Global
A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties.
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This dataset shows the global distribution of mangroves, and was produced as joint initiatives of the International Tropical Timber Organization (ITTO), International Society for Mangrove Ecosystems (
A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties.
This dataset contains the global distributions of habitat suitability for seven suborders of cold-water octocorals (Octocorallia) found deeper than 50 m: Alcyoniina, Calcaxonia, Holaxonia, Scleraxonia, Sessiliflorae, Stolonifera, and Subselliflorae.
Working document of the 14th Global Meeting of the Regional Seas Conventions and Action Plans
There is a rising concern regarding the accumulation of floating plastic debris in the open ocean. However, the magnitude and the fate of this pollution are still open questions. Using data from the Malaspina 2010 circumnavigation, regional surveys, and previously published reports, we show a worldwide distribution of plastic on the surface of the open ocean, mostly accumulating in the convergence zones of each of the five subtropical gyres with comparable density.
Abstract
Recent revisions to the satellite-derived vertical gravity gradient (VGG) data reveal more detail of the ocean bottom and have allowed us to develop a non-linear inversion method to detect seamounts in VGG data. We approximate VGG anomalies over seamounts as sums of individual, partially overlapping, elliptical polynomial functions, which allows us to form a non-linear inverse problem by fitting the polynomial model to the observations.
