MF6001 REM Monitoring of Common Skate By-Catch 2018

World Class Science for the Marine and Freshwater Environment

FINAL REPORT

Remote Electronic Monitoring (REM) of

Common Skate By-catch II

Part of ELECTRA MF6001: Work Package Task 1.3

Stuart J. Hetherington, Rose E. Nicholson, Paul Nelson, Rebecca Skirrow, Samantha Elliott, John Richardson, Thomas Barreau & Michael Spence

th 20 July 2018

Cefas Document Control

Sarah Jones and Jamie Rendell Submitted to:

20th July 2018 Date submitted:

Suzanna Neville Project Manager:

Hetherington et al. Report compiled by:

Thomas Catchpole & Suzanna Neville Quality control by:

Suzanna Neville, 20th July 2018 Approved by and date:

6 Version:

Version Control History

Author Date Comment Version

Hetherington et al 11th June 2018 First draft. V0 Further statistical Thomas Catchpole 11th June 2018 V1 analyses required. Suzanna Neville 20th June 2018 Clarification to the V1 text required. Additional statistical Hetherington et al 28th June 2018 V2 analyses complete. Thomas Catchpole 29th June 2018 Further clarification V3 to the text required. Hetherington et al 18th July 2018 Final draft. V4 Approved with minor Thomas Catchpole 18th July 2018 tracked changes to V5 text. Hetherington et al 20th July 2018 Final. V6

Project Title: Remote Electronic Monitoring (REM) of Common Skate By-catch II.

Defra Contract Managers: Sarah Jones and Jamie Rendell

Funded by: Department for Environment, Food and Rural Affairs (Defra)

Department for Environment, Food and Rural Affairs (Defra) Marine Science and Evidence Unit Marine Directorate 2 Marsham St, Westminster London SW1P 4DF

Authorship:

Stuart J. Hetherington1, Rose E. Nicholson1, Paul Nelson2, Rebecca Skirrow3, Samantha Elliott3, John Richardson4, Thomas Barreau5 & Michael Spence1.

1 Cefas, Lowestoft; 2 MMO, Hayle; 3 Cefas, Scarborough; 4 Shark Trust, Plymouth; 5 MNHN, Concarneau.

Disclaimer: The content of this report does not necessarily reflect the views of Defra, nor is Defra liable for the accuracy of information provided, or responsible for any use of the reports content.

This report can be cited as:

Hetherington, S. J., Nicholson, R.E., Nelson, P., Skirrow, R., Elliott, S., Richardson, J., Barreau, T., Spence, M. (2018). Remote Electronic Monitoring (REM) of Common Skate By- catch II (ELECTRA MF6001: Work Package Task 1.3). Project report (Cefas). 46 pp.

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Table of contents

Page How to use this report………………………………………………………………… 5 Executive Summary…………………………………………………………………… 6 Introduction…………………………………………………………………………….. 9 Background……………………………………………………………………… 9 Novel use of Remote Electronic Monitoring (REM)…………………………. 9 Rationale and purpose…………………………………………………………. 10 Adding to existing evidence on common skate catches to inform policy…. 11 Fishery-dependant approach………………………………………………….. 13 Building capacity………………………………………………………………... 13 Aim and Objective……………………………………………………………………... 14 Methods………………………………………………………………………………….. 14 Fishing Vessel, gear and REM equipment…………………………………... 14 Training & verification by an at-sea observer………………………………... 17 Sampling process aboard……………………………………………………… 18 Verification and validation process………………………………………….… 18 Estimation of total length and weight based on disc width…………………. 19 Statistical analysis……………………………………………………………… 19 Results…………………………………………………………………………………… 21 Estimation of catch and distribution of common skate……………………… 21 Blue skate biomass in relation to the total retained commercial catch…… 31 Length frequency of blue skate……………………………………………….. 32 Improvements to species identification………………………………………. 33 Discussion…………………………………………………………………………….… 38 Conclusion………………………………………………………………………….…… 40 Next Steps………………………………………………………………………….……. 40 Acknowledgements……………………………………………………………………. 41 References………………………………………………………………………………. 42 Annex 1………………………………………………………………………………..…. 44

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How to use this report

This report is an update to the previous project report titled Remote Electronic Monitoring (REM) of Common Skate By-catch;

Hetherington, S. J., Nelson, P., Searle, A., Bendall, V. A., Barreau, T., Nicholson, R. E., Smith, S. F., Sandeman, L. R. (2017). Remote Electronic Monitoring (REM) of Common Skate By- catch (ELECTRA MF6001: Work Package Task 1.3). Project report (Cefas). 30 pp.

This report, Remote Electronic Monitoring (REM) of Common Skate By-catch II, contains the 2016 REM results and findings from the previous report and the latest 2017 results, reported here together, along with advancements in the project.

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Executive Summary

Common skate is considered to comprise of two separate species, the larger bodied flapper skate Dipturus intermedius, and the smaller bodied blue skate Dipturus batis. This is referred to as the common skate complex (Dipturus batis complex).

By-catch of common skate (predominantly blue skate) caught by fishermen from the South- west of the UK operating in the Celtic Sea (ICES Division 7e-h) is of concern, both to fishermen and to Defra. Under EU fisheries legislation, common skate is classed as a prohibited species, therefore these fish cannot be targeted, retained, transhipped or landed. However, their aggregative nature and large size make them susceptible to by-catch, and with a prohibition on landings, common skate by-catch must be discarded. The level of by-catch and discards can be significant in the Celtic Sea trammel net fishery (Bendall et al., 2012; Ellis et al., 2015), with anecdotal information suggesting an increasing by-catch of juveniles in the otter and beam trawl fisheries of the western English Channel and Celtic Sea (ICES Division 7e). A high level of discarding of these species is not compatible with Defra’s principles for sustainable use of the marine environment, e.g. opposing wasteful discards when supported by scientific evidence (Bendall et al., 2017).

This collaborative pilot project between Cefas, the Marine Management Organisation (MMO), the Shark Trust and the Muséum National d'Histoire Naturelle, France (MNHN) aimed to assess whether Remote Electronic Monitoring (REM) can validate fishermen’s self-sampling records of common skate aboard a twin-rig otter trawler and beam trawler. Further methodological advances have been identified to increase the quality and utility of the data and are reported here. The species and number of common skate were recorded, and for a subsample of these fish, the disc width measured, and the estimated total length and weight calculated. The catch estimates provided by the skippers were compared with the estimates generated by an analysis of the REM data.

The REM analyst could not always be certain of the speciation of common skate, Dipturus batis complex, when reviewing the REM footage, so unless absolutely certain of the species identification (blue skate or flapper skate) the REM analyst recorded the individual as Dipturus species. Due to this difficulty for the REM analyst, validation of the skippers’ self-sampling records of blue skate and flapper skater were limited, with blue skate and flapper skate recordings combined for analysis.

The skipper provided comments about catch composition on 367 of 508 hauls in May to December 2016 (72%) and 318 of 377 hauls (84%) in July to December 2017. Hauls with no comments were assumed to have no data rather than zero common skate caught. For hauls with comments, but no record of common skate, it was assumed that zero common skate were caught, as the skipper only recorded the presence of common skate in the catch, and didn’t record the absence of common skate in the catch. Data from the 26 fishing trips made in 2017 by the participating twin-rig otter trawler, showed a significant but poor linear correlation (R2=0.496, p<0.05) between numbers of the common skate complex recorded by the skipper and the REM analyst.

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For 318 of 377 hauls in 2017 where the skipper provided comments on catch composition, the total estimated number of common skate caught based on the above correlation was 250 (equivalent to 0.55 common skate recorded by the skipper for each common skate recorded by the observer), with 95% lower and upper confidence limits of 196 and 345 (0.39-0.69 common skate recorded by the skipper for each common skate recorded by the observer. To account for the fact the skipper did not provide comments on catch composition for 59 of 377 hauls (16%), 59 hauls were randomly selected from the 318 hauls with skipper comments and added to the 318 hauls with comments to estimate the number of common skate caught over all 377 hauls. Based on 1,000 iterations of the random selection, the mean estimated number of common skate caught for all 377 hauls was 296, with estimates ranging from 199 and 566. This equates to 0.53-1.50 common skate caught per haul by the twin-rigged otter trawler. The estimates based on linear correlation were higher, and had a wider range, compared to estimates based on 1,000 iterations of a probabilistic modelling approach aiming to account for the high frequency of observations of no common skate and very low numbers of common skate, and the uncertainty associated with the skipper’s observations. The model yielded a mean estimate of 237 common skate individuals caught during the 26 fishing trips made in 2017, with minimal and maximal estimates of 171 and 312, equating to 0.45-0.83 common skate caught per haul.

Skipper self-sampling records and REM data were available for a second vessel, a beam trawler which fished on three distinct grounds. No common skate were recorded by the skipper and the REM analyst on two of the fishing grounds, with the exception of one haul with a record of one flapper skate. Both the skipper and REM analyst recorded the presence of common skate on the third fishing ground to the South-west of the Isles of Scilly, which was fished on only one occasion (of 14 fishing trips), and also had a scientific observer aboard. Due to possible, unintended consequences of the observer being aboard the vessel for this one trip, more commonly referred to as observer effect, the skipper may have been more vigilant in his self-reporting of common skate by-catch and/or influencing the fishing location, and that this one trip where common skate were recorded was not representative of the vessel’s typical fishing activity. Our statistical analyses have been restricted to this one trip for the specific objective of this work, to determine the feasibility of using REM to validate fishers self-sampling records of common skate. The skipper sampled every fourth haul (25% of hauls) of 49 hauls of the trip, recording 129 kg blue skate by-catch over all 12 hauls sampled. These data showed there was strong linear correlation (R2>=0.872, p<0.005) between the skippers’ records and the REM analyst’s records, particularly when disc width was measured and used to estimate the weight of blue skate by-catch (R2=0.976, p<0.005). These data have not been extrapolated to provide a catch estimate per haul or trip for this vessel due to the small number of hauls included in the analysis and the spatial variation in the common skate by-catch of this vessel, as common skate by-catch in one area is not representative of the vessel’s main fishing grounds. Similarly, probabilistic modelling was not attempted for this vessel due to the bias of common skate presence towards a single trip and the lack of disc width measurements from the REM analysis.

This pilot project adds value to, and complements the Defra funded, Cefas led, annual Common Skate Survey. The individuals caught in this study are typically smaller than those caught in the Common Skate Survey. The 2,394 lengths recorded in the Cefas Common Skate Survey ranged from 57cm to 149cm, with a mean length of 121cm, compared to a range

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of 20cm to 120cm for this study, with means of 63cm for 201 individuals captured by the beam trawler and 46cm for 87 individuals captured by the twin-rig otter trawler. The data presented (which also inform on the abundance, distribution and maturity of these species) yield new information on a currently underrepresented segment of the population of common skate in the fishery-dependant data collection programme in the Celtic Sea.

If the two separate common skate complex species are formally recognised by the International Commission for Zoological Nomenclature (ICZN), alternative management measures may be required for each species. Assessment scientists are more likely to accept independently verified and validated fishermen’s self-sampling data, as collected by this current study using REM, and feed it into new management strategies for the two species of common skate.

Advancements need to be made to mitigate the limitations of using REM to validate fisher’s self-sampling records of common skate. This pilot project has identified the key steps in improving the continuation of the REM of common skate by-catch programme. These are:

(1) To improve the quality and consistency of the skippers self-reporting, the burden on the skipper needs to be reduced by sampling 25% rather than 100% of hauls, and the REM coverage increased from 10% to 50% of hauls, for example, with both the skipper and REM analyst recording absence, as well as presence of common skate by-catch, providing valuable information on species distribution;

(2) To improve the validation of the skippers self-sampling record of speciation by the REM analyst, the REM analyst should apply a confidence level around speciation by the skipper, rather than defer to the ‘complex’ level;

(3) To modify the sampling methodology aboard so that species identification can be improved, and gender of common skate is better recorded, increasing biological understanding;

(4) To incentivise fishing vessels to participate, providing self-sampling data at the level and quality required;

(5) To standardise the common skate by-catch rate by applying a catch per unit effort (CPUE) to the REM data, for example, the number of common skate Km-1.h-1.

The driver of this project was the need for the fishing industry to generate robust policy- relevant data, validated by REM. This driver remains, as both the fishermen aboard the vessel and REM data are required together for effective fishery-dependant monitoring of the common skate complex. By engaging the fishing industry in data collection, more data are available, and the fishing industry is more likely to remain engaged and buy into any management measures that arise from the data. The challenge is to further increase, then maintain, the quality and robustness of the skipper self-sampling data, then modify our validation of the data using REM, through the mitigating actions identified above. The approach of using REM & fisher self-sampling data has the potential to monitor other less abundant, protected species, not just common skate, to generate robust evidence to inform policy.

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Introduction

Background Relatively little is known about elasmobranch (shark, skate and ray) populations within UK waters, compared with other commercially important species, such as cod (Gadus Morhua) and plaice (Pleuronectes platessa). As a result, current stock assessments are hampered by having only limited data available and consequently, management measures may be unnecessarily precautionary. Therefore, there is a strong need to gain more understanding of elasmobranchs in UK waters (e.g. distributions, by-catch levels, etc.) to better inform processes such as Regional Management Plans.

Once thought to be a single species, common skate is now considered to comprise of two separate species, the larger bodied flapper skate, Dipturus intermedius and the smaller bodied blue skate, Dipturus batis. This is referred to as the common skate complex (Dipturus batis complex). It is generally thought that the flapper skate has a more northerly range in the deep water off the west of Scotland compared to that of the blue skate in the Celtic Sea area, with an overlap between the two species off the western Irish coast. Historically, the common skate complex had a much wider distribution in UK waters, than at the current time (Brander, 1981). As a by-catch for several nations using fixed nets, beam trawls and otter trawls, there is some scientific evidence (albeit limited) and much anecdotal information that the population of blue skate extends over a large area of the continental shelf, extending from the Isles of Scilly, west and south west of the British Isles. The two separate species are yet to be formally recognised by the ICZN.

Novel use of Remote Electronic Monitoring (REM) REM systems on fishing vessels usually consist of cameras, global positioning system (GPS), and sensors for detecting net use. As reported by Hetherington et al., (2016), REM is not readily applied directly to fisheries management in the UK as it is in other countries, for example in the Canadian West coast hook and line fishery (Stanley et al., 2011). In Europe, REM is used primarily by enforcement agencies in trials for monitoring of the landing obligation (e.g. Kindt-Larsen et al., 2011, Roberts et al., 2015, the Scottish Government, 2011), rather than for scientific purposes.

However, scientists have begun to test the use of REM for gathering biological data. A review of Scottish scientific applications of REM by Needle et al., (2014) concluded that “while further development work is certainly needed, REM provides a rich source of fisheries information for science as well as for compliance and management”. This is, however, a use which is in its relatively early stages and the development of regular validation and maintenance protocols along with closer collaboration between scientists and enforcement agencies will help to improve the gathering of biological data with REM (van Helmond et al., 2014; Ulrich et al., 2015). Elson et al., (2016) reported Cefas investigations to determine (i) if REM could provide biological and catch data for EU fisheries data requirements, (ii) the accuracy of REM data collection compared to an at-sea observer and (iii) whether self-reported discard data by the fishing industry could be verified by REM. They concluded that REM data provided a potential rich source of information that could be used to inform on the outcome of management Page 9 of 46

measures, and that although REM systems could not consistently identify all commercial fish species, is less accurate in measuring all fish, and could not be used to sample age and maturity, REM data can corroborate fishermen’s self-sampling data.

More recently, a Cefas Fisheries Science Partnership project tested and evaluated the feasibility of using REM to validate fishermen’s self-sampling records of skates and rays in the Bristol Channel. Hetherington et al., (2018) concluded that “REM can be used to validate fishermen’s self-sampling records in the Bristol Channel skate and ray fishery, providing (i) fishery-dependent information to improve our knowledge and understanding of catches of skate and ray that can supplement traditional fishery-independent data sources for their assessment and management, (ii) information on the current levels of elasmobranch discarding, and (ii) fine scale, high resolution data of skate and ray spatial and temporal distribution and abundance”.

Similarly, the pilot project reported here assesses the use of REM to validate self-sampling data collected by fishermen, to verify fishery-dependant records of common skate by-catch in the Celtic Sea (ICES Division 7e-f & h), collecting data on species, size, weight and by-catch composition, by location.

Rationale and purpose Without independent validation of fisher’s self-sampling data, assessment scientists are unlikely to accept these data (Ellis et al., 2015), negating the very point for which it is collected, to inform fisheries management and policy. As described in Ellis et al., (2015), a previous Defra funded fishery-dependant data collection programme, the NEPTUNE Shark, Skate & Ray Scientific By-catch Fishery (October 2013 – December 2014), identified limitations of fishing industry self-sampling data, where REM was not used. This related to the consistency, accuracy, timeliness and the geolocation of data provided, e.g. it was difficult to consistently record reliable effort data for nets set under commercial conditions (in terms of total lengths, soak times etc.). REM addresses issues relating to geolocation through the use of GPS and sensors detecting net use, while quality and consistency can be evaluated and, potentially, quantified through independent analysis of the camera footage.

In the previous common skate by-catch self-sampling programme (Ellis et al., 2015; Hetherington et al., 2016) fishermen were provided with field data sheets which proved to be too burdensome and time consuming to complete during busy fishing operations. In 2016, three years from the commencement of the self-sampling programme, stakeholder fatigue led to the cessation of the field data sheet approach. The focus switched to the trial of REM, with some of the workload transferring from the fishermen to the REM equipment, such as recording fishing location and fishing duration, reducing stakeholder fatigue. Verification of fishermen’s self-sampling records using REM data on the location, fishing activity and catch, address many of the limitations of fishery-dependant self-sampling programmes identified by Ellis et al., (2015), such as:

• Independent verification of the fishermen’s self-sampling data by a trained analyst; • Resolution of data improved with exact coordinates and duration of all fishing activity, with precise counts of abundance possible;

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• Species identification improved with the development of an ID guide specifically designed for REM footage, for the size of individuals recorded by the REM analyst, improving accuracy, assuming the analyst data is the truth.

Adding to existing evidence on common skate catches to inform policy. Under EU fisheries legislation, common skate is classed as a prohibited species, meaning that it cannot be targeted, retained, transhipped or landed. However, their size and aggregative nature make them hard to avoid and susceptible to by-catch, with the prohibition on landings leading to a high level of discards. As reported by Bendall et al., (2016, 2017) and Hetherington et al., (2016), stakeholder consultation meetings show that the by-catch and dead discards of common skate is a concern of high priority to the fishing industry in the South- west of the UK. It is also of concern to policy makers. The high level of discards is not in-line with the principles of Defra’s sustainable use of the marine environment, e.g. opposing wasteful discarding when supported by scientific evidence. Fishermen in the South-west of the UK consider the prohibition of common skate to be a highly ineffective management measure as it is not in tune with what they encounter at sea, where they believe high levels of blue skate by-catch indicates local abundance.

Recognising Defra’s ambition of ‘collect once, use many times’ to ensure the efficient and effective use of its investment in data collection, this is a collaborative project between Cefas and the MMO to determine whether REM data collected for other joint Cefas/ MMO projects can be reused to enhance and validate fisher self-sampling data on the common skate complex in support of Cefas’ aim of better understanding the populations of the common skate complex in the Celtic Sea.

This project to trial REM to monitor common skate by-catch is part of a wider Defra funded research programme on common skate in the Celtic Sea, namely the Cefas led Common Skate Survey (Bendall et al., 2012, 2016, 2017; Hetherington et al., 2016), a fishery- dependant, time series survey of common skate abundance and distribution. This annual (2011, 2014, 2015, 2016 and 2017) week long survey charters a commercial fishing vessel, an offshore gill netter, deploying trammel nets to build a time series index of the spatial distribution, abundance and ‘health’ of the common skate complex population in a defined survey area of the Celtic Sea, to allow scientists and policy makers to develop more practical management measures. (Bendall et al., 2017)

This REM common skate project provides additional fishery-dependant information to supplement the Common Skate Survey and the ongoing national catch sampling or observer programme. New evidence in an adjacent area to the annual time-series survey of common skate is being provided, from different gears, increasing the spatial coverage of the Defra funded common skate data collection programme (Figure 1) whilst typically catching smaller individuals, providing information on a segment of the population underrepresented in our data collection programme to date.

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), compared with the REM of REM the with ), compared

turquoise

series survey (coloured (coloured survey series

and pink and

catch pilot project (coloured yellow (coloured project pilot catch

Spatial extent of the Cefas Common Skate time Skate Common the Cefas of extent Spatial

Figure 1: Figure By Skate Common

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Fishery-dependent approach This Defra funded research programme on common skate in the Celtic Sea is fishery- dependent. Rather than using more traditional fishery-independent approaches, such as research vessel surveys which do not optimally sample elasmobranchs, especially less abundant species, we work collaboratively with the fishing industry. Fishing vessels are used as scientific platforms, utilising fishermen’s knowledge and commercial fishing gears, to collect relevant data. Working collaboratively with the fishing industry who can encounter common skate on a more frequent basis than research vessels, proves an effective and pragmatic solution when studying less abundant species such as common skate, as dedicated research vessel surveys would prove prohibitively costly.

Building capacity This project is a collaboration between Cefas, MMO, the Shark Trust and MNHN building capacity within both Cefas and the MMO on alternative uses of existing Defra funded REM programmes, with the potential to make monitoring more ‘smart’, collecting policy relevant data simultaneously and cost effectively. MNHN have shared their taxonomic expertise of the common skate complex, developing expertise within all four organisations to differentiate between the blue skate and flapper skate on REM camera footage, especially for juveniles. To our knowledge, it is the first time this has been done with REM footage.

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Aim and Objective

The primary aim of this project was to collect fishery-dependant data on the level and distribution of common skate (Dipturus batis complex) by-catch from two fishing vessels operating in the Celtic Sea (ICES Division 7e-f & h) mixed demersal trawl fishery, furthering our biological understanding. The specific objective was to determine the feasibility of REM aboard a fishing vessel to validate fishers self-sampling records of common skate by-catch.

Methods

Fishing vessels, gear and REM equipment The FV Crystal Sea (Figure 2) is participating in the Cefas led, English Fisheries Data Enhancement Project (Catchpole et al., 2017), focusing on haddock caught in otter trawl fisheries. A REM system has been fitted to the Crystal Sea (SS118) since May 2016, and for the owner’s preceding vessel of the same name, since 2013. The Crystal Sea is a twin rig otter trawler which targets mixed demersal species in eastern Celtic Sea. The main target species are haddock (Melanogrammus aeglefinus), anglerfish (Lophius spp.) and megrim (Lepidorhombus whiffiagonis). The skipper and crew of the Crystal Sea volunteered to work with the MMO and Cefas for this project.

The second vessel was the FV Carhelmar (Figure 3), a beam trawler towing either two 4m or 8m beam trawls from derricks on either side of the vessel, with the size of the trawls varying accordingly. The main target species are cuttlefish (Sepiidae spp. and Sepiolidae spp.), sole (Solea solea), plaice (Pleuronectes platessa), anglerfish and lemon sole (Microstomus kitt). The vessel used to participate in the MMO Catch Quota Trial during 2012 and 2013. The skipper, crew and vessel owners (Interfish Limited) agreed to keep the REM system aboard on a voluntary basis to continue recording their catch. This pilot project was able to use the REM data and the skippers self-sampling, common skate by-catch data. The project used Electronic Monitoring Systems (Figure 4) created by Archipelago Marine Research Ltd, which supplies video from five cameras, location via GPS and sensors to interpret fishing activity.

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Figure 2: FV Crystal Sea

Photograph courtesy of Simon Armstrong, Cefas

Figure 3: FV Carhelmar

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Figure 4: Electronic Monitoring System (Courtesy of Archipelago Marine Research Ltd)

For the FV Crystal Sea the camera setup was comprised of 5 cameras. Camera 1 viewed a deck area where unwanted fish can be discarded. Camera 2 viewed the bins into which the catch is placed by the crew during the fish sorting operation. Camera 3 and camera 4 viewed the sorting belt which all fish passed along. This enabled the REM analyst to view common skate as they were sorted by the crew and to identify any which the crew may have missed. Camera 5 viewed a deck area where large common skate individuals were placed. The camera views are shown in Figure 5.

1 2

3 4 Figure 5: FV Crystal Sea camera views, numbered by camera. Page 16 of 46

For the FV Carhelmar, the camera setup was also comprised of 5 cameras. Camera 1 viewed the deck area where the catch is emptied from the trawls. Camera 2 and camera 5 viewed the sorting belt over which all fish passed along, again enabling the REM analyst to view common skate as they were sorted by the crew and to identify any which the crew may have missed. Camera 3 viewed the baskets into which the retained catch was placed before stowage. Camera 4 viewed the baskets in which the unwanted catch was placed, prior to discard. The camera views are shown in Figure 6.

1 2

3 4

Figure 6: FV Carhelmar camera views, numbered by camera

Training & verification by an at-sea observer Prior to the commencement of the project the skipper and crew of the FV Crystal sea were supplied with an identification guide for both species of the common skate complex, blue skate and flapper skate. An on-board observer went aboard the vessel in August 2016 to provide refresher training for species identification and review the sampling process with the skipper and crew. In 2017, species identification was revisited, with no further training deemed necessary.

For the FV Carhelmar, an on-board observer was aboard the vessel in 2017 for the national catch sampling programme, where they recorded common skate present and noted the crew were proficient in identification of the common skate complex.

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Sampling process aboard The typical commercial process that follows hauling was sufficiently similar for both vessels. The catch from both trawl cod-ends was deposited in the hopper(s) on the vessel. The trawl was then shot away before the crew began to sort and process the catch. The crew and skipper stood alongside the conveyor which drew the fish from the hopper. The retained fish were gutted and placed in one of several bins or baskets. When all the fish in front of the crew had been gutted, the conveyor was started again. In the case of the FV Crystal Sea, discarded fish were left on the conveyor to be deposited into the waste chute, whereas for the FV Carhelmar, they are removed from the conveyor and placed in baskets to be quantified, before being returned to the belt and discarded via the waste chute.

FV Crystal Sea This project required that all common skate were removed from the catch as they appeared on the conveyor so that they could be recorded separately to the commercial catch, once the commercial catch had been sorted. Small/medium common skate were placed in a basket at the end of the conveyor, large common skate on the deck beside the basket.

After the commercial catch had been processed, the skipper retrieved the basket of small/medium common skate. They were placed on the conveyor in view of the camera (camera 3 & 4) for a few seconds so that the identification could be later verified by the REM analyst. Similarly, for large common skate, individual fish were placed on deck in view of camera 5.

Once the crew had finished processing the catch from that tow, the skipper recorded the common skate by-catch by number in the REM log. For the periods May – December 2016 and July 2017 to December 2017, an entry was made of the number of common skate identified to species, for each tow, for each trip.

FV Carhelmar Aboard the FV Carhelmar, the skipper self-reported every 4th haul only, of every trip between April 2017 to January 2018. Common skate were separated from the catch along with all other discards as they appeared on the conveyor into a basket. Once the commercial catch had been processed, the basket of discards, including common skate, was emptied back onto the conveyor. The analyst relied on images from cameras 2 and 5 to identify the presence/ absence of common skate, to species where possible, amongst the other fish to be discarded. The skipper used a paper log sheet to record the weight (kg) of the common skate complex, rather than to species.

Verification and validation process

FV Crystal Sea The MMO carried out all the video processing and analysis for the FV Crystal Sea. The REM analyst fully reviewed a randomly selected ten percent of hauls from each trip, as is standard practice for the MMO Catch Quota Trial, noting the presence of common skate, therefore not all the skippers self-sampled hauls were analysed. To increase the coverage of analysed hauls where the skipper had recorded the presence of common skate, an additional nine hauls were analysed where records of common skate were present in the REM log, increasing the number Page 18 of 46

of analysed hauls for this project. For each selected haul, where present, common skate were identified to species level (blue or flapper). If species level identification was not achievable, the REM analyst recorded the species as Dipturus species. For a randomly selected number of blue skate, where an individual was appropriately positioned (unobstructed view, lying flat) a measurement was taken across the disc width from wing tip to wing tip using the software’s inbuilt measuring tool. A comparison of the count by species was then made with the skipper’s self-sampling records.

FV Carhelmar Cefas carried out all the REM analysis for the FV Carhelmar. The REM analyst fully reviewed a randomly selected 50% of hauls from each trip, therefore not all the skippers self-sampled hauls were analysed. For each selected haul and where present, common skate were identified to species level (blue or flapper) where possible. If species level identification was not achievable, the REM analyst recorded the species as Dipturus species. The REM system and conveyer aboard were not calibrated for measurement, so no disc widths could be taken. Observer data from the national catch sampling programme was available for one trip in the period analysed, where retained and discarded catch, including discarded common skate were measured to the nearest 1cm below. For this trip, the REM analyst fully reviewed all hauls for which on-board observer data were available. A comparison was then made with the skipper’s self-sampling records, the on-board observer data and REM data.

Estimation of total length and weight based on disc width The REM analyst measured the disc width of 87 blue skate captured by the FV Crystal Sea, and the on-board observer measured the disc width of 201 blue skate aboard the FV Carhelmar. Using the linear relationship between disc width and total length

퐷푊 = 0.7075 × 퐿푇 + 9.3838

where disc width 퐷푊 and total length 퐿푇 are expressed in mm (Barreau et al., 2016), the total length of each individual was estimated. The estimated weight of each individual was then calculated based on the power relationship

3.1289 푤 = 0.00003 × 퐿푇

where weight 푤 is expressed in kg and total length 퐿푇 is expressed in cm. This is approximately equivalent to a direct conversion from disc width in cm to weight in kg using the equation

푤 = 0.000006 × 퐷푊3.1233

Statistical analysis Linear regression analysis was conducted on numbers and weights of common skate by-catch according to the skippers’ self-sampling records against data from the REM analysis, and, for one trip where an observer was on board the FV Carhelmar, against equivalent observer data. This allowed preliminary estimates of common skate by-catch to be made based on skippers’ self-sampling records of which a subsample had been independently validated. Additionally, Page 19 of 46

a probabilistic model (Figure 7) was used to provide alternative estimates of common skate catch for all fishing trips in 2017 by the FV Crystal Sea. The model was trained using a Markov Chain Monte Carlo (MCMC) approach, using data form hauls with recordings by the skipper and the REM analyst, assuming that the video analysts observations were the truth.

Figure 7: Overview of the probabilistic model for estimation of common skate by-catch by the FV Crystal Sea in July – December 2017.

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Results Estimates of catch and distribution of common skate

FV Crystal Sea – Twin-rigged trawler REM data were available for 508 hauls over 27 trips in May to December 2016 and 377 hauls over 26 trips in July to December 2017. There were no skippers self-sampling reports available from the end of the first reporting period, December 2016, until reporting resumed in July 2017, as the skipper was disengaged with the process until the start of the joint Cefas- MMO English Fisheries Data Enhancement project in the summer of 2017 (Catchpole et al., 2017). 312 of 324 individual common skate (96%) recorded by the skipper over the two reporting periods were recorded at species level (blue skate or flapper skate). Across 116 of 938 hauls for which video footage was analysed (12%), the REM analyst identified 95 of 168 individuals (57%) as blue skate, while 73 individuals (43%) were identified as common skate complex (Dipturus species). Because of the uncertainty in species identification by the REM analyst and the low abundance of flapper skate, validation of the skippers self-sampling records against the REM analysis was limited to the common skate complex, with blue skate and flapper skate recordings combined.

The grounds fished by the FV Crystal Sea (Figure 1) were similar in 2016 and 2017, with the vessel’s main fishing grounds localised within ICES Division 7e, ICES rectangles 28E3 and 28E4. On one trip in 2016, the vessel fished to the South-west of its usual grounds, across the boundary between ICES Divisions 7e & h. The skipper did not record any common skate on this trip according to their REM log comments. However, the REM analyst recorded 45 individuals from the two hauls analysed from this trip, which suggests the skipper failed to include relatively large numbers of common skate (compared to other trips) in the REM log. The vessel also fished outside its main grounds on one trip in 2017, a short trip with only three hauls, targeting cuttlefish in ICES rectangles 28E6 and 29E7. The skipper did not record any common skate in any hauls from this trip according to their REM log entries and none of the hauls reviewed by the REM analyst showed common skate. Common skate were observed by the skipper and the REM analyst throughout the vessels main grounds, albeit in low numbers (Figure 8).

The skipper provided comments on catch composition on 367 of 508 hauls in May to December 2016 (72%) and 318 of 377 hauls (84%) in July to December 2017. For hauls with no comments, it was assumed that the skipper would not have recorded common skate, regardless of whether or not common skate were present, so these hauls were assumed to have no data rather than zero common skate caught according to the skippers records. For hauls with comments, but no record of common skate, it was assumed that zero common skate were caught, as the skipper only recorded the presence of common skate in the catch, and didn’t record the absence of common skate in the catch. Similarly in 2016, the REM analyst didn’t record zero observations of common skate in the catch, so only those hauls which were analysed with a presence of common skate could be identified from the database where REM analysis findings were recorded. To improve this, for the 2017 analysis, the REM analyst provided a list of all hauls from 2017 that they had analysed. The data available for statistical analysis are summarised in Table 1.

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As shown in Figure 9a, there was no correlation between numbers of skate recorded by the skipper and the analyst in 18 hauls in 2016 (R2=0.139, p=0.116). As approximately 25 unidentified hauls from 2016 with zero individuals observed by the REM analyst could not be matched to the skipper’s self-sampling records (see Table 1), validation of the skipper’s 2016 records was limited to concurrence in hauls where both the skipper and the analyst had observed one or more individual, with equivalent concurrence information also provided for 2017, to allow comparison (Table 2).

As shown in Figure 9b, there was poor, albeit statistically significant correlation between numbers of common skate recorded by the skipper and the REM analyst in 55 hauls in 2017 (R2=0.496, p<0.05). This was not unexpected because common skate individuals were very rare in the context of large hauls. Linear correlation is a simple method that is not well suited to a dataset (55 hauls) of less abundant species, with an absence of common skate observed in a very large proportion of hauls (85% of skipper’s records; 45% of the REM analyst’s records) and large proportions of hauls with very low numbers of individuals (14% of the skipper’s records with 1 - 5 individuals compared to <1% with 5 or more individuals; 52% of the REM analyst’s records with 1 - 5 individuals) compared to <3% with 5 or more individuals). As shown in Figure 10, the larger the number of common skate, the less frequently they are observed. Alternative, more complex modelling approaches are required to account for the low probability of common skate being, firstly, present, and secondly, recorded as present. The REM analyst recorded very low numbers more often than the skipper, which suggests that the skipper was more likely than the REM analyst to miss common skate. However, the skipper occasionally recorded more common skate individuals than the REM analyst, so, assuming the REM analyst’s records are correct, and discounting the possibility that the REM analyst might also miss common skate, it appears that the skipper may also have double- counted individuals on some occasions.

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December December

–

December 2016 and July July and 2016 December

–

catch by FV Crystal Sea in May May in Sea Crystal FV by catch

. .

Fishing grounds and common skate by skate common and grounds Fishing

Figure 8: Figure 2017, as recorded by the skipper (blue) and the REM analyst (green), excluding one trip to the East of the area shown, shown, area the of the toEast trip one excluding (green), analyst the REM and (blue) skipper the by recorded 2017, as caught were skate no common where

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Table 1: Summary of hauls conducted by the FV Crystal Sea in May – December 2016 and July – December 2017.

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Fishers self-sampling Analyst validation No. of No. of No. of No. of No. of No of Concurrence Concurrence Year Trip Haul No. Blue Flapper Dipturus Total Blue Flapper Dipturus Total (presence/ (Species ID) Skate Skate Species Skate Skate Species absence) 2016 1 11 5 1 6 4 4 67% 100% 2016 6 14 1 1 1 3 4 25% 100% 2016 8 7 10 10 11 11 91% 100% 2016 19 16 2 2 2 2 100% 0% 2016 21 17 1 1 1 1 100% 0% 2016 25 2 1 1 6 6 17% 0% 2017 11 3 1 1 1 1 100% 100% 2017 11 4 2 2 2 2 100% 100% 2017 16 5 1 1 1 1 100% 100% 2017 22 1 2 2 1 1 50% 0% 2017 22 4 2 2 1 1 50% 0% 2017 22 6 5 5 1 1 2 40% 50% 2017 22 7 2 2 3 3 67% 100% 2017 22 8 2 2 1 1 50% 100% 2017 22 9 2 2 1 1 2 100% 100% 2017 22 10 1 1 1 1 2 50% 100% 2017 22 16 1 1 1 1 100% 100% 2017 25 2 3 3 2 1 3 100% 67% 2017 25 3 1 1 1 2 3 33% 100% 2017 25 4 7 7 8 1 9 88% 100% 2017 25 5 1 1 1 1 100% 0% 2016 All hauls 18 2 1 21 27 0 1 28 2017 All hauls 33 0 0 33 23 0 10 33

Table 2: Concurrence between the skipper’s self-reported data and data collected by the REM analyst for the FV Crystal Sea in 2016 and 2017.

(a) (b)

Figure 9: Regression of numbers of common skate recorded by the skipper against numbers recorded by the REM analyst in (a) 2016 and (b) 2017.

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(a) (b)

Figure 10: Numbers of common skate recorded by (a) the skipper and (b) the REM analyst in 2017.

For 318 of 377 hauls in July – December 2017 where the skipper provided comments on catch composition, the total estimated number of common skate caught based on linear correlation between the skipper’s and the REM analyst’s records was 250. This is equivalent to 0.55 common skate recorded by the skipper for each common skate recorded by the observer. The 95% lower and upper confidence limits were 196 and 345, equivalent to 0.39-0.69 common skate recorded by the skipper for each common skate recorded by the observer. To account for the fact the skipper did not provide comments on catch composition for 59 of 377 hauls (16%), 59 hauls were randomly selected from the 318 hauls with skipper comments and added to the 318 hauls with comments to estimate the number of common skate caught over all 377 hauls. Based on 1,000 iterations of the random selection, the mean estimated number of common skate caught for all 377 hauls was 296, with estimates ranging from 199 and 566, equivalent to 0.53-1.50 common skate caught per haul.

The Markov Chain Monte Carlo approach aimed to address the uncertainty associated with very low numbers of common skate by-catch by the FV Crystal Sea in 2017. The total estimated numbers of common skate caught over the 377 hauls in July – December 2017 based on 1,000 iterations of the model ranged from 171 to 312, with a mean of 237. The range of estimation of 141 (312 – 171) is an improvement over the range of the 95% confidence limits for the linear correlation of 367 (566 – 199).

FV Carhelmar REM data were available for the period April 2017 to January 2018 for 14 fishing trips, with a total of 645 hauls. Of those 645 hauls, 155 were self-sampled by the skipper, of which 84 were analysed. The FV Carhelmar fished on three distinct grounds (Figure 11). The skipper and the REM analyst consistently recorded that no common skate were present on all hauls from 13 trips in ICES Divisions 7e & f, with the exception of one haul where the analyst recorded one flapper skate that was not observed by the skipper (Figure 11).

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Because of the apparent absence of common skate on most trips, and to meet the specific objective of the work to determine the feasibility of using REM to validate fishers self-sampling records of common skate, statistical analyses were restricted to nine hauls from a single trip where the vessel fished in ICES Division 7h, to the South-west of the Isles of Scilly. For the one trip where common skate were self-sampled by the skipper and observed by the REM analyst, observer data were also available (Table 3). Possible, unintended consequences of the observer being aboard the vessel for this one trip, more commonly referred to as observer effect, should be noted. For example, the presence of an observer may have made the skipper more vigilant in his self-reporting of common skate by-catch or influence the ‘one-off’ fishing location for this trip. The skipper, the REM analyst and the observer all recorded blue skate and/or Dipturus species on six hauls in the main ground fished on this trip. For three other hauls on this trip the skipper recorded 9-12 skate with no corresponding data from the REM analyst or the on-board observer (Figure 11).

The on-board observer recorded 67 individuals, identified as blue skate. Using the methods reported in Barreau et al., (2016) the estimated total weight of 104 kg was calculated from the on-board observer’s total length measurements. The skipper recorded 129 kg, a concurrence of 81%. The REM analyst and on-board observer counts were compared for all hauls where the on-board observer made a record. Where the REM analyst was unable to identify to species level (e.g. blue skate or flapper skate), they recorded them as Dipturus species. On all but one of the 30 hauls the concurrence between REM analyst and on-board observer on the number of Dipturus species was +/- 2 individual’s, with 100% concurrence for 43% of the hauls, with 75% concurrence for 83% of the hauls.

There was significant correlation (R2=0.991, p<0.005) between the numbers of blue skate recorded by the REM analyst and the on-board observer (Figure 12a). The REM analyst recorded 0.99 ±0.08 individuals for every individual recorded by the on-board observer. The largest difference between counts by the REM analyst and the on-board observer was 2. There was also significant correlation between the biomass of blue skate estimated by the skipper and biomass derived from the on-board observer data (R2=0.976, p<0.005), with the skipper typically over estimating total weights of blue skate compared with the on-board observer for smaller catches but not larger catches (Figure 12b). On this trip, where the skipper self-sampled every fourth haul and recorded common skate weights on three hauls that were not sampled by the on-board observer, based on the correlation between skipper and observer weights, the estimated weight of common skate for the trip was 797 kg, with 95% confidence limits of 734 - 948 kg.

The skipper’s estimated biomass was also correlated with the counts by the on-board observer (Figure 12c) and the REM analyst (Figure 12d), although these correlations were less reliable (R2=0.872 and R2=0.902 respectively, with p<0.005) due to variation in length distribution between catches. The numbers of individuals caught ranged from 3 - 18, so random variation in size of individuals is to be expected, although spatial variation cannot be ruled out. The estimated weight of common skate for the trip was 1,307 kg, with 95% confidence limits of 955 - 1,659 kg based on the skipper’s recordings and the on-board observer’s counts and 1,280 kg, with 95% confidence limits of 880 - 1,680 kg based on the skipper’s recordings and the REM analyst’s counts. The difference compared to weights alone highlights the limitation of linear correlation in these circumstances, as it does not account for the relationship between

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length, or disc width, and weight, which is a power correlation as opposed to a linear correlation.

It is unclear at present whether linear correlation could be routinely suitable to estimate catches of common skate by fishing vessels based on skippers’ self-sampling and REM analysis, in the absence of potential observer effect. Probabilistic modelling was not attempted for the FV Carhelmar due to the bias of non-zero data towards a single trip and the lack of disc width measurements from the REM analysis. However, this alternative approach is likely to be possible if disc width is consistently measured by the REM analyst and further modelling development is undertaken.

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recorded recorded by the skipper

common common skate

ows ows catching

area area showing all tows for the FV Carhelmar. T

Fishing

: :

Figure Figure 11 green coloured the analyst, by recorded and blue, coloured

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(a) (b)

Figure 12: Regression of (a) biomass of blue skate estimated by the skipper against biomass derived from numbers and lengths of individuals recorded by the on-board observer, (b) biomass of blue skate estimated by the skipper against numbers recorded by the on-board observer, (c) numbers of blue skate recorded by the REM analyst against numbers recorded by the on-board observer and (d) biomass of blue skate estimated by the skipper against numbers recorded by the REM analyst.

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Fishers Comparison Comparison self- between between Observer recording Analyst validation sampling Observer & Analyst & entry Fisher Observer Dipturus No. of No. of No. of Estimated No. of No. of No. of Haul Species Concurrence Concurrence Trip Blue Flapper Dipturus Weight Blue Flapper Dipturus Total Number weight (weight) (number) Skate Skate Species (Kg) Skate Skate Species (Kg) 2 4 0 0 0 0 0 100% 100% 2 8 0 0 0 0 0 100% 100% 2 12 7 7 7 1 7 8 100% 88% 2 16 30 10 24 3 8 11 80% 91% 2 24 36 18 26 3 15 18 72% 100% 2 28 16 9 16 2 1 6 9 100% 100% 2 36 7 10 10 3 5 8 70% 80% 2 40 5 4 4 2 1 3 80% 75% 2 48 28 9 18 1 8 9 64% 100% Total 129 67 0 0 104 15 1 50 66

Table 3: Concurrence between the skipper’s self-reporting data and data collected by the on-board observer and the REM analyst for the FV Carhelmar in 2017.

Blue skate biomass in relation to the retained commercial catch During 2016 and 2017 the REM analyst measured the disc width of a subsample of 81 blue skate from across 33 hauls from 21 fishing trips by the FV Crystal Sea. Using the methods reported in Barreau et al., (2016) the estimated total length and weight of each individual was calculated. The mean and total weight of blue skate was calculated at the trip level in relation to the total retained catch. For each fishing trip where blue skate were recorded by the skipper and measured by the REM analyst, the estimated blue skate by-catch weighed <0.4% of the total retained catch per fishing trip (Table 4). These data should be interpreted with caution as they’re not necessarily representative of the biomass of blue skate caught by otter trawlers in the Celtic Sea, as the fishing vessel avoids known areas of high common skate by-catch.

For the single trip for Carhelmar where blue skate were caught, the estimated blue skate by- catch weighed 10.5% of the total retained catch. However, this trip was not representative of the spatial distribution of fishing activity by beam trawlers in the Celtic Sea (Lee et al., 2010; Vanstaen and Breen, 2014; Enever et al., 2017), or indeed of this vessel’s activity, as shown in Figure 11). There was little or no common skate caught in the two main areas fished by the vessel, off the north Cornwall coast (ICES Division 7f) and off the south Devon coast (ICES Division 7e).

% blue skate by- No. blue skate No. measured by Mean estimated Estimated total Total weight of Year Trip catch of total recorded video analyst weight (±SD) weight (kg) retained catch (kg) retained catch 2016 1 6 2 3.77 (± 5.18) 22.62 8452.2 0.268% 2016 6 3 1 0.28 0.84 10287.9 0.008% 2016 8 55 8 0.24 (± 0.12) 13.2 9265.3 0.142% 2016 10 1 1 0.16 0.16 10575.8 0.002% 2016 21 1 1 0.27 0.27 10529.6 0.003% 2016 24 1 1 0.32 0.32 15778.5 0.002% 2016 25 4 1 0.18 0.18 11232.9 0.002% 2017 22 20 5 3.30 (± 4.19) 66 19863.3 0.332% 2017 25 11 11 0.74 (± 0.58) 8.14 8247.3 0.099%

Table 4: Recorded catches of blue skate caught by the FV Crystal Sea per trip in relation to the reported retained total catch. Fishing trips excluded where no blue skate were recorded by the skipper or data on the retained catch was not available. Page 31 of 46

Length frequency of blue skate The calculated length frequency of the 288 blue skate recorded during this project (87 by the FV Crystal Sea and 201 by the FV Carhelmar) were compared to the length frequency of 2,394 blue skate (Bendall et al., 2016, 2017; Hetherington et al., 2016) captured during the Cefas Common Skate Survey during September 2014 – 2017 (Figure 13). The lengths recorded in the Cefas Common Skate Survey ranged from 57cm to 149cm, with a mean length of 121cm. In contrast, the length of blue skate captured by FV Carhelmar adjacent to the Cefas Common Skate Survey transect (Figure 1) ranged from 20cm to 120cm, with a mean of 63cm. The length of blue skate caught by FV Crystal Sea, had a similar length range to that of the individuals captured by FV Carhelmar (20cm to 120cm), but a smaller mean of 46cm. Based upon length at 50% maturity (L50) data (Iglesias et al., 2010; Barreau et al., 2016), all but two individual blue skate measured were immature.

Figure 13: Length frequency of blue skate recorded during the Cefas Common Skate Survey September 2014 – 2017 (blue) and during the REM pilot project 2016 – 2017 (black for the FV Crystal Sea, red for the FV Carhelmar).

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Improvements to species identification Following analysis of the REM camera footage, it was apparent that the majority of common skate recorded were juveniles, and the REM analyst had difficulty identifying some individuals to species level. The uncertainty arose as a consequence of an identification guide which had been developed primarily for use with adult specimens which could be handled. It had not been designed for speciating juveniles, viewed remotely where the defining taxonomic features used to differentiate the 2 species of blue skate and flapper skate were less apparent or absent.

For this reason and to help with future analysis, an Expert Meeting on juvenile common skate identification using REM was convened in March 2017 (Annex 1). Its purpose was twofold, to determine and classify the taxonomic features visible in REM images to distinguish between juvenile Dipturus species found in the North-east Atlantic (blue skate, flapper skate, longnose skate Dipturus oxyrinchus and Norwegian skate Dipturus nidarosiensis), and to produce an identification guide for use with REM images. Those in attendance were UK and French experts in species identification of Dipturus species in the North-east Atlantic, REM application and usage and the production of elasmobranch species identification guides.

Following the Expert Meeting an outline methodology to identify juvenile Dipturus species in REM images from the North-east Atlantic was developed. During 2017 and 2018, this outline method was further developed by the Shark Trust, MNHN and Cefas. This common skate complex identification guide (Figure 14) is now complete and will be circulated to fishermen and REM analysts, providing a fit for purpose identification guide for speciating adults of the common skate complex. This will be especially useful for speciating juveniles viewed remotely where the defining taxonomic features used to differentiate the 2 species of blue skate and flapper skate are less apparent or absent. It is intended that this new identification guide will improve the accuracy of identification and in turn the level of concurrence between the skipper and REM analyst of blue and flapper skate.

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Figure 14: Common skate complex identification guide for use by fishermen and REM analysts.

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Discussion

For an 8-month period between May – December 2016 and a further 6 months between July

– December 2017, the skipper aboard the FV Crystal Sea recorded the common skate by- catch by number and species, per haul, during commercial fishing operations. Linear correlation between numbers of Dipturus species recorded by the skipper and the REM analyst was statistically significant but poor, due to this analytical method not being suited to the low numbers of common skate recorded. During typical commercial fishing, small juvenile Dipturus species can go unrecorded as it often makes up only a small component of the catch. For example, where it was identified by the REM analyst, common skate by-catch accounted for less than 0.4% of the total retained catch per trip by the FV Crystal Sea. We have shown here that linear regression analysis is not well suited to catches of low abundance species. Probabilistic modelling offers an alternative approach to estimating catches of less abundant species based on analysis of a small subsample of REM data and a larger sample of skippers’ self-sampling records, but further modelling development is required to overcome the limitations of REM to validate fisher’s self-sampling records in the context of low abundance species.

For a 10 month period between April 2017 and January 2018, the FV Carhelmar self-recorded catches of common skate in only 1 of 14 fishing trips. This trip took place in a different location to the other trips (Figure 11). This indicates that the abundance of Dipturus species in some parts of the Celtic Sea may be limited, and likely to be localised, in some areas within ICES Divisions 7e-h.

For the FV Carhelmar trip with notable blue skate by-catch, despite low numbers of individuals overall resulting in discrepancies of +/-2 individuals between the on-board observer counts and REM analyst counts, there was strong linear correlation between the skipper’s self- sampling records, the on-board observer’s records, and the REM analysts’ records, unlike for the FV Crystal Sea. One reason for this was that the numbers and sizes of individuals recorded were generally larger than those recorded for the FV Crystal Sea (around 10 individuals compared to 1 - 5, with a mean length of 63cm compared to 45cm), which reduces issues with small numbers of juveniles going unrecorded. Although observer effect (e.g. the presence of an on-board observer increasing the vigilance of the skipper in his self-sampling) cannot be discounted, it also appears that the FV Carhelmar data were improved by the on- board sampling design adopted, with the skipper recording estimated weights of blue skate only every fourth haul (25% of hauls), rather than counts of individuals on every haul as for the FV Crystal Sea. This reduces the burden of self-reporting, and probably improves accuracy during 24 hour operations at sea.

Moving forward, the sampling frequency used to verify skipper records needs to be considered further. For common species of fish (e.g. haddock) ten percent of hauls are randomly sampled per trip to provide an accurate representation (Stanley et al., 2011). The MMO REM analyst used the same sampling methodology for the FV Crystal Sea REM data. Of the 43 hauls, only 6 hauls had common skate by-catch of one or more individual reported by the skipper. To increase the occurrence of hauls where REM footage could be used to validate the skippers self-sampling report, another 10 hauls were selected with a skipper self-sampling report for verification. The data from this pilot project suggest that for common skate, a rarer species, a Page 38 of 46

higher percentage of hauls need to be sampled, similarly as recommended by Stanley et al., (2011) for basking sharks. For the FV Carhelmar, the REM analyst reviewed a randomly selected 50% of hauls from each trip. This follows a similar REM analyst sampling methodology as detailed in Hetherington et al., 2017 for skates and rays in the Bristol Channel.

Barreau et al., (2016) carried out extensive work on relationship between disc width, total length and weight of Dipturus species in the Celtic Sea and elsewhere. This project has successfully demonstrated that by the REM analyst taking one accurate measurement, the disc width, that total length and weight can be estimated using the regression methods and coefficients described in Barreau et al., (2016). This is of importance as an accurate total length by the REM analyst is unlikely, as the tail rarely lays straight, nor are there scales aboard to take weight measurements.

This project has furthered our understanding to date of the presence and distribution of juvenile common skate in the Celtic Sea, information that is lacking. For example, common skate were only caught by the FV Carhelmar in the South-west extremity of its fishing area, adjacent to the Cefas Common Skate Survey area, and the fishing ground of the FV Crystal Sea that catches common skate throughout its fishing grounds (Figure 1 and 8). Our analysis has shown that the common skate by-catch, by both twin rig otter trawl and beam trawl in this study are juveniles, a life history stage and size not typically recorded in the annual Cefas Common Skate Survey. Therefore, this project contributes evidence to the ongoing national catch sampling or observer programme and supports the Defra funded, common skate data collection programme in the Celtic Sea, providing information on abundance (Figures 8 and 11, Tables 1-3), distribution (Figures 8 and 11), and size/ maturity (Figure 13) for an area not covered and a segment of the population underrepresented in the Cefas Common Skate Survey.

Advancements need to be made to mitigate the limitations of using REM to validate fisher’s self-sampling records of common skate, identified in this project. For example, on occasion the skipper of the twin rigged otter trawler, FV Crystal Sea, did not record occasional, relatively large numbers of common skate (45 individuals on the two hauls analysed from one trip), whereas the REM analyst did. Although it may be tempting to explore REM in isolation to monitor less abundant species such as common skate, rather than engage with the fishing industry to improve the quality, consistency and robustness of the skipper generated self- sampling data, this is not advisable. Firstly, the assumption that the REM analyst data are the truth, or that they are more accurate and reliable is not necessarily sound. To ascertain whether REM data are accurate, you would need to have the same hauls analysed by 2 or more independent REM analysts and compare their findings. Secondly, given the occasional high by-catch events, independent use of REM data would require a high sampling frequency of the REM data. The sampling frequency can potentially be reduced if the skippers provide information that allows high by-catch events to be identified and validated. It is recommended that to improve the quality and consistency of the skippers self-reporting, the burden on the skipper needs to be reduced by sampling 25% rather than 100% of hauls, and the REM coverage increased from 10% to 50% of hauls, for example, with both the skipper and REM analyst recording absence, as well as presence of common skate by-catch.

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The level of concurrence between the skipper and REM analyst on species identification between blue skate and flapper skate needs to be improved, especially where the REM analyst records individuals as Dipturus species due to uncertainty. Speciation of the common skate complex is best conducted on deck where fish can be handled and examined for subtle differences to aid identification, rather than remotely by the REM analyst. The involvement of the fishing industry is critical for this task, and cannot be done by REM in isolation. It is anticipated that the circulation of the new identification guide to fishermen and REM analysts will reduce the level of uncertainty in speciation, thus improve concurrence between the skipper and REM analyst. In future, rather than providing absolute certainty, or not, the REM video footage should be used to provide a confidence level around speciation by the skipper, not that it is simply right or wrong, or can’t be done.

The driver of this project was the need for the fishing industry to generate robust policy relevant data, validated by REM. This driver remains, as both the fishermen aboard the vessel and REM data are required in unison for the effective fishery-dependant monitoring of the common skate complex. By engaging the fishing industry in data collection, available data are increased, and the fishing industry is more likely to remain engaged and buy into any management measures that arise from the data. The challenge is to further increase, then maintain, the quality and robustness of the skipper self-sampling data, then modify our validation of the data using REM, through the mitigating actions identified above.

Conclusion Working collaboratively with the MMO and MNHM, this project has demonstrated that REM can be used to validate fishermen’s self-sampling records on common skate to better understand the populations of blue skate and flapper skate. It is evident that REM of common skate by-catch can provide a means by which the burdensome paper record sheets traditionally used in self-sampling can be removed, instead only requiring fishermen to record fairly basic data requirements (in this case, number or weight by species), validated by REM. In addition, we have shown that for common skate, the workload of recording length, width, weight etc., can be transferred to the trained REM analyst and scientist, which is likely to be more accurate.

In conclusion, this approach of using REM & fisher self-sampling data has the potential to monitor other less abundant, protected species, not just common skate, to generate robust evidence to inform policy.

Next Steps For the REM of common skate by-catch programme to continue, to be a source of policy relevant data, the following steps need to be taken:

Improvements in data collection and analysis

(1) To improve the quality and consistency of the skippers self-reporting, the burden on the skipper needs to be reduced by sampling 25% rather than 100% of hauls, and the REM

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coverage increased from 10% to 50% of hauls, for example, with both the skipper and REM analyst recording absence, as well as presence of common skate by-catch;

(2) To improve the validation of the skippers self-sampling record of speciation by the REM analyst, the REM analyst should apply a confidence level around species identification by the skipper, rather than defer to the ‘complex’ level;

(3) To modify the sampling methodology aboard so that species identification can be improved, and gender of common skate is better recorded, increasing biological understanding;

Incentivisation

(4) The use of REM to validate fishers self-sampling records has been successfully demonstrated for skates and rays as described in Hetherington et al., 2018. One of the main differences between that programme and this, is that it was financially incentivised, with additional fishing quota also available. The REM of common skate by-catch programme is largely voluntary, with common skate by-catch not of particular concern to the participating vessels, therefore their buy-in to the programme is low. The skipper of the FV Crystal Sea initially turned down payment to participate in 2017. The skipper accepted payment later in the year when it was discussed again, where the level of self- reporting appears to have increased, although we cannot say for certain that this is linked to payment. Sufficient resource will need to be available to financially incentivise vessels to participate, providing self-sampling data at the level and quality required, or alternate methods to incentivise participation need to be found.

Standardisation

(5) A next step in providing further value from the REM data is to standardise the common skate by-catch rate by applying a catch per unit effort (CPUE) from the existing REM data without requesting further information from the skipper, i.e. gear details, so overly burdening him or her. For example, the number of common skate Km-1.h-1.

Acknowledgements

The authors of this report pass their sincere gratitude and thanks to the skipper, David Stevens, and crew of the FV Crystal Sea for their enthusiasm, ideas and commitment to self- sampling of common skate by-catch, to trial a novel use of REM in the UK. We thank Andrew Pillar of Interfish Limited and the skipper and crew of the FV Carhelmar for making available and entrusting the REM footage they voluntarily collected. Finally to Tom Catchpole of Cefas for reviewing this report.

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References Barreau T., Caraguel J.-M., Vuillemin S., Iglésias S.P. 2016. Programme POCHETEAUX, Rapport final. Muséum national d'Histoire naturelle, 100 p.

Bendall, V. A., Hetherington, S. J., Ellis, J. R., Smith, S. F., Ives, M. J., Gregson, J. and Riley, A. A. 2012. Spurdog, porbeagle and common skate by-catch and discard reduction. Fisheries Science Partnership 2011–2012, Final Report. 88 pp.

Bendall, V. A., Hetherington, S. J., Barreau, T., Nicholson, R., Winpenny L. (2016) Common Skate Survey Annual Report (ELECTRA MF6001: Workpackage Task 1.4) Cefas. 30 pp.

Bendall, V. A., Jones, P., Nicholson, R., Hetherington, S. J., and Burt., G., (2017) Common skate survey Annual Report (ELECTRA MF6001: Work Package Task 1.4) Cefas. 39 pp.

Brander, K. (1981). Disappearance of common skate Raia batis from Irish Sea. Nature 290 , 48–49.

Catchpole, T., Elliott, S., Elson, J., Benedet, R., Spence, M., Ribeiro Santos, A., Sandeman, L., Nelson, P. 2017a. Applying Remote Electronic Monitoring to improve estimates of commercial catches - a focus on haddock 7.b-k caught in otter trawl fisheries. Project report (Cefas). 34pp.

Ellis, J. R., Bendall, V. A., Hetherington, S. J., Silva, J. F. and McCully Phillips, S. R. (2015). National Evaluation of Populations of Threatened and Uncertain Elasmobranchs (NEPTUNE). Project Report (Cefas), 103 pp.

Elson, J., Elliott, S., O’Brien, M., Ashworth, J., Ribeiro Santos, A., Mangi, S., Dolder, P., Catchpole, T. (2016). Generating biological fisheries data using Remote Electronic Monitoring (REM) and the wider applications of REM data. Project Report (Cefas), 128 pp.

Enever, R., Lewin, S., Reese, A. and Hooper, T. (2017). Mapping fishing effort: Combining fishermen’s knowledge with satellite monitoring data in English waters. Fisheries Research, 189: 67-76.

Hetherington, S. J., Bendall, V. A., Barreau, T., Smith, S. F., Sandeman, L. R., Royston, A., Nelson, P. 2016. NEPTUNE 2.0: Monitoring of Common Skate in the Celtic Sea in partnership with the fishing industry. Final project report (Cefas). 76 pp.

Hetherington, S. J., Elliot, S., Pasco, G., Nelson, P., Elson, J. (2018). Remote Electronic Monitoring (REM) to validate fishermen’s self-sampling records of skates and rays in the Bristol Channel. Project report (Cefas). 25 pp.

Hetherington, S. J., Nicholson, R. E., Bendall, V. A., Catchpole, T. (2018). Real-time self- reporting by the fishing industry to enhance catch data collection and spatial management (avoidance) of unwanted catches. Project report (Cefas). 27 pp.

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Iglésias, S. P., Toulhoat, L. and Sellos, D. Y. (2010). Taxonomic confusion and market mislabelling of threatened skates: important consequences for their conservation status. Aquatic Conservation: Marine and Freshwater Ecosystems, 20: 319–333.

Kindt-Larsen, L., Kirkegaard, E., Dalskov, J. 2011. Fully documented fishery: a tool to support a catch quota management system. ICES Journal of Marine Science. 68(8): 1606-1610.

Lee, J., South, A.B., and Jennings, S. (2010). Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science. 67 (6): 1260-1271.

Needle, C.L., Dinsdale, R., Buch, T.B., Catarino, R.M.D., Drewery, J., Butler, N. 2014. Scottish science applications of Remote Electronic Monitoring. ICES Journal of Marine Science. Advance Access published December 15, 2014.

Roberts, J., Sandeman, L. R., Royston, A. 2015. North Sea Cod catch quota trials: Final Report 2014, 17 June 2015.

Stanley, R.D.; McElderry, H., Mawani, T., Koolman, J. 2011. The advantages of an audit over a census approach to the review of video imagery in fishery monitoring. ICES Journal of Marine Science. 68(8): 1621-1627.

The Scottish Government. Report on Catch Quota Management using Remote Electronic Monitoring (REM). Published 22nd August 2011. URL: http://www.gov.scot/Topics/marine/Sea-Fisheries/management/17681/CQMS082011 (accessed 19/04/16).

Ulrich, C., Olesen, H.J., Berggson, H., Egekvist, J., Håkansson, K.B., Dalksov, J., Kindt- Larsen, L., Storr-Paulsen, M. 2015. Discarding of cod in the Danish Fully Documented Fishery trials. ICES Journal of Marine Science. Advance Access published March 15, 2015. van Helmond, A.T.M., Chen, C., Poos, J. J. 2014. How effective is electronic monitoring in mixed bottom-trawl fisheries? ICES Journal of Marine Science. Advance Access published November 11, 2014.

Vanstaen, K. and Breen, P. (2014). Understanding the distribution and trends in inshore fishing activities and the link to coastal communities. Project report (Cefas). 88 pp.

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Annex 1

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About us Customer focus

The Centre for Environment, Fisheries and Aquaculture We offer a range of multidisciplinary bespoke scientific Science is the UK’s leading and most diverse centre for programmes covering a range of sectors, both public and applied marine and freshwater science. private. Our broad capability covers shelf sea dynamics, climate effects on the aquatic environment, ecosystems We advise UK government and private sector customers and food security. We are growing our business in on the environmental impact of their policies, overseas markets, with a particular emphasis on Kuwait programmes and activities through our scientific and the Middle East. evidence and impartial expert advice. Our customer base and partnerships are broad, Our environmental monitoring and assessment spanning Government, public and private sectors, programmes are fundamental to the sustainable academia, non-governmental organisations (NGOs), at development of marine and freshwater industries. home and internationally.

Through the application of our science and technology, We work with: we play a major role in growing the marine and freshwater economy, creating jobs, and safeguarding • a wide range of UK Government departments and public health and the health of our seas and aquatic agencies, including Department for the Environment resources Food and Rural Affairs (Defra) and Department for Energy and Climate and Change (DECC), Natural Head office Resources Wales, Scotland, Northern Ireland and Centre for Environment, Fisheries & Aquaculture governments overseas. Science • industries across a range of sectors including Pakefield Road offshore renewable energy, oil and gas emergency Lowestoft response, marine surveying, fishing and Suffolk aquaculture. NR33 0HT • other scientists from research councils, universities Tel: +44 (0) 1502 56 2244 and EU research programmes. Fax: +44 (0) 1502 51 3865 • NGOs interested in marine and freshwater. • local communities and voluntary groups, active in Weymouth office Barrack Road protecting the coastal, marine and freshwater The Nothe environments. Weymouth DT4 8UB

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