Data from: Taxonomic and numerical sufficiency in depth- and salinity-controlled marine paleocommunities

Numerical and taxonomic resolution of compositional datasets affects
investigator’s ability to detect and measure relationships between
communities and environmental factors. We test whether varying numerical
(untransformed, square-root- and fourth-root transformed relative
abundance, and presence-absence data) and taxonomic resolutions (species,
genera, families) reveal different insights into Lower to Middle Miocene
molluscan communities along bathymetric and salinity gradients. The marine
subtidal has a more even species-abundance distribution, a higher number
of rare species, and a higher species-family and species-genus ratio than
the three habitats with higher fluctuations in salinity and other physical
parameters (marine and estuarine intertidal, estuarine subtidal).
Taxonomic aggregation and numerical transformation of data result in very
different ordinations, although all habitats differ significantly from
each other at all taxonomic and numerical levels. Rank correlations
between species-level and higher-taxon, among-sample dissimilarities are
very high for proportional abundances and decrease strongly with
increasing numerical transformation, most notably in the two intertidal
habitats. The proportion of variation explained by depth is highest for
family-level data, decreases gradually with numerical transformation, and
is higher in marine than in estuarine habitats. The proportion of
variation explained by salinity is highest for species-level data,
increases gradually with numerical transformation, and is higher in
subtidal than in intertidal habitats. Therefore, there is no single best
numerical and taxonomic resolution for the discrimination of communities
along environmental gradients: the “best” resolution depends on the
environmental factor considered and the nature of community response to
it. Different numerical and taxonomic transformations capture unique
aspects of metacommunity assembly along environmental gradients that are
not detectable at a single level of resolution. We suggest that
simultaneous analyses of community gradients at multiple taxonomic and
numerical resolutions provide novel insights into processes responsible
for spatial and temporal community stability.