Summary 11 12The global ocean's near-surface can be partitioned into distinct provinces on the basis of regional 13 primary productivity and oceanography [1]. This ecological geography provides a valuable 14 framework for understanding spatial variability in ecosystem function, but has relevance only part and holds potentially huge fish resources [3][4][5]. It is, however, hidden from satellite observation, and 18 a lack of globally-consistent data has prevented development of a global-scale understanding. 19Acoustic Deep Scattering Layers (DSLs) are prominent features of the mesopelagic. These vertically-20 narrow (tens to hundreds of m) but horizontally-extensive layers (continuous for tens to thousands 21 of km) comprise communities of fish and zooplankton, and are readily detectable using 22 echosounders. We have compiled a database of DSL characteristics globally. We show that DSL and 23 acoustic backscattering intensity (a measure of biomass) can be modelled accurately using just 24 surface primary production, temperature and wind-stress. Spatial variability in these environmental 25 factors leads to a natural partition of the mesopelagic into ten distinct classes. These classes demark 26 a more complex biogeography than the latitudinally-banded schemes that have been proposed 27 before [6,7]. Knowledge of how environmental factors influence the mesopelagic enables future 28 change to be explored: we predict that by 2100 there will be widespread homogenisation of 29 mesopelagic communities, and that mesopelagic biomass could increase by c. 17%. The biomass 30 increase requires increased trophic efficiency, which could arise because of ocean warming and DSL 31shallowing. 32 33
The mesopelagic community is important for downward oceanic carbon transportation and is a potential food source for humans. Estimates of global mesopelagic fish biomass vary substantially (between 1 and 20 Gt). Here, we develop a global mesopelagic fish biomass model using daytime 38 kHz acoustic backscatter from deep scattering layers. Model backscatter arises predominantly from fish and siphonophores but the relative proportions of siphonophores and fish, and several of the parameters in the model, are uncertain. We use simulations to estimate biomass and the variance of biomass determined across three different scenarios; S1, where all fish have gas-filled swimbladders, and S2 and S3, where a proportion of fish do not. Our estimates of biomass ranged from 1.8 to 16 Gt (25–75% quartile ranges), and median values of S1 to S3 were 3.8, 4.6, and 8.3 Gt, respectively. A sensitivity analysis shows that for any given quantity of fish backscatter, the fish swimbladder volume, its size distribution and its aspect ratio are the parameters that cause most variation (i.e. lead to greatest uncertainty) in the biomass estimate. Determination of these parameters should be prioritized in future studies, as should determining the proportion of backscatter due to siphonophores.
Antarctic pack ice serves as habitat for microalgae which contribute to Southern Ocean primary production and serve as important food source for pelagic herbivores. Ice algal biomass is highly patchy and remains severely undersampled by classical methods such as spatially restricted ice coring surveys. Here we provide an unprecedented view of ice algal biomass distribution, mapped (as chlorophyll a) in a 100 m by 100 m area of a Weddell Sea pack ice floe, using under‐ice irradiance measurements taken with an instrumented remotely operated vehicle. We identified significant correlations (p < 0.001) between algal biomass and concomitant in situ surface measurements of snow depth, ice thickness, and estimated sea ice freeboard levels using a statistical model. The model's explanatory power (r2 = 0.30) indicates that these parameters alone may provide a first basis for spatial prediction of ice algal biomass, but parameterization of additional determinants is needed to inform more robust upscaling efforts.
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