Diatoms usually dominate microphytobenthic biofilms in coastal and estuarine intertidal environments. Yet, functional studies on biofilms often skip species analysis because benthic diatoms are notoriously difficult to extract from sediments and challenging to identify at that taxonomic level. Valid, less time-consuming alternatives would surely be welcomed and increase the inclusion of community structure information in microphytobenthos (MPB) ecophysiological studies. Starting with an original 181-species abundances matrix (OSM), obtained during a 2-year spatial–temporal survey in a Tagus Estuary intertidal flat with contrasting sediment textures, the current study assessed the effectiveness of several approaches to species abundances analysis. The effect of excluding abundance data or rare species, the influence of taxonomic resolution, or the use of size-based metrics on biotic multivariate patterns was examined by an objective comparison that replicated these different approaches on three different levels: (1) inter-matrix correlations, (2) performance in several non-parametric multivariate analyses (ANOSIM, MDS), and (3) correlations with the environmental dataset. When compared with the OSM, all matrices had strong or very strong positive correlations. All discriminated successfully spatial patterns, separating well assemblages from sandy and muddy sediments, and all had significant correlations with the environmental dataset. Apart from the relative biovolume species matrix (BSM), only the species matrices were able to discriminate significantly temporal patterns. The exclusion of the rarest species (48% of total) had a negligible effect, with the common and original species abundances matrices having a ρ > 0.99 correlation. Of the alternative approaches to species abundances, species presence/absence and the genera abundances matrices yielded the best results overall. Genera presence/absence and the size-class matrices had intermediate performances, with the former performing comparatively poorly with regard to seasonal patterns. BSM had the lowest correlation with the environmental variable dataset (ρ = 0.598) and the worst overall performance in the other multivariate routines. This means that either a high-taxonomic resolution qualitative analysis (i.e. species presence/absence) or, in alternatively, a genus-level analysis retaining abundance data may be sufficient to describe basic spatial differences in estuarine intertidal flats. However, if seasonal variations in mudflat diatom assemblage structure are to be detected, species-level abundance data are still necessary.