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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设计信息量更大的多驱动程序实验


几十年来，多驱动因素/压力源研究考察了未来将发生巨大变化的驱动因素之间的相互作用：温度、pH 值、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）无法对一般理解或预测能力做出贡献。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的环境条件下的行为。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。全因子响应面、分数阶因、二次响应面、自定义、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。使用这些设计的实验对于长寿生物体或在群落和生态系统水平上进行具有挑战性。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。