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.用于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的核心理论基础。我们综述并检验了12类用于构建全温度区间内生物过程温度响应模型的方程，涵盖超适温与亚适温范围。本研究重点关注将这些方程拟合至浮游植物生长的热响应曲线，但同时也针对多类生物的多种性状开展了方程测试。研究发现，多数被调研的方程在数据拟合能力与对数据质量的要求（包括测试温度点数与响应区间覆盖度）上表现相近，但针对临界温度以及这些温度下的生物速率的估算结果却存在显著差异。当将这些速率估算值应用于生物地理预测时，即便最优拟合模型之间的估算差异，也可能超过十年全球变暖所引发的全球生物变化预测总量。因此，针对温度全球变化下生物响应的相关研究，必须审慎考量模型选择与用于参数化这些模型的数据集质量。