Metacommunity Theory and Metabarcoding Reveal the Environmental, Spatial and Biotic Drivers of Meiofaunal Communities in Sandy Beaches.

Abstract

Understanding the processes that shape community assembly is a critical focus of ecology. Marine benthic meiofauna, microscopic invertebrates inhabiting sediment environments, play important roles in ecosystem functioning but have been largely overlooked in metacommunity studies due to the lack of community data. In this study, we quantify the relative contributions of environmental filtering, spatial processes, and biotic associations in structuring meiofaunal communities. We applied Generalised Dissimilarity Modelling (GDM) and Joint Species Distribution Modelling (JSDM) to an extensive metabarcoding dataset comprising 550 samples collected from sandy beaches along over 650 km of the Dutch and German North Sea coast. Our findings reveal that biotic associations, followed by environmental factors, particularly the distance from the low tide line and sediment grain size, are primary drivers of meiofauna community turnover, highlighting the influence of sharp environmental gradients. Spatial factors indicating dispersal limitations have no major impact on community composition, supporting the assumption that microscopic organisms have strong dispersal capabilities. JSDM results demonstrate that while species sorting is a key driver of community assembly, environmental factors are most important in environmentally distinct ('extreme') sites, whereas biotic associations significantly shape community assembly in both environmentally similar and dissimilar habitats, emphasising the need to incorporate species interactions into models of community assembly. By providing insights into the drivers of meiofaunal community structure, our study highlights the importance of environmental gradients and biotic associations in shaping biodiversity patterns and underscores the potential for similar approaches to enhance understanding of other ecosystems with small, highly diverse, but understudied taxa.