Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model

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.publishedVersio

Tuning and Development of an Individual-Based Model of the Herring Spawning Migration

Norwegian spring spawning herring is a migratory pelagic fish stock that seasonally navigates between distant locations in the Norwegian Sea. The spawning migration takes place between late winter and early spring. In this article, we present an individual-based model that simulated the spawning migration, which was tuned and validated against observation data. Individuals were modelled on a continuous grid coupled to a physical oceanographic model. We explore the development of individual model states in relation to local environmental conditions and predict the distribution and abundance of individuals in the Norwegian Sea for selected years (2015–2020). Individuals moved position mainly according to the prevailing coastal current. A tuning procedure was used to minimize the deviations between model and survey estimates at specific time stamps. Furthermore, 4 separate scenarios were simulated to ascertain the sensitivity of the model to initial conditions. Subsequently, one scenario was evaluated and compared with catch data in 5 day periods within the model time frame. Agreement between model and catch data varies throughout the season and between years. Regardless, emergent properties of the migration are identifiable that match observations, particularly migration trajectories that run perpendicular to deep bathymetry and counter the prevailing current. The model developed is efficient to implement and can be extended to generate multiple realizations of the migration path. This model, in combination with various sources of fisheries-dependent data, can be applied to improve real-time estimates of fish distributions.publishedVersio

Dynamic stochasticmodeling for adaptive sampling of environmental variables using an AUV

Discharge of mine tailings significantly impacts the ecological status of the sea. Methods to efficiently monitor the extent of dispersion is essential to protect sensitive areas. By combining underwater robotic sampling with ocean models, we can choose informative sampling sites and adaptively change the robot’s path based on in situ measurements to optimally map the tailings distribution near a seafill. This paper creates a stochastic spatio-temporal proxy model of dispersal dynamics using training data from complex numerical models. The proxy model consists of a spatio-temporal Gaussian process model based on an advection–diffusion stochastic partial differential equation. Informative sampling sites are chosen based on predictions from the proxy model using an objective function favoring areas with high uncertainty and high expected tailings concentrations. A simulation study and data from real-life experiments are presented.publishedVersio

Automation Concepts for Industrial-Scale Production of Seaweed

In order to industrialize macroalgal cultivation in Norway, new automated methods and solutions for seeding, deployment and harvesting need to be developed. Today's solutions are time and resource demanding, still yielding volumes nationally in the range of 100–200 tons per year in total (not including wild harvest), while the potential is in the megaton range. Standardization of equipment and automation can be one way to upscale production. Here we present results from a design study of a module-based solution for industrial cultivation, with specific solutions for spinning of thin seedling strings onto longlines, and a robotic module for interaction with the submerged farm at deployment and harvest. A reduced-scale physical prototype of the farm concept with the robot has been built for testing of deployment and harvesting techniques. The concept has been named SPOKe: Standardized Production of Kelp.publishedVersio

Automation Concepts for Industrial-Scale Production of Seaweed

In order to industrialize macroalgal cultivation in Norway, new automated methods and solutions for seeding, deployment and harvesting need to be developed. Today's solutions are time and resource demanding, still yielding volumes nationally in the range of 100–200 tons per year in total (not including wild harvest), while the potential is in the megaton range. Standardization of equipment and automation can be one way to upscale production. Here we present results from a design study of a module-based solution for industrial cultivation, with specific solutions for spinning of thin seedling strings onto longlines, and a robotic module for interaction with the submerged farm at deployment and harvest. A reduced-scale physical prototype of the farm concept with the robot has been built for testing of deployment and harvesting techniques. The concept has been named SPOKe: Standardized Production of Kelp.publishedVersio

Adaptive Underwater Robotic Sampling of Dispersal Dynamics in the Coastal Ocean

To get a better understanding of the highly nonlinear processes driving the ocean, efficient and informative sampling is critical. By combining robotic sampling with ocean models we are able to choose informative sampling sites and adaptively change our path based on measurements. We present models exploiting prior information from ocean models as well as real-time information from in situ measurements. The method uses compact Gaussian process modeling and objective functions to locate informative sampling sites. Our aim is to get a better understanding of ocean processes and improve real-time monitoring of dispersal dynamics. The case study focuses on a fjord located in Norway containing a seafill for mine tailings. Transportation of the deposited particles are studied, and the sampling method is tested in the area. The results from these sea trials are presented.acceptedVersio

Data assimilation with SINMOD

Data assimilation is the process of using measured data to correct the state of a mathematical model. This report documents the data assimilation process implemented in the ocean model system SINMOD, and the tests that have been run in the ELMO project. The report describes the choice of the EnOI assimilation method, its implementation in SINMOD, the input layer for providing measurement data to the model, and two test cases where the model has been run with assimilation of current data.Norges ForskningsrådpublishedVersio

2019:00075 A - D5.1 Industrial production line for seedlings - MACROSEA WP5

The production process for seedlings of macroalgae is analysed with regard to challenges and bottlenecks associated with industrial scale production. Based on issues identified for each sub-process, an action plan with rough estimates of time/cost requirements and proposed prioritization is given as a roadmap to achieve industrial scale seedling production.2019:00075 A - D5.1 Industrial production line for seedlings - MACROSEA WP5publishedVersio

D5.4 State of the art - MACROSEA WP5

The objective of the MACROSEA project is to facilitate industrial scale cultivation of seaweed in Norway. To achieve efficient large scale cultivation, development of cultivation technology is an important component, and in Work Package 5 - Seed/ing, Deployment and Harvest Technology- the objective is to identify requirements and bottlenecks for industrial scale seedling production systems, and evaluate and develop concepts for deployment and harvest operations. In order to understand the limitations and potential of today's cultivation methods, an initial work has been undertaken to get an overview of the methods used by today's producers. The opinions of the producers on future challenges and bottlenecks have also been requested. This report summarizes the questions that were asked of the producers, and the answers received.publishedVersio