AbstractAimWe analysed marine phytoplankton diversity data as a function of latitude, temperature, primary production and several environmental and biological variables to ascertain whether large‐scale variability in the diversity of marine nano‐ and microphytoplankton (including diatoms, dinoflagellates and coccolithophores) follows similar patterns to those observed for macroorganisms. For the first time we explored these relationships after correcting the observed patterns of species richness by sampling effort.LocationThe global ocean.MethodsTo standardize the estimates of species richness by sampling effort we used interpolation and extrapolation based on Hill numbers and shareholder quorum subsampling (SQS) methods. Then, we fitted linear and quadratic models to species richness data to explore their variability with latitude, inverse temperature and biomass. These relationships were compared with the patterns obtained from non‐standardized data. In addition, we used a stepwise multiple linear regression model to explain the variability of species richness as the combined effect of multiple drivers acting together.ResultsMarine phytoplankton diversity was weakly correlated with latitude, temperature or biomass. The hotspots of species richness at intermediate latitudes largely vanished after standardization for sampling effort. Neither latitude, temperature, primary production (as diagnostics of energy supply) nor any other variable or combination of variables, explained the patterns of phytoplankton species richness.Main conclusionsNone of the hypotheses tested explained a significant amount of the variability in species richness. The patterns observed for microorganisms in previous studies may have resulted at least partially from differences in sampling effort along productivity gradients and systematic undersampling of species. We conclude that large‐scale processes such as passive dispersal and recurrent habitat recolonization dominate the distribution of species. Sampling protocols and data analyses must be improved in order to obtain estimates of diversity that are comparable across ecosystems.