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 | 4 years ago | 168.7MiB | ||
Experimental.ipynb | 4 years ago | 280.9KiB |
|
|
Figures eLife.ipynb | 4 years ago | 322.3KiB |
|
|
Fisher Mehta neutral model annotated.ipynb | 4 years ago | 108.5KiB |
|
|
Influence interactions SOI and sgLV.ipynb | 4 years ago | 2.1MiB |
|
|
Noise color fit comparison (linear vs spline).ipynb | 4 years ago | 50.0KiB |
|
|
README | 4 years ago | 1.1KiB |
|
|
Study noise no interaction.ipynb | 4 years ago | 1.5MiB |
|
|
Study noise with interaction.ipynb | 4 years ago | 1.3MiB |
|
|
Understand noise color.ipynb | 4 years ago | 60.6KiB |
|
|
Understanding Fisher Mehta Figure 2B.ipynb | 4 years ago | 2.0MiB |
|
|
Width distribution dx.ipynb | 4 years ago | 184.7KiB |
|
|
article.ipynb Main | 4 years ago | 1.5MiB |
|
|
article.xml | 4 years ago | 135.8KiB |
|
|
article.xml.media | 4 years ago | 1.3MiB | ||
brownian.py | 4 years ago | 2.6KiB |
|
|
elife_settings.py | 4 years ago | 1.3KiB |
|
|
generate_timeseries.py | 4 years ago | 15.2KiB |
|
|
index.html | 4 years ago | 437.8KiB |
|
|
index.html.media | 4 years ago | 1.0MiB | ||
make_colormap.py | 4 years ago | 1.8KiB |
|
|
neutral_covariance_test.py | 4 years ago | 5.1KiB |
|
|
neutrality_analysis.py | 4 years ago | 4.0KiB |
|
|
noise_analysis.py | 4 years ago | 26.9KiB |
|
|
noise_color_analysis.py | 4 years ago | 2.7KiB |
|
|
noise_parameters.py | 4 years ago | 409.0B |
|
|
noise_properties_plotting.py | 4 years ago | 22.8KiB |
|
|
results | 4 years ago | 669.2MiB | ||
smooth_spline.py | 4 years ago | 4.6KiB |
|
|
timeseries_plotting.py | 4 years ago | 1.0KiB |
|