Data from: Metagenetic community analysis of microbial eukaryotes illuminates biogeographic patterns in deep-sea and shallow water sediments
Bik, Holly M. et al. (2011), Data from: Metagenetic community analysis of microbial eukaryotes illuminates biogeographic patterns in deep-sea and shallow water sediments, Dryad, Dataset, https://doi.org/10.5061/dryad.vd094
Microbial eukaryotes (nematodes, protists, fungi, etc., loosely referred to as meiofauna) are ubiquitous in marine sediments and likely play pivotal roles in maintaining ecosystem function. Although the deep-sea benthos represents one of the world’s largest habitats, we lack a firm understanding of the biodiversity and community interactions amongst meiobenthic organisms in this ecosystem. Within this vast environment key questions concerning the historical genetic structure of species remain a mystery, yet have profound implications for our understanding of global biodiversity and how we perceive and mitigate the impact of environmental change and anthropogenic disturbance. Using a metagenetic approach, we present an intensive assessment of microbial eukaryote communities across depth gradients (shallow water to abyssal) and ocean basins (deep-sea Pacific and Atlantic). Our results show that while some taxa can maintain eurybathic ranges and cosmopolitan deep-sea distributions, the majority of species appear to be regionally restricted in marine habitats. For OCTUs reporting wide distributions, there appears to be a taxonomic bias towards a small subset of taxa in most phyla; such bias may be driven by specific life history traits amongst these organisms. In addition, low genetic divergence between geographically disparate deep-sea sites suggests either a shorter coalescence time between deep-sea regions or slower rates of evolution across this vast oceanic ecosystem. While high-throughput studies allow for broad assessment of genetic patterns across microbial eukaryote communities, intragenomic variation in rRNA gene copies and the patchy coverage of reference databases currently present substantial challenges for robust taxonomic interpretations of eukaryotic datasets.