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The Pomo (or Jabuka) Pits area is one of the main fishing ground for Norway Lobster Nephrops norvegicus and European hake Merluccius merluccius within the GFCM Geographical Sub Areas 17 (Northern and Central Adriatic Sea) and it is shared by the Italian and the Croatian fleets. Furthermore, this represents a well-known nursery area for M. merluccius and hosts a distinct population of N. norvegicus, characterized by small-sized mature individuals. Due to a decline in landing of both species for the Adriatic Sea, since 2015 the Italian and the Croatian governments implemented some protection measures in that area. Eventually in 2018, the GFCM established a Fishery Restricted Area. Since 2009 the area is yearly monitored by CNR IRBIM in collaboration with IOF Split. From 2009 to 2019 (except 2011 and 2018), a spring UWTV survey was conducted in the Pomo Pits area jointly by CNR-IRBIM Ancona and IOF Split, on board the CNR R/V Dallaporta; the Pomo Pits UWTV time series has been recently included, as a tuning index, in new modeling approaches tested for the Adriatic N. norvegicus stock assessment. Trials on automatic burrow tracking and counting have also been recently conducted on the Adriatic UWTV footage in the framework of the EU H2020 NAUTILOS project.

The Working Group on Nephrops Surveys (WGNEPS) is the international coordination group for Nephrops underwater television and trawl surveys within ICES. This report summarizes the national contributions on the results of the surveys conducted in 2022 together with time series covering all survey years, problems encountered, data quality checks and technological improvements as well as the planning for survey activities for 2023. In total, 21 surveys covering 26 functional units (FU’s) in the ICES area and 1 geographical subarea (GSA) in the Adriatic Sea were discussed and further improvements in respect to survey design and data analysis standardization and the use of most recent technology were reviewed. The first exploratory UWTV survey on the FU 25 Nephrops grounds was also presented to the group. The results of the evaluation of reference sets for FU3&4 Skagerrak/Kattegat were accepted following the process set down by the 2018 workshop (WKNEPS). An alternative method estimate Nephrops abundance was shown to the group using the recently published R package sdmTMB. The group agreed to hold a workshop in 2025 to address burrow size estimations to update correction factors and terms of reference for this to be agreed at next meeting. Automatic burrow detection based on deep learning methods continues to show promising results where datasets from multiple institutes were used. Plans are being progressed for an international Nephrops UWTV database to be established at the ICES data centre with a sub-group.

29/03/2022-Minor corrections to report formatting. The Working Group on Nephrops Surveys (WGNEPS) is the international coordination group for Nephrops underwater television and trawl surveys within ICES. This report summarizes the national contributions on the results of the surveys conducted in 2021 together with time series covering all survey years, problems encountered, data quality checks and technological improvements as well as the planning for survey activities for 2022. In total, 19 surveys covering 25 functional units (FU’s) in the ICES area and 1 geographical subarea (GSA) in the Adriatic Sea were discussed and further improvements in respect to survey design and data analysis standardization and the use of recent technologies were reviewed. Due to the COVID-19 pandemic there were minimal disruptions to survey operations where one survey was not completed (GSA 17). A trial trawl Nephrops survey offshore Portugal was carried out on the new research vessel. Preliminary work on how to measure burrow system size was presented using high definition (HD) and standard definition (SD) image data. Further work on comparison of SD and HD indicates the change to HD system mounted with a different camera angle was not significantly different for two survey areas (FU 16 and FU 20-21). Automatic burrow detection based on deep learning methods continues to show promising results where datasets from multiple institutes were used. The working group members have agreed to draft a roadmap for automatic system technology requirements with links to the Working Group on Machine Learning in Marine Science (WGMLEARN) and current researchers. The working group is progressing plans for an international Nephrops Underwater television (UWTW) database to be established at the ICES Data Centre. End-users of UWTV datasets for epifauna reporting presented their work and showed the potential for adding value to the survey data, where many of the institutes are involved in providing data for similar research purposes.

The Pomo (or Jabuka) Pits area is one of the main fishing ground for Norway Lobster Nephrops norvegicus and European hake Merluccius merluccius within the GFCM Geographical Sub Areas 17 (Northern and Central Adriatic Sea) and it is shared by the Italian and the Croatian fleets. Furthermore, this represents a well-known nursery area for M. merluccius and hosts a distinct population of N. norvegicus, characterized by small-sized mature individuals. Due to a decline in landing of both species for the Adriatic Sea, since 2015 the Italian and the Croatian governments implemented some protection measures in that area. Eventually in 2018, the GFCM established a Fishery Restricted Area. Since 2009 the area is yearly monitored by CNR IRBIM in collaboration with IOF Split

The rise of new technology is continuously generating gigantic datasets, known as “big data”. Imagery to tackle biological and ecological questions, is no exception. Observing and learning from these data is crucial but remains a tedious and labour-intensive process. This project aims to address to what extent computer vision based on deep learning can solve ecological questions while minimizing – if not removing human validation. For this purpose, a convolutional neural network (CNN) was trained on two types of data representing different sampling conditions and species communities. The first aimed at detecting the attraction levels of different types of biodegradable baits using baited remote underwater videos (BRUV). The BRUV footage analysis showed promising results with an average precision (AP), the standard metrics to assess the performance of deep learning models, of 0.827 for fish for the best performing model. An Interest index was introduced to assess each of the different bait types and a cockle bait functioned as the control. The resulting analysis – manual and automated - showed that the biodegradable plastic bait C17 has the greatest potential of replacing an old-fashioned cockle’s bait. The UWTV footage had more diverse classes (17 species, genus, or other taxa) and showed more mitigated results. The fish Callionymus spp., the crustacean Munida spp. and the Pennatulaceidae classes were accurately detected with AP values of 0.86, 0.82 and 0.80 respectively. In comparison, the main focus class Nephrops norvegicus slightly underperformed, with an AP value of 0.69. Other classes were more difficult to identify as such as “hydrozoa” and “crustacean” (AP of 0.23 and 0.24), due to their high diversity of shapes, colours and sizes. Nevertheless, in regard to other studies and given the challenging nature of marine-derived data, these values are satisfying. This project highlights the promising potential of replacing the labour-intensive human-validated analysis, while identifying the gaps that still need to be overcome. The generated models will help moving toward non-invasive methods with direct applications in marine conservation and fisheries management.

13 pages, 4 figures, 3 tables, supplementary Information https://doi.org/10.1038/s41598-021-85240-3, Underwater Television (UWTV) surveys provide fishery-independent stock size estimations of the Norway lobster (Nephrops norvegicus), based directly on burrow counting using the survey assumption of “one animal = one burrow”. However, stock size may be uncertain depending on true rates of burrow occupation. For the first time, 3055 video transects carried out in several Functional Units (FUs) around Ireland were used to investigate this uncertainty. This paper deals with the discrimination of burrow emergence and door-keeping diel behaviour in Nephrops norvegicus, which is one of the most commercially important fisheries in Europe. Comparisons of burrow densities with densities of visible animals engaged in door-keeping (i.e. animals waiting at the tunnel entrance) behaviour and animals in full emergence, were analysed at time windows of expected maximum population emergence. Timing of maximum emergence was determined using wave-form analysis and GAM modelling. The results showed an average level of 1 visible Nephrops individual per 10 burrow systems, depending on sampling time and depth. This calls into question the current burrow occupancy assumption which may not hold true in all FUs. This is discussed in relation to limitations of sampling methodologies and new autonomous robotic technological solutions for monitoring, This work received financial support from the Spanish Ministerio de Economía y Competitividad (Contract TEC2017-87861-R Project RESBIO, RTI2018-095112-B-I00 Project SASES and CTM2017-82991-C2-1-R Project RESNEP), from the Generalitat de Catalunya “Sistemas de Adquisición Remota de datos. This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CE

This document is the product of an expert group under the auspices of the International Council for the Exploration of the Sea and does not necessarily represent the view of the Council. The Working Group on Nephrops Surveys (WGNEPS) is the international coordination group for Nephrops underwater television and trawl surveys within ICES. This report summarizes the national contributions on the results of the surveys conducted in 2019 together with time series covering all survey years, problems encountered, data quality checks and technological improve-ments as well as the planned for survey activities for 2020. In total, 19 surveys covering 25 func-tional units (FU's) in the ICES area and 1 geographical subarea (GSA) in the Adriatic Sea were discussed and further improvements in respect to survey design and data analysis, standardiza-tion and the use of most recent technology were reviewed. A new survey summary template by FU/GSA has been developed and adopted for future re-ports, which shall allow the data end users to extract the most relevant information on the survey results in a more easy way.Necessary actions and reviewer comments were addressed on the draft version of the Series of ICES Survey Protocols (SISP). Similarly, the working group reviewed the specifications for a Nephrops underwater TV database to be established at the ICES data centre and agreed on fur-ther action on this issue.First results from field studies on behaviour aspects of burrow emergence using bottom cages monitored by an automated camera system and on short-range migration using acoustic tracking are now available.Comparison of standard definition (SD) and high definition (HD) indicates the change to HD system mounted with a different camera angle may affect the detection rate and may thus require a revision of bias correction factors. New image reviewing software allows an easier way of an-notation of burrows than previous mosaicking methods, which has further advantages for inter-preting the results from different counters and for providing quality assured material for deep learning methods. The WG members agreed to collect information on burrow diameter size us-ing HD images and burrow annotation or mosaicking software because a change in the burrow size distribution may indicate recruitment events and the size of the burrow has an effect on bias correction factors in general. Automatic burrow detection based on deep learning methods applied to a test data set with an-notated burrow counts from a HD camera system showed promising results. The WG members were encouraged to provide more material with annotated burrow counts for further develop-ment of machine learning tools.