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Abstract：
We investigated the food-dependent growth and thermal response of the freshwater ciliate Colpidium kleini using numerical response (NR) experiments. This bacterivorous ciliate occurs in lotic water and the pelagial of lakes and ponds. The C. kleini strain used in this work was isolated from a small alpine lake and identified by combining detailed morphological inspections with molecular phylogeny. Specific growth rates (rmax) were measured from 5 to 21 °C. The ciliate did not survive at 22 °C. The threshold bacterial food levels (0.3 - 2.2 × 106 bacterial cells mL-1) matched the bacterial abundance in the alpine lake from which C. kleini was isolated. The food threshold was notably lower than previously reported for C. kleini and two other Colpidium species. The threshold was similar to levels reported for oligotrich and choreotrich ciliates if expressed in terms of bacterial biomass (0.05 - 0.43 mg C L-1). From the NR results, we calculated physiological mortality rates at zero food concentration. The mean mortality (0.55 ± 0.17 d-1) of C. kleini was close to the mean estimate obtained for other planktonic ciliates that do not encyst. We used the data obtained by the NR experiments to fit a thermal performance curve (TPC). The TPC yielded a temperature optimum at 17.3 °C for C. kleini, a maximum upper thermal tolerance limit of 21.9 °C, and a thermal safety margin of 4.6 °C. We demonstrated that combining NR with TPC analysis is a powerful tool to predict better a species\' fitness in response to temperature and food.
摘要：
我们使用数值响应（NR）实验研究了淡水纤毛虫柯氏藻的食物依赖性生长和热响应。这种细菌的纤毛虫发生在湖水和湖泊和池塘的上层。这项工作中使用的C.kleini菌株是从一个小型高山湖泊中分离出来的，并通过将详细的形态学检查与分子系统发育相结合进行鉴定。从5到21°C测量比生长速率(rmax)。纤毛虫在22°C下无法存活。阈值细菌食物水平（0.3-2.2×106个细菌细胞mL-1）与分离出克莱尼菌的高山湖中的细菌丰度相匹配。食物阈值明显低于先前报道的C.kleini和另外两种Colipdium物种的食物阈值。如果以细菌生物量（0.05-0.43mgCL-1）表示，则该阈值类似于针对富含寡聚物和富含植物的纤毛虫报告的水平。从NR结果来看，我们计算了零食物浓度时的生理死亡率。C.kleini的平均死亡率（0.55±0.17d-1）接近于其他不封闭的浮游纤毛虫的平均估计值。我们使用通过NR实验获得的数据来拟合热性能曲线(TPC)。TPC对C.kleini产生了17.3°C的最佳温度，最大耐热上限为21.9°C，和4.6°C的热安全裕度。我们证明，将NR与TPC分析相结合是一个强大的工具，可以更好地预测物种对温度和食物的适应性。
