For decades, multiple-driver/stressor research has examined interactions among drivers that will undergo large changes in the future: temperature, pH, nutrients, oxygen, pathogens, and more. However, the most commonly used experimental designs—present-versus-future and ANOVA—fail to contribute to general understanding or predictive power. Linking experimental design to process-based mathematical models would help us predict how ecosystems will behave in novel environmental conditions. We review a range of experimental designs and assess the best experimental path toward a predictive ecology. Full factorial response surface, fractional factorial, quadratic response surface, custom, space-filling, and especially optimal and sequential/adaptive designs can help us achieve more valuable scientific goals. Experiments using these designs are challenging to perform with long-lived organisms or at the community and ecosystem levels. But they remain our most promising path toward linking experiments and theory in multiple-driver research and making accurate, useful predictions.

中文翻译：

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设计信息更丰富的多驱动器实验

几十年来，多驱动因素/压力源研究一直在研究未来将发生巨大变化的驱动因素之间的相互作用：温度、pH、营养物质、氧气、病原体等。然而，最常用的实验设计——现在与未来和方差分析——无法促进普遍理解或预测能力。将实验设计与基于过程的数学模型联系起来将有助于我们预测生态系统在新环境条件下的表现。我们回顾了一系列实验设计，并评估了实现预测生态学的最佳实验路径。全阶乘响应面、分数阶乘、二次响应面、定制、空间填充，尤其是最优和顺序/自适应设计可以帮助我们实现更有价值的科学目标。使用这些设计进行的实验对于长寿生物体或在群落和生态系统层面进行是具有挑战性的。但它们仍然是我们在多驱动因素研究中将实验和理论联系起来并做出准确、有用的预测的最有希望的途径。