SeapoPym v0.1: implementation of the SEAPODYM low and mid trophic levels in Python with a flexible optimization framework

SEAPODYM-LMTL, the low and mid trophic level component of SEAPODYM, simulates mesozooplankton and micronekton biomass globally as an advection-diffusion-reaction system driven by physical and biogeochemical forcing. Its mesozooplankton parameterization remains incompletely calibrated, and its operational implementation couples the biological equations to spatial transport, so evaluating a parameter set requires running the full spatial model, which makes automated calibration costly. We present SeapoPym v0.1, an open-source Python re-implementation of the SEAPODYM-LMTL biological model that solves the dynamics locally, without transport. We apply it to the single epipelagic mesozooplankton group and estimate its five biological parameters with a covariance matrix adaptation evolution strategy (CMA-ES). Comparison with the operational product shows that omitting transport matters most in strongly advective regions and in cold high-latitude waters, where the long zooplankton life cycle keeps the biomass exposed to advection. At six contrasting stations the difference between the two models stays between 6 % and 12 % of the simulated biomass, five to seven times smaller than the model-observation gap where in-situ records allow that comparison. A Sobol analysis attributes the magnitude of the biomass to the energy-transfer and mortality parameters, and the timing of the seasonal peak to the recruitment parameters. Twin experiments then show that parameter identifiability depends on the environmental regime sampled. Wherever the search converged, energy transfer and mortality were recovered, whereas recruitment was recovered only in cold water. A single cold station constrained all five parameters as well as the six stations combined, so recovery follows the information content of the sampled regime rather than the number of stations. These results hold for noise-free synthetic observations generated by the transport-free model itself and driven by the exact forcing. The next step is to repeat them under realistic sampling and forcing error, then calibrate the model against in-situ records.