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Biology subjects

Beluche, O.

Publications and source records attributed to Beluche, O..

2 recordsLinked to original sources

From genome wide SNPs to genomic islands of differentiation: the quest for species diagnostic markers in two scleractinian corals, Pocillopora and Porites

Coral reefs are of paramount importance in marine ecosystems, where they provide support for a large part of the biodiversity. Being quite sensitive to global changes, they are therefore the prime targets for biodiversity conservation policies. However, such conservation goals require accurate species identification, which are notoriously difficult to get in these highly morphologically variable organisms, rich in cryptic species. There is an acute need for easy-to-use and resolutive species diagnostic molecular markers. The present study builds on the huge sequencing effort developed during the TARA Pacific expedition to develop a genotyping strategy to assign coral samples to the correct species within two coral genera (Porites and Pocillopora). For this purpose, we developed a technique that we called "Divergent Fragment" based on the sequencing of a less than 2kb long diagnostic genomic fragment determined from the metagenomic data of a subset of the corals collected. This method has proven to be rapid, resolvable and cost-effective. Sequencing of PCR fragments nested along the species diagnostic fragment allowed us to assign 232 individuals of the genus Pocillopora and 247 individuals of the genus Porites to previously identified independent genetic lineages (i.e. species). This genotyping method will allow to fully analyze the coral samples collected across the Pacific during the Tara Pacific expedition and opens technological perspectives in the field of population genomics-guided conservation.

evolutionary biology↗

Global observation of plankton communities from space

Satellite remote sensing from space is a powerful way to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here we present an approach to identify representative communities from a global plankton network that included both zooplankton and phytoplankton and using global satellite observations to predict their biogeography. Six representative plankton communities were identified from a global co-occurrence network inferred using a novel rDNA 18S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to train a model that predicted these representative communities from satellite data. The model showed an overall 67% accuracy in the prediction of the representative communities. The prediction based on 17 satellite-derived parameters showed better performance than based only on temperature and/or the concentration of chlorophyll a. The trained model allowed to predict the global spatiotemporal distribution of communities over 19-years. Our model exhibited strong seasonal changes in the community compositions in the subarctic-subtropical boundary regions, which were consistent with previous field observations. This network-oriented approach can easily be extended to more comprehensive models including prokaryotes as well as viruses.

ecology↗