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.

In contrast to generally sparse biological communities in open-ocean settings, seamounts and ridges are perceived as areas of elevated productivity and biodiversity capable of supporting commercial fisheries. We investigated the origin of this apparent biological enhancement over a segment of the North Mid-Atlantic Ridge (MAR) using sonar, corers, trawls, traps, and a remotely operated vehicle to survey habitat, biomass, and biodiversity. Satellite remote sensing provided information on flow patterns, thermal fronts, and primary production, while sediment traps measured export flux during 2007–2010. The MAR, 3,704,404 km2 in area, accounts for 44.7% lower bathyal habitat (800–3500 m depth) in the North Atlantic and is dominated by fine soft sediment substrate (95% of area) on a series of flat terraces with intervening slopes either side of the ridge axis contributing to habitat heterogeneity. The MAR fauna comprises mainly species known from continental margins with no evidence of greater biodiversity. Primary production and export flux over the MAR were not enhanced compared with a nearby reference station over the Porcupine Abyssal Plain. Biomasses of benthic macrofauna and megafauna were similar to global averages at the same depths totalling an estimated 258.9 kt C over the entire lower bathyal north MAR. A hypothetical flat plain at 3500 m depth in place of the MAR would contain 85.6 kt C, implying an increase of 173.3 kt C attributable to the presence of the Ridge. This is approximately equal to 167 kt C of estimated pelagic biomass displaced by the volume of the MAR. There is no enhancement of biological productivity over the MAR; oceanic bathypelagic species are replaced by benthic fauna otherwise unable to survive in the mid ocean. We propose that globally sea floor elevation has no effect on deep sea biomass; pelagic plus benthic biomass is constant within a given surface productivity regime.

Summary Prey distribution acts at multiple spatial scales to influence foraging success by predators. The overall distribution of prey may shape foraging ranges, the distance between patches may influence the ability of predators to detect and move between profitable areas, and individual patch characteristics may affect prey capture efficiency. In this study, we assessed relationships between spatially explicit patterns of prey capture by a central place forager, the little penguin (using GPS tracking and accelerometry), and the distribution of aggregations of potential forage fish prey (using boat‐based active acoustics) in eastern Australia. We used complementary resource selection functions to estimate the distribution of both prey captures and aggregations across the study area, based on a suite of habitat characteristics. We found that 99% of prey captures by penguins occurred in the top 20 m of the water column. The estimated distribution of prey captures across the study area was similar to the distribution of aggregations above 20 m depth, indicating that penguins effectively matched the local distribution of their prey. The distances between consecutive prey captures followed a bimodal distribution, with means of 8·1 ± 2·2 and 57·4 ± 1·7 m. Based on the length of aggregations and the distances separating aggregations along survey transects, this implies that foraging behaviour occurs on multiple spatial scales corresponding to within‐patch and between‐patch movements respectively. Morphological characteristics of aggregations above 20 m depth were important for explaining variance in the number of prey caught by penguins in an area, with penguins catching more prey where aggregations were relatively dense, compact and shallow. These results reveal spatially explicit patterns of prey capture, and provide a framework for understanding how features of prey distribution influence prey intake by predators in patchy environments. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12873/suppinfo is available for this article.

Many types of animals exhibit aggregative behavior: birds flock, bees swarm, fish shoal, and ungulates herd. Terrestrial and aerial aggregations can be observed directly, and photographic techniques have provided insights into the behaviors of animals in these environments and data against which behavioral theory can be tested. Underwater, however, limited visibility can hamper direct observation, and understanding of shoaling remains incomplete. We used multibeam sonar to observe three-dimensional structure of Antarctic krill shoals acoustically. Shoal size and packing density varied greatly, but surface area:volume ratios (roughnesses) were distributed narrowly about ∼3.3 m(-1). Shoals of clupeid fish (e.g., sardine, anchovy) from geographically and oceanographically diverse locations have very similar roughnesses. This common emergent shape property suggests common driving forces across diverse ecosystems. Group behavior can be complex, but a simple tradeoff--that we model--in which individual fish and krill juggle only their access to oxygen-replete water and exposure to predation can explain the observed shoal shape. Decreasing oxygen availability in a warming world ocean may impact shoal structure: because structure affects catchability by predators and fishers, understanding the response will be necessary for ecological and commercial reasons.