Individual-based modelling of adaptive physiological traits of cyanobacteria: Responses to light history

Mohammad Hassan Ranjbar, David P. Hamilton, Michael L. Pace, Amir Etemad-Shahidi, Cayelan C. Carey and Fernanda Helfer

Ecological Modelling, 2024, vol. 495, issue C

Abstract: Adaptive physiological traits of cyanobacteria allow plasticity of responses to environmental change at multiple time scales. Most conventional phytoplankton models only simulate responses to current conditions without incorporating antecedent environmental history and adaptive physiological traits, thereby potentially missing mechanisms that influence dynamics. We developed an individual-based model (IBM) that incorporates information on light exposure history and cell physiology coupled with a hydrodynamic model that simulates mixing and transport. The combined model successfully simulated cyanobacterial growth and respiration in a whole-lake nutrient enrichment experiment in a temperate lake (Peter Lake, Michigan, USA). The model also incorporates non-photochemical quenching (NPQ) to improve simulations of cyanobacteria biomass based on validation against cyanobacteria cell counts and chlorophyll concentration. The IBM demonstrated that physical processes (stratification and mixing) significantly affect the dynamics of NPQ in cyanobacteria. Cyanobacteria had high fluorescence quenching and long photo-physiological relaxation periods during stratification, and low quenching and rapid relaxation in response to low light exposure history as the mixing layer deepened. This work demonstrates that coupling adaptive physiological trait with physical mixing into models can improve our understanding and enhance predictions of bloom occurrences in response to environmental changes.

Keywords: Agent-based modelling; Dolichospermum; Hydrodynamic modelling; Non-photochemical quenching; Peter lake; Whole-lake manipulation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:495:y:2024:i:c:s0304380024001911

DOI: 10.1016/j.ecolmodel.2024.110803

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