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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

20th July 2018

Cefas Document Control

Submitted to: Sarah Jones and Jamie Rendell

Date submitted: 20th July 2018

Project Manager: Suzanna Neville

Report compiled by: Hetherington et al.

Quality control by: Thomas Catchpole & Suzanna Neville

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

Version: 6

Version Control History

Author Date Comment Version

Hetherington et al 11th June 2018 First draft. V0

Thomas Catchpole 11th June 2018 Further statistical

analyses required. V1

Suzanna Neville 20th June 2018 Clarification to the

text required. V1

Hetherington et al 28th June 2018 Additional statistical

analyses complete. V2

Thomas Catchpole 29th June 2018 Further clarification

to the text required. V3

Hetherington et al 18th July 2018 Final draft. V4

Thomas Catchpole 18th July 2018

Approved with minor

tracked changes to

text.

V5

Hetherington et al 20th July 2018 Final. V6

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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

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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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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

Figure 3: FV Carhelmar

Photograph courtesy of Simon Armstrong, Cefas

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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.

Figure 5: FV Crystal Sea camera views, numbered by camera.

1

5

4 3

2

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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.

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.

1 2

3 4

5

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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

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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

𝐷𝑊 = 0.7075 × 𝐿𝑇 + 9.3838

where disc width 𝐷𝑊 and total length 𝐿𝑇 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

𝑤 = 0.00003 × 𝐿𝑇3.1289

where weight 𝑤 is expressed in kg and total length 𝐿𝑇 is expressed in cm. This is

approximately equivalent to a direct conversion from disc width in cm to weight in kg using the

equation

𝑤 = 0.000006 × 𝐷𝑊3.1233

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,

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

July – December 2017.

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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.

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.

Year Trip Haul No.

No. of

Blue

Skate

No. of

Flapper

Skate

No. of

Dipturus

Species

Total

No. of

Blue

Skate

No. of

Flapper

Skate

No of

Dipturus

Species

Total

Concurrence

(presence/

absence)

Concurrence

(Species ID)

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 18 2 1 21 27 0 1 28

2017 33 0 0 33 23 0 10 33

All hauls

Analyst validation Fishers self-sampling

All hauls

(b) (a)

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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).

(a) (b)

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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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Fig

ure

11:

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of 46

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.

(a) (b)

(c) (d)

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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).

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.

Year TripNo. blue skate

recorded

No. measured by

video analyst

Mean estimated

weight (±SD)

Estimated total

weight (kg)

Total weight of

retained catch (kg)

% blue skate by-

catch of total

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%

Fishers

self-

sampling

entry

Comparison

between

Observer &

Fisher

Comparison

between

Analyst &

Observer

Trip Haul

Number

Dipturus

Species

weight

(Kg)

No. of

Blue

Skate

No. of

Flapper

Skate

No. of

Dipturus

Species

Estimated

Weight

(Kg)

No. of

Blue

Skate

No. of

Flapper

Skate

No. of

Dipturus

Species

TotalConcurrence

(weight)

Concurrence

(number)

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

Analyst validation Observer recording

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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

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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


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


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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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Annex 1

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