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…


Type Path Last modified Size Actions
Data 10 months ago 168.7MiB
Experimental.ipynb 10 months ago 280.9KiB
Figures eLife.ipynb 10 months ago 322.3KiB
Fisher Mehta neutral model annotated.ipynb 10 months ago 108.5KiB
Influence interactions SOI and sgLV.ipynb 10 months ago 2.1MiB
Noise color fit comparison (linear vs spline).ipynb 10 months ago 50.0KiB
README 10 months ago 1.1KiB
Study noise no interaction.ipynb 10 months ago 1.5MiB
Study noise with interaction.ipynb 10 months ago 1.3MiB
Understand noise color.ipynb 10 months ago 60.6KiB
Understanding Fisher Mehta Figure 2B.ipynb 10 months ago 2.0MiB
Width distribution dx.ipynb 10 months ago 184.7KiB
article.ipynb Main 10 months ago 1.5MiB
article.xml 10 months ago 135.8KiB
article.xml.media 10 months ago 1.3MiB
brownian.py 10 months ago 2.6KiB
elife_settings.py 10 months ago 1.3KiB
generate_timeseries.py 10 months ago 15.2KiB
index.html 10 months ago 437.8KiB
index.html.media 10 months ago 1.0MiB
make_colormap.py 10 months ago 1.8KiB
neutral_covariance_test.py 10 months ago 5.1KiB
neutrality_analysis.py 10 months ago 4.0KiB
noise_analysis.py 10 months ago 26.9KiB
noise_color_analysis.py 10 months ago 2.7KiB
noise_parameters.py 10 months ago 409.0B
noise_properties_plotting.py 10 months ago 22.8KiB
results 10 months ago 669.2MiB
smooth_spline.py 10 months ago 4.6KiB
timeseries_plotting.py 10 months ago 1.0KiB