Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System.

During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats-for which space and power onboard are often limited-as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities.

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight "suspicious" AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel-and the gear it adopts-is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder.

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.

Geochemical and Geophysical Monitoring of Hydrocarbon Seepage in the Adriatic Sea.

Hydrocarbon seepage is overlooked in the marine environment, mostly due to the lack of high-resolution exploration data. This contribution is about the set-up of a relocatable and cost-effective monitoring system, which was tested on two seepages in the Central Adriatic Sea. The two case studies are an oil spill at a water depth of 10 m and scattered biogenic methane seeps at a water depth of 84 m. Gas plumes in the water column were detected with a multibeam system, tightened to sub-seafloor seismic reflection data. Dissolved benthic fluxes of nutrients, metals and Dissolved Inorganic Carbon (DIC) were measured by in situ deployment of a benthic chamber, which was used also for the first time to collect water samples for hydrocarbons characterization. In addition, the concentration of polycyclic aromatic hydrocarbons, as well as major and trace elements were analyzed to provide an estimate of hydrocarbon contamination in the surrounding sediment and to make further inferences on the petroleum system.

Bridging the gap in fishing effort mapping: a spatially-explicit fisheries dataset for Campanian MPAs, Italy.

Recent technological advancements have facilitated the extensive collection of movement data from large-scale fishing vessels, yet a significant data gap remains for small-scale fisheries. This gap hinders the development of consistent exploitation patterns and meeting the information needs for marine spatial planning in fisheries management. This challenge is specifically addressed in the Campania region of Italy, where several Marine Protected Areas support biodiversity conservation and fisheries management. The authors have created a spatially-explicit dataset that encompasses both large-scale (vessels exceeding 12 meters in length) and small-scale (below 12 meters) fishing efforts. This dataset (available at https://doi.org/10.6084/m9.figshare.23592006 ) is derived from vessel tracking data and participatory mapping. It offers insights into potential conflicts between different fishing segments and their interactions with priority species and habitats. The data can assist researchers and coastal management stakeholders in formulating policies that reduce resource competition and promote ecosystem-based fisheries management. Furthermore, the provided mapping approach is adaptable for other regions and decision-making frameworks, as we are committed to sharing the tools and techniques we employed.

The BlueBio project's database: web-mapping cooperation to create value for the Blue Bioeconomy.

Funding innovation requires knowledge on previous/on-going research and identification of gaps and synergies among actors, networks and projects, but targeted databases remain scattered, incomplete and scarcely searchable. Here we present the BlueBio database: a first comprehensive and robust compilation of internationally and nationally funded research projects active in the years 2003-2019 in Fisheries, Aquaculture, Seafood Processing and Marine Biotechnology. Based on the previous research projects' database realized in the framework of the COFASP ERA-NET, it was implemented within the ERA-NET Cofund BlueBio project through a 4-years data collection including 4 surveys and a wide data retrieval. After being integrated, data were harmonised, shared as open and disseminated through a WebGIS that was key for data entry, update and validation. The database consists of 3,254 "georeferenced" projects, described by 22 parameters that are clustered into textual and spatial, some directly collected while others deduced. The database is a living archive to inform actors of the Blue Bioeconomy sector in a period of rapid transformations and research needs and is freely available at: https://doi.org/10.6084/m9.figshare.21507837.v3 .

COVID-19 lockdowns reveal the resilience of Adriatic Sea fisheries to forced fishing effort reduction.

The COVID-19 pandemic provides a major opportunity to study fishing effort dynamics and to assess the response of the industry to standard and remedial actions. Knowing a fishing fleet's capacity to compensate for effort reduction (i.e., its resilience) allows differentiating governmental regulations by fleet, i.e., imposing stronger restrictions on the more resilient and weaker restrictions on the less resilient. In the present research, the response of the main fishing fleets of the Adriatic Sea to fishing hour reduction from 2015 to 2020 was measured. Fleet activity per gear type was inferred from monthly Automatic Identification System data. Pattern recognition techniques were applied to study the fishing effort trends and barycentres by gear. The beneficial effects of the lockdowns on Adriatic endangered, threatened and protected (ETP) species were also estimated. Finally, fleet effort series were examined through a stock assessment model to demonstrate that every Adriatic fishing fleet generally behaves like a stock subject to significant stress, which was particularly highlighted by the pandemic. Our findings lend support to the notion that the Adriatic fleets can be compared to predators with medium-high resilience and a generally strong impact on ETP species.

An integrated approach to assess fish spatial pattern around offshore gas platforms: A pilot study in the Adriatic Sea.

Research on abundance and composition of fish assemblages surrounding offshore extraction platforms is essential to evaluate their impact as well as to understand relationships between natural and artificial habitats. Also decommissioning practice, which belongs to the lifecycle of these structures, can be encouraged or discouraged if fish school behaviour in the close proximity of the platform is well understood. With thousands of platforms to be decommissioned around the world in coming decades, understanding such dynamic interactions is key to improve spatial management of marine ecosystems. In this context, this study drafts a work plan that can be used to investigate fish presence and abundance, school movement and qualitative species composition around a platform over long time periods. It integrates fishing captures, multibeam echosounder (MBES) investigations, and drop camera shootings to overcome the limitations of the individual methods. Monthly samplings were conducted at a three-leg gas extraction platform placed at ~80 m depth in the central Adriatic Sea, for one year after its installation. MBES completely insonified the studied area, providing data on school shape, volume, surface area and position throughout the water column. Fishing captures were useful for MBES targets' identification by measuring the presence/abundance of nekto-benthic and pelagic species both in the nearby of the structure and in the open sea, while drop camera shootings added evidence of a few species in close proximity to the poles, which were not censused by the other methods. Results underlined the strong attraction exerted by the platform and the significant influence of the explanatory variable distance on the schools' nominal volume.