Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet study sponge behavior over time. analyzed large time series of hourly high-resolution still images marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 2015...

Sponges (Porifera) are among the earliest evolving metazoans. Their filter-feeding body plan based on choanocyte chambers organized into a complex aquiferous system is so unique among metazoans that it either reflects an early divergence from other animals prior to the evolution of features such as muscles and nerves, or that sponges lost these characters. Analyses of the Amphimedon and Oscarel...

Sigmaxinella hipposiderus sp. nov. is described from morphological and molecular datasets, based on a single known specimen collected from the upper margin of a submarine canyon on the edge of the continental shelf, south-east of coastal Victoria (Tasman Sea), Australia. Morphologically, the species is clearly assigned to the genus Sigmaxinella, and preliminary molecular data (COI mt DNA) suppo...

Tedania ignis (Duchassaing and Michelotti, 1864), the fire sponge, is common throughout the tropical western Atlantic, and is a popular species for studies of ecology, larval biology, and chemistry. T. ignis is readily consumed by seagrass-dwelling starfish, and so finding sponges similar to this species in a seagrass meadow provoked closer scrutiny. A variety of ecological, morphological, and ...

Viruses are among the most abundant biological entities in ocean, largely responsible of modulating nutrients fluxes and influencing microbial composition functioning. In marine invertebrate holobionts like sponges their associated microbiomes, little is known about virome composition. Here, we characterized Haliclona fulva hologenome, an encrusting low-microbial abundance sponge found across W...