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. 2023 Oct 20:2023:baad067.
doi: 10.1093/database/baad067.

Fisheries data management systems in the NW Mediterranean: from data collection to web visualization

Jordi Ribera-Altimir  1   2 , Gerard Llorach-Tó  1   2   3 , Joan Sala-Coromina  1   2 , Joan B Company  1   2 , Eve Galimany  1   2
Affiliations

Fisheries data management systems in the NW Mediterranean: from data collection to web visualization

Jordi Ribera-Altimir et al. Database (Oxford). .

Abstract

The European Union Data Collection Framework (DCF) states that scientific data-driven assessments are essential to achieve sustainable fisheries. To respond to the DCF call, this study introduces the information systems developed and used by Institut Català de Recerca per a la Governança del Mar (ICATMAR), the Catalan Institute of Research for the Governance of the Seas. The information systems include data from a biological monitoring, curation, processing, analysis, publication and web visualization for bottom trawl fisheries. Over the 4 years of collected data (2019-2022), the sampling program developed a dataset of over 1.1 million sampled individuals accounting for 24.6 tons of catch. The sampling data are ingested into a database through a data input website ensuring data management control and quality. The standardized metrics are automatically calculated and the data are published in the web visualizer, combined with fishing landings and Vessel Monitoring System (VMS) records. As the combination of remote sensing data with fisheries monitoring offers new approaches for ecosystem assessment, the collected fisheries data are also visualized in combination with georeferenced seabed habitats from the European Marine Observation and Data Network (EMODnet), climate and sea conditions from Copernicus Monitoring Environment Marine Service (CMEMS) on the web browser. Three public web-based products have been developed in the visualizer: geolocated bottom trawl samplings, biomass distribution per port or season and length-frequency charts per species. These information systems aim to fulfil the gaps in the scientific community, administration and civil society to access high-quality data for fisheries management, following the Findable, Accessible, Interoperable, Reusable (FAIR) principles, enabling scientific knowledge transfer. Database URL https://icatmar.github.io/VISAP/(www.icatmar.cat).

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Map showing the study area along the Catalan coast (NW Mediterranean Sea), where the bottom trawling samplings take place). Sampling fishing ports are displayed on the map.
Figure 2.
Figure 2.
Diagram of the data workflow: data collection, processing, storage, extraction and products.
Figure 3.
Figure 3.
Examples of the data input website displaying interactive charts and maps to perform visual validations of the entered data. The scatter chart displays the individuals’ lengths and weights to detect possible outliers. The interactive chart is displaying the onboard positions (blue pointers), together with the uploaded GPS points (blue circles) and the GPS points detected as fishing (yellow circles).
Figure 4.
Figure 4.
Spatial distribution of the fishing effort (h/km2), landings (km/km2) and revenues per unit of effort (€/h· km2) produced by the bottom trawling fishing fleet in 2021.
Figure 5.
Figure 5.
Comparison of the length-frequency distributions of blue and red shrimp (A. antennatus—red) and deep-water rose shrimp (P. longirostris—pink). The abundances on the chart are the average of all areas and years (number of individuals/km2).
Figure 6.
Figure 6.
Catch composition per port and season of the deep-water rose shrimp (P. longirostris). The value of the central circle is the average of the species’ biomass per port or season (kg/km2).
Figure 7.
Figure 7.
Catch geolocation of a bottom trawling sampling. An additional layer with the biomass extracted for all the fishing fleet (kg/km2) is added to the map.
Figure 8.
Figure 8.
Example of the data input website to prevent incorrect data. The website displays a line chart with the length frequency and species’ length ranges. If some lengths are out of the range, they can be detected in the length frequency chart and the website triggers a warning with the lengths that need to be checked. Chart showing the lengths out of range with a warning detecting the outliers (A). Chart showing the fixed lengths that fall within the range values (B).
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