Spatially implicit plankton population models: Transient spatial variability

Area of Science:

• Oceanography
• Ecology
• Mathematical Biology

Background:

• Ocean plankton models are crucial for ecosystem prediction.
• The mean field approximation in models fails when grid cells are not well-mixed, leading to inaccurate population dynamics due to biological Reynolds fluxes.

Purpose of the Study:

• To investigate the domain-scale behavior of plankton models with spatial variability.
• To explore methods for improving the accuracy of large-scale plankton model predictions.

Main Methods:

• Analysis of two-component, 2D reaction-diffusion plankton models with transient dynamics.
• Investigation of implicit spatial resolution methods, including spatial moment closure and a two-spike approximation.

Main Results:

• Spatial moment closure models significantly improve upon the mean field approximation for weak to moderate nonlinearity, especially at low mixing rates.
• The two-spike approximation is accurate for stronger nonlinearity and weaker transience at low mixing rates.

Conclusions:

• The mean field approximation's failure in ocean plankton models can be substantial.
• Spatial moment closure and two-spike approximations offer improved predictions for marine ecosystem dynamics.
• These methods may enhance our understanding and prediction of large-scale marine ecosystem behavior.