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Stochastic logistic models reproduce experimental time series of microbial communities.

ABSTRACT: We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, that is, without interactions, even the niche character of the experimental time series. Linear noise is associated with growth rate stochasticity, which is related to changes in the environment. This suggests that fluctuations in the sparsely sampled experimental time series may be caused by extrinsic sources.

SUBMITTER: Descheemaeker L

PROVIDER: S-EPMC7410486 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Stochastic logistic models reproduce experimental time series of microbial communities.

Descheemaeker Lana L de Buyl Sophie S

eLife 20200720

We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, t ...[more]

PMID: 32687052

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Project description:Seamounts are ubiquitous in the ocean. However, little is known about how seamount habitat features influence the local microbial community. In this study, the microbial populations of sediment cores from sampling depths of 0.1 to 35 cm from 10 seamount summit sites with a water depth of 1,850 to 3,827 m across the South China Sea (SCS) Basin were analyzed. Compared with nonseamount ecosystems, isolated seamounts function as oases for microbiomes, with average moderate to high levels of microbial abundance, richness, and diversity, and they harbor distinct microbial communities. The distinct characteristics of different seamounts provide a high level of habitat heterogeneity, resulting in the wide range of microbial community diversity observed across all seamounts. Using dormant thermospores as tracers to study the effect of dispersal by ocean currents, the observed distance-decay biogeography across different seamounts shaped simultaneously by the seamounts' naturally occurring heterogeneous habitat and the limitation of ocean current dispersal was found. We also established a framework that links initial community assembly with successional dynamics in seamounts. Seamounts provide resource-rich and dynamic environments, which leads to a dominance of stochasticity during initial community establishment in surface sediments. However, a progressive increase in deterministic environmental selection, correlated with resource depletion in subsurface sediments, leads to the selective growth of rare species of surface sediment communities in shaping the subsurface community. Overall, the study indicates that seamounts are a previously ignored oasis in the deep sea. This study also provides a case study for understanding the microbial ecology in globally widespread seamounts. IMPORTANCE Although there are approximately 25 million seamounts in the ocean, surprisingly little is known about seamount microbial ecology. We provide evidence that seamounts are island-like habitats harboring microbial communities distinct from those of nonseamount habitats, and they exhibit a distance-decay pattern. Environmental selection and dispersal limitation simultaneously shape the observed biogeography. Coupling empirical data with a null mode revealed a shift in the type and strength, which controls microbial community assembly and succession from the seamount surface to the subsurface sediments as follows: (i) community assembly is initially primarily driven by stochastic processes such as dispersal limitation, and (ii) changes in the subsurface environment progressively increase the importance of environmental selection. This case study contributes to the mechanistic understanding essential for a predictive microbial ecology of seamounts.

Dataset Information

Stochastic logistic models reproduce experimental time series of microbial communities.

Publications

Stochastic logistic models reproduce experimental time series of microbial communities.

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