Complexity–stability trade-off in empirical microbial ecosystems

Yonatan, Yogev; Amit, Guy; Friedman, Jonathan; Bashan, Amir

doi:10.1038/s41559-022-01745-8

Cited by 41 publications

(41 citation statements)

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“…Therefore, the emergent phases of biodiversity and dynamics that we observed in this study may occur in a wide range of ecological communities. Future work should study whether these phases generalize across spatiotemporal scales, environmental conditions, and organism types to understand their prevalence and importance in shaping major ecological patterns ( 37 , 38 ).…”

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confidence: 99%

Emergent phases of ecological diversity and dynamics mapped in microcosms

Amor

Barbier

et al. 2022

Science

137

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From tropical forests to gut microbiomes, ecological communities host notably high numbers of coexisting species. Beyond high biodiversity, communities exhibit a range of complex dynamics that are difficult to explain under a unified framework. Using bacterial microcosms, we performed a direct test of theory predicting that simple community-level features dictate emergent behaviors of communities. As either the number of species or the strength of interactions increases, we show that microbial ecosystems transition between three distinct dynamical phases, from a stable equilibrium in which all species coexist to partial coexistence to emergence of persistent fluctuations in species abundances, in the order predicted by theory. Under fixed conditions, high biodiversity and fluctuations reinforce each other. Our results demonstrate predictable emergent patterns of diversity and dynamics in ecological communities.

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confidence: 99%

Emergent phases of ecological diversity and dynamics mapped in microcosms

Amor

Barbier

et al. 2022

Science

137

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“…However, for more advanced ecological modelling to be applied, usually a minimum of five to six replicates are required; this will usually satisfy criteria for the null models which often underpin community assembly analysis (e.g., [ 18 , 44 , 45 ]). Finally, for even more involved methods such as network inferences, upwards of 35 samples per category are often necessary to obtain reliable results (e.g., [ 46 , 47 ]).…”

Section: Study Design Considerations: Planning For Statisticsmentioning

confidence: 99%

“…Instead, to maintain stability, microbial communities have evolved to be either small and highly interacting, or large but weakly interacting. This trade-off between community complexity and stability emphasizes the role of interspecific interactions in governing overall species richness of a community [ 47 ]. Generally, calculating the complexity-stability across different communities has been challenging because while the species richness can, in theory, be measured, the degree of interspecific interactions is more challenging.…”

Section: Network Inferences: Identifying Relationships and Revealing ...mentioning

confidence: 99%

“…The authors suggest that in addition to evaluating complexity-stability relationships, the effective connectance may give insights into the degree to which local perturbations, which cause shifts in the abundance of one or a few species, will propagate and affect the whole community. Communities with high effective connectance will be more substantially affected by a few changes in abundance than communities with low effective connectance [ 47 ].…”

Section: Network Inferences: Identifying Relationships and Revealing ...mentioning

confidence: 99%

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Beyond Basic Diversity Estimates—Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

et al. 2022

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Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

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“…Given that living systems are usually not fully interconnected in order to buffer against environmental stochasticity (May, 1973;Gravel et al, 2016), the microbial networks to be reconstructed should be sparse (Goyal et al, 2022;Yonatan et al, 2022). There are two strategies that can be used to ensure network sparsity.…”

Section: Sparsity Of Microbial Network Through Variable Selection And...mentioning

confidence: 99%

Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis

et al. 2022

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Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.

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Complexity–stability trade-off in empirical microbial ecosystems

Cited by 41 publications

References 50 publications

Emergent phases of ecological diversity and dynamics mapped in microcosms

Emergent phases of ecological diversity and dynamics mapped in microcosms

Beyond Basic Diversity Estimates—Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis

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