Monte Carlo Sensitivity Analysis & High‑Dimensional Parameter Space Exploration

I implemented a comprehensive Monte Carlo sensitivity analysis to quantify how key physiological, ecological, and physical parameters control benthic microalgae productivity and dynamics within coupled physical-biological models. By efficiently sampling multi‑dimensional parameter spaces and applying statistical metrics (e.g., Spearman correlations, normalized standard deviations) alongside parallel coordinate plotting, I identified critical drivers, parameter interactions, and threshold effects.

Parallel coordinates of the benthic microalgae annual primary production
Figure: Parallel coordinates of the benthic microalgae annual primary production (g C m⁻² yr⁻¹) according to the temperature optimum for microalgae growth (Tempₒₚₜ), the temperature maximum for microalgae growth (Tempₘₐₓ), the temperature amplitude between the optimal and maximal temperatures (Tempₐₘₚ), the light saturation parameter (Eₖ), the half-saturation constant for light use (Kᴇ), the temperature optimum for grazing by grazer (Tₒₚₜᴢ), and the shape parameter of the temperature grazing function (αᴢ) for 10,000 combinations tested in the Monte Carlo sensitivity analysis.

The goal was to rigorously quantify parameter uncertainty and its propagation through the modeling framework, yielding actionable insights for both model optimization and ecological interpretation. To do this, I selected critical biological constants that were varied simultaneously across observed ranges using a low‑discrepancy random sequence, yielding a structured, space‑filling design that efficiently sampled the multi‑dimensional parameter space. This approach enabled 10,000 simulations from identical initial conditions, allowing robust statistical characterization of benthic microalgae dynamics across parameter combinations. Spearman’s rank correlation coefficients, parameter averages, and normalized standard deviations were used to assess parameter influence, while parallel coordinate plotting revealed complex, nonlinear interactions and threshold effects. This combined workflow of stochastic sampling, statistical attribution, and high‑dimensional visualization provides a robust, data‑science–oriented approach for understanding and optimizing complex models. By making parameter sensitivities and interdependencies explicit, it strengthens parameterization, improves predictive skill, and advances the reliability of coupled simulations.

Read the full story here: Article in Biogeosciences.