Abstract Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented.
Antarctic krill ( Euphasia superba ) are a key component in the Southern Ocean ecosystem, especially in the Atlantic sector, where the majority of the population is concentrated. The Norwegian commercial krill fishery exclusively targets three subareas in the Antarctic: the western Antarctic Peninsula, and the northern shelves of both the South Orkney Islands and South Georgia. Given it's reliance on oceanic transport from other regions and the potential impact of rising sea temperatures on the northern habitat, the South Georgian krill population is particularly sensitive to altering environmental conditions. The relative distance from the peninsular regions to South Georgia means that choosing to trawl in this region implies a higher risk, which is why it is exclusively targeted in winter when extensive sea-ice makes peninsular regions unsafe and inaccessible to commercial fishery operations. In this article, we show that relative to operations at South Orkney and the Antarctic Pensinsula, average catches at South Georgia have been lower with higher variability over the past 15 years. Using a Lagrangian modelling approach, we illustrate that variability in advection from source regions in the Antarctic Peninsula are correlated with proceeding catch values at South Georgia. This was not the case for source release sites at the South Orkney Islands. The dominant transport pathways for krill were strongly determined by position of regional fronts and the source sites of recruits to South Georgia were related to the position of fronts at both the Antarctic Peninsula and South Orkney Islands. This study highlights the importance of advective patterns on the variability in krill fishing activity and supports the hypothesis that South Georgia is a sink region for krill in the Southern Ocean while the western Antarctic Peninsula is a central source site.
In this study, we explored the potential of using mathematical models for studying the effects of physical scale of production units on the growth performance of Atlantic salmon (Salmo salar L.). Atlantic salmon are typically produced in large sea cages, but for ethical, practical and economic reasons, most research experiments are performed in tanks or cages of comparatively small volumes, and it is therefore important to consider the representability of small-scale experiments with regard to growth performance. Based on an existing model, we developed a model for estimating the effects of changes in physical scale on salmon growth performance. The model was verified using experimental data obtained from a laboratory study featuring growth experiments in tanks of different sizes, and found able to predict the effects of increasing tank scale. We also used the model in a series of virtual experiment studying how sensitive the scaling effect is towards how (i.e. changing only radius, only depth or both) the volume is scaled. The results from the virtual studies indicate that larger production volumes lead to improved feed ingestion and growth, provided the increase in volume is achieved through horizontal or horizontal plus vertical expansion of the units, but also implies that the nature of the scaling effects depends on other factors such as tank cross section.
This article presents a novel method for estimating large scale spatiotemporal distribution patterns of fish populations modelled at the individual level. A single realization of an individual-based model calibrated on historic data has weak predictive capacity, given the underlying uncertainties faced when modelling a relatively small cluster of individuals operating in a high dimensional spatial plane. By incorporating real-time data sources to update these models, we can improve their predictive capacity. When correcting estimates from a large population of individuals, we don't have access to information about individual histories, such as information derived from tagging data. We propose mapping individuals to derived density matrices, which can be corrected using conventional data sources which describe a mass of individuals e.g. catch data. An ensemble of derived states are used as forecast inputs to an assimilation procedure, that calculates an analysis state matrix of the same form. An individuals' position and biomass values are updated based on the analysis values. To assess the effect of corrections, we setup a simulation experiment to explore the impact the number of measurement points has on the updated spatiotemporal distribution. The measurement points were sampled from derived states of a twin model that resembles the original model. The output of the twin model serves as the true distribution. With an increasing number of measurement points the centre of mass of the modelled distribution converges on the true distribution and the two distributions increase in overlap. Additionally, the absolute error between model and true values decreases. This estimation method, applied to individual-based models and coupled with real-time fisheries data, can improve spatially explicit estimates of fish distributions.
A large fraction of costs in wild fisheries are fuel related, and while much of the costs are related to gear used and stock targeted, search for fishing grounds also contributes to fuel costs. Lack of knowledge on the spatial abundance of stocks during the fishing season is a limiting factor for fishing vessels when searching for suitable fishing grounds, and with better planning and routing, costs can be reduced. Strategic and tactical decision-making can be improved through operational decision support tools informed by real-time data and knowledge generated from research. In this article, we present a model-based estimation approach for predicting catch potential of ocean areas. An individual-based model of herring migrations is combined with an estimation approach known as Data Assimilation, which corrects model states using incoming data sources. The data used to correct the model are synthetic measurements generated from neural network output. Input to the neural network was vessel activity data of over 100 fishing vessels from 2015-2018, targeting mainly herring. The output is the predicted normalized density of herring in discrete grid cells. Model predictions are improved through assimilation of synthetic measurements with model states. Characterizing patterns from model output provides novel information on catch potential which can inform fishing activity.
The assimilation of ocean temperature measurements into ocean models provides useful insights on how to design heterogeneous ocean observation systems. In systems of this kind, ocean models can be complemented with multiscale operational assets such as satellites and in-situ unmanned vehicles. In this article, the authors simulate three different ocean model domains with horizontal resolutions of 20 km, 4 km and 800 m. The assimilated data sets are a global observation product including sea surface temperature, a vertical temperature profile measurement data set from the Norwegian Sea, and sea surface temperature measurements from an unmanned surface vehicle operating in the coastal waters of Frohavet (Central Norway). The key outcomes of the study suggest that global covering data sets should be assimilated in coarse model domains when available, while the intermediate and local data sets can be assimilated if they are covering areas of specific interest, and can be omitted otherwise.
