SPEAD 1.0 – Simulating Plankton Evolution with Adaptive Dynamics in a two-trait continuous fitness landscape applied to the Sargasso Sea

Authors: Guillaume Le Gland, Sergio M. Vallina, S. Lan Smith, and Pedro Cermeño

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Diversity plays a key role in the adaptive capacity of marine ecosystems to environmental changes. However, modelling the adaptive dynamics of phytoplankton traits remains challenging due to the competitive exclusion of sub-optimal phenotypes and the complexity of evolutionary processes leading to optimal phenotypes. Trait diffusion (TD) is a recently developed approach to sustain diversity in plankton models by introducing mutations, therefore allowing the adaptive evolution of functional traits to occur at ecological timescales. In this study, we present a model called Simulating Plankton Evolution with Adaptive Dynamics (SPEAD) that resolves the eco-evolutionary processes of a multi-trait plankton community. The SPEAD model can be used to evaluate plankton adaptation to environmental changes at different timescales or address ecological issues affected by adaptive evolution. Phytoplankton phenotypes in SPEAD are characterized by two traits, the nitrogen half-saturation constant and optimal temperature, which can mutate at each generation using the TD mechanism. SPEAD does not resolve the different phenotypes as discrete entities, instead computing six aggregate properties: total phytoplankton biomass, the mean value of each trait, trait variances, and the inter-trait covariance of a single population in a continuous trait space. Therefore, SPEAD resolves the dynamics of the population's continuous trait distribution by solving its statistical moments, wherein the variances of trait values represent the diversity of ecotypes.

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PRODIGIO is an EU-funded project that plan to establish a base of knowledge for the development of system failure prediction technologies that increase the performance of microalgae production and anaerobic digestion systems and advance towards more favourable techno-economic, environmental and social performance to achieve more sustainable microalgae biogas. The project aims to decode the triggers, identify early-warnings, define threshold values, and calculate warning times for critical state transitions in bioreactors. The technology behind the project could contribute to increasing resource and energy efficiencies >50% throughout the production chain, which would translate into OPEX savings and GHG emissions reduction. The results of the project will pave the way for moving the entire microalgae biogas production chain efficiently towards its theoretical maximum, enabling the development of a fully integrated and truly sustainable microalgae biogas production industry and contributing to strengthening the EU's leadership in renewable fuel technologies. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007006. Visit for more information.

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