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Chlorophyll (Chl) is widely taken as a proxy for phytoplankton biomass, despite well-known variations in Chl:C:biomass ratios as an acclimative response to changing environmental conditions. For the sake of simplicity and computational efficiency, many large scale biogeochemical models ignore this flexibility, compromising their ability to capture phytoplankton dynamics. Here we evaluate modelling approaches of differing complexity for phytoplankton growth response: fixed stoichiometry, fixed stoichiometry with photoacclimation, classical variable-composition with photoacclimation, and Instantaneous Acclimation with optimal resource allocation. Model performance is evaluated against biogeochemical observations from time-series sites BATS and ALOHA, where phytoplankton composition varies substantially. We analyse the sensitivity of each model variant to the affinity parameters for light and nutrient, respectively. Models with fixed stoichiometry are more sensitive to parameter perturbations, but the inclusion of photoacclimation in the fixed-stoichiometry model generally captures Chl observations better than other variants when individually tuned for each site and when using similar parameter sets for both sites. Compared to the fixed stoichiometry model including photoacclimation, models with variable C:N ratio perform better in cross-validation experiments using model-specific parameter sets tuned for the other site; i.e., they are more portable. Compared to typical variable composition approaches, instantaneous acclimation, which requires fewer state variables, generally yields better performance but somewhat lower portability than the fully dynamic variant. Further assessments using objective optimisation and more contrasting stations are suggested. 

Ecosystem models need to capture biodiversity, because it is a fundamental determinant of food web dynamics and consequently of the cycling of energy and matter in ecosystems. In oceanic food webs, the plankton compartment encompasses by far most of the biomass and diversity. Therefore, capturing plankton diversity is paramount for marine ecosystem modelling. In recent years, many models have been developed, each representing different aspects of plankton diversity, but a systematic comparison remains lacking. Here we present established modelling approaches to study plankton ecology and diversity, discussing the limitations and strengths of each approach. We emphasize their different spatial and temporal resolutions and consider the potential of these approaches as tools to address societal challenges. Finally, we make suggestions as to how better integration of field and experimental data with modelling could advance understanding of both plankton biodiversity specifically and more broadly the response of marine ecosystems to environmental change, including climate change.