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 3 years, 1 month ago 168.7MiB
Experimental.ipynb 3 years, 1 month ago 280.9KiB
Figures eLife.ipynb 3 years, 1 month ago 322.3KiB
Fisher Mehta neutral model annotated.ipynb 3 years, 1 month ago 108.5KiB
Influence interactions SOI and sgLV.ipynb 3 years, 1 month ago 2.1MiB
Noise color fit comparison (linear vs spline).ipynb 3 years, 1 month ago 50.0KiB
README 3 years, 1 month ago 1.1KiB
Study noise no interaction.ipynb 3 years, 1 month ago 1.5MiB
Study noise with interaction.ipynb 3 years, 1 month ago 1.3MiB
Understand noise color.ipynb 3 years, 1 month ago 60.6KiB
Understanding Fisher Mehta Figure 2B.ipynb 3 years, 1 month ago 2.0MiB
Width distribution dx.ipynb 3 years, 1 month ago 184.7KiB
article.ipynb Main 3 years, 1 month ago 1.5MiB
article.xml 3 years, 1 month ago 135.8KiB
article.xml.media 3 years, 1 month ago 1.3MiB
brownian.py 3 years, 1 month ago 2.6KiB
elife_settings.py 3 years, 1 month ago 1.3KiB
generate_timeseries.py 3 years, 1 month ago 15.2KiB
index.html 3 years, 1 month ago 437.8KiB
index.html.media 3 years, 1 month ago 1.0MiB
make_colormap.py 3 years, 1 month ago 1.8KiB
neutral_covariance_test.py 3 years, 1 month ago 5.1KiB
neutrality_analysis.py 3 years, 1 month ago 4.0KiB
noise_analysis.py 3 years, 1 month ago 26.9KiB
noise_color_analysis.py 3 years, 1 month ago 2.7KiB
noise_parameters.py 3 years, 1 month ago 409.0B
noise_properties_plotting.py 3 years, 1 month ago 22.8KiB
results 3 years, 1 month ago 669.2MiB
smooth_spline.py 3 years, 1 month ago 4.6KiB
timeseries_plotting.py 3 years, 1 month ago 1.0KiB