Would that it were so simple: Interactions between multiple traits undermine classical single-trait-based predictions of microbial community function and evolution.
Would that it were so simple: Interactions between multiple traits undermine classical single-trait-based predictions of microbial community function and evolution.
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DOI：
10.1111/ele.13861
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发表时间：
2021-08
期刊：
影响因子：
8.8
通讯作者：
Richard J. Lindsay;Alys Jepson;Lisa Butt;Philippa J. Holder;Bogna J. Smug;I. Gudelj
中科院分区：
文献类型：
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作者：
Richard J. Lindsay;Alys Jepson;Lisa Butt;Philippa J. Holder;Bogna J. Smug;I. Gudelj
了解微生物性状如何影响微生物群落的进化和功能，对于改善有害微生物的管理，同时促进有益微生物的发展至关重要。几十年来，进化生态学研究的重点是研究微生物的合作，多样性，生产力和毒力，但有一个关键的限制。所考虑的性状，如公共产品生产和对抗生素或捕食的抗性，往往被认为是孤立的。然而，在现实中，多种性状经常相互作用，这可能导致宏观生物体健康和生态系统功能的意外和不希望的结果。这是因为在单一性状背景下产生的许多预测旨在促进多样性，降低毒力或控制抗生素耐药性，但对于多性状相互作用的系统可能会失败。在这里，我们提供了一个非常需要的讨论和最新研究的综合，以揭示多性状相互作用的广泛和多样性及其对预测和控制微生物群落动态的影响。重要的是，我们认为，合成微生物群落和多性状数学模型是管理微生物群落的有益和有害影响的有力工具，这样就不会重复过去的错误，比如那些关于抗菌剂管理的错误。
Understanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single-trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi-trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi-trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.