Abstract：
Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.
摘要：
了解人为压力源之间的相互作用对于有效保护和管理生态系统至关重要。淡水科学家已投入大量资源进行阶乘实验，以通过测试其个体和综合效应来解决压力源相互作用。然而,所研究的压力源和系统的多样性阻碍了该研究机构先前的综合。为了克服这一挑战，我们使用了一个新的机器学习框架，从超过235,000种出版物中确定了相关研究。我们的合成产生了一个新的数据集，该数据集包含2396个淡水系统中的多压力源实验。通过总结这些研究中使用的方法，量化所调查压力源的流行趋势，并进行共现分析，我们对迄今为止这一多样化的研究领域进行了最全面的概述。我们提供了将909调查的压力源分为31个类的分类法，以及数据集的开源和交互式版本（https://jamesaorr。shinyapps.io/淡水多重压力源/）。受到我们结果的启发,我们提供了一个框架来帮助澄清由阶乘实验检测到的统计相互作用是否与感兴趣的应激源相互作用一致，我们概述了与任何系统相关的多压力源实验设计的一般指南。最后，我们强调了更好地了解面临多种压力源的淡水生态系统所需的研究方向。
