Aim: Understanding large-scale spatial distribution patterns is not only a central goal of ecology but is also essential for conservation planning. Nevertheless, the biogeographical patterns of diversity and composition remain unclear for microorganisms and the role of various factors in structuring their assemblages is still poorly known. Here, we tested whether the diversity and community structure of ciliates are driven by both local environmental and regional dispersal-related processes.

Location: Coasts of China.

Taxon: Benthic ciliates.

Results: We found that local environmental factors including BI, salinity and MPS were more important in shaping ciliate alpha diversity than latitude. However, the Shannon alpha diversity index decreased with latitude, perhaps due to anthropogenic disturbances. DistLM analysis emphasized regional processes in shaping community structure. Both NMDS and PERMANOVA supported a clear separation among the three clusters matching with the ecoregional delineations suggested for macroorganisms. We also note that community trait composition was partially explained by a local factor, i.e. the maximum spring tide range of beaches.

Main conclusions: Ciliate communities were driven by both local environmental and regional factors. We suggest that these biogeographic patterns may have stemmed from large-scale environmental filtering related to the outflow of Yangtze River, rather than from dispersal limitation or historical events. The current study provides a new understanding of the biogeographic patterns and underlying mechanisms of marine microorganisms, thus helping improve their management and conservation in the face of future global change.

We sampled ciliates at 30 sandy beaches covering a latitudinal range of over 2500 km from October to November 2020. We first used generalized linear models to examine the most important predictors of ciliate alpha diversity. Then, distance-based linear models (DistLM) were used to identify the relative importance of local and regional variables in explaining community structure variability. We further used nonmetric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) to reveal patterns in community structure.