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DOI10.1038/s41559-022-01745-8
Complexity–stability trade-off in empirical microbial ecosystems
Yonatan Y.; Amit G.; Friedman J.; Bashan A.
发表日期2022
ISSN2397-334X
英文摘要May’s stability theory, which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge with inconsistent results. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks. Here we introduce a computational framework for estimating an ecosystem’s complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites and sponge-associated microbial communities from different geographical locations, we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity. ? 2022, The Author(s), under exclusive licence to Springer Nature Limited.
语种英语
来源期刊Nature Ecology & Evolution
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/257193
作者单位Physics Department, Bar-Ilan University, Ramat-Gan, Israel; Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel
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Yonatan Y.,Amit G.,Friedman J.,等. Complexity–stability trade-off in empirical microbial ecosystems[J],2022.
APA Yonatan Y.,Amit G.,Friedman J.,&Bashan A..(2022).Complexity–stability trade-off in empirical microbial ecosystems.Nature Ecology & Evolution.
MLA Yonatan Y.,et al."Complexity–stability trade-off in empirical microbial ecosystems".Nature Ecology & Evolution (2022).
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