Data from: Predictions of response to temperature are contingent on model choice and data quality

The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and sub-optimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth, but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured), but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.用以阐释包括生长速率与代谢速率在内的生物过程温度依赖性的各类方程，是我们预测全球生物地球化学与生物地理学如何响应全球气候变化而发生变化的核心基础。我们综述并检验了12种可用于模拟全温度区间（涵盖亚最优与超最优温度）内生物过程温度依赖性的方程的应用效果。我们以将这些方程拟合至浮游植物（phytoplankton）生长的热响应曲线为核心研究场景，同时也在多种类群的生物的多样性状上对这些方程进行了测试。研究发现，诸多被考察的方程在数据拟合能力与对数据质量的要求（测试温度点数与所覆盖的响应范围）上不相伯仲，但在临界温度（cardinal temperatures）以及这些温度下的生物速率的估算结果上却存在显著差异。当这些速率估算值被用于生物地理学预测时，即使是拟合效果最优的模型，其估算结果之间的差异也可能超过十年全球变暖所引发的全球生物变化预测值。因此，针对全球温度变化的生物响应相关研究，必须审慎考量模型选择与用于参数化这些模型的数据质量。