stab_analyzer

This repository contains the code which has been used to study the temporal stability of ecosystems in (Occhipinti et al. 2023, Occhipinti et al. 2024, Occhipinti et al. 2025):

• lyapunov.py A python class which computes the maximum Lyapunov exponent of a timeseries following Wolf et al. (1985).
• lyapunovV.py A faster version of the class lyapunov.py, thanks to a vectorization of procedures.
• seamless-notebooks-guido The code used in the studies (Occhipinti et al. 2023, Occhipinti et al. 2024), it make uses of the fabm and parsac framework embedded in BoldingBruggeman/seamless-notebooks/
  • setups: the setup files and the python scripts to launch models
  • parsac: contains scripts for sensitivity analysis
  • extern: contain a model to reproduce the results of Fussmann et al. (2002) and a modification to Parsac sensitivity analysis tool.
• ExternalForcings The code used in the study (Occhipinti et al. 2025), it make uses of the fabm and parsac framework embedded in BoldingBruggeman/seamless-notebooks/
  • setups: model configuration file and scripts to launch numerical simlations
  • Scripts: python scripts to analyze model output and produce the paper figures

References

Fussmann, G. F., & Heber, G. (2002). Food web complexity and chaotic population dynamics. Ecology Letters, 5(3), 394-401.

Occhipinti, G., Solidoro, C., Grimaudo, R., Valenti, D., & Lazzari, P. (2023). Marine ecosystem models of realistic complexity rarely exhibits significant endogenous non-stationary dynamics. Chaos, Solitons & Fractals, 175, 113961.

Occhipinti, G., Piani, S., & Lazzari, P. (2024). Stochastic effects on plankton dynamics: Insights from a realistic 0-dimensional marine biogeochemical model. Ecological Informatics, 83, 102778.

Occhipinti, G., Solidoro, C., Grimaudo, R., Valenti, D., & Lazzari, P. (2025). Plankton Communities Behave Chaotically Under Seasonal or Stochastic Temperature Forcings. Ecology and Evolution, 15(8), e71930.

Wolf, A., Swift, J. B., Swinney, H. L., & Vastano, J. A. (1985). Determining Lyapunov exponents from a time series. Physica D: nonlinear phenomena, 16(3), 285-317