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Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple‐stressor experiments
Ecology Letters ( IF 7.6 ) Pub Date : 2024-06-26 , DOI: 10.1111/ele.14463
James A. Orr 1, 2 , Samuel J. Macaulay 1 , Adriana Mordente 1 , Benjamin Burgess 3 , Dania Albini 1 , Julia G. Hunn 4 , Katherin Restrepo‐Sulez 1 , Ramesh Wilson 1 , Anne Schechner 5, 6 , Aoife M. Robertson 7 , Bethany Lee 1 , Blake R. Stuparyk 8 , Delezia Singh 9 , Isobel O'Loughlin 1 , Jeremy J. Piggott 7 , Jiangqiu Zhu 10 , Khuong V. Dinh 11 , Louise C. Archer 12 , Marcin Penk 7, 13 , Minh Thi Thuy Vu 11 , Noël P. D. Juvigny‐Khenafou 14, 15 , Peiyu Zhang 10 , Philip Sanders 1 , Ralf B. Schäfer 16, 17 , Rolf D. Vinebrooke 8 , Sabine Hilt 5 , Thomas Reed 18 , Michelle C. Jackson 1
Affiliation  

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.

中文翻译:


研究淡水生态系统中人为压力源之间的相互作用:对 2396 个多重压力源实验的系统回顾



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。淡水科学家投入了大量资源进行析因实验,通过测试压力源的单独影响和综合影响来理清压力源的相互作用。然而,所研究的压力源和系统的多样性阻碍了该研究机构之前的综合。为了克服这一挑战,我们使用新颖的机器学习框架从超过 235,000 篇出版物中识别相关研究。我们的综合产生了淡水系统中 2396 个多重压力源实验的新数据集。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,我们对这一多样化研究领域进行了迄今为止最全面的概述。我们提供了将 909 个调查的压力源分为 31 个类别的分类法,以及数据集的开源和交互式版本 (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/)。受我们的结果的启发,我们提供了一个框架来帮助澄清阶乘实验检测到的统计相互作用是否与感兴趣的压力源相互作用一致,并且我们概述了与任何系统相关的多压力源实验设计的一般准则。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。
更新日期:2024-06-26
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