Chaotic dynamics in blooms of Ostreopsis cf. ovata modelled from environmental time series using ordinary differential equations systems
Abstract
Blooms of the toxic dinoflagellate Ostreopsis cf. ovata can cause respiratory irritation in beach users, yet their environmental drivers and temporal dynamics remain poorly understood. Here, we use a dynamical-systems approach to investigate how environmental conditions shape bloom dynamics from long-term time series of algal abundance and abiotic variables. Using the global modelling framework GPoM, we reconstruct nonlinear ordinary differential equation systems and identify candidate models capable of reproducing key features of the observed dynamics, including chaotic behaviour. Topological and statistical analyses reduce the candidate models to two systems that best capture the observed distributions and dynamical patterns. These models reveal distinct combinations of environmental conditions associated with bloom occurrence and amplitude, providing a framework for interpreting bloom trajectories and a potential basis for short-term prediction.
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BibTeX
@misc{almeida2026chaotic,
title = {{Chaotic dynamics in blooms of Ostreopsis cf. ovata modelled from environmental time series using ordinary differential equations systems}},
author = {Andy de Almeida and Samuel Hocquart and Martin Rosalie},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.754431},
url = {https://hal.science/hal-05790579v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Chaotic dynamics in blooms of Ostreopsis cf. ovata modelled from environmental time series using ordinary differential equations systems
AU - Andy de Almeida
AU - Samuel Hocquart
AU - Martin Rosalie
PY - 2026
DA - 2026/09/25
PB - HAL
DO - 10.64898/2026.09.25.754431
UR - https://hal.science/hal-05790579v1
AB - Blooms of the toxic dinoflagellate Ostreopsis cf. ovata can cause respiratory irritation in beach users, yet their environmental drivers and temporal dynamics remain poorly understood. Here, we use a dynamical-systems approach to investigate how environmental conditions shape bloom dynamics from long-term time series of algal abundance and abiotic variables. Using the global modelling framework GPoM, we reconstruct nonlinear ordinary differential equation systems and identify candidate models capable of reproducing key features of the observed dynamics, including chaotic behaviour. Topological and statistical analyses reduce the candidate models to two systems that best capture the observed distributions and dynamical patterns. These models reveal distinct combinations of environmental conditions associated with bloom occurrence and amplitude, providing a framework for interpreting bloom trajectories and a potential basis for short-term prediction.
ER -