Keywords

Fisheries provide jobs and income to coastal communities and are expected to contribute to long-term European food security and economic growth. No other bioeconomy sector appears to be as regulated and monitored as fisheries, with numerous data inputs collected (i.e., catch and effort, stock sampling, ocean environment, fishing vessel activity, sales and transactions) to better understand and control the industry. Still, there is little coordinated use of big data technologies in the sector.

Fuel consumption is a challenge for most fisheries, as it represents 60–70% of the total annual costs of a vessels’ activity [1,2,3,4]. Ocean-going pelagic fishing vessels employ both energy efficient gear, such as purse seines, and energy intensive gear, such as trawls. The vessels are frequently searching for fish between fishing operations, since schooling pelagic species are migratory. The vessels have been engineered to become very flexible in their production, routing, and consumption of energy onboard [5], and several methods have been proposed for adapting vessels’ operations to these variations [6, 7]. Still, the crew often operate the vessel based on habits and preferences for certain configurations of the power system.

Fishing trip planning and routing are important factors for reducing the fuel consumption within fisheries and achieving better margins. Decisions about when, where, and how to harvest are taken by expert fishers based on their own experience, information gathered from industry contacts and publicly available data. In most cases, such information is limited to meteorological forecasts, catch reports and communication with a small number of collaborating fishermen. The subjectively perceived market development is an important factor for fisheries planning, but there are no tools to assist fishermen in this respect.

Fish stock assessment is traditionally carried out based on measurements from yearly campaigns. These campaigns follow a preestablished pattern and apply both test fishing and hydroacoustic observation to sample the spatial distribution of fish in the ocean. The data from these campaigns are used in statistical models for stock estimation and resource management advice. The International Council for the Exploration of the Sea (ICES) determines quota recommendations for the national authorities, which have jurisdiction over these fish stocks. Great effort is expended in the collection of this critical data, but its spatial and temporal coverage is limited by the associated costs. In addition, consideration of market variations is important for fisheries planning to optimize the value created by fisheries. These market variations are caused by the relationship between supply and demand, which is influenced by multiple factors such as fisheries effort, fish distribution, quotas, weather conditions, competing products, and economic factors.

Part VII focuses on two separate types of pelagic fisheries: The Spanish tropical tuna fisheries in Indian Ocean and the Norwegian small pelagic fisheries in the North Atlantic Ocean. The areas encompassed by these pilots have an annual capture production above 13 million tonnes. Six separate pilot cases have been defined, addressing three separate viewpoints: (i) immediate operational choices, (ii) fishing vessel trip and fisheries planning, and (iii) fisheries sustainability and value, as shown in Fig. 28.1.

Fig. 28.1
figure 1

Overview of fisheries pilots

Full size image

The two “Operation” pilots aim at providing crew with information to help them perform current operations in the most energy efficient way possible, while avoiding unscheduled maintenance. To achieve this, the vessels’ energy systems are monitored, and various methods are employed to supply crew direct advice.

The goal of the two “Planning” pilots is to provide information that benefits fisheries planning. The information will be provided based on extensive historical datasets of fisheries activity (e.g., VMS, GPS tracking), catch statistics (e.g., logbooks and public records), oceanographic conditions (e.g., SST, salinity, chlorophyll), meteorological conditions, and FAD data (e.g., GPS data, echosounder data, SST). The hypothesis is that large amounts of historical data combined with ocean environment near real-time information can be used to accurately forecast species distribution, by using machine learning or other techniques. This will reduce fuel consumption through targeted effort and more efficient engine operation.

The two “Sustainability” pilots investigate how long-term fish market forecasts can benefit long term fisheries planning, in particular for best timing of different fisheries to maximise economic revenue. These pilots also investigate how oceanographic simulations using fishing fleet data, as an inexpensive biomass and physical property source of measurements, can benefit pelagic fish stock assessments.

These pilots require a large amount of data to reach their goals. In addition to its volume, data collected on a large scale from a diverse set of sensors, published records and regional observation systems, also exhibits other unique characteristics as compared with data collected for a single purpose and from a single source. This data is commonly unstructured and requires more real-time analysis [8]. Many of these aspects are present in the fisheries pilots. The pilots are likely to end up producing over 5 TB of data per year and coming from many different sources. Such sources include earth observations, sensors onboard fishing vessels (i.e., acoustics, machinery, operations), simulations (i.e., meteorological, oceanographic, and marine biology) and human annotations. The update frequency, regularity, and volumes of these sources are on very different scales, affected by simulation times, vessel communications, and satellite orbits. The lack of data acquisition standardization on board vessels and data structuring poses another challenge for these pilots (Table 28.1, Fig. 28.2).

Table 28.1 Data production by DataBio fisheries pilots
Full size table
Fig. 28.2
figure 2

Echebastar company tuna fishing vessel within the DataBio project

Full size image