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.
In studies of the population dynamics of parasitic sea lice and the implications of outbreaks for salmon farms, several types of mathematical models have been implemented. Delay differential equation models describe the temporal dynamics of average adult lice densities over many farm sites. In contrast, larval transport models consider the relative densities of lice at farm sites by modelling larval movements between them but do not account for temporal dynamics or feedbacks created by reproduction. Finally, several recent studies have investigated spatiotemporal variation in site lice abundances using statistical models and distance-based proxies for connectivity. We developed a model which integrates connectivity estimates from larval transport models into the delay differential equation framework. This allows representation of sea lice developmental stages, dispersal between sites, and the impact of management actions. Even with identical external infection rates, lice abundances differ dramatically between farms over a production cycle (dependent on oceanographic conditions and resulting between-farm connectivity). Once infected, lice dynamics are dominated by site reproduction and subsequent dispersal. Lice control decreases actual lice abundances and also reduces variation in abundance between sites (within each simulation) and between simulation runs. Control at sites with the highest magnitude of incoming connections, computed directly from connectivity modelling, had the greatest impact on lice abundances across all sites. Connectivity metrics may therefore be a reasonable approximation of the effectiveness of management practices at particular sites. However, the model also provides new opportunities for investigation and prediction of lice abundances in interconnected systems with spatially varying infection and management.
Most biomass in the mesopelagic zone (200-1000 m) comprises zooplankton and fish aggregated in layers known as sound scattering layers (SSLs; they scatter sound and are detectable using echosounders). Some of these animals migrate vertically to and from the near surface on a daily cycle (diel vertical migration; DVM), transporting carbon between the surface and the deep ocean (biological carbon pump; BCP). To gain insight to potential global variability in the contribution of SSLs to the BCP, and to pelagic ecology generally (SSLs are likely prey fields for numerous predators), we report here regional-scale (90000 km2) community depth structure based on the fine-scale (10s of m) vertical distribution of SSLs. We extracted SSLs from a near-global dataset of 38 kHz echosounder observations and constructed local (300 km by 300 km) SSL depth and echo intensity (a proxy for biomass) probability distributions. The probability distributions fell into six spatially coherent regional-scale SSL probability distributions (RSPDs). All but one RSPD exhibited clear DVM, and all RSPDs included stable night-time resident deep scattering layers (DSLs; SSLs deeper 2 than 200 m). Analysis of DSL number and stability (probability of observation at depth) revealed 2 distinct DSL types: 1.) Single-Shallow DSL (single DSL at c. 500 m), and 2.) Double-Deep DSL (two DSLs at c. 600 and m). By including consideration of this fine-scale depth structure in biogeographic partitions and ecosystem models, we will better understand the role of mesopelagic communities in pelagic food-webs and consequences for them of climate change.
Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple “rule” that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have, however, been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 million tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) for 49 081 manually classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.
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