均方根误差为 1.5 °C。一年中不同时间的预测误差差异突出了在模型解释中考虑物种生态的重要性。然而，仅使用一年中相关时间的数据时模型拟合的改进表明，如果应用得当，可以在管理领域有效地使用相对简单的模型。