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. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes). Patterns of benthic standing stocks were positive functions of surface primary production and delivery of the particulate organic carbon (POC) flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management.

Macrobenthos of the deep, northern Gulf of Mexico (GoM) was sampled with box cores (0.2 m2) along multiple cross-depth transects extending from depths of 200 m to the maximum depth of the basin at 3700 m. Bathy- metric (depth) zonation of the macrofaunal community was documented for 6 major taxa (a total of 957 species) on the basis of shared species among geographic locations; 4 major depth zones were identified, with the 2 intermediate- depth zones being divided into east and west subzones. Change of faunal composition with depth reflects an underlying continuum of species replacements without dis- tinct boundaries. The zonal patterns correlated with depth and detrital particulate organic carbon (POC) export flux estimated from remotely-sensed phytoplankton pigment concentrations in the surface water. The Mississippi River and its associated mesoscale eddies, submarine canyon, and deep sediment fan appear to influence the horizontal zonation pattern through export of organic carbon from the ocean surface and the adjacent continental margin. On the local scale, near-bottom currents may shape the zonation pattern by altering sediment grain size, food availability, and larval dispersal. This study suggests a macroecological relationship between depth, export POC flux, and zona- tion; parsimonious zonal thresholds need to be tested independently for other continental margin ecosystems.

The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality of satellite ocean-colour datasets in relation to ground truth data in the Gulf. Here, using a unique set of in situ Chlorophyll-a measurements (Chl-a; an index of phytoplankton biomass), collected from 24 locations in four transects in the central Gulf over six recent research cruises (2015–2016), we evaluated the performance of VIIRS and other merged satellite datasets, for the first time in the region. A highly significant relationship was found (r = 0.795, p < 0.001), though a clear overestimation in satellite-derived Chl-a concentrations is evident. Regardless of this constant overestimation, the remotely sensed Chl-a observations illustrated adequately the seasonal cycles. Due to the optically complex environment, the first optical depth was calculated to be on average 6–10 m depth, and thus the satellite signal is not capturing the deep chlorophyll maximum (DCM at ~25 m). Overall, the ocean colour sensors’ performance was comparable to other Case II waters in other regions, supporting the use of satellite ocean colour in the Gulf. Yet, the development of a regional-tuned algorithm is needed to account for the unique environmental conditions of the Gulf, and ultimately provide a better estimation of surface Chl-a in the region.