Inshore Fishing Activities Intensity report from Cefas by Through the Gaps

Cefas contract report C5401

MB0117: Understanding the distribution and trends in inshore fishing activities and the link to coastal communities

Authors: Koen Vanstaen and Patricia Breen

Issue date: 02/12/2014

Cefas Document Control

Title: Understanding the distribution and trends in inshore fishing activities and the link to coastal communities Submitted to:

Carole Kelly, Defra

Date submitted:

02/12/2014

Project Manager:

Koen Vanstaen

Report compiled by:

Koen Vanstaen and Patricia Breen

Quality control by:

Janette Lee

Approved by & date: Edmund McManus, 01/12/2014 Version:

Final Draft

Version Control History Author

Date

Comment

Version

KV & PB

26/03/2014

Draft

0.1

28/03/2014

Internal review

KV & PB

29/03/2014

Final Draft

1.0

01/12/2014

Final

2.0

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Project Title: Understanding the distribution and trends in inshore fishing activities and the link to coastal communities Project Code: MB0117 Defra Contract Manager: Leila Fonseca/Carole Kelly, Defra, Marine and Fisheries Science Unit

Funded by: Department for Environment Food and Rural Affairs (Defra) Marine and Fisheries Science Unit Marine Directorate Nobel House 17 Smith Square London SW1P 3JR

Authorship: Koen Vanstaen and Patricia Breen Centre for Environment, Fisheries and Aquaculture Science (Cefas) Pakefield Road Lowestoft Suffolk NR33 0HT United Kingdom tel: + 44 (0)1502 562244 email: koen.vanstaen@cefas.co.uk www.cefas.co.uk

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.

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MB0117: Understanding the distribution and trends in inshore fishing activities and the link to coastal communities

Authors: Koen Vanstaen and Patricia Breen

Issue date: 02/12/2014

Head office Centre for Environment, Fisheries & Aquaculture Science Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK Tel +44 (0) 1502 56 2244 Fax +44 (0) 1502 51 3865 www.cefas.defra.gov.uk

Cefas is an executive agency of Defra

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Executive Summary Knowledge about which human activities take place where, when, and how often, is essential for developing a network of marine protected areas, marine planning, the management of human activities and to understand how the different activities relate to one another. Whereas the location and footprint of most human activities is well known (e.g. aggregate extraction, wind farms, cables and pipelines), information on fishing activities remains patchy and in some instances is closely guarded, especially for the inshore fleet. The overarching aim of this project was to gain a better understanding of the inshore fishing fleet. The project brought together data from the Inshore Fisheries and Conservation Authorities, Welsh Government and the Marine Management Organisation. The same data were analysed in a number of different ways to inform management requirements. Specifically the objectives of this project were: 1. To develop inshore fishing activity data layers from IFCA and MMO data covering years 20102012; 2. To provide insights into temporal changes of inshore fishing activities and changes in relation to closed areas (e.g. MPAs or renewable developments); 3. To develop the concept of Zones of Influence for coastal fishing ports around England; 4. To present a case study on the potential application of the data layers developed; and 5. To report and disseminate the outcomes of the project.

This project was able to produce fishing activity data layers for England and Wales covering the years 2010-2012. The data layers were produced using routinely collected data by IFCAs and the MMO. The fishing distribution and intensity data layers are presented by gear type and complement the previous data layers covering the period 2007-2009. A first attempt was made to investigate temporal changes in fishing activities in inshore waters, using the data covering the six year period between 2007 and 2009. At a national scale, areas with repeated fishing activity on an annual basis were identified. At a local scale, around a marine protected area, displacement and fishing activity intensity were studied.

Results confirmed

observations from local experts, thereby suggesting the data are able to describe changing trends in fishing activity. The Zones of Influence concept was successfully rolled out to all fishing ports around England and Wales.

The approach links fishing grounds to coastal fishing communities and allows better

assessment of current and future spatial conflicts between stakeholders. The Zones of Influence

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were also used to explore a new approach to value inshore fishing grounds. As with any approach, it has some limitations, but also offers some valuable new insights. The project has delivered all its objectives and results have already been used to inform conservation management and marine planning, by informing marine conservation zone impact assessments and MMO South Coast plan development. The key outputs from this project and their policy relevance are summarised in the tables on the next page.

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Output

Policy requirement

Outcome

2010-2012 fishing activity intensity data layers

Knowledge of which, and where, inshore fishing activities take place.

Data layers showing where inshore fishing activities take place and their relative intensity. Broken down by gear types: trawling, dredging, potting, netting and lining & commercial angling. Based on fishing activity day records, over 98% of trawling by vessels under 15 m length will involve towing gear over the seabed. Reductions in data recordings were observed and consideration should be given to how such data will be obtained in future. iVMS could be a solution, but only if rolled out to all vessels.

Regional and local temporal trends in inshore fishing activities

To understand trends in inshore fishing activities. Changes in distribution and intensity over time.

The spatial distribution of inshore fishing activity is relatively stable across the years. Annual updates of the data may therefore not be required.

Investigate whether ‘core fishing grounds’ exist or whether fishing grounds shift annually.

Clear ‘core fishing grounds’ exist, with peripheral areas around these which are visited less frequently over time. For static gear activities the ‘core fishing ground’ is large compared to the peripheral area, whereas for mobile gears the reverse is the case. Static gear fleets have less displacement opportunity.

Assess the changes in distribution and intensity as a result of management measures.

The Lyme Bay case study provided quantitative evidence on the effects of the towed gear closure on local fishing activities. Static gear effort increased inside the closed area, but also in the wider surrounding area. A slight increase (displacement effect) in mobile gear effort was seen immediately outside the site, but decreased to reduced levels over time. Displacement of inshore activities is therefore not a simple redistribution of effort across the wider area.

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Output

Policy requirement

Outcome

Zones of Influence

Understand the impact of management measures on coastal fishing communities.

The Zone of Influence concept for ports around England and Wales was demonstrated. It allows spatial visualisation of the areas fished by each fleet and allows the study of interactions with proposed offshore developments or management measures.

Economic valuation of (r)MCZs

Understand the economic impact of It is possible to use ZOI and management measure interactions to assess different management scenarios for the potential losses in landings values and vessel numbers per port. the fishing industry. Use best Static gears overlap more with (r)MCZ than mobile gears and economic available data and approaches. impact per kilometre square closure is often greater. Link economic impacts to fishing Limitations with the landings values and assumptions about spreading communities affected. costs evenly throughout the area should be carefully considered when using this method.

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Table of contents

Introduction ................................................................................................................................... 2

1.1

Background ............................................................................................................................. 2

1.2

Policy background ................................................................................................................... 2

1.3

Overview ................................................................................................................................. 3

1.4

Aims and objectives ................................................................................................................ 4

1.5

Scope ....................................................................................................................................... 4

Data and methods ......................................................................................................................... 6

2.1

2.1.1

Data ................................................................................................................................. 6

2.1.2

Methodology ................................................................................................................... 8

2.2

Temporal analysis ................................................................................................................. 13

2.2.1

National trends analysis ................................................................................................ 13

2.2.2

Local trends analysis ..................................................................................................... 14

2.3

Relative inshore fishing activity analysis ................................................................................. 6

Zone of Influence .................................................................................................................. 15

2.3.1

Data ............................................................................................................................... 15

2.3.2

Methodology ................................................................................................................. 16

2.3.3

Economic spatial valuation â&#x20AC;&#x201C; A Case Study................................................................... 17

2.3.3.1

Introduction .............................................................................................................. 17

2.3.3.2

Methodology ............................................................................................................. 18

Results ......................................................................................................................................... 20

3.1

Data sources and trends ....................................................................................................... 20

3.2

Relative inshore fishing analysis ........................................................................................... 24

3.3

Temporal analysis ................................................................................................................. 39

3.3.1

National trends analysis ................................................................................................ 39

3.3.2

Local trends analysis ..................................................................................................... 47

3.4

Zone of Influence .................................................................................................................. 54

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3.5 4

Case study: valuation of inshore fisheries ............................................................................ 61

Limitations ................................................................................................................................... 70

4.1

Relative fishing intensity ....................................................................................................... 70

4.2

Temporal analysis of inshore fishing activity ........................................................................ 70

4.3

Zone of Influence .................................................................................................................. 71

4.4

Economic valuation of fishing activities ................................................................................ 71

Conclusions ................................................................................................................................. 74

5.1

Data and methodology ......................................................................................................... 74

5.2

Relative fishing intensity maps ............................................................................................. 75

5.3

Temporal changes in distribution of inshore fishing activities ............................................. 76

5.4

Zone of influence .................................................................................................................. 77

5.5

Economic analysis - A Case Study ......................................................................................... 78

References ................................................................................................................................... 80

Annex I: Vessel activity codes ................................................................................................... 84

Annex II: â&#x20AC;&#x2DC;Râ&#x20AC;&#x2122; ZoI valuation script ............................................................................................... 86

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Acknowledgments

This project was made possible thanks to the support of Chief Fishery Officers at participating Inshore Fisheries and Conservation Authorities. Inshore Fisheries and Conservation Officers assisted by providing access to data, review of the resulting data layers, and provided advice throughout the project when necessary. In addition, data were made available by the Welsh Government. The Marine Management Organisation made sightings and landings value data available to this project.

Reproduction of materials presented in this report The data presented and described in this report were made available by the Inshore Fisheries and Conservation Authorities, Welsh Government and Marine Management Organisation. Any figures reproduced from this report or the digital data outputs should always acknowledge the source of the data. Example statement: "Produced from data provided by the Inshore Fisheries and Conservation Authorities, Welsh Government and Marine Management Organisation".

Data availability The inshore fishing activity data presented in this report are freely available from the Marine Environmental Data and Information Network (MEDIN) Fisheries Data Archive Centre (FishDAC): http://www.oceannet.org/ http://www.cefas.defra.gov.uk/publications-and-data/fishdac.aspx

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1 Introduction 1.1

Background

Knowledge about which human activities take place where, when, and how often, is essential for successful marine planning, the management of human activities and to understand how the different activities relate to one another (MSPP, 2006; Eastwood et al., 2007; Gilliland & Laffoley, 2008). Whereas the location and footprint of most human activities is well known (e.g. aggregate extraction, windfarms, cables and pipelines), information on fishing activities remains patchy and in some instances is closely guarded. Increased knowledge on offshore fishing activities has become available through the processing of satellite-based vessel monitoring systems (VMS) for vessels over 15 m (Lee et al., 2010). Inside the 6 nautical mile limit the fishing activities are dominated by smaller vessels, often under 15 metres in length. In England and Wales, this part of the fleet makes up over 90% of registered fishing vessels (MMO, 2011). Defra funded project MB0106 successfully demonstrated how the knowledge of inshore fishing activities can be improved using data routinely collected by the Inshore Fisheries and Conservation Authorities (IFCAs). The initial work provided a snapshot of activities between 2007 and 2009. To ensure that knowledge of where activities take place is kept up to date there is a need to continue such data analysis. The current project works towards this and expands on the use of these data to develop and improve the knowledge of the inshore fishing fleet.

1.2

Policy background

The UK Government is committed to the establishment of a network of Marine Protected Areas (MPAs) to conserve marine ecosystems and biodiversity (Defra, 2010a). The EC Habitats Direct (92/43/EEC) has led to a number of sites, considered to be of European and international importance, being designated or proposed as Special Areas of Conservation (SAC) or Special Protection Areas (SPA).

The Marine and Coastal Access Act 2009 enables sites of national

importance to be protected as part of a network of Marine Conservation Zones (MCZs). A UK MPA network will also assist in the achievement of Good Environmental Status (GES) under the EU Marine Strategy Framework Directive (MSFD). Within English territorial waters, four Regional MCZ Projects were tasked with the recommendation of MCZs for their area. Using the guidance provided by Natural England and the Joint Nature Conservation Committee (JNCC) (Natural England and JNCC, 2010) and the best available data, stakeholder groups recommended 127 sites to Government. In November 2013, the first 27 sites were designated by the UK Government (Defra, 2013).

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The process for the designation of MCZs requires analysis of a range of environmental and socioeconomic data layers. Understanding the economic impact of management measures requires knowledge of the activities taking place within the zones. This project addresses the current lack of data on the distribution, intensity, and economic value of fishing activities closest to the shoreline, often by vessels under 15 m in overall length. Whereas the implementation of marine protected areas was the primary policy driver for this work, the outcomes of the work are also directly relevant to other policy areas. For example, improved knowledge of inshore fishing activities will also inform marine planning, the management of fisheries effort in coastal waters and facilitate assessments required under the water framework directive (WFD) or MSFD.

1.3

Overview

Defra funded project MB0106 indicated that analysis of more up-to-date data would provide the best available information on where vessels are fishing out to the 12 nautical mile limit. Furthermore, expanding the information base will allow assessment of the inter-annual behaviour of fishermen. Currently, knowledge on how inshore fishermen move is reliant on information from interviews (des Clers et al., 2008; des Clers, 2010) and models (Tidd et al., 2011; Witt et al., 2012). Knowledge on how temporal intensities fluctuate through time can feed into management decisions about key areas for fishing activities. With European and UK law requiring management plans within conservation areas, measures affecting fishing fleets will be implemented. To be successful, questions about core fishing areas and how fishing activity is dispersed both spatially and temporally, will need to be addressed in order to reduce conflict with stakeholders. Continually updating these data will also allow for subsequent assessment of how fishermen have behaved after a closure has been implemented. This can inform future predictions of management effects. While fishing intensity maps are useful for spatial pressure and activity assessment, the data do not provide any information regarding the coastal fishing communities contributing to this activity. It is important for policymakers to understand where the effects of management measure are likely to occur, which stakeholders are mostly likely to be affected and how any effects are distributed throughout the country. The zones of influence (ZOI) concept was developed to address this requirement (Vanstaen et al., 2010). Zones of Influence link fishing activity to the registered home port of a fishing vessel and allows us to begin exploring socio-economic information about the fleets and those areas of the sea which are most valuable to them. We can then begin to make judgements about the effects, and magnitude, of management decisions on fishing communities. This work

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complements the work of other projects such as the EU funded Geography of Inshore Fishing and Sustainability project (GIFS, http:// http://www.gifsproject.eu). The overarching aim of this project is to gain a better understanding of the inshore fishing fleet. By using the same data we hope to answer several questions about fishing intensity in the 0-12 nm zones, how these intensities vary through time, and the significance of different sea areas to the fishing communities which rely on them for a living. This improved understanding will be of value to policy and decision makers as they implement marine policies and seek to balance sustainable use of the marine environment and conservation effort.

1.4

Aims and objectives

Cefas were commissioned by Defra under contract MB0117 to continue the inshore fishing activity mapping initiated under project MB0106 and to build upon this work to explore further applications of the dataset. Specifically the objectives of this component of the contract were: 1. To develop a series of data layers illustrating the location and intensity of inshore fishing activities from IFCA and MMO data covering years 2010-2012 and, where necessary, to provide support to IFCAs in the recording and analysis of the data. 2. To provide insights into the inter-annual changes in the location and intensity of inshore fishing activities and changes in relation to closed areas (e.g. MPAs or renewable developments). 3. To develop and demonstrate the concept of Zones of Influence for coastal fishing ports around England. 4. To present a case study on the potential application of the data layers developed. 5. To report and disseminate the outcomes of the project.

1.5

Scope

This work addresses the needs of Defra and the Statutory Nature Conservation Bodies (SNCBs) to have access to information on inshore fishing activities in order to assist in the assessment and designation of Marine Conservation Zones. The outputs also have wider relevance in support of marine planning and the assessment of human pressures in the marine environment. The geographical scope of the project was the entire coastline of England and Wales and the extent of IFC Districts, defined as 6 nautical miles from the baselines as they existed at 25th January 1983 (Defra, 2010b). Because IFCA and MMO data holdings extended beyond this area data analysis was extended to 12 nautical miles.

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2 Data and methods 2.1 2.1.1

Relative inshore fishing activity analysis Data

Around the English coastline there are 10 Inshore Fisheries and Conservation Authorities (IFCAs) tasked with the sustainable management of inshore sea fisheries resources in their local area (Figure 1). Around Wales the management of inshore sea fisheries resources is the responsibility of the Welsh Government.

Figure 1: Location of Inshore Fisheries and Conservation Authorities (Defra, 2011).

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Surveillance and enforcement of national and local fisheries regulations form a major component of the work of the IFCAs. Fishery patrol vessels and rigid inflatable boats (RIBs) are operated to undertake these surveillance and enforcement activities (Figure 2). The Welsh Government utilises similar resources to monitor fishing activities in Welsh waters, while the Marine Management Organisation operates an at-sea surveillance programme using Royal Navy Fisheries Protection Vessels and an aerial surveillance programme conducted under contract by Directflight Ltd. (MMO, 2014b) (Figure 2). In addition to vessel-based surveillance, IFCAs, Welsh Government and MMO also undertake extensive shore and port based enforcement activities.

Figure 2: Fisheries Patrol Vessels at sea (Source: Northeastern IFCA, Isles of Scilly IFCA, Royal Navy, Directflight Ltd).

The geographical intensity of these surveillance activities varies greatly depending on the local fisheries and their management requirements. Some fisheries are strictly regulated and require a considerable amount of patrol effort to enforce, while others are well managed by the local fishers and require less patrol effort. Other regulations are more effectively enforced by land-based Fishery Officers. It has long been recognised that knowing where fishing activities take place allows more effective management of the activities. The location and type of fishing vessel are therefore now routinely collected by IFCAs, Welsh Government and the MMO. As part of project MB0106 standardised data recording and analysis tools were introduced. With IFCAs and the Welsh Government now using the

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standardised data recording spreadsheets, collation of all IFCA data was significantly easier when compared to the MB0106 experience. However, the process of collating data remains reliant on individuals preparing, extracting and sending data. In some cases this led to delays in receiving data or data not being received at all.

All districts provided sightings data for this project: an

improvement on MB0106 which had to deal solely with boarding data for two districts. The minimum sightings data required for this project are: 

Sightings date (and time)



Fishing vessel location (Latitude & Longitude)



Fishing method (gear type)



Vessel name and registration (optional)

In addition to sightings data, the surveillance effort by the patrol vessels or aircraft is required to undertake the data analysis. There were some differences in the surveillance data available from the different IFCAs, the Welsh Government and MMO. Generally regular patrol track data were available, with a maximum separation time of 2 hours. Only the surveillance aircraft did not have track data available. Instead, information on the ICES sub-rectangles observed was used to calculate the surveillance effort. The sightings and surveillance effort data used in this project covered the period 1st January 2010 – 31st December 2012. For most IFCAs data were available for the entire period. As for the previous work, data were not available for the Isles of Scilly IFCA.

2.1.2

Methodology

The MB0106 methodology for analysing the data was used in this project. A schematic of the data analysis approach is detailed in Figure 3. Full details on the method can be found in Vanstaen & Silva (2010) with a summary provided below. The methodology builds on the work of Eastwood (2005) to analyse relative fishing intensity from sightings data. The method requires accurate positional data of locations where fishing vessels were seen fishing (sightings) and a measure of how often the location was observed to check for fishing activity. By taking account of the surveillance effort in an area, the sightings data are normalised to remove bias resulting from the surveillance programme.

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The fishing activities were categorised using standard gear codes (Annex I), building on the gear codes used by the MMO. The IFCA specific codes provide additional detail and would allow specific activities to be mapped separately. For the purposes of this project, maps were produced using five high-level gear classes: ď&#x201A;ˇ

Dredging

ď&#x201A;ˇ

Trawling

ď&#x201A;ˇ

Potting

ď&#x201A;ˇ

Netting

ď&#x201A;ˇ

Lining and commercial angling

An additional layer was produced for any gear types not captured by the above gear groups. This included activities such as handgathering, diving and spear fishing. It excluded those activities recorded as recreational angling, as extensive research into recreational angling activities was undertaken in the Sea Angling 2012 project (Armstrong et al., 2013). Additionally, mapped outputs were produced summarising the data into mobile (dredging and trawling) and static (potting, netting, lining and commercial angling) gear types. As part of generating the surveillance effort, a buffer was applied around the patrol tracks to take into account that fishing vessel observations can be made at a distance away from the vessel. The buffer distance was based on feedback from Inshore Fishery Officers and set at 2 kilometres. Spatial analysis of vessel locations relative to the patrol track undertaken as part of MB0106 confirmed this distance was appropriate. Although using an average of 2 kilometres will at times over- or underestimate the actual visibility distance, it was not possible to account for this using the data available. Using a time period of three years it is assumed that any such effects are averaged out. Once the data were standardised and arranged in a suitable format, the relative effort calculation was based on the following formula: đ?&#x2018;&#x2020;đ?&#x2018;&#x2013;đ?&#x2018;&#x201D;â&#x201E;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ?&#x2018; đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;˘đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;Ą đ?&#x2018;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x153;đ?&#x2018;&#x;đ?&#x2018;Ą (đ?&#x2018;&#x2020;đ?&#x2018;&#x192;đ?&#x2018;&#x2C6;đ??¸) =

đ?&#x2018; đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;&#x153;đ?&#x2018;&#x201C; đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x201D;â&#x201E;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ?&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;˘đ?&#x2018;&#x;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;?đ?&#x2018;&#x2019; đ?&#x2018;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x153;đ?&#x2018;&#x;đ?&#x2018;Ą

The data were analysed using a spatial grid at the resolution of 1/400th of an ICES rectangle as recommended by Eastwood (2005). This equates to a resolution of 0.05 degrees in a longitudinal direction and 0.025 degrees in a latitudinal direction, or approximately 3 by 3 kilometres. The grid size was recommended by Eastwood (2005) based on a compromise of needing to have small enough grid cells to be relevant for management purposes yet coarse enough to account for the limitations of the data. The results from this methodology were previously validated by Eastwood (2005) and Vanstaen and Silva (2010) using local expert knowledge. Vanstaen (2010) applied the same methodology to data

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for vessels over 15 metres length only and correlated results with VMS data. Good correlations were found, validating the approach and results.

Figure 3: Schematic of data analysis approach.

The varying surveillance effort affects the confidence of the resulting data layers presented in this report. It is therefore important for the end-user to appreciate the confidence of the data. For example, grid cells visited on a daily basis by the patrol vessel are more likely to accurately represent the fishing intensity in the area, whereas grid cells only visited a few times each year are less likely to present an accurate picture. The confidence classification adopted in this report is described in Table 1. This classification differs from that used by Vanstaen & Silva (2010) due to the improved MMO dataset used. Whereas for the 2007-2009 data analysis no surveillance effort was available for the overflight data, MMO have since started recording which ICES sub-rectangles were observed during each flight. The 2007-2009 dataset used a track created by connecting sightings data points as a proxy for the surveillance effort. The new surveillance effort is a lot more accurate but also results in significantly increased

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surveillance levels. Hence, the absolute surveillance efforts are not comparable. However, by using relative confidence levels, it is possible to compare old and new confidence classifications. Table 1: Confidence classification system

Average Surveillance Effort Confidence class over 1 year More than once in every 6 days High Less than once in every 6 days, but Moderate more than once in 2 weeks Less than once in 2 weeks Low No surveillance effort No Data The colour scheme used to visualise the relative fishing intensity was similar to that used by Vanstaen & Silva (2010). The colour scheme is optimised for visualisation of relative differences within a map and applied to all maps presented in this report, allowing comparison of relative intensities between different maps. A frequency histogram of data values demonstrates that the data distribution is heavily skewed towards low levels of activity (Figure 4). The geometric sequence adopted means each class upper limit is three times larger than the upper limit of the previous class (Table 2). Digital GIS files are also supplied with this report, which allow the end-user to develop alternative classification systems.

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Figure 4: (Top) Frequency distribution of the relative mobile fishing effort. (Bottom) Frequency distribution of the relative static fishing effort. Table 2: Data visualisation colour scheme and ranges

Class minimum

Class maximum

0 0.000001 0.012346 0.037038 0.111112 0.333334

0 0.012345 0.037037 0.111111 0.333333 1.0

Symbol colour

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2.2

Temporal analysis

The temporal analysis undertaken as part of this project focussed on two different scales and applications. At a national scale, temporal analysis of sightings data was undertaken to investigate trends in the location and intensity of inshore fishing activities. At a local scale, changes in fishing activity intensities were studied around closed areas. 2.2.1

National trends analysis

To undertake the national trends analysis two types of data analysis were undertaken. A first analysis concentrated on the combined 2007-2009 (MB0106) and 2009-2012 (MB0117) dataset and assessed inter-annual changes in the distribution of fishing activity. A second analysis compared the location of fishing activities between the 2007-2009 and 2010-2012 datasets. As the purpose of this analysis was to study changes in the distribution of fishing activities, the analysis did not make use of the absolute data values, but converted the relative fishing intensity values into presence (1) or absence (0) values. Presence and absence values were then summed for each grid cell for the different gear types to give an indication of the temporal change in activity (Table 3, Table 4). The data were visualised in ArcGIS where cells were coloured according to the presence/absence value for the different year combinations.

Table 3: Temporal activity classification (2007-20012 analysis)

Grid value

Nature of temporal activity

No fishing activity observed in periods 2007-2012 Fishing activity observed in one of six years Fishing activity observed in two of six years Fishing activity observed in three of six years Fishing activity observed in four of six years Fishing activity observed in five of six years Fishing activity observed in all six years

1 2 3 4 5 6

Colour scale

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Table 4: Temporal activity classification (2007/08/09 vs 2010/11/12 analysis)

Grid value

Nature of temporal activity

No fishing activity observed in periods 2007-2009 and 2010-2012 Fishing activity only observed in periods 2007-2009 or 2010-2012 Fishing activity observed in both 2007-2009 and 2010-2012

1 2

2.2.2

Colour scale

Local trends analysis

In order to investigate the effect of management measures on inshore fishing activities, data were reviewed for smaller areas.

The test locations considered were marine protected areas and

windfarm sites. In order to assess the impact on the fishing activity it was essential that the closure occurred during the timeframe of the data available. In this case, the data used covered the period 2007-2009. A number of windfarms were erected in the Thames Estuary during this period. In many cases, when the development is completed, fishing activities no longer take place within the windfarm. However, the construction period often spans months or, in some cases, years and so a trend analysis may be biased if an area was not fully closed at the time. We therefore limited analysis to windfarms where construction was mainly undertaken in 2008, allowing comparison between fishing activity levels in 2007 and 2009. The marine protected area considered as part of this study is the Lyme Bay and Torbay Site of Community Importance (SCI) and, in particular, the Lyme Bay portion of this site. The Lyme Bay area is an area of conservation interest because of the presence of rocky and stony reef features and sea fans (Eunicella verrucosa). The area has also long been an area of interest to local scallop fishermen. In 2001 a voluntary agreement was put in place closing part of the area to bottom trawling. In 2008 a Statutory Instrument was introduced closing a larger area to all demersal fishing activity using dredges or trawls. Changes in fishing activity levels between 2007 and 2009 were therefore studied at this site. Differences in absolute fishing effort values for the different years were calculated. Where a positive score resulted, fishing effort was higher in the earlier year than in the later year and where a negative score resulted, fishing effort was higher in the later year. Therefore a positive value indicated that there was a decrease in fishing effort between years while a negative value indicated an increase in fishing effort between years. The data were analysed and visualised in ArcGIS. Cells which had a zero effort for both years were excluded from the analysis to distinguish them from the cells which scored a zero because the effort was the same for both years.

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2.3

Zone of Influence

To complement the relative fishing intensity maps produced as part of this project, further work was undertaken to relate the spatial occurrence of fishing activities to coastal ports and fishing communities. This work builds on earlier work by Vanstaen et al. (2010) which explored the use of 'zones of

Zone of influence is defined as the geographic area where fishing activity contributes to the characteristics of the social, economic

and/or

environmental conditions.

influence' along the Sussex coastline. The United Nations Food and Agriculture Organisation (FAO, 2005) state that “the spatial distribution of the fishing activity is explained by the spatial distribution of three components: the abundance zone (i.e. the location of targeted biomass), the accessible zone and an authorized zone (i.e. the jurisdiction enforced in the area)”. Figure 5 shows a theoretical framework model of the causal relationships between fleet segment (in this study referred to as sector) and the spatial distribution of fishing effort.

Figure 5: Relationships between a fleet segment and the spatial distribution of its fishing activity (source FAO, 2005).

The purpose of this approach is an attempt to use empirical data to describe the “activity zone” and thus provide decision makers with an insight into the ‘zone of influence’ of inshore fishing vessels. 2.3.1

Data

A visualisation of the zone of influence was developed from the sightings data collected by IFCAs, the Welsh Government and MMO. Using the vessel registration associated with each sighting data point

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and vessel registration details available from the MMO (MMO, 2014a), the registered home port of each fishing vessel was associated with each vessel sighting data point. The analysis was limited to vessels under 15 metres overall length because personal communication suggested that the majority of these vessels operate from their home port on a daily basis. Further, VMS equipment fitted to vessels over 15 metres overall length would allow analysis of that part of the fleet. 2.3.2

Methodology

Using spatial analysis tools in ArcGIS, an envelope was developed around all sightings data associated with each fishing port (Figure 6). The zone of influence analysis assumes that the fishing vessel operates from the fishing port where the vessel is registered. In some cases, the vessel may have changed ownership and registration details have not been updated, and sometimes vessels may be temporarily based in another fishing port because of increased fishing opportunities in the area. As the aim of the zone of influence analysis is to identify the zone that contributes to local social and economic characteristics, those fishing vessel operating away from their registered home port were excluded from the analysis. This was achieved by excluding all vessel sightings more than 100 km from their home port. Further manual cleaning of outliers from the resulting zone of influence was undertaken. For example, around headlands where the distance from port would exceed 100 km.

Vessel sightings

Home port Figure 6: Development of a visual representation of the zone of influence from fishing vessel sightings data.

Using the zones of influence developed and the â&#x20AC;&#x2DC;polygon in polygonâ&#x20AC;&#x2122; analysis routines within ArcGIS, areas of overlap between zones of influence were calculated. The sum of the number of overlapping zones of influence was used to create a map illustrating interactions between zones of influence. Initially, zones of influence were developed using the sightings data covering the period 2007-2009. When all 2010-2012 data became available, a separate series of zones of influence were developed for this period.

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2.3.3 2.3.3.1

Economic spatial valuation – A Case Study Introduction

In order to demonstrate a practical application of the zones of influence concept, a case study was developed which linked these zones of influence to the 127 recommended marine conservation zones (rMCZs) around the UK. In order to designate these sites and to introduce management measures within them, UK Government requires information on how closing certain areas of the sea to fishing is likely to affect the local fishing population. Previous attempts to describe the spatial value of inshore fisheries were made by Vanstaen and Silva (2010). The validity of the results was questioned because landings data were only available at the scale of ICES rectangles and due to under-reporting in parts of the fleet. Figure 7 demonstrates this in an area to the south of Plymouth, where two ICES rectangles meet. Although relative fishing intensity data showed an even intensity in activity south of Plymouth, the spatial valuation showed a distinct difference as a result of the reporting at ICES rectangle scales. Using zones of influence we attempt to link fishing activity to a particular port and, therefore, fishing community. For this case study we have used landings values and number of vessels registered per port for the years 2007-2009. These statistics were obtained from the MMO’s Fishing Activity Database (FAD). Details on this dataset can be found in the MMO’s annual UK Sea Fisheries Statistic publication (MMO, 2013). The results presented in this report are for demonstration purposes of the concept only.

Figure 7: Extract of spatial valuation of inshore fisheries from Vanstaen and SIlva (2010).

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2.3.3.2

Methodology

Individual zones of influence (ZOI) were prepared and analysed using ArcGIS 10.1. ZOI for static and mobile gears were then overlapped with all 127 recommended marine conservation zones (rMCZs) to quantify common areas (in km²). Attribute tables for the mobile and static ZOI and for mobile and static overlaps were exported as text files and loaded into statistical software ‘R’. Information on total landings and total number of vessels per port for vessels under 15 m length for the years 2007-2009 was downloaded from the landings data register and also loaded into ‘R’. A statistical script was written which performed the following steps: 1. Combine the ZOI area table with the area of intersect table. 2. Calculate the percentage overlap of the intersecting area with the entire ZOI. 3. Summarise the landings values and number of vessel tables so two objects showing the sum of landings values and number of vessels for static and mobile gears for the years 20072009 were created. 4. Merge the tables so the tables created in step 1 now include the landings values and number of vessels per port. 5. Calculate the percentage of landings values and number of vessels potentially affected using the percentage overlap calculated in step 2. Final tables were exported as csv files and results interpreted for six example (r)MCZs and the relevant ZOIs with which they interact. The R script used for analysis can be found in Annex II. Using the zones of influence and the landings data available for each port, a spatial visualisation of the annual landings value per square kilometre was developed. The landings value (Great British Pounds) and area of the zone of influence (in km²) were used to obtain a monetary value per square kilometre. A 1 km² grid was used to summarise the overall landings values. As landings values were a combination of 2007, 2008 and 2009 data, the resulting value was divided by three to obtain an indicative annual landing value per square kilometre.

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3 Results 3.1

Data sources and trends

Sightings data are the source for the analyses presented in this report. Collation of all sightings data from IFCAs, Welsh Government and MMO for the period 2010-2012 resulted in 53,975 data points available for analysis purposes. This is a small decrease when compared to the total number of sightings available for the period 2007-2009 (at 55,743 data points). Comparison of the total number of sightings data collected by IFCAs and Welsh Government during the period 2007-2012 shows a decreasing trend in the number of sightings (Figure 8). It is believed that this reflects the decrease in surveillance effort by IFCAs as a result of funding pressures. For example, Eastern IFCA sold their patrol vessel ESF Protector III in 2012 (EIFCA, 2013), which previously was a major contributor to the sightings dataset. The vessel is being replaced by 2 Rigid Inflatable Boats (RIBs) which are less suitable to record sightings data due to the lack of space for recording equipment. A similar trend is seen in other IFCAs, where RIBs are increasingly being used for enforcement purposes. In other cases, the decrease in patrol effort can be associated with high operating costs for the larger patrol vessels.

Figure 8: Variation in sightings numbers recorded over time by IFCAs and Welsh Government.

The distribution of the sightings data is relatively similar for the period 2010-2012 compared to 2007-2009 (Figure 9). The main areas where a lower density of sightings is observed is off the Cumbrian coast and in the Wash. A higher density of sightings can be observed around Cornwall. Previously Cornwall IFCA only collected boardings data, which resulted in a lower number of data

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points. Cornwall IFCA has now adopted the same approach used by other IFCAs with the resulting increase in data volume. The confidence map for the data layers developed in this project is presented in Figure 10 and compared against the confidence data layer covering 2007-2009. As previously discussed, the 20102012 dataset uses an improved MMO surveillance effort dataset compared to 2007-2009. The result is improved knowledge of the actual surveillance effort, but also an overall increase in the actual surveillance effort. As a result the absolute surveillance effort values are not comparable between both datasets, however, the relative distribution shows a similar picture. Within large parts of the 06 nautical mile zone, where both IFCAs and MMO collect data, confidence is generally moderate to high. Beyond the 6 nautical mile boundary, and outside of the area of responsibility of the IFCAs, confidence is generally moderate to low, except in the southeast where high confidence is maintained in this area. In the 2007-2009 dataset areas along the south coast, Cornwall and Cumbria were presented using a different colour scale due to the reduced accuracy of the source data. Following the recommendation of Vanstaen & Silva (2010) the relevant IFCAs modified their procedures in order to obtain the appropriate data. As a result, the 2010-2012 does not have any areas with low accuracy sightings or surveillance data. The lower confidence outside of 6 and 12 nautical miles is of less concern as most vessels under 15 metres overall length will not fish in this area (Vanstaen & Silva, 2010) and higher confidence data from VMS will be available for the larger vessels fishing in this area. Since vessel activity data for vessels over 15 m length is captured by the satellite based Vessel Monitoring System (VMS), the dataset derived from the sightings data is complementary to the VMS data by providing relative fishing intensity data for the 0 to 12 nautical mile zone, almost exclusively fished by vessels under 15 metre length. Based on the generally lower confidence outside the 12 nautical mile limit, combined with the reduced number of smaller vessels, it is recommended that the relative fishing intensity maps presented in the report are primarily used within the 0 to 12 nautical mile zone. Where confidence is low within the 0 to 6 nautical mile zone, it is recommended that the maps presented in this report are not used in isolation.

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Figure 9: Comparison of sightings recorded in 2007-2009 (left) and 2010-2012 (right). Results presented included IFCA, Welsh Government and MMO data.

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Figure 10: Comparison in relative confidence between the relative fishing intensity maps covering 2007-2009 (left) and 2010-2012 (right).

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3.2

Relative inshore fishing analysis

Using the data available, a series of maps were produced for the main gear types used by inshore fishing vessels as summarised in the table below. Gear type Dredging

Fishing activities

Figure number All fishing methods Figure 16 using towed dredges or suction dredges

Example vessel

Figure 11: Example scallop dredger www.trawlerphotos.co.uk - User: jimmyt).

Trawling

(From

All bottom and pelagic Figure 17 trawling activities. The activities include beam trawling, otter trawling, and seines. Figure 12: Example of trawling vessel (From www.trawlerphotos.co.uk - User: valhalla).

Netting

All static fishing Figure 18 methods using nets, including gill, trammel and drift nets.

Figure 13: Example of netting vessel (From www.trawlerphotos.co.uk - User: BOBE).

Potting

All potting activities Figure 19 such as crab and lobster potting; whelk potting; and potting for prawns, Nephrops or cuttlefish.

Figure 14: Example of potting vessel (From www.trawlerphotos.co.uk - User: watchman).

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Gear type

Fishing activities

Figure number Lining and All fishing methods Figure 20 Commercial using handlines and Angling longlines, as well as commercial angling operations.

Example vessel

Figure 15: Example of lining vessel www.trawlerphotos.co.uk - User: sam).

Other gears

Mobile gears

Static gears

(From

Any fishing activity not Figure 21 captured by the above gear groups Dredging and trawling Figure 22 data were combined to produce relative fishing intensity maps for mobile fishing gears. Netting, potting and Figure 23 lining and commercial angling activities were combined to produce relative effort maps for static fishing gears.

Figure 16 shows that the main areas used by vessels fishing dredge gears in 2010-2012 are situated along the Essex coast, the Solent, East Devon and Cardigan Bay. In comparison with the 2007-2009 data it can be noted that dredging activity has disappeared from the Wash. This is a result of cockle gathering in this area being restricted to hand gathering since 2009 (Pers. Comm. Ron Jessop, Eastern IFCA). Also notable is the decrease of activity in the Lyme Bay region where, since 2008, a closure was introduced to all bottom trawling gears. Figure 17 shows that the main areas used by vessels fishing trawl gears in 2010-2012 are situated in similar areas to those found in 2007-2009. The areas experiencing the highest intensity remain situated east of Newcastle, along the south coast and off the Cumbrian coast. However, it is notable that the area trawled off the Cumbrian coast has reduced in extent. A similar observation can be made in the Wash, where activity has concentrated into smaller areas. Sightings data cannot differentiate between pelagic and demersal trawling so further analysis was undertaken from fishing activity database records. The activity days data for vessels under 15 m length for England and Wales showed that just 1.76% of all fishing activity days for trawlers was undertaken using midwater trawls, and so 98.24% of activity days by trawlers involved demersal Distribution and trends in inshore fishing activities and the link to coastal communities

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gears. Therefore, over 98% of the trawling effort represented in Figure 17 will be demersal trawling, towing gear over the seabed. Figure 18 again shows similarity in the relative fishing intensity for netting between 2010-2012 and 2007-2009. The main areas of activity are situated along the North-eastern and South-eastern coastline. The activity remains restricted to the areas closest to the shoreline. Figure 19 shows the intensity and distribution of potting effort. Overall, the relative effort for 20102012 is similar to that observed in 2007-2009. The coastline north of the Humber shows high levels of activity, and activity is also high and widespread along the south coast. A notable increase in activity can be observed in the Lyme Bay region, whereas the data suggest a decrease in activity around Wales, although data confidence in these areas is moderate to low. The lining and commercial angling effort shown in Figure 20 shows major differences between the 2007-2009 and 2010-2012 datasets. Whereas activity was limited, and of low intensity, in 20072009 by 2010-2012 more areas of intense activity can be observed, including Northumberland, South Wales, and the south and south-east coast. It is likely that the lining and commercial angling effort was underestimated in the 2007-2009 dataset due to the differences in recording of the effort at the time. The standardised recording format introduced by Vanstaen & Silva (2010) has provided clarity and standardisation between IFCAs and has ensured commercial and recreational effort are defined as separate activities. Previously this was not always the case and on that basis some data had previously been excluded from the analysis. Figure 21 brings together the remaining vessel sightings which were not associated with any of the above gear classes. This activity may be associated with e.g. hand gathering (as observed in the Wash), diving and spear fishing. Recreational angling is excluded from this category. Activity intensity is very high within the Wash where the fishery has changed from a dredge fishery to a hand-raked mollusc fishery.

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Figure 16: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using dredges.

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Figure 17: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using trawls.

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Figure 18: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using nets.

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Figure 19: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using pots.

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Figure 20: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using lines.

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Figure 21: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using other gears.

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Figure 22: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using mobile gears.

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Figure 23: Comparison in relative fishing intensity between 2007-2009 (left) and 2010-2012 (right) for fishing vessels using static gears.

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Although the absolute value ranges of the 2007-2009 and 2010-2012 datasets differ, using the same approach to develop the colour scale for both datasets has allowed visual comparison of the results. While visual assessment is useful, it does not allow quantitative comparison of the data and the correlation between both datasets was therefore explored. Using data from high confidence areas only, the relative fishing intensity data values from the 2007-2009 and 2010-2012 datasets were compared with each other for mobile and static gear types.

Figure 24: Correlation between 2007-2009 and 2010-2012 relative fishing intensity for mobile (top) and static gear types (bottom).

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Figure 25: Correlation between relative fishing intensity for individual gear types.

Figure 24 shows the correlation between both datasets for mobile and static gears. Figure 25 presents results for individual gear types. In most cases the correlation coefficient is greater than 0.6, indicating a statistically strong relationship although there is significant noise in the dataset. Distribution and trends in inshore fishing activities and the link to coastal communities

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This noise is likely to be an artefact of the methodology used to describe the effort, providing a relative measure of effort as opposed to absolute value, as well as the inter-annual variation in the distribution and intensity of fishing effort. In most cases the relative fishing intensity value in 2007-2009 was approximately twice as high as that for 2010-2012. This is a result of the more accurate estimate of surveillance effort from MMO aerial surveillance, which was underestimated in the 2007-2009 dataset due to lack of accurate data. For potting activity, the 2007-2009 relative effort was only 1.3 times higher than that of 2010-2012. For lining and commercial angling, a weak negative correlation was found which can be explained by the significant differences between both datasets as a result of the modifications to the data recording, which led to major improvements in the 2010-2012 assessment. Although generally strong correlations were found, care should be taken when using the actual correlation equations to combine or compare both datasets to undertake quantitative assessments of changes the distribution and intensity of inshore fishing activities.

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3.3 3.3.1

Temporal analysis National trends analysis

A series of analyses were carried out to investigate temporal changes in the distribution of inshore fishing activities. For most gear types and areas similar trends can be observed: a core area where fishing activity takes place most years, surrounded by areas of gradually decreasing frequency of activities taking place over the 6 year period. An example from the Cardigan Bay area is shown in Figure 26.

Figure 26: Detail of core area with annual fishing activity surrounded by areas of decreasing frequency of activity. See Table 3 for colour legend.

Whereas for most fishing activities the area with repeated activity is often restricted, results for trawling showed extensive areas with year on year activity, although not necessarily for all years. This suggests that, of all fishing activities considered, trawling is the most nomadic in its distribution. For most gear types, core areas can be recognised, showing that fishers revisit the same area year after year. The proportion of grid cells visited every year is larger for static gears than for mobile gears, with the area of lower frequency around the core area often smaller for static gears than mobile gears. This is also illustrated by the summary histograms shown in Figure 27.

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Figure 27: Proportion of grid cells with fishing activity in multiple years. Red lines shows percentage of total number of grids cells with no fishing activity. See Table 3 for colour legend.

Figure 28 and Figure 29 illustrate changes in distribution over a six year period. Blue hues indicate areas where activity was observed in the majority of years, while yellow hues indicate areas where activity only took place in a few of the years. The main outlier in the results is the map presented for lining and commercial angling. As discussed before, this is likely to be a result of improved recording, which underreported this activity in the period 2007-2009.

Comparison between Figure 28, Figure 29 and Figure 16 to Figure 23 suggests that in many cases the areas of repeated fishing activity coincide with those areas with highest levels of fishing intensity. Figure 30 presents the results of a correlation analysis between both datasets for mobile and static gears. The relative effort data used in this case covers the period 2010-2012, but the same analysis using 2007-2009 showed similar results. It shows how, in areas where fishing activity took place in just a few of the 6 years, the relative effort was generally low. In those areas where fishing activity took place in most years, relative fishing intensity are more likely to be higher. The red line in these graphs show the average relative effort and demonstrates the increasing trend in relative fishing intensity with increased frequency.

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Figure 28: Classification according to number of years within which fishing activity took place in each analysis grid cell during period 2007-2012 (trawling, dredging, potting, and netting).

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Figure 29: Classification according to number of years within which fishing activity took place in each analysis grid cell during period 2007-2012 (lining & commercial angling, mobile, and static gears).

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Figure 30: Correlation between frequency of fishing effort and relative fishing intensity: (top) mobile gears; (bottom) static gears.

An additional analysis was undertaken to compare the distribution of activity in 2007-2009 and 2010-2012. The purpose of this analysis was to identify longer term changes in the distribution of the activity, rather than inter-annual changes. Figure 32 and Figure 33 illustrate the changes in the distribution for the different gear types. In many areas similar patterns can be observed as for the inter-annual comparison, with the area fished in both datasets surrounded by grid cells only visited in one of the years.

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For mobile, trawling, static, and potting activities, the maps suggested roughly equal areas fished in one of the periods versus areas fished in both periods. This was confirmed by quantitative analysis of the data (Figure 31) which showed that, for those gear groups, the proportion of grids cells fished in one of the periods (2007-2009 or 2010-2012) was roughly equal to the proportion of cells fished in both years (50% +/- 10%). For netting and dredging a larger proportion of grid cells was fished in only one period compared to both. For dredging activities, the differences can partly be explained by the shift from dredging to handgathering of molluscs in the Wash. For lining, a small proportion of grid cells was found to be fished in both periods. As discussed before, changes in data recording will be a major source for this difference as opposed to real changes. Although the results suggests that netting and dredging are subject to more long term shifts in distribution, it should be noted that these activities are also more spatially restricted and therefore opportunities may be still more restricted than for activities taking place over wide areas such as trawling.

Figure 31: Percentage of grid cells fished in 2007-2009 and 2010-2012.

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Figure 32: Classification comparing location of fishing activity between 2007-2009 and 2010-2012 dataset (trawling, dredging, potting, and netting).

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Figure 33: Classification comparing location of fishing activity between 2007-2009 and 2010-2012 dataset (lining & commercial angling, mobile, and static gears).

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3.3.2

Local trends analysis

Whereas the previous section explored large scale differences in the distribution of fishing activities, this section explores local changes in fishing patterns in response to management action. A case study was undertaken in the Lyme Bay area where a marine protected area banned bottom trawling from mid 2008 onwards. Due to the need for enforcement of the fisheries byelaw, regular patrols were undertaken in the area, resulting in good confidence in the relative fishing intensity estimates. Figure 34 illustrates the changes in relative fishing intensity for four different gear types. Lining and commercial angling was excluded from the analysis as no such activity had been recorded in this area. As expected, results show a decrease in towed gear activity within the Lyme Bay SI closure area, an increase in netting and a significant increase in potting effort. Two grid cells within the closed area suggest a small increase in dredging effort between 2007 and 2009. Due to the closure, no such effort should have taken place within the area. As these cells are located near the edge, it is possible they are due to minor errors in the reporting of the vessel position. Figure 36 demonstrates how the data successfully identify the decreased mobile gear activity following the closure of the Lyme Bay SI closure in 2008. As discussed previously, differences in reporting have resulted in the 2007-2009 and 2010-2012 datasets providing different absolute values. Section 3.2 discussed correlations between the 2007-2009 and 2010-2012 datasets, which provide an indication of the relationship between both datasets. To undertake detailed local analysis, local correlations were assessed using the 2009 data, which were analysed using the MB0106 (old) and MB0117 (new) approach. Due to the reduced mobile gear effort, no correlation could be produced for the Lyme Bay SI closure area and the correlation as presented in section 3.2 was used. The correlation for fishing activities using potting gear is shown in Figure 35. Overall, the results show that, after 2008, a significant decrease in fishing activities using mobile gear was observed within the Lyme Bay SI area with such activity reducing to negligible levels. At the same time, a doubling in potting effort can be observed within the site. Outside the closed area, a significant increase in dredging effort can be seen to the south and southeast. Further afield, increases in dredging effort can also be seen off Exmouth and in Start Bay. For trawling activities, in the majority of areas a decrease in activity was observed, especially in inshore areas. Further offshore along the Devon coastline, increase in trawling activity can be observed. Potting activities generally show an increase in relative effort in most areas.

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Figure 34: Changes in relative fishing intensity between 2007 and 2009 following the 2008 SI closure to mobile fishing gears in Lyme Bay.

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Figure 35: Correlation between old and new analysis methodology for 2009 data for potting activities within the Lyme Bay SI closure area.

Figure 36: Temporal change in relative fishing intensity for mobile and potting gear within the Lyme Bay SI closure area.

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With mobile gears and potting showing most changes as a result of the closure (Figure 34), further analysis was undertaken for these gear types. Analyses were undertaken to compare the changes in fishing activity levels within the closed area, compared to the area outside of the closed area. Fishing activity levels were summarised in buffered areas 2, 10, 25 and 50 kilometres around the closed area, with data beyond 6 nautical mile excluded from the analysis due to reduce data confidence in this area. Figure 37 shows a subset of the temporal changes in relative effort levels for mobile and potting gear types. For mobile gears a significant decrease in activity was seen within the site as a result of the SI closure. Just outside of the site, in 2009 an increase in activity was seen. Over time, intensity levels just outside, and in the wider area, decreased for mobile gear activities. In the wider area, overall mobile gear effort reduced to levels of approximately half of those seen before the SI closure. For potting gears, a doubling of effort was seen after the SI closure within the site and just outside. Further afield, similar trends of increase potting effort can be observed although as distance from the closed area increased, the smaller was the relative increase in potting effort.

Figure 37: Changes in relative fishing intensity for mobile and potting gears within the Lyme Bay SI closure area, in the area immediately outside (2 km) and in buffered area 50 km outside of the Lyme Bay SI closure area.

Splitting the mobile gear effort into dredging and trawling components (Figure 38) shows that an increase in activity is observed only in the area immediately beyond the closure. Trawling effort in particular increased in this area following the SI closure. Dredging effort increased in 2009 following a decrease in 2008, but reduced to levels below those observed in 2007 from 2010 onwards. Distribution and trends in inshore fishing activities and the link to coastal communities

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Further afield, dredging activity levels remained relatively stable over time, whereas trawling effort gradually decreased in all areas.

Figure 38: Changes in relative fishing intensity for dredging and trawling activities within the Lyme Bay SI closure area, in the area immediately outside (2 km) and in buffered area 25 km outside of the Lyme Bay SI closure area.

Results therefore suggested that, as a result of the SI closure, there has not only been a decrease of mobile gear activity within the site, but also within the wider area. The decrease in mobile gear effort seems to be compensated by an increase in potting effort, especially in areas nearest the closure. These quantitative observations generally agree with the qualitative assessments made by Mangi et al. (2012) who reported increased static gear activity and displacement/reduction in towed gear activities. Mangi et al. (2012) also reported an increase in recreational angling and charter boat activities. Figure 39 confirms the high intensity of lining and commercial angling taking place within the Lyme Bay SI closure area (2010-2012 data), with low levels in surrounding areas: a significant change when compared to 2007-2009, when apparently no such activity took place. However, care should be taken as the 2010-2012 benefits from improved clarity in the recording of angling activities and in the differentiation between recreational and commercial angling activities, which was not always possible for the 2007-2009 data. Temporal analysis in the Lyme Bay region provided useful insights on changes in the distribution and intensity of fishing activities following the introduction of fisheries management measures. Other local comparisons around small windfarms, where the area is closed gradually to fishing activities Distribution and trends in inshore fishing activities and the link to coastal communities

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due to the gradual increase in turbine installations over time, proved less successful. Both the small size of the 'closed areas' and the gradual closure are expected to have contributed to this. Therefore it may not be possible to use these data to undertake temporal analysis in all cases, especially where the size of the closed area is smaller than, or of similar size to, the analysis grid cells.

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Figure 39: Comparison between 2007-2009 and 2010-2012 lining and commercial angling activity in Lyme Bay.

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3.4

Zone of Influence

Zones of influence were produced from all sightings data for vessels under 15 metres length. The work by Vanstaen et al. (2010) showed that the zone of influence for static and mobile gear fleets were very different. This study therefore separated, for the first time, the zones of influence by gear type. The analysis was undertaken both on the 2007-2009 and 2010-2012 sightings dataset. For every fishing port around England and Wales, both static and mobile gear zones of influence were developed. Where a zone of influence was made from less than 5 sightings data points it was excluded from the final data layer developed (Table 5). It was deemed that with such a low number of data points the zone of influence could not be accurately described. Table 5: Ports for which the mobile or static gear ZoI is made up of 5 or less sightings.

Mobile Cemaes Bay Eastbourne Eyemouth Felixstowe Fishguard Helford river Holyhead Ipswich

Newquay Porthleven Ramsgate Salcombe Seaham Seahouses Tollesbury Wivenhoe

Littlehampton

Static Aberdaran Aldeburgh Amlwch Axmouth Blakeney Boscastle Broadstairs Bude Caernarvon

Cardiff Conwy Cowes Cullercoats Eyemouth Felixstowe Flamborough Hythe Leigh-on-Sea

Margate Maryport Mullion Penzance Sheringham St Mawes West Mersea

Due to extensive overlap between the zones of influence of nearby ports, visualisation of the results is difficult using a single figure in this report. The results are best explored interactively in a Geographic Information System (GIS). Figure 40 demonstrates the results for a number of ports in Cornwall and Devon. The results show how the size of the zone of influence varies significantly between ports. For example, the static gear zones of influence for Looe is about twice the size of the zone of influence for Salcombe, and the static gear zone of influence for Newlyn is 2 - 3 times the size of the zone of influence of Looe. The mobile gear zone of influence for Newquay was excluded due to low numbers of sightings making up the zone (Table 5), while no mobile gear sightings were associated with vessels from Port Isaac. In most cases, and demonstrated by the zones of influence of Bideford and Looe in Figure 40, the zone of influence for mobile gear vessels was found to be larger than that for static gear vessels. Figure 41 shows the average area covered by all zones of influence and shows that the average static gear zone of influence is about half the size of the mobile gear zone of influence. This result confirms the observations by Vanstaen et al. (2010) and reaffirms the need to separate static and mobile zones of influence.

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Figure 40: Zone of influence for selected ports in southwest England.

Figure 41: Average area of static and mobile gear zones of influence.

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Figure 42 demonstrates a practical application of the zone of influence concept in the context of marine protected areas or offshore developments (the red rectangle on the map). Whereas the relative fishing activity maps are useful for spatial identification of those areas where most activities take place, they present challenges in relating the activity back to coastal fishing communities. In the case of marine protected areas or offshore developments, which have the potential to reduce fishing opportunities, an understanding of which fishing communities are likely to be impacted is crucial. Figure 42 provides an example showing the different impacts on the fishing fleets from Exmouth and Beer. Restriction of fishing opportunities in the red area will have an impact on both the static and mobile gear fleet from Exmouth. The impact on the static gear fleet is likely to be more significant, as the red area covers approximately 25% of the static gear zones of influence, compared to less than 10% of that for the mobile gear fleet. By contrast, the impact on the Beer fleet will be more significant for the mobile gear fleet, as almost half of their zone of influence coincides with the red area. The static gear fleet from Beer is unlikely to be affected by any management measures as their zone of influence does not overlap the red area. Application in the context of the introduction of marine conservation zones is further explored in section 4.4.

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Figure 42: (Top) Exmouth mobile (pink) and static gear (blue) zone of influence and interaction with fictional closed area; (Bottom) Beer mobile (pink) and static gear (blue) zone of influence and interaction with fictional closed area.

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Zones of influence for static and mobile gears were summarised as density plots (Figure 44). Where zones of influence for different ports were found to intersect, the number of zones overlapping were counted. The result is a heat map which highlights those areas of importance to the largest number of coastal fishing communities. The map allows identification of areas where there is highest potential for conflict, or greatest likelihood of a large number of fishing communities being affected by the introduction of fisheries restrictions. For example, the area to the southwest of Plymouth has the highest number of mobile gear fleets operating within the same spatial area. Currently, this heat map only visualises the number of intersecting zones of influence. There are a number of applications that can be further explored to build on this work. For example, the size of the fleet for each port could be linked to the zone of influence and, rather than the number of fishing communities affected by any fisheries restrictions, the resulting map would give an idea about the number of vessels that would potentially be affected. This would be more relevant in the context of a socio-economic assessment. Such an approach would be similar to the theoretical modelling work undertaken by Witt et al. (2012), but with the added benefit of using actual zones of influence instead of predicted, maximum, circular extents. Another approach, using landing values, is further explored in the case study in the next section.

The majority of results presented in this report make use of the zones of influence developed for 2007-2009, as they could be developed at an early stage in the project. Zones of influence were also derived from 2010-2012, but only came available at a late stage in this project due to delays in getting hold of the data. Correlation between the area of the zone of influence for the 2 datasets (2007-2009 and 2010-2012) shows a reasonable 1:1 correlation for mobile gears, whereas the correlation for static gears is poor to very poor (Figure 43). The large variation in the correlation can be explained by the fact that zones of influence are based on the maximum extent. As detailed in 3.3.1, inshore activities seem focussed in core areas visited year after year, with peripheral areas visited less frequently. Those peripheral areas visited less frequently will have a major influence on the shape and extent of the zone of influence. Additional work could be undertaken to better understand temporal changes in the zone of influence.

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Figure 43: Correlation between zone of influence area based on 2007-2009 and 2010-2012 sightings data for vessels under 15m. (Top) Mobile gears; (Bottom) Static gears.

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Figure 44: Spatial overlap between static (left) and mobile (right) zones of influence.

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3.5

Case study: valuation of inshore fisheries

Table 6 shows a subset of results from the case study which explored overlaps between zones of influence and (recommended) MCZs. The potential impact on landings values and number of vessels affected by worst case scenario management measures (total closure to fishing) for six (r)MCZs are shown in this table. Each of these results are illustrated and discussed below.

The Cumbria Coast MCZ overlaps with the mobile gear zones of influence of three ports: Fleetwood, Maryport and Whitehaven (Figure

45).

However,

the

percentage

overlap of the MCZ compared to the size of the zones of influence is very small, 0.55%, 0.38% and 0.55% for Fleetwood, Maryport and Whitehaven respectively (Table 6). This has negligible affect on landings values (£1199, £2691 and £5628 respectively) and number of vessels affected per port (0.29, 0.36, 0.87 respectively). Although the MCZ Figure 45: Overlap of mobile gear zones of influence for the Cumbria Coast MCZ.

interacts with several zones of influence the effect on the mobile fishing fleets is very small.

The Skerries Bank and Surrounds MCZ overlaps with the Brixham and Plymouth mobile gear zones of influence (Figure 46). This example shows the greatest affected landings values from any port for mobile gears. The introduction of the MCZ could potentially lose over £850,000 across three years from Brixham port and affect 35 vessels. The loss from Plymouth is less but still

substantial

£315,000

and

approximately 19 vessels. The Brixham Figure 46: Overlap of mobile gear zones of influence for the Skerries Bank and Surrounds MCZ.

value indicates a potential annual loss for

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the port of over £250,000 should mobile fishing gears be banned within the MCZ. Looking at the map this might come as a surprise since, compared to the size of the zones of influence, the MCZ is relatively small with both ports having approximately 5% overlap. However, landings to these ports are large and so this represents a large sum of money overall. The largest percentage overlap for mobile zones of influence is between the Thames Estuary and Canvey Island. The small inshore zone of influence lies almost completely within the Thames Estuary rMCZ, with a 98% overlap. This relates to £241,275 of potential vessel landings with more than 50 vessels potentially affected (Table 6). The Holderness Offshore and Inshore rMCZs have the highest percentage overlap (Holderness Inshore) and the largest

affected

landings

value

(Holderness Offshore) for the static fleet (Figure 47). Holderness Inshore overlaps completely with two zones of influence: Hornsea and Withernsea. Closures for static gears in these rMCZs have the potential to lose 100% of static fishing available to both fishing communities, equating to £1,353,042 and 34 vessels for Figure 47: Overlap of static gear zones of influence for the Holderness Offshore and Holderness Inshore rMCZs.

Hornsea and £612,503 and 32 vessels for

Withernsea. The Holderness Offshore rMCZ with static gear closures would also affect 38% of the Bridlington static zone of influence but, due to high landings value over three years, this could mean a potential loss in excess of £5 million affecting more than 100 vessels (Table 6).

The Bembridge rMCZ overlaps with the static zones of influence of five different ports: Langstone Harbour, Portsmouth, Hayling Island, Isle of Wight and Selsey (Figure 48). It has the potential to have to the largest effect on Langstone Harbour and Portsmouth with Figure 48: Overlap with static gear zones of influence for the Bembridge rMCZ.

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potential landings value losses of £110,910 and £116,460 respectively and affecting 15 and 14 vessels respectively. The zones of influence for the other three ports have relatively small overlaps, low monetary values, and small numbers of vessels affected (£1 752, £28 448 and £2 281; 0.24, 2.81 and 0.13 for Hayling Island, Isle of Wight and Selsey) (Table 6).

We reported in section 4.3 that the average size of the zone of influence was approximately 1,200 km2 for mobile gears and 600 km2 for static gears. The size of an average ICES rectangle around England and Wales is approximately 3,800 km2. The approach of using zones of influence to redistribute landings value therefore has benefits over the approach using ICES rectangles as used by Vanstaen and Silva (2010). The zones of influence have much improved spatial definition and therefore the landings value is more closely associated with the area fished, thereby improving the spatial value estimates for inshore fisheries.

Table 7 shows the five highest percentage overlaps between an rMCZ and zones of influence for both static and mobile gears. For mobile gears these are: Thames Estuary with Canvey Island; Padstow Bay and Surrounds with Newquay; Skerries Bank and Surrounds with Dartmouth; Blackwater, Crouch, Roach and Colne Estuaries overlapping with Burnham-On-Crouch; and Holderness Offshore and Bridlington. The maximum overlap is almost 100% with the remainder having overlaps of between 30-45%. For static gears the top five overlaps are: Holderness Inshore with Hornsea, Withernsea and Hull; Cromer Shoal Chalk Beds with Cromer; and Holderness Inshore with Grimsby. It is interesting to note that four of these top five relate to overlaps with a single rMCZ (Holderness Inshore). These overlaps for static gears range from 70 to 100% indicating that overlaps with static gears tend to be higher than overlaps for mobile gears due to static gear zones of influence tending to be smaller and closer inshore.

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Table 6: Subset of (r)MCZ, ZOI overlaps showing the area of overlap, total landings and number of vessels for each port as well as the calculated potentially affected landings values and number of vessels.

Gear

Mobile

(r)MCZ Name

Cumbria Coast (multiple ZOIs affected)

Skerries Bank and Surrounds (largest affected landings)

Mobile

Thames Estuary (largest percentage overlap)

Static

Holderness Offshore (largest affected landings)

Static

Holderness Inshore

Static

Bembridge (multiple ZOIs affected)

Port Name

ZOI area 2 (km )

Area of overlap 2 (km )

Percentage overlap

Total landings for port (£) (2007-2009)

FLEETWOOD

305.45

1.68

0.55

218,458

Total number of vessels for port 52

MARYPORT

2486.33

9.38

0.38

713,557

WHITEHAVEN

1453.74

7.93

0.55

BRIXHAM

3883.82

219.31

PLYMOUTH

4532.40

Potentially affected landings (£) (2007-2009)

Potentially affected vessels

1,199

0.29

2,691

0.36

1,031,219

160

5,628

0.87

5.65

15,067,969

632

850,870

35.69

232.93

5.14

6,142,517

365

315,679

18.76

9.96

9.80

98.32

245,391

241,275

50.14

2043.95

771.76

37.76

13,729,315

310

5,183,928

117.05

HORNSEA

56.84

100

1,353,042

34.00

WITHERNSEA

36.87

100

612,503

32.00

178.80

53.05

29.67

373,817

110,910

14.83

2011.33

84.77

4.21

2,763,122

323

116,460

13.61

134.07

2.72

2.03

86,271

1,752

0.24

1205.95

24.42

2.02

1,405,169

139

28,449

2.81

511.95

0.39

0.08

2,977,356

174

2,282

0.13

CANVEY ISLAND

BRIDLINGTON

LANGSTONE HARBOUR PORTSMOUTH HAYLING ISLAND ISLE OF WIGHT SELSEY

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Table 7: Ranking by percentage overlaps from highest to lowest for mobile and static zones of influence separately.

Rank

Gear

(r)MCZ Name

Port Name

ZOI area 2 (km )

Area of overlap

Mobile

Thames Estuary

CANVEY ISLAND

9.96

9.80

98.32

245,391

241,275

Mobile

Padstow Bay and Surrounds

NEWQUAY

3.07

1.38

44.93

53,911

24,221

Mobile

Skerries Bank and Surrounds

DARTMOUTH

316.23

137.81

43.58

220,827

96,234

Mobile

Blackwater, Crouch, Roach and Colne Estuaries

BURNHAM-ONCROUCH

85.62

28.92

33.78

19,969

6,746

Mobile

Holderness Offshore

BRIDLINGTON

2149.03

687.31

31.98

246,908

78,967

Static

Holderness Inshore

HORNSEA

56.84

100.00

1,353,042

Static

Holderness Inshore

WITHERNSEA

36.87

100.00

612,503

Static

Holderness Inshore

HULL

62.39

99.99

165,097

165,087

Static

Cromer Shoal Chalk Beds

CROMER

80.65

80.41

99.70

1,287,429

1,283,520

Static

Holderness Inshore

GRIMSBY

183.44

133.15

72.58

3,553,471

2,579,257

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Percentage overlap (%)

Total landings for port (£) (2007-2009)

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Total number of vessels for port

Potentially affected landings (£) (2007-2009)

Potentially affected vessels

While Table 6 and Table 7 provide a tabular summary of the statistics which can be derived from the data, Figure 49 and Figure 50 provide a visual representation of the spatial distribution of affected value using the zones of influence approach. For mobile gear activities the results show highest affected annual values off Newcastle, in the Wash, the Thames estuary and along large stretches of the south coast. This distribution parallels the distribution of the highest fishing activities intensities shown in Figure 22. For static gear, higher values per square kilometre can be observed compared to mobile gear activities. This confirms earlier observations that static gear activities are spatially more restricted and more intense in these areas, resulting in higher landings values per area fished. High values can be observed along large stretches of the coastline, with highest values off the Northeast coast, the Sussex and south Devon coast. Again, this distribution parallels the distribution of the intensity of static gear activity observed in Figure 23. It should however be noted that some ports, with significant landings values, were excluded from this analysis on the basis that their zone of influence was made up of very few sightings (Table 5). Figure 44 presented areas with greatest overlap of zones of influence, however Figure 49 and Figure 50 demonstrate that it is not necessarily in those areas where highest landings returns are obtained. Therefore, although management measures in these areas are likely to affect a greater number of fishing communities, the economic impact may still be less than in other areas. The approach taken here to visualise the landings data spatially using zones of influence can also be applied using different metrics. For example, the number of vessels based in each port could be linked with the zone of influence and a spatial representation of the number of vessels using each part of the seabed could be visualised. The number of vessels affected by a spatial closure could therefore be assessed.

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Figure 49: Spatial valuation using the zones of influence approach for mobile gear activities.

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Figure 50: Spatial valuation using the zones of influence approach for static gear activities.

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4 Limitations 4.1

Relative fishing intensity

Availability of data on inshore fishing activities remains relatively poor compared to the more data rich offshore activities which have accurate VMS data and electronic logbooks. The relative fishing intensity maps presented in this report were produced using the best available data. However, due to the data poor nature of the dataset, a number of limitations should be kept in mind by end users: 

The patrol effort by IFCAs, Welsh Government and MMO FPVs and patrol aircraft are optimised for enforcement purposes and not for the collection of sightings data. Areas with fewer fisheries enforcement issues are therefore likely to be visited less often and result in lower data confidence;



An observation buffer is applied around each track, generally set at 2 kilometres. This is likely to over- or under-estimate the actual observation distance locally, but we anticipate the effect being compensated by averaging the data over 3 years;



The majority of patrols will be undertaken during daylight hours, the dataset may therefore not accurately capture activities taking place at night;



Those areas that are visited most frequently by patrol vessel and/or aircraft will provide a better indication of the fishing effort in the area. It is suggested that the relative effort maps not be used in isolation where confidence is low;



Maps are indicative of areas where fishing activities occur. The fact that no fishing activity has been observed in some areas does not mean that no fishing activities have taken place in these areas;



Changes in data reporting and recording means that the 2007-2009 and 2010-2012 data are not directly comparable. Correlations between both datasets were assessed but users should be aware of the large variation in the data and care should be taken when combining and using the derived results.

4.2

Temporal analysis of inshore fishing activity

Assessments of temporal changes in inshore fishing activities were undertaken as part of this work. While results were in line with expectations, users should be aware of the limitations discussed in section 4.1 and their impact on the temporal assessments. Because of these limitations, data lend themselves better to presence/absence comparisons, as opposed to absolute intensity comparisons. Absolute value comparisons were undertaken in the Lyme Bay area and results confirmed qualitative assessments made as part of stakeholder surveys (Mangi et al., 2012). Whereas this was successful Distribution and trends in inshore fishing activities and the link to coastal communities

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for a relatively large area such as Lyme Bay, trials with smaller sites were not successful. Therefore the data do not lend themselves to studies of changes in fishing activity in small areas. Best results are most likely to be obtained where the study area is several times the size of the grid cells used as this allows for averaging of local inaccuracies.

4.3

Zone of Influence

Zones of Influence are developed from sightings data. Their accuracy therefore partially relies on the correct recording of sightings data. To link vessels to home ports we initially made use of vessel registration details available from the MMO website. In several cases it was found that the vessel operates a long way away from the registered home port. In this case the registered home port is different from the operational home port. These outliers were removed from the ZOI of the registered home port, but could not be added to the operational home port as such knowledge was not available. For the 2010-2012 zone of influence development use was made of data held in the MMO's Fish Activity Database (FAD). The port from which the vessel operated most frequently during a calendar year was used as home port. Although this is an improvement over use of the registered home port, outliers were still found to be present where a vessel changed operational home port during the year. In some cases, the generated ZOI may therefore not capture the entire extent of the true zone of influence. The zone of influence spatially represents the area within which fishing activity takes place. The intensity of the activity is unlikely to be spread evenly across these areas. Where the surveillance effort is low, the zone of influence will be less well defined. Hence the confidence layer developed in support of fishing intensity maps should also be considered when using zone of influence results.

4.4

Economic valuation of fishing activities

The limitations described above for the development of the zones of influence are also relevant to our case study example and should be considered limitations when the data are used to explore potential effects on landings values and number of vessels affected by the implementation of any management measures. Three further limitations should also be considered with regard to the economic analysis of landings values and number of vessels. Firstly, as mentioned in 4.3, the intensity of fishing activity is unlikely to be evenly distributed across the area. For the economic analysis we have assumed that this is the case. By using the percentage overlap between an (r)MCZ and zone of influence we are assuming that all landings and the number of vessels from the port are evenly distributed throughout the zone

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of influence. In reality this is unlikely to be the case and therefore the actual potential landings values or number of vessels affected could be significantly higher (or lower) depending upon the true distribution of activity within the zone of influence. Secondly, EU and national legislation has no requirements for skippers of vessel under 10 m length to declare their catches. There are in excess of 4,200 vessels under 10 m length in the UK. Sales notes are used to obtain monetary values for this part of the fleet and it is likely that reporting does not capture the complete landings value of catch (MMO, 2013). This would mean that our calculations are potentially under-estimates of the true loss for a particular fleet by any (r)MCZ but particularly the inshore (r)MCZs. Lastly, due to a lack of data, the zone of influence for some ports was poorly developed, or in some cases excluded altogether from the analysis. In these cases, the presented landing values may not accurately reflect the true spatial value. Results should therefore be considered in the context of the ports excluded from the analysis. It should be noted that in our assessments we did not consider the type of catch to demonstrate the concepts. Some species have a high monetary value, whereas others have a much lower value. To achieve the same landings value, a different level of fishing effort may therefore be required depending on the species. For detailed socio-economic assessments, consideration should therefore be given to the catch composition.

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5 Conclusions 5.1

Data and methodology

This project built on a previous project which developed the first national inshore fisheries data layer for England and Wales (MB0106). The major challenge of project MB0106 was to combine the disparate datasets collected by the IFCAs around the country. To improve this process, project MB0106 developed a standardised Excel sightings recording spreadsheet with built-in quality control features. Standardised, IFCA specific, gear codes were built-in to the spreadsheet to ensure uniform classification of fishing activities across IFCAs. Finally, a data analysis tool was developed for use with MapInfo GIS, using the standardised spreadsheet as one of the data inputs. While IFCAs were provided with the tools to undertake data analysis themselves, few were found to be analysing the data on a regular basis. Bugs were found in the software tool developed as part of MB0106 which caused parts of the data analysis to fail. All data analysis undertaken as part of this project used the previously developed tool. This allowed identification of a number of bugs and opportunities for improvement. These changes were made to the software and the tool will be rolled out again to the IFCAs through the IFCA Technical Advisory Group in 2014.

Another

contributing factor for limited uptake of regular data analysis has been the significant increase in workload for IFCAs following the transition, in April 2011, from Sea Fisheries Committees to Inshore Fisheries and Conservation Authorities. The standardisation introduced by project MB0106 greatly reduced the time required for data preparation and cleaning as part of this project. However, the collation of data from each of the 10 IFCAs, Welsh Government and MMO took significantly longer than anticipated.

Collation of

surveillance tracks proved time-consuming and caused delays in the transfer of data. Concern around sharing of data in the context of the Data Protection Act (1998) also caused delays in some cases. To facilitate improvements in the flow of data there are a few solutions which could be considered: 1. Following the upgrade of the MMO's Monitoring, Control and Surveillance System (MCSS) with IFCA specific gear codes it is possible for all IFCAs to record sightings data in this database. IFCAs already have access to this database and some, but not all, IFCAs already record their sightings data in this database. If all IFCAs were to use this database for sightings recording, all data can be sourced from a single source without delays. The MapInfo GIS data analysis tool has already been updated to accept MCSS database extracts for data analysis. 2. The surveillance effort data proved to be the cause of most delays. Making use of VMS or iVMS technology on board IFCA patrol vessels (already used for Royal Navy FPVs) and storage in the MCSS

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database would lead to all surveillance data being stored in a single database with easy access. Furthermore it would standardise the source of surveillance effort across IFCAs. These two changes to the data recording process would dramatically reduce the time taken to obtain the necessary data and undertake the data analysis.

Although improvements to the process for obtaining data can be made, in the context of reduced numbers of patrols, and therefore reduced volumes of sightings data, consideration should be given to different solutions to assess inshore fishing effort. Further, whereas the relative fishing intensity maps provide useful insight to planning management decisions, they are not suitable to assess the effectiveness of management measures nor to undertake detailed assessments of displacement following the introduction of the management measures. Caslake et al. (2012) and Vanstaen (2012) demonstrated the benefits of inshore VMS solutions and efforts by the MMO are now underway to introduce such systems for inshore enforcement purposes by IFCAs. While this development is encouraging, in order to replace the relative fishing intensity mapping using sightings data, such systems would need to be rolled out to all vessels. At present, consideration is only being given to implementation in selected areas and fleets. Consideration should therefore be given to rolling out these systems to all fishing vessels.

5.2

Relative fishing intensity maps

A series of relative fishing intensity maps, by gear type, were produced as part of this project. The maps cover the period 2010-2012 and provide an average intensity over this period. The maps were presented alongside previous results (MB0106, 2007-2009) and allowed direct comparison. Although absolute values are not comparable due to variations in the input data used, the distribution of relative intensities can be compared between both datasets and showed similar trends emerging from both datasets. Overall, a reasonable correlation could be found between the 2007-2009 and 2010-2012 datasets. Considering the changes that may have taken place in the distribution of the activities over that time and the decline in surveillance effort, this suggests that the maps continue to provide a good indication of the location and intensity of inshore fishing activities. The results from this work have already been used support marine biodiversity advice to Defra. There has also been a continued interest from the MMO in this work, who have published the MB0106 project results on their Marine Planning Portal as one of the reference datasets.

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5.3

Temporal changes in distribution of inshore fishing activities

The results from the temporal comparison of fishing activity distribution would suggest that the location of inshore fishing activities do not move significantly over time. Fishermen using particular gear types fish core areas each year, with results suggesting that areas outside of these core areas are fished less regularly. We do not know at this stage whether this is real or an artefact of the source data, which may be less effective at describing activity in peripheral areas. These core fishing grounds give an indication of areas where which are most important to the fishing industry. Moreover it gives an indication of areas where there is likely to be most conflict between fisheries and other marine activities. Further evidence that the intensity and distribution of inshore fishing activity is stable over time was provided by the correlation between 2007-2009 and 2010-2012 data. Areas where significant changes were observed (e.g. Lyme Bay, Wash) were primarily influenced by the introduction of management measures. For static gear types the areas fished repeatedly were often extensive, with peripheral areas of reduced fishing frequency often relatively small. For mobile gear activities, areas fished annually were often restricted in size, but peripheral areas of reduced fishing frequency were often extensive. From a management perspective, this indicates reduced opportunities for the static fleet if access to their repeatedly fished, core areas, is restricted. The results indicate that distribution and intensity of inshore fishing grounds remain relatively unchanged over time. This implies that an analysis of fishing patterns may not be required on an annual basis, unless management action has impacted on fishing opportunities.

In 2008 a spatial closure to trawling and dredging was implemented in Lyme Bay. Within the SI closure area, results show a shift from mobile gear activity to fishing activities using static gear types. These results provide quantitative evidence of the qualitative results obtained by Mangi et al. (2012). Environmental issues associated with fishing displacement do not always suggest benefits to the local ecosystem. Increases in the fishing effort surrounding closed areas and potential negative effects on benthic invertebrates are possible consequences of displaced fishing (Dinmore et al., 2003; Greenstreet et al., 2009). An increase in both trawling and dredging effort could be seen in the immediate vicinity of the site in the year following the closure. Whereas increased trawling levels were sustained for a number of years, dredging effort fell to levels observed before the closure from 2010 onwards. There was no significant increase in effort by mobile gears further from the site.

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The static gear effort doubled within the Lyme Bay SI closure area, with increases in static gear activity also observed in the wider area. The Lyme Bay closure aims to protect benthic habitats, in particular those characterised by the pink sea fan (Eunicella verrucosa). Eno et al. (2001) indicate that replacement of mobile gears with static gears will not have as significant an effect on epibenthic species and therefore that such replacement is unlikely to affect the conservation objectives of the closure. Indeed, Atrill et al. (2012) found that recovery was taking place within the closed area, despite the increased static gear effort. Results from the Lyme Bay area do not display clear displacement of mobile gear effort away from the closed area into the vicinity of the site. Rather a reduction mobile fishing effort in the nearby area was observed, and an increase in static gear effort throughout the area. This may be the result of local fishing vessels changing gear type from mobile gears to static gears, or the mobile gear vessels relocating to nearby ports of leaving the area altogether, and static gear vessels relocating from other parts of the UK to Lyme Bay due to increased opportunity in the areas. Whereas the data presented in this report allows to explore changes in fishing intensity as a result of management measures, additional work would be needed to fully explain the causes of these changes.

5.4

Zone of influence

Building on the work by Vanstaen et al. (2010) zones of influence were developed for ports around England and Wales. Since Vanstaen et al. (2010) found large differences in the characteristics of static and mobile gear fleets, the zones of influence for these fleets were developed separately. The concept of zones of influence is similar to the FisherMap approach (des Clers et al., 2008; des Clers, 2010) to mapping fishing activities whereby the fishing grounds for each port are mapped spatially. Unlike FisherMap, the zones of influence approach is based on field observations and therefore on objective assessment. Furthermore, the approach can easily be repeated using annual updates of the sightings database. Whereas the relative fishing intensity maps provide the end-user with a tool to identify those areas of great importance to the fishing industry, they do not allow assessment of effects on coastal fishing communities. In the past, the majority of nearby stakeholders would often claim they would be impacted by offshore developments or management measure. The zones of influence approach provides an objective assessment of the area actually fished by each fishing community. Using the zones of influence developed as part of this project, it is possible to start exploring which fishing communities will be impacted if spatial management measures are introduced in any coastal area. The average mobile gear zone of influence was twice as large as the static gear zone of influence. In most cases this means that the mobile fleet will have more opportunities for displacement to nearby

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areas compared to static gear fleets. Similar conclusions were reached from the temporal analysis undertaken in this project. To minimise impact on fishing communities, particular attention should be paid to the interaction between management measures and the static gear fleet.

5.5

Economic analysis - A Case Study

Using landings values and number of vessels per port from the landings database we have demonstrated the application and benefits of the zones of influence concept. The approach makes use of routinely collected data and can therefore be updated on a regular basis. Whilst it is important to note the limitations of this analysis, we have also demonstrated how zones of influence could be used to support economic analysis of the implications of management measures on local fishing fleets and coastal communities.

Using such a method to study economic effects of

designating MCZs in the UK can add to existing methods of economic analysis to broaden the information base or fill in gaps where other methods are not appropriate. The results of the study show that many (r)MCZs overlap with several zones of influence, therefore potentially impacting on several fishing communities. Generally the percentage overlap is greater between (r)MCZs and zones of influence for static gears. The example from the Holderness Inshore rMCZ demonstrated this and showed how entire fishing communities could be impacted by significantly reduced opportunities should static activities be banned in the area. In the worst case scenario context whereby all fishing activities are banned in an area, on average 8.72% percent of static gear and 3.85% of mobile gear zones of influence will be impacted. Aside from the opportunity to assess monetary losses, zones of influence can also be used to assess number of fishing communities and vessels affected. Using additional data, this could provide insights into employment impacts within the local community. High overlap does not necessarily mean large monetary or vessel opportunity losses, but it could mean high stakeholder conflict and dissatisfaction. The approach therefore provides decision makers various options for evaluation in order to prioritise losses. Whilst it is not possible, nor appropriate, to rank (r)MCZs by those most likely to have the greatest affect on fishing fleets, these results allow decision makers to rank MCZs using a range of metrics thereby supporting the decision making process for both designation of MCZs and management implementation. Our case study utilised the 2007-2009 zones of influence. With the methodology developed and concept demonstrated, and more recent data available (2010-2012 zones of influence, as well as landings and fleet data) it is possible to produce more up-to-date valuations. As data continue to be collected by IFCAs and MMO, it will be possible to repeat the analysis in future.

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The limitations of developing the zones of influence on a relatively data poor dataset means that care should be taken when using these data for economic analysis. Some of these limitations could be addressed through further research or through the implementation of VMS and e-logbook systems. However, at present, these data represent the best available information on where the under 15 m fishing fleet are active.

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7 Annex I: Vessel activity codes MMO Code A ACE B BDF

Description

E F G H

PELAGIC SIDE TRAWLER SIDE TRAWLER (PELAGIC/DEMERSAL) PELAGIC STERN TRAWLER STERN TRAWLER (PELAGIC/DEMERSAL) DEMERSAL SIDE TRAWLER DEMERSAL STERN TRAWLER BEAM TRAWLER LONG LINER

PURSE SEINER

BOTTOM SEINER (ANCHOR/DANISH/FLY/SCOTS) GILL NETTER

SFC Specific Code

DRIFT NETTER

SCALLOP DREDGER (FRENCH/NEWHAVEN) ROD AND LINE

K1 K2

Trammel nets Demersal medium gauge gill nets (cod/sole) Skate/Turbot nets (large gauge) Fixed beach nets/enclosures seafish Fixed beach nets/enclosures salmonids Lobster & Edible Crab Whelk Prawn (Nephrops) Prawn (Non Nephrops species) Velvet Crab (specific pots) Cephalopod trap (e.g. Cuttlefish) Fish Trap Pelagic (mackerel/herring etc) Bass Salmonids

Netting Netting

Registered Fishing Vessel Commercial Activity Charter Recreational Private Recreational

Angling

L1 L2 L3 L4 L5 L6 L7 M1 M2 M3

O1 O2 O3

P Q R S T

SHRIMPER KLONDYKER INDUSTRIAL TRAWLER (SANDEELER) FREEZER TRAWLER (PELAGIC/DEMERSAL) TRAWLER (ALL)

U V W

TRIO TRAWLER (ALL) PAIR TRAWLER (ALL) SUCTION DREDGER

X Y

UNKNOWN HANDGATHERING

Trawling Trawling Trawling Lining Lining Trawling Trawling Trawling

Mechanically hauled Hand Hauled Vessel Beach Seine

K5 POTTER/WHELKER

Main Gear Class Trawling Trawling Trawling Trawling

H1 H2 I1 I2

K3 K4

SFC Specific Description

Netting Netting Netting Potting Potting Potting Potting Potting Potting Potting Netting Netting Netting Dredging

Angling Angling Trawling Trawling Trawling

T1 T2 T3

Single Rigged Twin Rigged Triple Rigged

W1 W2 W3 W4

Mussel Dredging Cockle dredging Razor fish Unidentified Bivalves

Trawling Trawling Trawling Trawling Trawling Dredging Dredging Dredging Dredging

Mollusc Raking

Hand

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8 Annex II: â&#x20AC;&#x2DC;Râ&#x20AC;&#x2122; ZoI valuation script static_intersect = merge(ZOI_MCZ_overlap_stc, ZOI_stc_area, by= "PORT") mobile_intersect = merge(ZOI_MCZ_overlap_mbl, ZOI_mbl_area, by= "PORT")

static_intersect$percent = (static_intersect$overlap_area / static_intersect$ZOI_area)*100 mobile_intersect$percent = (mobile_intersect$overlap_area / mobile_intersect$ZOI_area)*100

scratch = ddply(values,.(landing_year, gear_group, landing_port_name), summarise, sum_landings = sum(landings_value), sum_vessels = sum(count_rss_no)) mobile = scratch [scratch$gear_group == "Mobile", ] static = scratch [scratch$gear_group == "Static", ] mobile = mobile [mobile$landing_year %in% c ("2007", "2008", "2009"), ] static = static [static$landing_year %in% c ("2007", "2008", "2009"), ] mobile = ddply(mobile,.(landing_port_name), summarise, sum_landings = sum(sum_landings), sum_vessels = sum(sum_vessels)) static = ddply(static,.(landing_port_name), summarise, sum_landings = sum(sum_landings), sum_vessels = sum(sum_vessels))

static_merge = merge(static_intersect, static, by="PORT") mobile_merge = merge(mobile_intersect, mobile, by="PORT")

static_merge$affected_landings = (static_merge$sum_landings / 100) * static_merge$percent static_merge$affected_vessels = (static_merge$sum_vessels / 100) * static_merge$percent mobile_merge$affected_landings = (mobile_merge$sum_landings / 100) * mobile_merge$percent mobile_merge$affected_vessels = (mobile_merge$sum_vessels / 100) * mobile_merge$percent

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