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We compared two mutation and selection (MuSe) models for phytoplankton adaptation. Both individual-based (MuSe-IBM) and multi-compartment (MuSe-MCM) models yielded similar results for temperature adaptation, with MCM being computationally efficient.

Keywords:
Adaptive evolutionIndividual based model (IBM)Multi-compartment model (MCM)NPZD-Type modelThermal adaptationTrait diffusion model

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Area of Science:

  • * Ecological modeling
  • * Evolutionary biology
  • * Phytoplankton adaptation

Background:

  • * Phytoplankton adaptation to environmental changes is crucial for marine ecosystems.
  • * Modeling trait value changes requires understanding mutation and selection dynamics.
  • * Different modeling frameworks may yield varying results for evolutionary processes.

Purpose of the Study:

  • * To compare two distinct modeling approaches for phytoplankton adaptation: MuSe-IBM and MuSe-MCM.
  • * To evaluate model performance under various environmental scenarios, including temperature shifts and fluctuations.
  • * To assess the influence of thermal reaction norms and life cycle dynamics on adaptation.

Main Methods:

  • * Individual-Based Model (MuSe-IBM): Simulates individual phytoplankton with random mutations during cell division.
  • * Multi-Compartment Model (MuSe-MCM): Uses a deterministic approach with multiple genotypes, treating mutations as biomass transfer between genotypes.
  • * Scenarios included sudden temperature change, seasonal variation, and high-frequency fluctuations, alongside varying thermal reaction norms and growth/resting phases.

Main Results:

  • * Both MuSe-IBM and MuSe-MCM demonstrated negligible differences in modeling phytoplankton adaptation to temperature.
  • * Both models successfully reproduced known and plausible adaptive features.
  • * MuSe-IBM offers mechanistic insights, while MuSe-MCM is computationally less demanding.

Conclusions:

  • * MuSe-IBM and MuSe-MCM are comparable in predicting phytoplankton adaptation outcomes.
  • * MuSe-MCM's computational efficiency makes it suitable for integration into larger 3D ecosystem models.
  • * The choice of model depends on the balance between mechanistic detail and computational resources.