Stochastic logistic models reproduce experimental time series of microbial communities
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.Read more…
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.Read more…
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Data | 3 years, 9 months ago | 168.7MiB | ||
Experimental.ipynb | 3 years, 9 months ago | 280.9KiB |
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Figures eLife.ipynb | 3 years, 9 months ago | 322.3KiB |
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Fisher Mehta neutral model annotated.ipynb | 3 years, 9 months ago | 108.5KiB |
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Influence interactions SOI and sgLV.ipynb | 3 years, 9 months ago | 2.1MiB |
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Noise color fit comparison (linear vs spline).ipynb | 3 years, 9 months ago | 50.0KiB |
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README | 3 years, 9 months ago | 1.1KiB |
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Study noise no interaction.ipynb | 3 years, 9 months ago | 1.5MiB |
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Study noise with interaction.ipynb | 3 years, 9 months ago | 1.3MiB |
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Understand noise color.ipynb | 3 years, 9 months ago | 60.6KiB |
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Understanding Fisher Mehta Figure 2B.ipynb | 3 years, 9 months ago | 2.0MiB |
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Width distribution dx.ipynb | 3 years, 9 months ago | 184.7KiB |
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article.ipynb Main | 3 years, 9 months ago | 1.5MiB |
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article.xml | 3 years, 9 months ago | 135.8KiB |
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article.xml.media | 3 years, 9 months ago | 1.3MiB | ||
brownian.py | 3 years, 9 months ago | 2.6KiB |
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elife_settings.py | 3 years, 9 months ago | 1.3KiB |
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generate_timeseries.py | 3 years, 9 months ago | 15.2KiB |
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index.html | 3 years, 9 months ago | 437.8KiB |
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index.html.media | 3 years, 9 months ago | 1.0MiB | ||
make_colormap.py | 3 years, 9 months ago | 1.8KiB |
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neutral_covariance_test.py | 3 years, 9 months ago | 5.1KiB |
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neutrality_analysis.py | 3 years, 9 months ago | 4.0KiB |
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noise_analysis.py | 3 years, 9 months ago | 26.9KiB |
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noise_color_analysis.py | 3 years, 9 months ago | 2.7KiB |
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noise_parameters.py | 3 years, 9 months ago | 409.0B |
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noise_properties_plotting.py | 3 years, 9 months ago | 22.8KiB |
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results | 3 years, 9 months ago | 669.2MiB | ||
smooth_spline.py | 3 years, 9 months ago | 4.6KiB |
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timeseries_plotting.py | 3 years, 9 months ago | 1.0KiB |
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