Feasibility study on applying a spatial footprint approach to quantifying fishing pressure

Contract Reference: MMO1108

Final Report November 2015

Defra Contract Manager: Laura Weiss 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 Commissioned by: This work was commissioned by Defra’s Marine Biodiversity Impact Evidence Group (IEG). The Group was established in March 2014 to co-ordinate evidence collection concerning impacts of human activities. Membership of the Group is as follows:

Department for Environment Food and Rural Affairs (Defra)

Welsh Government

Natural England – IEG Chair

Centre for Environment, Fisheries and Aquaculture Science (Cefas)

Environment Agency (EA)

Inshore Fisheries Conservation Authorities (IFCAs)

Joint Nature Conservation Committee (JNCC)

Marine Management Organisation (MMO)

Natural Resources Wales (NRW) Report prepared by: MRAG Ltd in association with Envision Mapping Ltd. Authorship: MRAG Ltd in association with Envision Mapping Ltd. First published: November 2015 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. Acknowledgements: We would like to thank Daniel Steadman for his steering and oversight throughout the project, all the IFCAs who contributed (and in particular Eastern, North Western and Sussex IFCAs for their inputs to the case studies), and the Marine Management Organisation, Natural England, Natural Resources Wales and Defra for their feedback.

Contents

1. Executive Summary .......................................................................................... 1 2. Introduction ....................................................................................................... 3

2.1 Project Aim .................................................................................................... 4

2.2 Structure of the Report .................................................................................. 5 3. Approach and Methodology ............................................................................. 6

3.1 Project Inception and Planning ...................................................................... 6 3.2 Footprint Methodology Evaluation ................................................................. 6

3.2.1 Identification of Data Requirements and Limitations ............................... 6 3.2.2 Considerations and assumptions within the methodology ...................... 7 3.2.3 Best practice/‘Gold Standard’ ................................................................. 7

3.3 Case Study Identification and Implementation .............................................. 7 4. Literature Review .............................................................................................. 8

4.1 Assessing Fishing Pressure .......................................................................... 8

4.2 Footprint approach ........................................................................................ 8 4.3 Impact Estimation Parameters ...................................................................... 9

4.3.1 Offshore Fisheries Spatial Assessment ................................................ 10 4.3.2 Inshore Fisheries Spatial Assessment .................................................. 11

4.3.3 Impact Assessments of High Risk Gears.............................................. 14 4.3.4 Impact Assessments of Low Risk Gears .............................................. 18

4.4 Protected Feature Extents .......................................................................... 18

4.5 Habitat Sensitivity (Recoverability/Resilience) ............................................ 20 4.6 Impacts of Fishing in Relation to Natural Disturbance ................................. 21

5. Feasibility of applying the fishing impact equation ..................................... 22

6. Fishing Impact Estimation Parameters ......................................................... 23

6.1 Fishing Effort (E) ......................................................................................... 23 6.1.1 Potential data sources for Fishing Effort ............................................... 24

6.2 Area of Interaction (A(i)) ............................................................................... 25

6.2.1 Potential data sources for Area of Interaction ....................................... 30 6.3 Area of Feature (A(f)) ................................................................................... 31

6.3.1 Potential data sources for Area of Feature ........................................... 31 6.4 Worked Example of Fishing Impact Estimation ........................................... 32

7. Other Parameters ............................................................................................ 36

7.1 Temporal Patterns of Fishing Effort and Activity ......................................... 36 7.2 Temporal Variation in Habitats and Features .............................................. 36 7.3 Sensitivity of Habitats and Features to Fishing Activities ............................ 37

7.4 Estimation of Cumulative Impact ................................................................. 39 8. Modifications to the Equation ........................................................................ 41

8.1 Developing a Framework for an Applied Methodology ................................ 41 8.2 Developing a Geospatial System ................................................................ 43

9. Data Sources, Availability and Limitations ................................................... 46

9.1 Summary of best practice and ‘Gold Standard’ ........................................... 49

10. Questionnaire Completion and Responses .................................................. 50 11. Case Study Examples ..................................................................................... 59

11.1 Eastern IFCA shrimp beam trawl (Wash, North Norfolk and Lincolnshire coast) ................................................................................................................... 60 11.2 NW IFCA pot fishery (crab and lobster) (Lune Deep EMS) ......................... 62 11.3 Sussex IFCA net fishery (Kingmere MCZ) .................................................. 63

11.4 GIS Example ............................................................................................... 65 11.5 Feedback on the Fisheries Footprint Approach from IFCAs ....................... 68

11.5.1 Eastern IFCA ..................................................................................... 68 11.5.2 North Western IFCA .......................................................................... 69 11.5.3 Sussex IFCA ..................................................................................... 69

12. Recommendations .......................................................................................... 70 13. Acronyms ........................................................................................................ 72 14. References ....................................................................................................... 73

Annex 1: Summary of Typical Effort Measurements for Gears in the MMO Matrix. Annex 2: Summary of Estimated Proportions of Bottom Impact for Gears in the

MMO Matrix. Annex 3: Summary of Typical Area Measurements for Gears in the MMO Matrix. Annex 4: Lists of EMS Generic Sub-Features in the MMO Matrix, MCZ Broad

Scale Habitats (BSH) and Habitat Features of Conservation Importance (FOCI).

Annex 5: Project Information Flyer. Annex 6: Fisheries Footprint Questionnaire. Annex 7: Quantification of Fishing Pressure – Spatial Footprint Approach -

Calculation Sheet Annex 8: Eastern IFCA Case Study – Calculation Sheet Annex 9: North Western IFCA Case Study – Calculation Sheet Annex 10: Sussex IFCA Case Study – Calculation Sheet Annex 11: Completed Questionnaires Eastern IFCA

Northeastern IFCA North Western IFCA Northumberland IFCA Sussex IFCA

Figures

Figure 1 ‘Footprint Equation’ with possible data sources and influencing factors ..... 22 Figure 2: Summary of data types from low standard to best practice (high) for estimation of fishing effort. ....................................................................................... 25

Figure 3: Summary of data types from poor (left) to best practice (right) for estimation of fishing gear impact. .............................................................................................. 26 Figure 4: Graphical representation of point/area fishing activity. .............................. 28 Figure 5: Graphical representation of net/line fishing activity. .................................. 28 Figure 6: Graphical representation of string fishing activity. ..................................... 29

Figure 7: Graphical representation of trawl/dredge fishing activity. .......................... 29 Figure 8: Summary of data types from lower level information to higher standards for estimation of the area impacted. .............................................................................. 31

Figure 9: Summary of data types from lowest usable data to highest quality data for estimation of the feature area. .................................................................................. 32 Figure 10: Summary of data types from poor (left) to best practice (right) for estimation of the sensitivity of feature in relation to fishing gear. ............................. 39

Figure 11: Flow diagram for a methodical approach to applying the fishing impact equation for all fishing activities and features present. ............................................. 42

Figure 12: Input layers and stages for geospatial calculations. ................................ 43 Figure 13: Hypothetical response curves for habitats to fishing activity. .................. 45

Figure 14: Matrix for data checks and identification of standard of data. ................. 47 Figure 15: Examples of data sources and availability, with limitations and implications for use................................................................................................... 48

Figure 16: Stages of data processing for the GIS example. ..................................... 67

Tables

Table 1: Examples of evolution of complexity of footprint models. ............................. 9 Table 2 Examples of Parameters and Equations Used in Selected Studies Quantifying Spatial Extent of High Risk Gears ......................................................... 17

Table 3: Summary of information available, limitations and recommended data collection methodologies for “Fishing Effort”. ........................................................... 24 Table 4: Summary of categories of gear with respect to interaction area calculation methods. .................................................................................................................. 26 Table 5: Summary of information available and recommended data collection methodologies for “Area Impacted”. ......................................................................... 30 Table 6: Example format for Collecting Temporal Variation in Fishing Gear Use. .... 36 Table 7: Example format for Collecting Presence/Absence Data on-Critical Periods during Seasonal Variations of Features. .................................................................. 37 Table 8: Summary of information available and recommended data collection methodologies for Sensitivity of Habitats and Features to Fishing Activities. ........... 39 Table 9: Summary of IFCA Telephone Interviews. ................................................... 51

Table 10: Summary of Number of Responses to IFCA Questionnaire – Annex 1 – Gear Types. ............................................................................................................. 53

Table 11: Summary of Number of Responses to IFCA Questionnaire – Annex 2 – Gear Dimensions...................................................................................................... 55

Table 12: Summary of IFCA Questionnaire Responses – Annex 3 - Features Present. .................................................................................................................... 57

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1. Executive Summary

The Department for Environment, Food and Rural Affairs (Defra) revised policy regarding the approach to the management of commercial fisheries in European Marine Sites (EMS) aims to ensure that all existing and potential commercial fishing operations occurring in EMS are managed in line with Article 6 of the EU Habitats Directive. This has resulted in the need for the Inshore Fisheries and Conservation Authorities (IFCAs) and the Marine Management Organisation (MMO) (with the assistance of Natural England) to assess the impacts of all fisheries on designated features and habitats within Marine Protected Areas (MPAs) in England and to complete these evidence-based assessments, and have any respective management in place, by the end of 20161. A footprint approach to the quantification of fishing pressure has been proposed as a mechanism to standardise assessments of impacts on designated features and habitats for inshore fisheries. However, knowledge and quantitative data of the spatial distribution of activities in most inshore areas is currently a significant evidence gap for fisheries management. This study tested the feasibility of the spatial footprint approach and identified the data available for inshore fisheries that will be needed to quantify fishing pressure, i.e. to quantify fishing effort (vessel days) expended within the feature area, the area fished by an individual vessel in one day and the total area of the feature that is being fished (i.e. by an entire fleet). A comprehensive literature review of methods used to assess fishing pressure, previous attempts at footprint approaches, quantification of the impact of fishing gear and the identification and quantification of protected feature extents was completed. This review also briefly explored habitat sensitivity in terms of the rates of disturbance, recoverability and resilience of features. Data gathered through questionnaires and telephone interviews with IFCAs illustrated that, for the majority of inshore fisheries, data required to conduct a footprint approach are directly available or estimates can be made based on anecdotal information or from other sources (e.g. average values from neighbouring fisheries). Data quality may not be currently available at the best practice level in all cases, but for each data type best practice data have been identified to allow IFCAs to develop or modify data collection to feed into a footprint approach. Outputs from the literature and data review include proposed modifications to the simple footprint approach equation and a structured methodology framework with which to provide context for use of the equation. Suggestions have also been made for modifying the equation to relate fishing activity and effort with habitat distribution spatially to assist with the problem of clumped or uneven fishing activity and also to enable assessment of any habitat or resource to be better understood. The underlying principles of the footprint equation could be used as building blocks within the development of a geospatial system (a simple example of which is given).

1 In addition to all EMSs, this revised approach also applies to all Marine Conservation Zones designated in 2013

(https://www.gov.uk/government/collections/marine-conservation-zone-2013-designations)

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Following consultation with IFCAs three case study scenarios were selected to test the methodology. These case study examples were developed using information gathered from three different IFCAs, with differing areas, gears and scales to test the applicability of the spatial footprint approach in quantifying fishing effort. We would recommend that these case studies are only used within the scope of this project and not repeated or used outside of the study. The three case studies used were as follows:

Eastern IFCA shrimp beam trawl fishery (Wash, North Norfolk and Lincolnshire coast; including The Wash & North Norfolk Coast SAC);

North Western IFCA pot fishery (crab and lobster) (Lune Deep EMS); and

Sussex IFCA net fishery (Kingmere MCZ).

The footprint approach enabled an estimation of fishing pressure to be developed reasonably simply. However, it is suggested that a more sophisticated modelling approach (with a GIS component) would be a better approach to account for the variety of gear, operational parameters and geospatial aspects of fishing activity and of the features themselves that have been simplified in the case studies. Recommendations from the project are made on how the approach could be tested more fully with a greater variety of data types, and how it could be progressed using the modifications suggested within the report. An example is a geospatial modelling approach which could better account for the variety of parameters involved, and to help address the temporal and spatial variability in fishing effort. Suggestions are also made regarding data gaps where research could potentially be undertaken to refine the input data to the footprint approach, thereby improving the accuracy and confidence of the results. Further recommendations are made regarding the integration of the inshore fisheries footprint approach with approaches employed from offshore fisheries and other marine sectors, and for a mechanism to maintain an evidence register for ease of access to information by all relevant bodies.

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2. Introduction

The concern over fishing impacts in Europe and the UK is evident in recent policy revisions. The Department for Environment, Food and Rural Affairs (Defra) have recently published (August 2014) revised policy regarding their approach to the management of commercial fisheries in European Marine Sites (EMS). This aims to ensure that all existing and potential commercial fishing operations occurring in EMS are managed in line with Article 6 of the EU Habitats Directive2. As a result of this policy, management measures for the conservation of high risk EMS features were to be identified and implemented by 2016 (Defra, 20143). To ensure EMSs receive the requisite level of protection, Inshore Fisheries and Conservation Authorities (IFCAs) and the Marine Management Organisation (MMO) are required to assess the impacts of all fisheries on designated features and habitats within MPAs in England. Working with Natural England, the IFCAs and MMO must complete these evidence-based assessments, taking into account any respective management in place, by the end of 2016. Whilst the location and footprint of most human activities are well known (e.g. aggregate extraction, windfarms, cables and pipelines), it is widely acknowledged that spatial information for inshore fishing activities remains a significant evidence gap for EMS/MCZ assessments, as most inshore vessels are less than 12m in length and therefore not included in vessel monitoring system (VMS) data. Furthermore, even with these data (being developed through the inshore VMS project4), the presence of a vessel actively fishing does not necessarily convey the full spatial pressure exerted by that vessel, or the extent to which an EMS/MCZ feature is impacted. The application of a “footprint” approach has been promoted by previous authors (such as Jennings et al., 2012) as a method to quantify fishing pressure and inform political processes and debate surrounding marine spatial plans. A footprint approach to quantifying pressure is commonly used across marine sectors with fixed locations and easily defined extent, however this is a more complex measure when considering fisheries, due to the variable nature of fishing both spatially and temporally. To quantify the impact of fishing on EMS/MCZ features, a simple “fishing impact equation” has been suggested:

Where: E = fishing effort (vessel days) expended within the feature area; A(i) = the area fished by an individual vessel in one day; A(f) = the total area of the protected feature; P = fishing footprint.

2 Council Directive 92/43/EEC on the Conservation of natural habitats and of wild fauna and flora

3 https://www.gov.uk/government/collections/fisheries-in-european-marine-sites-implementation-group 4 https://www.gov.uk/inshore-vessel-monitoring-system-project

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This takes into account the fishing effort (vessel days) within a feature area and the area fished by a vessel in one day, over the total area of the protected feature. However, the feasibility of such a method needs to be tested, considering the limitations of inshore fisheries data and the number of variables contributing to the level of impact on a protected area. The factors involved in calculating the area of interaction and level of impact can be complex depending on the range of vessels, fishing effort and gear types used in the area, temporal or spatial patterns of activity within the fishery, the frequency of impacts and resilience of the habitats concerned, and any cumulative impacts of different types of gear. The incorporation of these factors will need to be considered to appraise the feasibility of the equation, along with the availability and robustness of data to provide such information for current and future assessments. The value of P represents the proportion of a feature that is actively fished, and this could potentially be used to inform assessments concerning the level of impact that is acceptable for maintaining integrity of the site or feature. The approach can also be used by regulators to help define the spatial extent of the fisheries activities (in relation to feature size) or simply identify where interactions exist with features (which may in itself signify adverse effect and warrant management measures). The equation can also be used to model “worst case” scenarios to help define upper limits of potential impact, which can be refined to more realistic levels with local expert judgement. The fishing impact equation therefore represents a potentially useful tool that could inform MPA management and marine spatial planning processes. If fishing pressure can be quantified spatially and footprint values generated for individual vessels and fleets, then with information regarding acceptable levels of fishing activity/pressure on individual EMS/MCZ features, assessments can be made and appropriate management measures implemented (such as effort or gear restrictions)5. Furthermore, the application of this approach to fisheries would help to standardise the assessment process between regulators, align it with other industries and, by using a standard measurement unit of pressure, allow regulators to quantitatively assess cumulative pressures across multiple fisheries and sectors.

2.1 Project Aim

The overarching aim of this project was to collaborate with several IFCAs and the MMO to collect initial data for the purpose of assessing the feasibility of applying a “footprint” approach to quantifying fishing pressure. The project aimed to produce an agreed set of principles and a method for evaluating the spatial footprint of fishing gears for subsequent application of the method to Marine Protected Area (EMS/MCZ) fishery assessments. The expected outputs from this project were:

5 In terms of how a Statutory Nature Conservation Body (SNCB) would identify acceptable levels of fishing

activity via the footprint approach, the project authors have been advised that whilst SNCBs “can only provide advice on the Conservation Objectives and whether they are met (or not)”, [they] “feel that this approach will provide useful evidence in support of the assessment conclusions” (Natural England Fisheries Lead Advisor, pers. comm.)

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1. A review of current/previous approaches to spatially quantifying fishing pressure. 2. An analysis of the proposed method, including broad-level caveats about how it could/should be applied. 3. A framework for what data needs to be collected in order to generate footprint values for all fishing activities, including specific caveats around the particularities of quantifying footprint for specific gears. 4. Example data to populate this framework collected through IFCA/MMO liaison that accounts for regional variation and includes confidence limits and/or ranges/means. 5. Final reporting

2.2 Structure of the Report

The study was carried out in two parts, Part 1 involved a desk study to evaluate applicability of the proposed method and Part 2 involved the collection of data for case studies to provide examples of implementing the proposed methodology. Section 3 of this report briefly outlines the approach and methodology used during Parts 1 and 2 of the study. Sections 4 to 9 encompass tasks completed under Part 1 of the study and Sections 10 and 11 include outputs from Part 2 including presentation of the case study examples. Annexes are provided for summaries of data collected, project related documentation such as questionnaires and flyers, returned questionnaires from IFCAs and worked examples for case studies.

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3. Approach and Methodology

The approach adopted for completing Part 1 comprised two key steps. The first step involved an extensive literature review of the available formal and “grey” literature for published and ongoing studies on similar approaches to quantifying fisheries pressure on habitats and species. The second step involved a critical review of the proposed footprint methodology. The critical review involved evaluation of the proposed fishing impact equation and associated parameters, incorporating an appraisal of associated data requirements and current availability, as well as a summary of gold standards for such data, and included suggestions for modifications to the equation (see section 3.2). Part 2 approach is described in section 3.3.

3.1 Project Inception and Planning

The inception and planning phase was used to refine and finalise aspects of the approach originally put forward in the project proposal. The project inception meeting was held via teleconference on 2nd March 2015 and was attended by representatives from the MMO, Eastern IFCA, MRAG Ltd. and Envision Mapping Ltd. The project inception report provides a summary of the inception meeting; a review of the project scope and objectives; the approach and methodology to be undertaken; related projects and data to be reviewed; and, a review of the project deliverables.

3.2 Footprint Methodology Evaluation

Utilising the outputs and information acquired from the literature review, an evaluation of the fishing impact equation was undertaken. The process highlighted methodological issues which are inherent in a simple equation approach. Datasets and possible sources were identified and discussed, along with likely limiting factors, confidence levels and constraints. The implications of any issues to the application of the method and where alternatives or proxies may be introduced to overcome significant obstacles or information gaps were highlighted. Recommendations were made to introduce additional factors and variables to improve the effectiveness of the methodology, along with expansion and development of the fishing impact equation.

3.2.1 Identification of Data Requirements and Limitations

A requirement of the footprint approach is to calculate the spatial parameters for various fishing gears. The MMO “Fisheries in EMS Matrix” (hereafter known as the MMO matrix)6 was used to identify gear types which are likely to be used within IFCA areas and to assess the dimensions to be used when calculating a gears impact area (Annex 1). Each of the gear types within the matrix are addressed by the study (see Annexes 1-3).

6 https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/310811/matrix.xls

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Other variables and values required to develop and implement a footprint approach were then considered. Many of these were identified from previous studies through the literature search or from knowledge of the impact of different fishing gears. The equation was then reviewed with alternatives and modifications considered. It is noted that, as data sources are continually being improved, the incorporation of future iVMS data along with improved resource distribution data, information pertaining to seabed sensitivity and recovery would likely futureproof the approach as far as possible. 3.2.2 Considerations and assumptions within the methodology

The development of the footprint methodology involves assumptions and these were explored, along with caveats and limitations inherent within source data. For example, repeated impacts with different, similar or identical fishing gears can be cumulative and considerations were given to enable this to be accounted for. 3.2.3 Best practice/‘Gold Standard’

Following the analysis of the fishing footprint equation parameters, options for best practice with ‘gold standards’ were identified in terms of data requirements for the fishing impact equation. The gold standard scenario is summarised in section 9.1 and is envisioned as a stage where sufficient data of high enough resolution and quality can be input into an equation and can model a range of scenarios based on a variety of gear types and dimensions (along with fishing activity levels) to give a value for how much of a feature is affected by fishing activity. This will help to provide a standardised, transparent and evidence-based approach to assessment of fisheries and their potential impact.

3.3 Case Study Identification and Implementation

A project information flyer (see Annex 5) was developed and circulated to all IFCAs at the start of the project to provide them with information on the project, contacts and timelines. This was followed with a detailed questionnaire (see Annex 6) to identify if the information required to populate the fishing footprint equation was available for their gears and those features that the gears are active upon. Once these questionnaires had been completed, a choice was made on three contrasting case studies to verify that data were available and capable of populating the equation for a variety of situations and gears.

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4. Literature Review

This review examines the published and ongoing studies regarding quantifying fisheries pressure on habitats and species. Section 4.1 begins by briefly describing the current requirement for assessing fishing pressure, and section 4.2 introduces the studies that have used a footprint approach with some examples of models with increasing complexity. Section 4.3 reviews the literature to identify the common parameters that are used to calculate fishing impact focussing on a variety of different fisheries. The review then goes on to outline in section 4.4 the existing literature/sources for defining feature extents, and section 4.5 and section 4.6 follow by introducing how the issues of habitat sensitivity and the relative impact of natural disturbance to fishing impact have been studied.

4.1 Assessing Fishing Pressure

In several large-scale studies assessing the anthropogenic impacts in the marine environment, including cumulative impact assessment of human pressures, demersal fishing gears (beam trawls, otter trawls, dredges) have been identified as having one the most significant impacts (Diesing et al., 2013; Eastwood et al., 2007; Kaiser et al. 2002; Kaiser et al., 2006, Hiddink et al., 2006b). Therefore, accurate information on the extent and intensity of fishing pressure is an essential component when assessing anthropogenic impacts on seabed habitats, and should be quantified in a similar way and at a similar resolution or spatial scale to other industries so that impacts are comparable and can be summed and weighted for cumulative assessment (Eastwood et al., 2007). The MMO must ensure that cumulative effects are identified and assessed appropriately, and a fisheries footprint approach to assess spatial extent of fisheries could feed into this process, which is currently lacking data and methods of quantification7. In order to develop a consistent approach to the identification and consideration of cumulative effects that can be applied at the strategic level across all relevant MMO functions, the MMO have recently commissioned work to review current evidence and create a framework to identify and scope cumulative effects, and to explore a series of options for management and mitigation measures (MMO1055 - MMO, 2014a), in particular with respect to offshore wind farms (MMO1009 - MMO, 2013).

4.2 Footprint approach

A footprint approach is commonly used across marine sectors with fixed locations and easily defined extent, however this is a more complex measure when applied to fisheries, due to the variable nature of fishing both spatially and temporally. Several studies have used this approach for assessing the spatial patterns of offshore fisheries (Eastwood et al., 2007; Diesing, 2013; Hiddink, 2006b), taking into account many of the different aspects of fishing behaviour, however the more complex and detailed the model becomes, the greater the levels of uncertainty and assumptions

7 This is especially pertinent to the MMO’s statutory licensing and Marine Planning functions, both are which

require the organisation to assess the environmental impacts of multiple industries (including non-fisheries industries) in MPAs.

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involved (Hiddink et al., 2006b). A current study undertaking global footprint calculations of a wide variety of different gear types is soon to be published8. In summary, we can identify four potential footprint models that can be used (see Table 1). Table 1: Examples of evolution of complexity of footprint models.

Type Description References

Simple presence/absence The original footprint idea consisted of just an indication of where fishing had or had not taken place over a known spatial grid.

des Clers et al. (2008)

Effort measurement Simple fishing pressure footprint evaluating the combined effort for a single gear over a known spatial grid.

Eastwood et al. (2006) Piet et al. (2007) Stelzenmuller et al. (2008a)

Cumulative “normalised” effort

Combined a number of simple effort plots for different gears with the relative impact taken into consideration to provide a fishing pressure footprint of the cumulative normalised effort.

Stelzenmuller et al. (2008b) Hiddink et al. (2006b) Kraan et al. 2007 Sharp et al. (2009)

Cumulative “normalised” effort with habitat impacts

As for the cumulative normalised effort type of analysis but taking into consideration the effects of different types of fishing gear on different habitats and the risk to those habitats.

Diesing et al. (2013)

4.3 Impact Estimation Parameters

A variety of methods aiming to quantify the impact of fisheries on marine habitats and species have been described in the literature. These methods are diverse (reflecting the wide range of fishing gears used), however a number of common parameters are used by multiple studies to calculate fishing impact; for example, data on spatial and temporal distribution of fishing effort are important prerequisites to calculating the impact of fishing activities. This section summarises the methods and indicators used to convey and estimate impact in terms of spatial and temporal activity as well as physical impacts on habitats and species.

8 https://trawlingpractices.wordpress.com/

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Methods used to assess the spatial extent of offshore and inshore fisheries are initially described, followed by descriptions of methods used to assess the impact of high-risk and low-risk fishing gears on habitats and species. Where relevant, we provide a brief summary of each study’s outputs, data requirements, and equations used to calculate fishing impact.

4.3.1 Offshore Fisheries Spatial Assessment

A large body of work has been undertaken using Vessel Monitoring Systems data, which provides high resolution positional information for large, offshore fishing vessels only (over 12m overall length), and often considers only fisheries using mobile demersal gear (identified as the fishing activities with the highest impacts (Kaiser et al., 2002, Kaiser et al., 2006, Diesing et al., 2013). The VMS data can be filtered to identify records when vessels are more likely to be fishing (using speed and directionality rules, removing records near ports and harbours etc.) and extrapolated between position records to find vessel paths, which can then be buffered according to gear width to determine spatial extent of vessel tracks. This method has been explored and tested in Mills et al., 2007 and, when validated by observer data, has correctly identified trawling and steaming activity, in 99% and 95% of cases, respectively. Using straight line tracks between position records can generate an underestimation of fishing effort, and this has been refined by a variety of methods including validation by observer/sightings recordings (Eastwood et al., 2007), use of dmax (the maximum distance a vessel could deviate between two points) (Eastwood et al., 2007), or other methods considered more accurate for reconstruction of trawling tracks such as the cubic Hermite spline (Piet & Hintzen., 2012) which can also give a confidence interval around the estimated trawl track. These tracks are then often associated with vessel and gear type from logbook data (Eastwood et al., 2007), as well as catch composition and fishing effort (days per trip) data (Piet & Hintzen., 2012). This has been explored further through the Defra project MF12179, which reviews the assessment and applications of low-cost VMS data analysis. To calculate either general fishing effort extent, or fishing intensity, the VMS tracks can then be attributed to grid cells, which summarises the data within a standardised spatial system. The grid cells often need to be of an appropriate spatial scale to fit with other data, such as catch and landings data (3 x 3km for ICES rectangles – Jennings et al., 2012), comparative assessments with other marine sectors (Eastwood et al., 2007) or to use a scale at which fishing effort is considered to become random (below 1n.mi.2 (Rijnsdorp et al. 1998) in Mills et al., 2007) reflecting the patchiness of trawling fishery patterns, and allowing areas of un-impacted seabed to be identified. However the scale at which the trawling effort is recorded has been shown to have a large influence on calculations of trawling area, and some studies have explored the use of different scale grid cells. Piet & Hintzen (2012) concluded that high resolution cells (60 x 60m), gave far greater accuracy, but was too high resolution for some of the modelling, and the 600 x 600m grid cells were in fact a more realistic compromise to the larger usual scale of 3km x 3km. Trawling

9http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=2&ProjectID=1

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impact estimates can be considerably larger when calculated with large-scale estimates of mean trawling intensity (Dinmore et al., 2003; Mills et al., 2007, Lee et al., 2010). 4.3.2 Inshore Fisheries Spatial Assessment

It is widely acknowledged within the literature reviewed that inshore fisheries are underrepresented due to the reliance on VMS data for positional data of fishing (Vanstaen et al., 2010). Fishing vessels under 12m account for approximately 75%, 2014 value, (MMO, 2014b) by number of the UK fleet, but have only limited positional information as few have vessel monitoring systems in place and may therefore be underestimated in many impact assessment studies (Diesing et al., 2013).

However, this is the focus of a recent study by Breen et al., (2014) where sightings from enforcement and patrol officers were used to quantify the spatial extent of inshore fisheries with some success (Breen et al., 2014). The method uses sightings per-unit-effort (SPUE) estimates calculated from fisheries enforcement data (number of sightings over surveillance effort) to describe distribution and intensity of inshore fishing activity, with an estimation of uncertainty provided. This was validated for larger inshore vessels with VMS data and showed good agreement (although better for static than mobile gears), and so provides simple and repeatable methodology for mapping of inshore fishing activity.

Records of vessel sightings are logged by the IFCAs and MMO, and further sightings data are available from MMO aerial surveys, and surveillance intensity is estimated so fishing activity could be reported as SPUE. Sightings and surveillance effort were aligned with ICES rectangles to link landings data to estimates of fishing activity. Tracks were created by assuming straight line paths between position records from on-board navigation systems and buffers applied around the tracks to represent the area covered by the observers. The number of tracks intersecting with each grid were summed to give surveillance effort. The results of the study were presented as fishing activity maps. Weaknesses in this methodology are clear, in that patrol vessels do not randomly sample all areas fished, presenting an inherent bias to the estimates. Bottom impacting static gears such as nets, pots etc. would be underestimated using this method as these activities they can only be observed when vessel are actively setting, fishing or hauling gear.

Vanstaen et al., (2010) described fishing intensity and distribution in aggregate extraction sites and REC areas, primarily through use of VMS data. To address the lack of comparable knowledge on the distribution and intensity of inshore fishing activities, a methodology was developed to integrate the best available data on inshore activities with the VMS data for offshore areas, and trialled over the entire English and Welsh coastline (Vanstaen & Silva, 2010). This project brought together all sightings and boarding data from the Sea Fisheries Committees of England and Wales, and from MMO sightings data, and produced national data layers on the distribution and intensity of fishing activities within inshore waters, as well as a GIS toolbox to improve inshore sighting data analysis. Although this provided only a snapshot of activities between 2007 and 2009, the techniques used, data collated and its analysis are a useful basis for assessing inshore fisheries, excluded hand gathering and recreational fishing.

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Other research has looked at ways of quantifying fishing effort spatially for inshore or smaller scale fisheries over a broader area. Stelzenmuller et al. (2008) provides a spatial assessment of fishing effort of artisanal fleets around five European protected areas. Fishing effort data were directly gathered on-board fishing vessels operating in the proximity of case study MPAs. The positions of fishing gear deployments were monitored using GPS; the type of fishing gear and the type of bottom substrate were also recorded. Annual total fishing effort was then calculated as the mean number of days of gear deployment multiplied by the number of vessels employing each specific gear type. A measure of effort density was then derived (Number of gear deployments per km2). Stewart et al. (2010) characterised fishing effort and spatial extent of coastal fisheries in six ocean regions with poorly documented coastal fishing effort. The study first compiled a database of fishing effort metrics and spatial limits of fisheries from FAO data, national fisheries reports and published research. Three basic metrics, which were commonly reported, were extracted: number of vessels, length of vessels, and the spatial boundary of the fishery. Using these metrics the effort for each of the fisheries was calculated as the product of number of vessels and vessel length, giving an indicator of fishing effort as “vessel-metres”. Data were then input into a spatial analysis programme to map regional-scale patterns of fishing effort density in 1km2 grid cells. The study therefore creates fishing effort envelopes for 364 fisheries across 96 countries, and highlights where fishing pressures are high. Further studies incorporate spatial elements for gears with limited impact, or from small scale or data poor fisheries. Some of these have also used observed fishing vessel sightings by patrol vessels to assess fishing activity, such as the Northumberland potting fishery, and have compared this information with perceived fishing activity elicited through interviews with local fishers (Turner et al., 2013; Turner et al., 2015). Interviews contained a map-based component that required fishers to indicate areas that they fish, including any seasonal changes. Using the resulting maps the study then used data on the seasonal use and the total quantity of gear used by each fisher to estimate fishing effort in pot months km-2yr-1; one pot month being equal to one pot worked for one month. Although the two datasets showed varying correlation amongst ports, it suggested that vessel sightings are more likely to better represent variable intensity of fishing activity, while interview data may more accurately capture the absolute extent of grounds important to fishers (Turner et al., 2013; Turner et al., 2015). Small-scale fisheries are particularly challenging when it comes to quantifying fishing effort and impact, primarily due to a paucity of data caused by limited funding, oversight or infrastructure. Methods to quantify fishing effort therefore rely on interview methods (McCluskey & Lewison, 2008) and model simulations (Gomez-Munoz, 1990; Otero et al., 2005; Okada et al., 2005). However, McCluskey and Lewison (2008) notes that the methods discussed are capable of generating high resolution temporal and spatial information on fishing effort in certain circumstances, and can be extrapolated to unfished areas. However, McCluskey and Lewison (2008) also acknowledge the limitations of such studies; access and trust of fishing communities is required; reliance on fisher accuracy; high cooperation requirements; and interview data can contain considerable errors.

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Ongoing inshore studies of static gears include work commissioned by the MMO (MMO1086 – Potting Impacts), which will collate and evaluate all available evidence on the impacts of potting on benthic features. The evidence will be assessed and synthesised, and used to develop a tool that can support the assessments of potting activity in MPAs. This study has the potential to provide a valuable source of information which can be utilised within a fisheries footprint methodology.

Within the UK, nationwide initiatives have been carried out to help quantify and assess the status of inshore fisheries. FisherMap was a UK Government funded initiative aiming to map the nature and extent of fishing activities and fishers’ knowledge of marine ecosystems across all the MCZ regions in England, and was carried out through a process of interviews (des Clers et al., 2008)10. Liaison officers asked individual fishers to draw the areas that they used on maps, and fill in a questionnaire on the nature of their activity, i.e. fishing methods and gear type used, species targeted, and months of the year when the activity was carried out. This information was subsequently entered into a database, and a Geographic Information System (GIS). The areas drawn on the maps were digitised into the GIS, and linked with the information entered into the corresponding questionnaires. The information held in the GIS database was used to produce summary maps, showing combined information from all or a subset of the interviews (des Clers et al., 2008).

ScotMap, a Marine Scotland initiative, undertook similar work to provide spatial information on the fishing activity of Scottish registered commercial fishing vessels under 15m in overall length. The data were collected during face-to-face interviews with individual vessel owners and operators and relate to fishing activity for the period 2007 to 2011. Interviewees were asked to identify the areas in which they fish, and to provide associated information on their fishing vessel, species targeted, fishing gear used and income from fishing. The dataset, as of July 2013, is based on interviews of 1,090 fishermen who collectively identified 2,634 fishing areas or ‘polygons’, the majority of which relate to creel (pot) fishing. The data collected were aggregated and analysed to provide raster data and mapped outputs of the monetary value, relative importance (relative value) and the usage (number of fishing vessels and number of crew) of seas around Scotland (Kafas et al., 2013).

FishMap Môn was a Welsh project set up by Natural Resources Wales (previously Countryside Council for Wales) to survey commercial, recreational and charter vessel fishers, map the distribution and intensity of fishing activity around Anglesey and combine it with seabed habitat maps and their sensitivities to fishing activity. Fishing activity data collected during 2011/2012 from 47 commercial fishers, 26 charter vessel operators and over 500 recreational users provides stakeholders and managers with a unique level of detail about where different fishing activities take place and at what intensity. The project also developed an innovative GIS tool to allow users to explore maps of fishing grounds, benthic habitats and their sensitivity to current and future fishing activities. Eastern IFCA, through Habitats Regulations Assessment (HRA) work, has also compiled charts of different fisheries activities from interview data as part of the

10 (http://data.gov.uk/dataset/2012-marine-conservation-zone-project-stakmap-commercial-fishing-under-15m-

vessels-fishermap)

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‘Fisheries Mapping Project’ (ESFJC, 201011). It has also undertaken a prioritisation exercise, looking at a very diverse range of fisheries (e.g. net/line/tightly controlled shellfish), and started assessments with mobile fisheries (initially shrimp trawling) using evidence such as the levels of activity, patterns of effort and landings etc. overlain with feature extent data, backed up by generic literature review; however spatial quantification of activities is currently a key missing link in the assessments. 4.3.3 Impact Assessments of High Risk Gears

The impacts of towed fishing gears have been subject to much discussion and a range of studies have therefore focused on evaluating the effects of these fisheries. Bottom-towed gears, designed to catch demersal species, directly interact with the seabed in a variety of manners and are regarded as highly destructive due to their capability to alter both the physical and ecological structure of benthic ecosystems. The advent of vessel monitoring systems (VMS) has advanced the study of the spatial and temporal distributions of fishing effort as it provides high quality positional data for fishing vessels. In recent years a large number of studies have utilised VMS information to quantify the spatial and temporal pressure of fisheries deploying mobile gears. The following review section summarises some of the relevant research, and Table 212 details examples of the equations and parameters used by this research to define the extent of fisheries pressure. Eastwood et al. (2007) utilised spatial data to assess the physical pressure on the seabed from a number of human activities in UK waters, including fishing. The study evaluated fishing pressure for mobile gears (beam trawlers, otter trawlers and shellfish dredgers) using vessel monitoring (VMS) data and corresponding logbook information. VMS locations were filtered on the basis of speed to indicate periods of fishing activity; speeds of 1–6 knots were determined to correspond to fishing activity of otter trawlers and scallop dredgers, with 2–8 knots corresponding to fishing activity of beam trawlers. Linear fishing tracks were then interpolated between consecutive fishing locations (VMS transmission points) using straight-line paths. To estimate the spatial extent of fishing activity, fishing track lines were estimated using assumed gear width dimensions: 24m for beam trawlers (2x12 m wide beams), 4m for otter trawlers (2x2 m scour tracks left by trawl doors), and 20.4m for shellfish dredgers (24 0.85 m wide dredges) (Eastwood et al., 2006 and references contained therein). Calculated fishing track areas were layered to estimate the total area of the seabed impacted by demersal trawling. Piet et al. (2007) describes the ecological impact of the Dutch beam-trawl fleet using four pressure indicators:

1. Fleet Capacity – the number of vessels in a fishery; 2. Fishing Effort – fleet capacity multiplied by vessel activity; 3. Frequency Trawled – e.g. the total area of the swept by the gear in a specified

time period; 4. Annual Fishing Mortality – e.g. indicators from the stock assessments for the

target species.

11

http://www.eastern-ifca.gov.uk/index.php?option=com_content&view=article&id=130&Itemid=199 12

Not all studies presented a breakdown of equation parameters, therefore only a selection are presented within this table.

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Fleet capacity, fishing effort and other parameters of the Dutch trawl fleet were taken directly from information available from the Dutch-based VIRIS and APR/VMS databases, and the swept area calculated for each metier.

In a similar manner to Piet et al. (2007), Stelzenmuller et al. (2008a) assessed the spatio-temporal distribution of fishing pressure on various marine landscapes (habitat types) by trawlers in UK waters again using VMS data for large commercial vessels. The study assessed the proportion of marine landscapes that are fished, and provided an estimation of average annual fishing pressure for each marine landscape, by calculating the annual average proportion of a spatial unit affected by beam trawling, otter trawling, and scallop dredging.

Piet and Hietzen (2012) calculated a suite of indicators which convey the impact of trawl fisheries on the seafloor within the Dutch EEZ. Indicators included:

Total area fished;

Proportion of the surface area fished;

Proportion of the surface area fished by a specific portion of effort;

Proportion of the surface area fished at a specific intensity;

Cumulative proportion of the surface not impacted over a specific period. The study utilises VMS and logbook information to obtain the required parameters to calculate each of the indicators. Total area fished, within georeferenced cells, for example, was calculated based on the gear width, vessel speed and time spent within each cell. The proportion of the total surface area fished was also calculated. This differs from total area fished as it only accounts for a presence / absence of fishing within a given cell (over a specific period) without any consideration of how much or how often the area was fished. The proportion of the surface area that is fished by a specific amount of effort was calculated through the summation of the effort in that specific area; the indicator demonstrates the total relative amount of fishing effort expended in a defined area. Diesing et al. (2013) also used VMS data to determine “fishing disturbance” (trawled areas) within 12x12km grid squares in the North Sea. Similar to the study conducted by Eastwood et al. (2007), non-fishing data were filtered from the VMS database on the basis of vessel speed. To estimate fishing disturbance each VMS observation was assumed to be an area of impacted seabed and an equation (Table 2) was used to derive the area trawled (AT), which was then converted to the trawled proportion (T). T = 1 if an area equivalent to the area of the grid cell is fished once per year. The use of VMS data to quantify fishing impacts on epifauna was further explored by Lambert et al. (2012), specifically the implications of using alternative methods of VMS data analysis. Lambert argued that successive positioning records are too infrequent to capture intricate movements of fishing vessels, and therefore collected high-frequency positioning data with the aim of comparing how different polling frequencies influence estimates of fishing impact. Fishing intensity was calculated by estimating the area swept by point summation. Point summation methods involved attributing a swept area to individual VMS points and summing the number of points within a defined area. The area swept was calculated by multiplying the time interval between positions, the fishing speed, and the breadth of the gear. Using log book

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data, otter trawls were determined to be 18.3m wide, and UK dredges were assumed to have a gear width of 7.6 m inshore (0–3 nautical miles) and 12.16m farther offshore (3–12 nautical miles). As not all vessels can deploy the maximum width of gear allowed (Murray et al., 2011), these assumptions were determined to likely overestimate fishing activity and therefore represent a precautionary approach to determining fishing impact. Estimates of swept area associated with individual position records were then calculated and fishing intensity calculated as the summed swept-area divided by the surface area of the geographic grid cells. The study concluded that descriptions of fishing intensity are influenced significantly by analytical methods and that transmission intervals for VMS of 30 minutes provides a desirable compromise between achieving precise estimates of fishing impacts and minimising data collection costs. Kraan et al. 2007 evaluated the impact of dredging in the Dutch Wadden Sea using species abundance counts taken using sediment cores at fixed sampling stations. The metric used to convey the impact of the dredges was the relative occurrence of the species between dredged and undredged sites. Hiddink et al. (2006a) described the impact of mobile gears on the benthic communities in the North Sea using state and pressure indicators. The study predicted the recovery time of soft-sediment communities after trawling using a size-based model. Trawling frequency was calculated using VMS data; records of vessels with speeds over 8 knots were eliminated as fishing would be unlikely to be occurring at these speeds. Halpern et al. (2008) developed a quantitative method to estimate the impact of certain anthropogenic drivers on specific ecosystems using a multiscale spatial model to synthesise 17 global datasets of anthropogenic drivers, including fishing. Cumulative impact scores were calculated for each of the 1km2 cells of the ocean. The global impact of single drivers were calculated using log-transformed and normalized values for anthropogenic drivers at various locations, with weightings for impact level of drivers. Polet and Depestele (2010) offered a qualitative and quantitative description of a number of fishing methods present in the North Sea; a quantitative evaluation of the physical impact of a range of towed fishing gears is presented using three parameters:

1. Penetration depth – indicates the depth to which gear impacts the substrate and therefore what range of species will be impacted based on their position relative to the surface;

2. Surface fished – the size of the surface area that will be impacted; 3. Sediment displaced – quantifies the amount of sediment that is brought into

suspension.

The formula used to calculate these parameters are summarised in Table 2, and suggests penetration depth or a proxy for each gear type should also therefore be considered along with effort to produce an estimate of fishing pressure.

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Table 2 Examples of Parameters and Equations Used in Selected Studies Quantifying Spatial Extent of High Risk Gears

Study Impact Equation Parameters Equation

Piet et al., (2007)

Days at sea (E); Hours fished (HR); Trawling speed (S); Beam width (W)

E * HR * S * 1852 * 2 * W

Stelzenmuller et al. (2008a)

Annual average proportion of a spatial unit affected by trawling/dredging (AvAFP SU), Total annual fishing pressure TFP (m2) per grid cell (~2x2 miles), Spatial unit surface area (SUA; m2).

Diesing et al. (2013)

Area trawled (AT), Number of observations/VMS transmissions (n), Vessel speed (S), Interval between VMS “pings” (I), Gear width (w), and a given VMS observation (i), then Trawled proportion of the grid cell (T), Sea area of the grid cell (Ag).

then

Macdonald et al. (1996)

Sensitivity (S), Speed of recovery (R – four point scale), Fragility (F - three point scale); and Intensity of gear impact (I - three point scale).

Kraan et al. 2007

Average density of focal species (N – averaged over all areas), dredged area (d) or control areas (c).

Halpern et al. (2008)

Anthropogenic driver (i), log-transformed and normalized value, between 0 and 1 (Di -), location (i), presence or absence (Ej) of ecosystem j (1 or 0), and mi, impact weight (j) for the anthropogenic driver i and ecosystem j

Polet and Depestele (2010)

Penetration depth fishing gear (PGear); Penetration depth of gear component i (Pi); Width of the gear component (Wi).

Surface fished by gear (SFGear); Width of gear (Wtot); Towing speed (TS); hours per fishing day (tprod).

Sediment displaced by gear (SDGear)

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4.3.4 Impact Assessments of Low Risk Gears

As would be expected, there has been far less effort expended on studies of low impact gears, as the need is not considered as urgent. Eno et al. (2001) provides a qualitative and quantitative assessment of the impact of fishing with crustacean traps on epifaunal species perceived as susceptible to direct physical damage. The quantitative evaluation used a control-impact study design to assess the mean abundance of benthic species pre-potting and post-potting. Divers were utilised to record abundance of focal species along transect lines. Analysis revealed no difference between control and experimental replicates. The Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) have put in place measures to protect vulnerable marine ecosystems (VMEs) within the CCAMLR Regulatory Area in order to address concerns raised through the United Nations General Assembly and Resolution 61/105. Measures have been put in place to identify VMEs with a registry or database recording the locations and characteristics of each through an online GIS13 and to assess the impacts of fishing on species within the VMEs. Sharp et al. (2009) provides an analysis of the cumulative impact of fishing gears on vulnerable species in the Ross Sea, a method which is currently applied as a management measure across CCAMLR fisheries. This study focuses on the impact of bottom longline gear and uses a 6 step process to calculate the cumulative fishing impact:

1. Description of fishing gear; 2. Description of fishing activity; 3. Description of non-standard gear deployment; 4. Vulnerability assessment of VME species; 5. Description of historical fishing impact; 6. Calculation of total cumulative impact.

Step 2 of the process defines the spatial footprint of the specific gear, i.e. the maximum spatial envelope within which impacts on VME species are restricted (expressed in m2); for benthic longlines this is defined as 1m wide. This is then converted into a standard footprint per unit effort. Step 4 of the process is a vulnerability assessment of various VME taxa to fishing impacts; this involves classifying organisms into three categories dependent on the likely impact of the focal gear: no impact/ non-lethal impact/ lethal impact. Step 5 involved the definition of historical fishing impacts in the Ross Sea; effort was converted into units commensurate with the definition of footprint impacts (i.e. km of longline set). Finally, step 6 was to calculate the cumulative impacts by multiplying the size of the standard gear set per unit effort by the historical footprint.

4.4 Protected Feature Extents

To establish the proportion of feature that is fished using the spatial footprint equation, or even to compare the relative spatial scale of fisheries activity with feature extents, it is necessary to ascertain the area of the protected feature which may be impacted. The UK over recent years implemented a series of MCZs which are MPAs designed to protect nationally important habitats and species (features)

13

https://www.ccamlr.org/en/data/online-gis

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designated under the Marine and Coastal Access Act 2009 (MaCAA). Alongside European Marine Sites, MCZs aim to contribute to an ecologically coherent network of UK MPAs. Each MPA has been designated for containing either a single or a range of features of conservation importance and management measures have been put into place for each of the 27 MCZs, and EMSs. For each MPA, the areas and the features or species of conservation importance are identified along with an evaluation of the level and type of risk each features in the Matrix of fisheries gear types and European marine site protected features14. Several of the broad scale habitats/ sediment types have been assessed to be under various levels of disturbance from fishing (Jennings & Kaiser, 1998, 1998, Collie et al, 2000, Hiddink et al., 2006b) with deeper seabed being more disturbed by fishing pressure as a result of less natural disturbance. An inverse relationship between natural disturbance and disturbance by fishing was also modelled along with the comparative effects of trawling on various seabed sediments (Diesing et al., 2013). For each MPA there are base maps of habitat types from either predictive models (EUSeaMap) or based on surveyed data (EMODnet) with the surveyed data being of higher confidence. As a minimum, the spatial extent of the features of conservation importance can be determined from these, although the resolution or confidence will vary between areas. Each MPA/MCZ also has a factsheet (Natural England, 2013), which identifies the feature of conservation importance although not all have distribution maps, but it is expected these will be updated as a priority with spatial distributions being produced. The data contributing to such distribution maps will also vary greatly in spatial resolution and quality. EMSs (SACs & SPAs) also have Habitat Directive Regulation 33 supporting documentation (i.e. Natural England and Scottish Natural Heritage’s "Berwickshire and North Northumberland Coast European Marine Site”) along with data in support of, (a) the conservation objectives and (b) any operations which may cause deterioration of natural habitats or the habitats of species, or disturbance of species. There may also be data available from commercial survey undertaken as impact assessment for development of infrastructure or other commercial activities in the marine environment, which can add to the spatial data available for features in many areas. In addition to the distribution of marine habitats, there are certain species features, which are affected directly, or indirectly, by fishing pressure, whether it is through disturbance, loss of habitat or loss of food resource. Whilst spatial information on species may be limited, a range of site-specific data may be applicable in these cases and data on habitat or other resources are available as mentioned above. The relationships between the various resources should be considered and highlighted when applying the spatial equation tool in relation to single species. Mobile species may also be FOCI and where possible the range and seasonality of their spatial ranges should be considered.

14

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/310811/matrix.xls

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4.5 Habitat Sensitivity (Recoverability/Resilience)

The resulting impact of a fishing gear on a feature is dependent upon the sensitivity of the feature. When disturbed, an ecosystem may be damaged and recover; it may resist the disturbance and remain fundamentally unchanged; or it may change irreversibly, persisting in a different state for a long period of time. All three of these attributes of ecosystem stability—recovery, resistance, and reversibility—are measurable metrics of ecosystems, and can serve as empirical targets for experimental and comparative studies (Levin & Lubchenco, 2008). These components of resilience, however, are nearly impossible to study in isolation. They are embedded in complex ecosystems, and as a result, their components must be studied within the context of their day-to-day function and how they operate within functioning ecosystems. Macdonald et al. (1996) provides an assessment of the sensitivities of seabed types and benthic species in the context of a single passing of fishing gear followed by a recovery period in which no fishing activity occurs. The study considers a wide variety of gears, including mobile and static gears. The sensitivity of each of the species is calculated using an equation, involving the speed in which a species can recover from a disturbance, scored on a four point scale; the fragility of the focal species, scored on a three point scale; and the intensity of the impact of the gear, scored on a three point scale. The results of the study therefore provide sensitivity indices for species in relation to a range of gears. MarLIN (Tyler-Walters et al., 2001, and Tyler-Walters & Jackson, 1999) provides information on the sensitivity of 216 species and 131 biotopes. The MarLIN approach to sensitivity assessment examines the likely effects of human activities and natural events on marine species and habitat. The approach was designed in liaison with national Government, countryside agencies and marine experts. The MarLIN Sensitivity Rationale defines 'Sensitivity' as being: ‘dependent on the intolerance of a species or habitat to damage from an external factor and the time taken for its subsequent recovery’ and assesses the intolerance of a species to external change, and the likely recoverability of the species following cessation of the human activity or natural event. Intolerance and recoverability are then combined and result in categories of sensitivity, ranging from ‘very high’, ‘high’, ‘moderate’, ‘low’, ‘very low’ to ‘not sensitive’ and ‘not relevant’.15 The work on sensitivity assessment was updated and further developed through the Defra project MB0102 (Tillin et al. 2010), as well as further work currently being undertaken on abrasion pressure assessment by the Joint Nature Conservation Committee (JNCC) (NE5421). Tillin et al. 2010 also considered the resistance (tolerance) and resilience (recovery) of a feature and developed a comprehensive sensitivity matrix for protected habitats and species (EUNIS Level 3 broad-scale habitats, OSPAR threatened and/or declining habitats and species and the UK BAP habitats and species listed in Annex A), which were assessed and tested in relation to pressure benchmarks in 40 different pressure categories, with confidence scores relating to the evidence on which the assessment was based.

15

Sensitivity assessment rationale in http://www.marlin.ac.uk/sensitivityrationale.php

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Although a great amount of detail was incorporated into this approach, it was still acknowledged that it provided only initial ‘broad-brush’ risk assessments for what are typically complex and site-specific considerations, and did not take account of spatial or temporal scale. It was further noted that sensitivities and pressure levels would be site specific and need consideration, that some pressures had no benchmark levels (e.g. litter), that the approach used expert judgement in several cases where evidence was not available, and that a wider range of features could be assessed e.g. marine mammals. This approach graded resilience (tolerance) into four categories (none, low, medium, high) and resilience (recovery) into four categories (very low, low, medium and high) and when combined resulted in a sensitivity rankings of:

High sensitivity - high percentage mortality/destruction and recovery > 10 years;

Medium sensitivity - either medium resistance and zero to low recovery or zero to low resistance and medium to high recovery (recovery from 2 – 10 years);

Low sensitivity - high resistance or where recovery from any impacts caused by pressure is rapid (recovery within 2 years);

No sensitivity – high resistance or rapid recovery (within 2 years). These sensitivity assessments incorporating recoverability and resilience should be

considered in relation to the fishing gear/ activity when applied in the spatial footprint

methodology.

4.6 Impacts of Fishing in Relation to Natural Disturbance

The impact of fishing is often compared to the natural levels of disturbance in an area and the potential that the relative impact of the fishing pressure is low compared to already high levels of natural disturbance (e.g. from storms). However, large-scale studies have shown that for the majority of the seabed, physical disturbance from fishing exceeds the natural disturbance experienced (Diesing et al., 2013). The level of background pressure could, however, combine with natural disturbance to have a cumulative impact on some features, and this could also be considered when assessing the impact fishing gear has upon a habitat. It should also be noted that the majority of inshore fishing has been occurring for decades and it is more than likely that almost all the habitats and species that are present now have been, and continue to be, subject to some level of disturbance from fisheries pressure. For offshore habitats Hiddink et al. (2006b), supported by Collie et al., (2000), noted the first passage of a trawl has the greatest effect, while an increase of trawling effort on communities that were already heavily trawled had little additional effect which has influence on the cumulative effect of certain fishing gear types. Additionally, the disturbance from fishing gear upon a habitat may in effect maintain a habitat in its current state and removal of fishing pressure may alter the condition from the recognised state of the habitat.

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5. Feasibility of applying the fishing impact equation

The proposed equation is a straightforward approximation of the area impacted by fishing as a proportion of the total area of protected feature, and has the potential as the basis for an uncomplicated methodology for assessing the spatial impact of fisheries and standardising this assessment within the UK. Figure 1 ‘Footprint Equation’ with possible data sources and influencing factors

E = fishing effort (vessel days) expended within the feature area A(i) = the area of interaction with an individual vessel in one day; A(f) = the total area of the protected feature P = fishing footprint As previously discussed, some of the main processes affecting the impact of fishing are missing from this equation, and these are represented in Figure 1, along with some of the potential sources of data which could be used in its application. The feasibility of applying the equation and incorporating these factors with available data are discussed in more detail within the following sections.

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6. Fishing Impact Estimation Parameters

Generating a “footprint value” (P) aims to define the level of pressure for a single average day of effort for a reference vessel or fisher (land-based) within a fleet, taking into account the gear used. This value could be multiplied by the number of vessels or fishers to give the total pressure for a particular gear over a specific time period e.g. a calendar year. Where the total fishing pressure for an area could be calculated, and where management thresholds have been determined, fishing pressures could be assessed against these thresholds and compared with other pressures (e.g. renewables, dredging and fishing) for a particular site or site feature. The MMO gear matrix details the level of risk posed to EMS generic sub-features’ Conservation Objectives should interaction occur with the gear types listed. Within the MMO matrix, 40 individual gear types have been compiled into 13 gear categories. Each of these gear categories have specific common characteristics that define the amount of fishing effort and the area impacted by each unit of fishing activity. This will enable the proportion of the impact per km² to be determined for a specific gear for a year (or month) as required. In order to calculate the fishing pressure effectively for each gear, a clear understanding of the three parameters that define the fishing pressure must be obtained. The measurement of these three variables and potential data sources are described in the following sections: fishing effort (Section 6.1), area of interaction (Section 6.2) and area of the feature (Section 6.3). A worked example of the calculation of the fishing pressure P is presented (Section 6.4) for three example indicators of P: fleet – year, vessel – year and vessel – day.

6.1 Fishing Effort (E)

Fishing effort is a critical element in determining the level of fishing activity that interacts with a specific feature within a specific period. In order to calculate this parameter there are two specific variables that must be defined for each gear type;

effort (the number of effort units for a particular gear type) and

area of interaction (the area a unit of gear contacts) (see section 6.2). Effort is generally reported as an amount of gear (e.g. number of pots, length of net, width of trawl) and an indication of the time the gear was active (e.g. soak time or trawl duration). For the calculation of a footprint analysis, the effort measurement of time is only of importance when determining the cumulative impact in a single location. Therefore, a simple count of effort units, usually from a recognised standard source (e.g. logbooks) would be required. The typical standardised measures of effort for each of the gear types in the MMO matrix can be found in Annex 1. This will provide an indication of the level of fishing effort/frequency. Alternative methods for calculating effort are numerous, including using the total number of vessels within a fleet combined with an estimate of the proportion of the

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fleet actively fishing at any time (obtained from control and enforcement authorities, stakeholder consultation or available logbook data). Additionally estimates of the number of days per fishing trip, the number of hours fished per trip and the mean trawling speed of vessels can all be used to estimate the number of hours, which relates directly to effort, fished on a particular feature.

6.1.1 Potential data sources for Fishing Effort

There are a number of potential sources of data for fishing effort. Firstly, fleet numbers are relatively simple to calculate from vessel registers or licensing schemes that will contain details of the vessel size, engine power and gear types. The identification of the number of active vessels can be achieved through a variety of means from anecdotal information from local experts or fishers, questionnaires and surveys, through to more accurate data from logbook records or VMS style recording. Secondly, the level of fishing effort based on gear quantity and deployments per vessel or fisher should be identified. Similar methods of data collection as for fleet numbers can be employed. Average values for gear numbers and deployment times can be gained through anecdotal information and through questionnaires and surveys although these data will be subject to bias or inaccuracies and caution is advised when employing these data. Credible records in the form of logbooks or other reporting will be of greater value as these can be verified through inspection of the vessel and through paper records. Logbook or landing records, in one form or another, should be available for the majority of the fishing vessels operating in the inshore fleet. Relatively accurate estimates of time spent actively fishing can be calculated using ‘high-resolution’ GPS or VMS data, with vessel movement being used as a proxy for activity, and these data can be verified against observed data to add confidence to any resulting figures. VMS or high resolution GPS may not be currently available for all inshore fishing vessels but it is becoming much more prevalent throughout the fleet as costs are reduced and the hardware is becoming specifically designed for smaller inshore vessels. Typical data sources for fishing effort data have been identified in Table 3 and a summary of the progression from simplest to best practice data can be seen in Figure 2. Table 3: Summary of information available, limitations and recommended data collection methodologies for “Fishing Effort”.

Information Available Methodology Recommended

Anecdotal Information Estimates from an understanding of the fishery and historical records only. No quantitative information available to generate a value is available. This information would only be used in the absence of all other sources of information.

Fisher Interviews Interviews with fishers can be conducted to provide an estimate of the amount of effort conducted (e.g.

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Information Available Methodology Recommended

total number of days fished, number of pots set per day, number of trawls per days, average trawl speed and duration) and what would be considered typical. Data are limited without verification and are not collected in real-time. Information on gear can be enhanced through combination with inspection data on sizes (e.g. mesh size, pot openings).

Logbook/fishing records Data on effort can be gained either from logbooks or other fishing records (IFCA sightings data, or landings records can provide number of active days). Data should be recorded in real-time and would be relatively accurate and verified. Data recorded in this way can be cross-checked against VMS or GPS data submitted independently for times and locations of active fishing effort.

VMS/GPS data VMS or simple high frequency GPS data can indicate number of days at sea and time spent fishing. These can then be used with average reference values to indicate the level of effort conducted. This represents high quality independent data that can be linked to logbook data to verify and merge catch and effort datasets.

Figure 2: Summary of data types from low standard to best practice (high) for estimation of fishing effort.

6.2 Area of Interaction (A(i))

In order to define what constitutes the impact of a “typical vessel” for a fleet we need to consider what values (i.e. dimensions, deployment and physical properties of the gear utilised) are needed to estimate the individual daily impact for a “typical” vessel. The critical information required here would allow an estimation of the area of interaction with a unit of fishing activity at a certain location (i.e. not just when a vessel is present) and when the gear deployed would then interact with the feature, using the physical dimensions of the gear that will come into contact with the seabed (or other interaction with the feature). The proposed approach would be to define an impact proportion for each gear type which reflects the actual impact of the individual gear type based on the proportion of

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the gear in contact with the bottom. These proportions would ideally be based on experience from UK inshore fisheries, but it is unlikely that direct estimates for each gear type are readily available and estimates based on expert judgement and experience from other fisheries using similar gear types may be required initially to populate any predictive model. Estimates of proportions of bottom impact for gear types used in UK inshore fisheries as listed in the MMO matrix have been proposed and can be found in Annex 2. The physical dimensions for each of the gear types defined in the MMO Matrix are detailed in Annex 3 (e.g. the swept area for shrimp beam trawling would be average length of trawl / dredge x (width of beam (m) + shoe width)). A summary of the data types for estimation of fishing gear impact are summarised in Figure 3 from poor to best practice. Figure 3: Summary of data types from poor (left) to best practice (right) for estimation of fishing gear impact.

There are four broad classifications of gear within the extensive range of possible gear types used within UK inshore fisheries: point or area based gears, nets and lines, strings of pots or traps and trawls or dredges. The gear types and standard generic methods for calculation of interaction area are shown in Table 4. Table 4: Summary of categories of gear with respect to interaction area calculation methods.

Category Description of interaction area calculation method

Point / Area Single position, or defined area. Gear types of this type include the simplest single handline from shore to multiple fishers hand-gathering shellfish across a defined area. For the quantification of the area of seabed impacted by these types of gear the position would be recorded, and a nominal range for a single position, or the estimated area for larger area based fishing gears.

Net / Line To accurately measure the area of interaction of the seabed with net or line gears, the two dimensions required would be the length of the line and a measure of the movement of the line horizontally during fishing operations. This would vary with the net or line types, the degree of anchoring employed and the pattern of shooting and hauling which

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Category Description of interaction area calculation method

may cause the line to be hauled at an angle to the setting direction thereby causing an increased area to be impacted and should therefore be estimated for instance and type of gear assessed. Where high quality positional information is available for line or net setting, the area of interaction may be calculated more accurately by creating a buffer around the line and using this as the estimate of area, as lines may be set in non-regular patterns to target specific features. Impact would be based on the length of line + buffer either side to account for drift and hauling impacts.

String As for “Net/Line” with n point impacts of known size for the pots or traps. The string is an extension of the net and line category where the two dimensional line has a number of point impacts along the length of the line. The additional point impacts would be defined based on the shape and size of the gear (pots or traps) that impact the seabed. If traditional pots, these would be simple areas based on the product of the two longest dimensions of the pot, with the conservative assumption of maximum impact. In the case of pots or traps with legs to keep them off the substrate only the number and dimensions of the legs would be considered.

Trawl / Dredge The most complex category are those gears that are towed across the seabed, trawls and dredges. In these cases the measurements of each part of the gear that is in contact with the seabed should be measured. These will include all doors, feet, shoes, ground ropes, chains and the net itself if this is in contact with the seabed at any time. An estimate of the proportion of contact should also be made for each part of the gear which will give an estimate of the effective width of the fishing gear. The second measurement is the length of the trawl. The product of these two values will give the swept area for that gear. The trawl length can be calculated based on the duration and speed of the vessel. Where high quality positional information is available, trawl paths can be interpolated and the effective fishing time estimated based on the analysis of fishing speeds giving an accurate estimation for the area fished. Defined swath width (m) based on width of impacting gear × length of trawl (m) (duration × speed in knots (x1852)) (m)

The area impacted for each of the gear type categories is described below and illustrated in Figure 4 to Figure 7. Point/Area – This type of effort refers to fishing activity that appears at or around a single point where the effort is conducted or within an area that simply describes a non-standardised area such as a patch of intertidal mud used to drag for bait or a subtidal area used to dive for scallops.

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Figure 4: Graphical representation of point/area fishing activity.

Net/Line – This type of effort refers to a linear fishing activity where a line or net is set in the water. The length of the net or line can be measured and the effort and impact determined. This can cover both demersal (static) and pelagic (mobile) gears the only difference being the potential shift in gears between setting and hauling. Figure 5: Graphical representation of net/line fishing activity.

String – A string is similar to the “net/line” category in that a single line of fishing gear is set, but the line has attached a number of pots, traps or creels. Therefore in

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addition to the potential impacts of the line, the individual pots may have an impact on the bottom substrate. Figure 6: Graphical representation of string fishing activity.

Trawl/Dredge – The fourth category covers mobile gears, again both demersal and pelagic. Here a known width of gear (including attachments such as otter boards) is towed through the water for a known distance and hence the area potentially impacted by the gear can be calculated i.e. the swept area. Figure 7: Graphical representation of trawl/dredge fishing activity.

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6.2.1 Potential data sources for Area of Interaction

The best available data should be utilised to identify the geographic location of fishing activities. Table 5 provides a summary of potential data sources and how each might be applied and Figure 8 summarises how these data types range in suitability to the task. Where detailed effort information is available, the level of effort would ideally be calculated over a standardised grid system or, where possible, used to generate individually georeferenced records of fishing activity, as shown in Figure 4 to Figure 7, to enable linking of fishing effort to feature location. Where actual position information of fishing activities is available, tracks can be buffered to indicate area of potential impact based on gear type using typical gear dimensions. Where only anecdotal or interview data are available, a simple map or chart of activities at presence/absence level could be produced, but this would only define a uniform distribution of effort by respective grid locations. If a slightly higher level of effort data are available, the proportion of effort by grid could be allocated across the area. Table 5: Summary of information available and recommended data collection methodologies for “Area Impacted”.

Information Available Methodology Recommended

Anecdotal/Fisher Interviews Simple maps and charts defined from anecdotal (presence/absence) or fisher interviews (some allocation of effort).

Surveillance data Extrapolation of observations of fishing effort to the entire “fleet” providing spatial footprint of effort.

Logbook/fishing records Extracts of fishing locations from logbooks that can be georeferenced or preferably data extraction from georeferenced fishing logbook databases. The data from the FisherMap/ESFJC Fisheries Mapping Project represent data of this type that have already been georeferenced to provide a georeferenced polygon of where particular fishing activities have taken place.

VMS/GPS data

VMS or simple high frequency GPS data (at higher frequency than the 2 hour VMS interval) can indicate the areas fished by vessels. These data can be filtered further by appropriate vessel speeds to indicate when/where vessels have been actively fishing. Where high frequency data are available it is possible to interpolate between the positions reports either as straight lines or splines to define the most likely course of the vessel. These data can then be analysed and aggregated to an appropriate grid scale.

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Figure 8: Summary of data types from lower level information to higher standards for estimation of the area impacted.

6.3 Area of Feature (A(f))

To calculate P (proportion of a feature fished) using the spatial footprint equation, the area of the protected feature (A(f)) which may be impacted is an essential component of the equation.

As assessed within the MMO matrix, the protected features for EMS sites have been categorised into generic sub-features (with associated sub-features for SACs and SPAs considered) and their interaction with different fisheries and gear types prioritised by level of risk. For the purposes of this feasibility study, it is also important to consider the MCZ broad scale habitats (BSH) or Habitat Features of Conservation Importance (FOCI). The EMS features have been listed in Annex 4, alongside MCZ broad scale habitats and habitat FOCI. 6.3.1 Potential data sources for Area of Feature

As already summarised above, information on the distribution and extent of protected features ought to be available for each MPA. This information ranges from base maps of habitats types from either predictive models (EUSeaMap) or based on surveyed data (EMODnet), MPA factsheets (e.g. Natural England, 201316) which identify features of conservation importance and may have distribution maps, to the Habitat Directive Regulation 33 supporting documentation for EMSs which defines the protected features and gives extents, along with data in support of the Conservation Objectives and sources of impact on the habitats or species. Further detail on extents of features also exist in the form of Natural England Evidence Project Maps. There may also be further habitat distribution maps or data available from research in the area, or from impact assessment for infrastructure in other marine sectors or from other industry activities. If such opportunistic data are free to access within the public domain, this can also be used to collate the best available spatial information to feed into fisheries footprint estimation.

Due to the range of data sources and varying quality that could exist for different areas, probably the most important implication of using these existing data is that any caveats and assumptions inherent in the datasets used must be clearly stated

16 http://jncc.defra.gov.uk/protectedsites/sacselection/n2kforms/UK0017072.pdf

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and understood for each area. The MESH confidence scoring approach does go some way to help define the confidence limits for the data, but also has its own limitations. The vintage of the data should be taken into account, along with the robustness of the data collection methodologies.

Whilst MPAs are required to be monitored every 6 years, this often involves only condition monitoring of features, and updated feature distributions may not be a product of this process. It may therefore be impossible to detect change over time spatially of these features in response to any impact, and it should be noted that best practice would aim to use the most robust and up to date distribution data available.

It is also important to consider that spatial data for different areas may be at a different scale, and possibly at a lower resolution than required for the intended spatial assessment of the impact. In certain environments, or for certain features of a more ephemeral nature, natural variability may also affect feature extent over relatively short timescales, and in these instances high spatial resolution records of extent may never be possible, or desirable. Any such issue arising during an assessment should be noted and clearly stated when presenting the data, and the full implications explained with regard to how it affects use of a footprint equation. Figure 9 summarises examples of low to high quality data types that might be used in the estimation of the feature area. Figure 9: Summary of data types from lowest usable data to highest quality data for estimation of the feature area.

6.4 Worked Example of Fishing Impact Estimation

This section presents a series of simple worked examples for a fictional case study of a beam trawl fishery, where interactions occur with three different features of conservation interest within the fisheries’ area of operation. This worked example has been based on the example given out with the Fisheries Footprint Questionnaire (see Annex 6). Gear Type: Beam Trawl Gear Description: All vessels use the same gear - 6m wide beam trawl consisting

of 2 x 400mm shoes at either end, 5 bobbins of 200mm width

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spaced evenly along the beam. (Data Source: Gear measured and checked by IFCOs at regular intervals as maximum sizes defined in local bye-laws.)

Typical operation (unless otherwise stated) – Daily operation

consisting of 6 trawls of one hour duration each at 3 knots. (Data Source: Fisher interviews cross-checked independently against subset of vessels using iVMS).

NB: See Annex 1 for typical gear configuration dimensions.

These dimensions represent a medium sized beam trawler operating in inshore areas. See section 6.1 for other methods of estimating the impact of different gear types)

Fleet Description: The “fleet” under consideration consists of only four vessels that

operate on average 250 days each year. The days of operation are representative of the typical total number of days a medium sized beam trawler would be operational within a year but the time allocated to each feature has been allocated by the authors to produce a variety of results. (Data Source: Logbook records cross-checked independently against subset of vessels using iVMS).

Features: The fishery operates over a wide area which includes three

features of conservation interest, subtidal gravel and sand (Feature 1), subtidal muddy sand (Feature 2) and native oyster beds (Feature 3). (Data Source: Areas would be derived from current resource maps, example areas are used here).

Feature 1: Subtidal gravel and sand (area estimated at 75 km²)

Whilst operating on subtidal gravel and sand the vessels will operate on a day by day basis consisting of 6 trawls of one hour duration each at a typical speed of 3 knots. They will spend 180 days per year on this feature type.

Feature 2: Subtidal muddy sand (area estimated at 70 km²)

Operation as above but with reduced speed to 2.5 knots average speed to avoid excessive disturbance of mud. They will spend 60 days per year on this feature type.

Feature 3: Native oyster beds FOCI (area estimated at 6 km²) Daily operation consisting of 6 trawls of one hour duration each

at 3 knots. They will spend only 10 days per year on this feature type.

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Calculation of Fishing Pressure: Fishing pressure P is calculated here at three different levels that will be of use to different stakeholders for different uses. These are the fishing pressure for the entire fleet for a year (P FLEET (YEAR)), the fishing pressure for a typical vessel over a year (P

TYPICAL VESSEL (YEAR)) and the fishing pressure for a single day of effort for a typical vessel (P TYPICAL VESSEL (DAY)). Feature 1: Subtidal gravel and sand

Area Fished = 3 kts x 1852 m * (2 shoes x 0.4 m + 5 bobbins x 0.2 m) per trawl = 10000.8 sq. m per trawl 4 vessels, 6 trawls per day and 180 days operation per year = 4320 trawls

Therefore 10000.8 sq. m x 4320 trawls = 43,203,456 sq. m (43.2 sq. km)

Area fished / year = 43.2 sq. km

Fishable area = 75 sq. km

P FLEET (YEAR) = 0.576 P TYPICAL VESSEL (YEAR) = 0.144 P TYPICAL VESSEL (DAY) = 0.0008 Feature 2: Subtidal muddy sand

Area Fished = 2.5 kts x 1852 m * (2 shoes x 0.4 m + 5 bobbins x 0.2 m) per trawl = 8334 sq. m per trawl 4 vessels, 6 trawls per day and 60 days operation per year = 1440 trawls

Therefore 8334 sq. m x 1440 trawls = 12,000,960 sq. m (12 sq. km)

Area fished / year = 12 sq. km

Fishable area = 70 sq. km

P FLEET (YEAR) = 0.171 P TYPICAL VESSEL (YEAR) = 0.04275 P TYPICAL VESSEL (DAY) = 0.00071 Feature 3: Native oyster beds FOCI

Area Fished = 3 kts x 1852 m * (2 shoes x 0.4 m + 5 bobbins x 0.2 m) per trawl = 10000.8 sq. m per trawl

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4 vessels, 6 trawls per day and 10 days operation per year = 240 trawls

Therefore 10000.8 sq. m x 240 trawls = 2,400,192 sq. m (2.4 sq. km)

Area fished / year = 2.4 sq. km

Fishable area = 6 sq. km

P FLEET (YEAR) = 0.4000 P TYPICAL VESSEL (YEAR) = 0.1000 P TYPICAL VESSEL (DAY) = 0.0100

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7. Other Parameters

7.1 Temporal Patterns of Fishing Effort and Activity

The temporal variation in fishing activity is also a dimension that should be considered in relation to fishing activity. Complex temporal mapping of fishing activities could potentially be modelled. However, for use within the current equation and to keep the approach simple and appropriate to the data which is likely to be available, the following monthly information on the level of fishing would be useful to identify times when fishing activity will coincide with seasonality of features or critical periods for species or habitats. The presence or absence of a fishing activity at certain times during the annual cycle of the protected features or associated species, could either give greater weight to a fishing impact, or remove the interaction altogether. Data could be collected simply using the format suggested in Table 6. . Table 6: Example format for Collecting Temporal Variation in Fishing Gear Use.

Fishing Gear Type J F M A M J J A S O N D

Beam trawl (whitefish)

Beam trawl (shrimp)

Beam trawl (pulse/wing)

Heavy otter trawl

Multi-rig trawls

Light otter trawl

Pair trawl

Anchor seine

Scottish/fly seine

Mid-water trawl (single)

Mid-water trawl (pair) Key:

High activity

Medium level of activity

Low or zero activity

Unknown or not applicable

7.2 Temporal Variation in Habitats and Features

As the majority of fisheries have seasonal variations, the features themselves may also have temporal variations in extent or abundance, and periods where they are more or less sensitive or vulnerable to fishing impact. However, the extent to which seasonality will influence the level of impact will be feature specific and dependent on species present. A wide variety of fluctuations and processes can occur

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(spawning/reproduction times, migration times, recruitment, etc.), and should be assessed on a site by site basis. Whilst it may be possible to produce precise spatial and temporal distributions of a feature, resource limitations are likely to mean the most feasible approach would be to identify/assess whether the habitats/FOCI undergo ‘critical periods’ throughout the year. At those times, the coincidental occurrence of fishing activity during the same period will then affect or interact with the feature at a greater level. Conversely, periods during the year when a feature is not present within an MPA (i.e. migratory birds, fish) would remove the interaction with fishing activities and that specific impact would no longer apply. This could feasibly be recorded on the same scale as that used for the collection of seasonal variation in fisheries activity, i.e. on a month by month basis (Table 7). This is particularly relevant for a subset of the generic sub-features on the MMO matrix (see Table 7), but would need to be assessed on a site by site basis: Table 7: Example format for Collecting Presence/Absence Data on-Critical Periods during Seasonal Variations of Features.

Feature J F M A M J J A S O N D

Benthic feeding seabirds

Estuarine birds

Estuarine fish community

Grey and Common Seal

Kelp Forest Communities

Pursuit and plunge diving birds

Reed beds

River and sea lamprey

Salmon

Saltmarsh spp, Salicornia, Seablite

Surface feeding birds

Twaite and Allis shad

Key:

Present and critical period

Present but not critical period

Not present

Unknown or not applicable

7.3 Sensitivity of Habitats and Features to Fishing Activities

Estimating or ranking sensitivity, has been explored in detail through the work of initiatives such as MarLIN and Tillin et al. (2010), and has involved a comprehensive review of the traits and responses of a large range of species and habitats (MCZ

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features) with relevance to their sensitivity to a change in impact from various perturbations with pressure benchmarks. In order to determine the level of impact of interaction with fishing activity, the sensitivity of the feature should be incorporated into the proposed fisheries footprint calculation to help determine the extent to which the interaction is likely to cause an adverse effect. In large scale studies into the effect of fishing on benthic habitats, it was emphasized that if the frequency of fishing is greater than the period of time required for a habitat/species to recover, then the habitat or species is likely to remain in permanently altered state, as discussed in Collie et al. (2000). This study also noted that recovery is rarely less than 100 days for any substrate, implying that even the most resilient habitats could perhaps withstand only 2-3 incidences of physical disturbance a year before changing markedly in character, although this can easily be exceeded. These studies also recognised the impact of fishing pressure in relation to natural disturbance, and found that the impact of fishing was less in areas where natural disturbance was already high. An overall pattern indicated that the significance of fishing disturbance was likely to increase with increasing water depths, as natural disturbance tends to decrease with same depth gradient (Diesing et al., 2013). This suggests that depth could potentially be used as a proxy for incorporating a general level of natural disturbance in an area. This is also incorporated into the biotope classification system with level of exposure, and therefore may have the potential to be inferred by general substrate types of the generic sub-features and related biotopes (Level 3). Also of relevance to the process of recovery of features, are extreme levels of disturbance, where catastrophic events may remove habitat altogether, and no recovery would be possible (e.g. removal of biogenic reef by dredging). At the other end of the scale, indirect effects may also come into play. Whilst fishing pressure may not occur on a feature, fishing activity in adjacent areas could also affect a feature, for example through re-suspension of the sediment and smothering of neighbouring species. The sensitivity assessment work and matrix from the MB0102 project for MPA network and planning (Tillin et al. 2010) is currently being updated for EMS features, in addition to MCZ features already considered. The approach results in sensitivity rankings (high, medium, low and no sensitivity) and it may be appropriate to use this type of categorisation to help weight the fishing footprint equation, and give greater value to the impact on more sensitive features. However, within the sensitivity matrix developed by Tillin et al., (2010), fishing pressure falls under several pressure types: surface abrasion; surface penetration; sub-surface penetration; siltation rate change; removal of target species and removal of non-target species, and fishing is not specific to gear type in each instance. The sensitivity scorings for separate features would then require combination to result in a single figure that could be applied to weight an interaction between a specific gear type and a feature overall, and for application to the fisheries footprint equation. The range of data sources and availability is outlined in Table 8 and summarised in Figure 10. In a best practice situation, it might be possible to use the detailed

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information from the sensitivity assessment matrix (Tillin et al, 2010), but this would require further thought and time to develop. It might also be useful to include for best practice a value to indicate the level of natural disturbance in an area, which could be used to decrease the value of P as fishing impact decreases in relation to higher levels of natural disturbance. As discussed previously, there is potential to use depth, or exposure (as described in the biotope system) as a proxy value. More sophisticated models could also be developed by estimating the number of days in a year the seabed was disturbed by tides and waves (by predicting the number of days in a year wave/current stress exceeded a grain size-dependent threshold) as in Diesing et al., (2013). However, such complex models also increase the number of assumptions made and introduce uncertainty of their own. Table 8: Summary of information available and recommended data collection methodologies for Sensitivity of Habitats and Features to Fishing Activities.

Information Available Methodology Recommended

No research available, or anecdotal information only

Use precautionary level of impact response

Pre-determined non-linear matrix of feature sensitivity

Use predetermined or hypothetical levels of sensitivity for use within equation

Existing research (species/habitat specific)

Use previous research to inform level of sensitivity to fishing impact

Purpose led research Design specific research to calculate sensitivity response curves to impact

Figure 10: Summary of data types from poor (left) to best practice (right) for estimation of the sensitivity of feature in relation to fishing gear.

7.4 Estimation of Cumulative Impact

There are a number of spatial/temporal considerations which apply to fishing pressure that have implications for cumulative level of impact. These include the non-uniform distribution (clustering) of fishing effort, the cumulative effects of multiple gear types and estimates of recovery times for features. Fishing effort varies in terms of both the spatial and temporal distribution and is determined by availability of fishing opportunities and the catch rates achieved. This may lead to clustering or non-uniform distribution of fishing effort across a single

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feature. This effect is not captured by the fisheries footprint equation in that, where P=1, this can indicate that an entire area of feature has been fished, but uneven distribution of effort may in reality mean that only a fraction of the area has experienced any impact. However, it has also been suggested that the fisheries footprint equation could be used to give a value of P which describes an upper estimate of an annual impacted area, or the largest possible area that could be impacted within a year. This can then be used to consider the worst case scenario for different fisheries, and could be used to compare fisheries with each other using this relative value, or between different sectors. However, comparison of relative values of P between fishing gears could be misleading, knowing that the spatial distribution of the fishing effort has not been captured. The level of repeat coverage, combined with the clustering of effort on a particular feature, will determine the number of times particular parts of a feature are fished in a year. In these cases, the effects of fishing effort may not be linear (in that twice the effort has twice the impact etc.) and that the first pass of fishing gear impacts highly and subsequent effort has a lesser effect. Currently, the equation does not incorporate this possible effect but uses a precautionary approach in that the fishing pressure from multiple impacts is additive and applied without any decrease with cumulative effects. Where a high degree of clustering of fishing effort is observed or indicated through stakeholder consultation, or habitat distribution is of a heterogeneous nature it is recommended that the feature under consideration is split spatially between the high and low level areas to allow these to be assessed individually. An alternative approach may be to calculate and accumulate the area of feature that is unimpacted (as per Piet and Hintzen, 2012) to give some indication of the area of feature that has not been affected by the fishing pressure and therefore has its integrity maintained, which would have management implications over and above the level of fishing pressure over an area without any indication of patchiness of effort. Where multiple gear types are used on a single feature it is recommended that each are assessed individually (giving due consideration to the seasonality and level of impact of each of the gear types used). Individually, each fishing gear may be below any prescribed threshold level but without periods of recovery between the combined impacts it may reach and exceed the threshold. It is recommended therefore that all individual gears should then be combined to provide an overall level of fishing pressure for a feature and also identify any periods of inactivity where recovery could occur. A possible best practice concept for modelling the spatial variation in fisheries would be to grid the spatial patterns of fishing activity geospatially, to build up additive effects of subsequent impacts using impact/response curves for each feature and building up a “hotspot” type approach.

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8. Modifications to the Equation

The evaluation of the fisheries impact equation has identified various factors in the application of the equation which mean if the fisheries impact equation is applied in an unstructured manner, critical phases or data along with crucial considerations may be omitted. Framing the fisheries impact equation within a structured methodology could ensure these are captured and fully encompassed. Further development of the concepts within the fisheries impact equation could be progressed by incorporation of these and additional theories within a geospatial system. These possible developments are described below.

8.1 Developing a Framework for an Applied Methodology

The current form of the equation uses mathematical calculations to relate the extent of fishing effort to the feature extent based upon ‘footprints’ or area measurements. This application of the equation can be used out of context which may cause confusion or poorly interpreted results. A suggested pathway to modify the equation would be to incorporate the mathematical principles into a more structured methodology or system which would provide context and enable a user to consider any results with references to some of the spatial, temporal and cumulative effects mentioned above. A suggested methodology (similar to that undertaken by the MMO in management of the EMS sites in the 6-12 nm zone) is presented in Figure 11 which shows the basic assumption that a fishing activity takes place within a management area and that a feature is also present within that area. The process then poses a series of questions for which simple answers are required, with cross reference to the MMO matrix for categories of vulnerability of features to various fisheries/gear types. This process is designed to ensure that if any interaction between fishing activity and feature occurs, that is it considered fully with temporal and spatial variability incorporated. The process also assumes each fishing activity or type has a cumulative impact which is simply additive. This may be simplistic but supports the use of the precautionary principle for management purposes. When applying the suggested methodology, the metadata for all the best available datasets, and assumptions and limitations associated with them, should be carefully considered. The application of the methodology itself may identify data gaps or lack of confidence in the data, which should be recorded as part of the spatial footprint assessment.

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Figure 11: Flow diagram for a methodical approach to applying the fishing impact equation for all fishing activities and features present.

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8.2 Developing a Geospatial System

In addition to the development of an applied methodology, modifying the equation to relate fishing activity/effort and habitat distribution spatially may assist with clumped or uneven fishing activity and also enable assessment of any habitat or resource to be better understood. The underlying principles of the footprint equation could be used as building blocks within the development of a geospatial system. Spatial and temporal data can be obtained from data sources used to map fishing activity. An assessment can then be made as to whether this fishing effort has an impact on any given habitat. Using the distribution of the habitat it will be possible to spatially cross tabulate fishing effort (E) with habitat distribution (A(f)). Figure 12 illustrates the data inputs and basic stages of data processing and analysis. Figure 12: Input layers and stages for geospatial calculations.

It may often be the case that either fishing effort data or feature extent data may be at dissimilar spatial resolutions, therefore it is proposed a standard grid based system is used to spatially cross reference the two datasets. The scale of the grid based system should be appropriate to the management requirements and data availability. If high resolution data are available then a small grid spacing can be used whereas if coarser resolution data are available then a larger grid spacing can be used. Should the grid spacing lack sufficient resolution, this may have

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implications for the level of detail that is required to support appropriate management actions (see Section 4.3.1). The cross-tabulation between fishing effort and feature distribution could also be modified. As noted above in section 7.4, fishing activity and habitats can interact in a variety of ways and using a mathematical approach the cumulative effects of fishing can result in P exceeding 1 and, cumulatively, this can be interpreted in different ways depending on the fishery. It may be that an index based approach would allow these impact values to be cumulated or combined in a standard manner and as they are represented spatially the undamaged area can be taken in to account. To develop an index system the interaction between fishing gear and habitat requires better assessment and a possible means for this is the utilisation of response curves or scales for each habitat and gear interaction. Whilst there are theoretically thousands of possible interactions of which some are extremely unlikely to occur and some will be better understood, it is proposed a small range of response curves could be utilised. These curves could be based upon depletion curves for trawl impact studies (Ocean Studies Board, 2002) or MacDonald et al. (1996) (assuming that trawling or dredging have the largest physical impact) and supplemented by other targeted information where possible. A precautionary principle could be used if no data or information exists and this would assume that a single unit or fishing effort has a catastrophic effect (100% damage). Figure 13 gives some examples of hypothetical response curves which could be used including a precautionary curve and low, medium and high response curves.

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Figure 13: Hypothetical response curves for habitats to fishing activity.

This in effect would replace the Ai/Af component in the equation and gives E x effect per unit area i.e. if a 20m grid cell contained a FOCI which is trawled (high response) 3 times, the impact value is 0.75. If the area is then potted, the value from the potting response curve (low) would be simply added (it is likely potting will have a lower response curve than trawling and so little would be added e.g. 0.08). If the area is then dredged, then this value would also be added which is likely to have a high response curve (0.6) resulting in a high overall value (1.43). If the index value exceeds 1 this may highlight a problem, as it is possible 100% of the feature in that grid square would be damaged. As the system is based on grid squares and the interaction is carried out spatially the undamaged or unaffected areas of habitat can also be measured and assessed to highlight when a feature is likely to be lost or irreparably damaged. An example of how geospatial analysis could be employed is presented in section 11.4.

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9. Data Sources, Availability and Limitations

To facilitate the application of the equation, data sources need to be identified and also checked against the range of options presented as part of this study. Using the low to high standards presented, each data source can be checked and matched for each application of the equation / methodology using the matrix in Figure 14. As acknowledged previously, datasets used for assessment should ideally be the best available, and all such datasets should be accompanied by appropriate metadata. However, this may not always be the case and it is recommended that any assumptions and caveats relating to the data are documented. The implications of these assumptions and any risks should be fully considered and also stated when being used for resource assessments. Local expert judgement should also be used to give insight as to whether data and results are applicable and whether they reflect realistic situations in the field. The methodology for applying the fisheries impact equation requires a range of input data and within these there will be a range of quality and therefore confidence within each input dataset. Additionally data may have restricted access or licence conditions associated, which may limit their use or effectiveness within the fisheries impact equation. Figure 15 provides an indication of some of the likely data sources along with example considerations and possible implications of limitations which can be associated with each data source. The resulting outputs from the fisheries impact equation should be accompanied with references to the source data made and any assumptions stated. Additionally, where data of low confidence has been employed attention should be drawn to this. Where input data is of poor quality, confidence or is unsuitable to be used to determine management objectives, then any outputs should be caveated and recommendations made to alleviate the paucity of data.

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Figure 14: Matrix for data checks and identification of standard of data.

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Figure 15: Examples of data sources and availability, with limitations and implications for use.

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9.1 Summary of best practice and ‘Gold Standard’

To ensure marine resources are utilised and managed in a sustainable manner whilst maintaining healthy marine ecosystems, habitats and species, management measures must consider and use sound scientific principles and evidence to underpin management rationale. To this end, a best practice option for the application of the fishing impact methodology should use the best available data. These data can be identified using the matrix in Figure 14, ‘gold standard’ or best practice application of the methodology would require the highest standard of data (right-hand column) to be used. Where there are knowledge gaps or data are at a low standard, the assumptions and caveats for these should be fully understood and stated along with the likely implications (see section 0. Application of the fishing impact equation uses an uncomplicated procedure to assess the effect of fishing upon a protected or important feature(s) which inherently applies a precautionary approach to the assessment. This approach could also be used to model a range of scenarios based on a variety of gear dimension detail and fishing activity levels to give a value for how much of a feature is affected by fishing activity. This will help to provide a more standardised, transparent and evidence-based approach to assessment of fisheries and their potential impact. As knowledge or evidence becomes available, the application of the equation becomes more complex and in order to incorporate this new or updated information it should be possible to incorporate the underlying principles into a more sophisticated methodology and geospatial system as discussed in section 8.

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10. Questionnaire Completion and Responses

Information on this project was initially circulated to all 10 IFCAs in a project identification flyer providing background information and introducing how each of the IFCAs could be involved within the development of the approach. A copy of the flyer is attached as Annex 5. A questionnaire was distributed to all IFCAs at the end of the Part 1 (sections 4-9) of the project and was discussed in a series of teleconference meetings (see Table 9) to walk-through the questionnaire and to identify and address any problems that were encountered when completing the information requested. The questionnaire can be found in Annex 6. Interviews with representatives from 8 of the Inshore Fisheries and Conservation Authorities (IFCAs) were held during the week commencing 13th April 2015, to gather the views and data available from each IFCA on the applicability of the spatial footprint approach in quantifying fishing effort. Interviews with 2 further IFCAs were scheduled but representatives could not attend due to unforeseen work commitments. Questionnaires were sent out to IFCAs (and to the MMO Marine Protected Area (MPA) Team) prior to the interviews, with questions detailed below. The questions addressed each of the elements of the fishing impact equation (fishing effort, area of impact and area of feature), factors that are relevant to the cumulative impact (fishing behaviour and seasonality) and any examples of best practice that can be identified, i.e. the “gold standard”. The questions were designed to be open-ended and wide ranging in order to collect data which would allow an evaluation of this method to assess the spatial pressure of inshore fishing activities, and also to assess the level of information readily available. During the interviews, the questions were run through sequentially and information discussed for each IFCA was noted. This allowed the variety of information from the different regions and IFCAs to be highlighted and to raise issues/differences between the IFCAs that might not have been considered if completing the questionnaire in isolation. IFCAs were then asked to complete and return the questionnaires in as much detail as possible within the timescales allowed for the study. Responses were received from 4 of the 8 IFCAs interviewed, 1 IFCA that had been unable to attend interview, and the MMO MPA team who responded with information at a generic level, but gave specific information for one example site. Each of the completed questionnaires are included in Annex 11 to the report, and some brief summary information is included below. The responses were then reviewed to ascertain which case studies could be drawn from the information obtained to illustrate use of the fishing footprint equation, whilst highlighting regional differences, gear type differences, spatial differences (of feature) and levels of data availability. Discussion arising at interview concerned the variety of and within gear types used in each IFCA, and the varying effort throughout the year (including multi-gear vessels, and changing gear due to catch availability), as well as the variability in feature extent and condition, and whether this could be captured by the footprint equation. Also mentioned were other natural pressures/processes such as background

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eutrophication, deposition of sediment from rivers and natural disturbance (storms) levels which potentially outweigh fishing impacts and are not thought to currently be adequately recorded or therefore potentially captured within the equation. There was also general comment regarding the level of time it would take to gather the relevant information for this study, if it were to be completed in detail for all fisheries, gear types and all features present within IFCA regions. As a result, it was agreed that information should be completed at least for the 3 most common/contentious fisheries within their management. Table 9: Summary of IFCA Telephone Interviews.

IFCA Name of interviewees

Position/role of interviewee

Interview Date

Sussex (SUS)

Sean Ashworth

Dr Alberto Kavadellas

Charlie Hubbard

Chief Fisheries & Conservation Officer

Senior Fisheries Officer

Marine Operations Manager & Master of Vessels

14/04/2015

Eastern (E)

Daniel Steadman

Ron Jessop

Environment Officer

Senior Research Officer

14/04/2015

Kent and Essex (K&E)

Jane Heywood Lead Scientific and Conservation Officer

14/04/2015

Southern (S) Simon Pengelly Conservation Officer 14/04/2015

North Western (NW)

Erik Thinnesen

Sarah Temple

Senior IFCO

Science Officer 15/04/2015

Northumberland (N)

Emma McLoughney Environment & Scientific Officer

15/04/2015

Devon and Severn (D&S)

Sarah Clark

Lauren Parkhouse

Kat Grey

Fisheries Officer

Environment Officer

Environment Officer

17/04/2015

Cornwall (C) Colin Trundle Research/Enforcement Officer

17/04/2015

North Eastern (NE)

Tim Smith Environmental and Scientific Officer

Questionnaire only

MMO Elaine Young

Andy Wills

Marine Environment Manager

Questionnaire only

The number of responses to question 2 (fishing effort) and 3 (area of impact) of the

questionnaire can be found in summary in Table 10 and Table 11 respectively. A

third Annex to the questionnaire listed the generic sub-features identified in the MMO

Matrix, allowing the IFCAs to show which features were present in their district, and

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is summarised in Table 12. The area of feature (Question 4) included a number of

data sources from which the area of a feature and fishing upon that feature could be

identified including vessel sighting positions and activity maps (NW, N, SUS, NE),

fishing effort maps (SUS, NE), regular targeted surveys, including yearly Sabellaria

alveolata reef (NW, E) surveys, stock assessment of mussels beds (NW), cockle or

mussels spat settlement and bed surveys conducted as and when required (NW, E),

subtidal boulder and cobble reef surveys (E), and seagrass monitoring studies (E).

MMO have FisherMap data for all sites, and have developed expert opinion protocol

for MMO coastal/IFCA representatives to inform on gear specifications and fishing

ground areas.

Question 5 on fishing behaviour and how fishers interact with particular features gave highly variable responses as expected with the variety of fishing resources, features and environmental variables encountered in different regions. The level of data on such trends in behaviour was also highly variable in its availability. Generally, information tended to be anecdotal rather than formally recorded. Fishing effort and activity mapping has been tentatively indicated in work such as the Fisheries Mapping Project, the National Inshore Fisheries Data Layer and ‘in-house’ IFCA data layers (E, SUS, NE). The MMO use site assessments on fishing activity from statistics and expert opinion.

Responses on question 6 relating to habitat sensitivity and recovery showed that

information was limited and the majority of information discussed in response to

these questions was anecdotal, apart from research in localised case studies for e.g.

Lyme Bay. Similarly, when asked to highlight any best practice examples for data

gathering and analysis (Question 7), responses were limited to the byelaw permit

systems and monthly return forms for hand gathering mussels and cockles (NW) and

potting (Northumberland) and where specific case studies have been conducted

(MCZs, SACs) with high levels of research such as those studies on Kingmere MCZ

and Lyme Bay.

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Table 10: Summary of Number of Responses to IFCA Questionnaire – Annex 1 – Gear Types.

Gear Category Gear Type Active in IFCA (Y/N) (No. of IFCAs with this gear type activity)

Typical (or range of) vessel size in fishery

Size of fleet for each gear.

Typical (or range of) speed of vessel when fishing

Typical (or range of) length of fishing trip

Percentage coverage of inshore vessels by VMS, if present.

Towed (demersal) Beam trawl (whitefish) 4 + MMO 2+ MMO 1+ MMO 1 1 1

Beam trawl (shrimp) 3 + MMO 3 + MMO 2 + MMO 2 2

Beam trawl (pulse/wing) MMO

Heavy otter trawl 2 + MMO 2 1 1 1 1

Multi-rig trawls 2 + MMO 2 1 1 1 1

Light otter trawl 4 + MMO 2 2 + MMO 2 2

Pair trawl 2 + MMO 2 1 1 1 1

Anchor seine

Scottish/fly seine

Towed (demersal/pelagic)

Towed (pelagic) Mid-water trawl (single) 1 + MMO 1

Mid-water trawl (pair) 1 + MMO 1

Industrial trawls MO

Dredges (towed) Scallops 2 1 2 1 1 1

Mussels, clams, oysters 3 1 2 2 1

Pump scoop (cockles, clams)

Dredges (other) Suction (cockles) 2

Tractor

Intertidal handwork Hand working (access from vessel)

3 1 1 1

Hand work (access from 5 1

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Gear Category Gear Type Active in IFCA (Y/N) (No. of IFCAs with this gear type activity)

Typical (or range of) vessel size in fishery

Size of fleet for each gear.

Typical (or range of) speed of vessel when fishing

Typical (or range of) length of fishing trip

Percentage coverage of inshore vessels by VMS, if present.

land)

Static - pots/traps Pots/creels (crustacea/gastropods)

5 + MMO 5 + MMO 5 + MMO N/A 4 1

Cuttle pots 1 1 1 N/A 1

Fish traps 2 2 2 N/A 1

Static - fixed nets Gill nets 5 + MMO 3 + MMO 3 + MMO 1 2

Trammels 4 + MMO 2 + MMO 2 + MMO 1

Entangling 4 + MMO 2 2 1

Passive - nets Drift nets (pelagic) 3 + MMO 2 2 1

Drift nets (demersal) 3 + MMO 1 1 1

Lines Longlines (demersal) 4 2 2 1

Longlines (pelagic) 2 +MMO 2 +MMO 2 + MMO 1

Handlines (rod/gurdy) 5 2 2 1 2

Jigging/trolling 4 1 1 1 1

Seine nets and other

Purse seine

Beach seines/ring nets 1

Shrimp push-nets 3

Fyke and stakenets 3 1 1 1

Miscellaneous Commercial diving

Bait dragging

Crab tiling 3 1

Bait collection Digging with forks 4 + MMO 1 2 *Values within the table indicate the number of IFCAs/MMO that have responded with available data for that cell. Where cells have not been filled, this may indicate data are

not relevant, or not available with any degree of confidence. Some IFCAs responded only with information for their 3 most common/contentious fisheries, the MMO responded for one example site: Margate and Long Sands cSAC. So far, 5 IFCAs have completed questionnaires.

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Table 11: Summary of Number of Responses to IFCA Questionnaire – Annex 2 – Gear Dimensions.

Gear Category Gear Type Typical Gear Dimensions

Towed (demersal) Beam trawl (whitefish)

Beam trawl (shrimp) 3

Beam trawl (pulse/wing)

Heavy otter trawl 1

Multi-rig trawls 1

Light otter trawl 1

Pair trawl 1

Anchor seine

Scottish/fly seine

Towed (demersal/pelagic)

Towed (pelagic) Mid-water trawl (single)

Mid-water trawl (pair)

Industrial trawls

Dredges (towed) Scallops 1

Mussels, clams, oysters 2

Pump scoop (cockles, clams)

Dredges (other) Suction (cockles)

Tractor

Intertidal handwork Hand working (access from vessel)

1

Hand work (access from land) 1

Static - pots/traps Pots/creels (crustacea/gastropods)

3 + MMO

Cuttle pots 1

Fish traps

Static - fixed nets Gill nets 1 + MMO

Trammels 1 + MMO

Entangling 1

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Gear Category Gear Type Typical Gear Dimensions

Passive - nets Drift nets (pelagic) 1

Drift nets (demersal) 1

Lines Longlines (demersal)

Longlines (pelagic)

Handlines (rod/gurdy) 1

Jigging/trolling 1

Seine nets and other Purse seine

Beach seines/ring nets

Shrimp push-nets 1

Fyke and stakenets 1

Miscellaneous Commercial diving

Bait dragging

Crab tiling 1

Bait collection Digging with forks 1 *Values within the table indicate the number of IFCAs/MMO that have responded with available data for that cell. 3 IFCAs responded to this section, 1 with only the 3 most common/contentious fisheries (others reported insufficient time to complete this information).

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Table 12: Summary of IFCA Questionnaire Responses – Annex 3 - Features Present.

Feature Type Present in IFCA

Sand (high energy) 1

Subtidal gravel and sand 3

Subtidal muddy sand 3

Seagrass (SACs) 3

Seagrass (SPAs) 2

Maerl

Mussel bed on boulder and cobble skears 1

Estuarine rock (boulder, cobble and bedrock) 1

Estuarine fish community 3

Subtidal mud 2

Intertidal mud 3

Intertidal mud and sand 3

Intertidal gravel and sand 2

Intertidal sand (high energy)

Mussel beds on mixed and sandy sediments 1

Intertidal mixed sediments 2

Subtidal mixed sediments 1

Brittlestar beds 1

Coastal lagoons 2

Coarse sediment (high energy)

Tideswept communities

Intertidal bedrock reef 1

Intertidal boulder and cobble reef 1

Intertidal chalk reef

Subtidal bedrock (including chalk) reef 2

Subtidal boulder and cobble reef 2

Sabellaria spp reef 2

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Feature Type Present in IFCA

Subtidal mussel bed on rock

Kelp forest communities

Saltmarsh spp, Salicornia and Seablite 3

Annual vegetation of driftlines 2

Intertidal sea caves

Subtidal sea caves

Twaite and Allis shad

River and sea lamprey 2

Salmon

Grey and Common Seal 1

Surface feeding birds 3

Estuarine Birds 3

Pursuit and plunge diving birds 2

Benthic feeding seabirds 2 Sheltered muddy gravels FOCI

Native oyster beds FOCI

Peat & clay exposures FOCI 2

Seapen + burrowing megafauna communities FOCI

Coral gardens FOCI

File shell beds

Fragile sponge/anthozoan communities

NB: 3 IFCAs have filled out this section (others reported insufficient time to complete this information)

FISHERIES FOOTPRINT QUESTIONNAIRE

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11. Case Study Examples

Following consultation, the project aimed to investigate how the equation could be applied using data identified from 3 of the IFCAs. These case study examples have been developed using information gathered from a number of different IFCAs who responded to the questionnaire, with differing areas, gears and scales to test the applicability of the spatial footprint approach in quantifying fishing effort. It should be noted that these case studies have been developed using average data for gear and operational parameters and in some cases parameters have only been derived from anecdotal information and not actual quantitative studies. As a result of this, we would recommend that these case studies are only used within the scope of this project and not repeated or used outside of the study. The three case studies used are as follows:

Eastern IFCA shrimp beam trawl (Wash, North Norfolk and Lincolnshire coast);

North Western IFCA pot fishery (crab and lobster) (Lune Deep EMS); and

Sussex IFCA net fishery (Kingmere MCZ).

All three were capable of producing results reasonably easily, though a more sophisticated modelling approach would be better to account for the variety of gear and operational parameters that have been simplified in the case studies. A calculation worksheet (see Annex 7) has been developed to collate and assist in the calculation of the four typical indicators that allow the fishing pressure to be quantified in simple terms:

Total fishing pressure P Fleet Year - for an entire year for the fleet of vessels under consideration;

Fishing pressure P Vessel Year - for an average single vessel for a year.

Fishing pressure P Vessel Day - for an average single vessel for a typical day fishing.

Proportion Feature Fished – The proportion of the feature under consideration that is considered to be fished by the fleet (i.e. not the calculated fishing pressure, but a geographical understanding of the parts of the feature that are actively prosecuted by fishing vessels).

It should again be noted that calculation of the fishing pressure for the entire fleet for a year on one feature is likely to be an over-estimate; however the calculation provides a worst case scenario and an upper threshold for levels of impact on any given feature.

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11.1 Eastern IFCA shrimp beam trawl (Wash, North Norfolk and Lincolnshire coast)

The following case study uses data related to the shrimp beam trawl fishery in the Wash and North Norfolk Coast SAC site, which is a 1,077.61 (km2) intertidal and subtidal site. Marine areas occupy 51% of the site, intertidal and estuarine areas occupy 46%, with the final 3% occupied by saltmarsh areas. The site is designated for eight marine Annex I habitats features (including subtidal sandbanks, intertidal mudflats and sandflats, coastal lagoons, reefs, Salicornia and other annuals colonising mud and sand, Atlantic salt meadows, and Mediterranean saltmarsh scrub) and one Annex II species feature (grey and common seal). Known extractive fishing activity is extensive in this site and includes hand-working and dredging of common cockle (Cerastoderma edule) and blue mussel (Mytilus edulis) beds, beam trawling of brown shrimp (Crangon crangon) and pink shrimp (Pandalus montagui), static potting for brown crab (Cancer pagurus), common lobster (Homarus gammarus) and common whelk (Buccinum undatum) as well as various netting (gill nets, trammels, entangling, drift nets (demersal)), longlining (demersal) and bait-digging activities. Aquaculture activity is undertaken in the site through several lays for various molluscan species. Activities also identified in the area that may cumulatively cause impact are maintenance dredging (licensed), dredge disposal, and future windfarm cable laying (Race Bank OWF led by DONG Energy). Beam trawling for shrimp is one of the most common and important fishing activities occurring within The Wash and North Norfolk Coast SAC. Two species are targeted by this activity; brown shrimp (Crangon crangon) and pink shrimp (Pandalus montagui), with the brown shrimp fishery making up approximately 90% of the UK landings from this site (ICES, 2010). Shrimping activity can potentially access any subtidal or intertidal feature in the Wash, Lincolnshire coast or North Norfolk Coast, except in closed areas covering 9.27 km2 (or ~1% of site area). High risk interactions (amber and green) in The Wash and North Norfolk Coast SAC have been identified for shrimp beam trawling with subtidal coarse sediments, subtidal mixed sediment, subtidal muddy sand, intertidal coarse sediments, intertidal mud, and intertidal mud and sand. The interaction with the grey and common seal has been classified as low risk. The fishery is depth constrained in that shrimp tend to be found in <10m depths. Fishing activity can be concentrated where there is the most water (i.e. sandbank trough) and may not access the top of a sandbank as the target is unlikely to be present. Some vessels stay completely constrained within one trough of one sandbank in a day, or may access several sandbank edges/troughs in one day. Vessels are known to “chase” target and so may leave MPAs and come back over the course of the season, which may also mean very high effort may be concentrated on relatively small patches. Vessels participating in the C. crangon fishery work almost exclusively inshore and are small in size (ranging from 8 to 16m in length) and participate in brief trips (24-48 hours per trip): most employ twin beam set-ups and nets predominantly have cod end mesh sizes of 18-26mm. The fishing gear used by vessels in this fishery tends to be much lighter than that used by deeper water offshore fisheries targeting flatfish

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species, employing relatively light rollers and no tickler chains. Veils are used to reduce by-catch of larger fish and catches are riddled to discard smaller species. The average number of tows is 3 per day, and average tow duration is 2 hours, with a tow speed of 1.5 knots. However, the fishery can largely be split into two types of vessel: under 12m, which have 5-6m beams and the length of fishing trip is up to one day, often with a seabed contact time of approximately 6 hours (3 x 2 hour hauls) at a lower speed of 1.5 knots. Larger vessels (over 12m) have beams up to 8m and fish for up to 48 hours, and potentially have considerably longer seabed contact time as not restricted by tidal cycles. There is anecdotal evidence to suggest that smaller independently run vessels are more likely to deploy their gear optimally, with ground ropes set shallow so that rollers remain lateral and have less penetration into the substrate, whereas larger vessels have the power to tow at greater speeds and may set their nets in a deeper curve causing edges to dig into the substrate and can cause surface abrasion pressure from the entirety of the bottom of the net (P. Garnett, pers. comm.). Generally though, the small beam length, lighter rollers and absent tickler chains are vital in considering the environmental impacts of this activity on subtidal and intertidal habitats and in distinguishing this activity from other, heavier impacting beam trawling. Shrimp beam trawling is noted to cause physical loss, surface abrasion, shallow penetration and biological disturbance to benthic communities (R. Jessop, pers. comm.). This has been identified as have a likely significant effect on the SAC’s subtidal sandbanks feature , which exhibits low to medium sensitivity to shallow penetration, but variable sensitivity (up to high) to surface abrasion (Eastern IFCA, unpublished data). The trawl feet are also thought to cause a high rate of sedimentation (Tillin et al., 2010). The case study presented in Annex 8 is an example based on the information provided by enforcement officers of the Eastern IFCA for this fishery, and is calculated for the EMS sub-feature of subtidal mixed sediments, which has an area of 80.93 km2. This example provides estimates as follows: The entire fleet would exert a surface fishing pressure P of 7.227 over a single year. In single vessel terms this would represent a fishing pressure P of 0.2492 per vessel per year or 0.00103 per vessel day. This example highlights how the equation can provide a worst case scenario for an entire fleet fishing with full capacity on a single feature over a year, and very likely represents an overestimation of effort on this one feature. However, this does provide a useful indication that a fishery has the potential to exert significant pressure upon a specific feature of management concern. This case study was also used as an exercise to investigate how the fisheries footprint calculations may be refined using GIS to process the data further. This example can be found in section 11.4.

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11.2 NW IFCA pot fishery (crab and lobster) (Lune Deep EMS)

The North Western IFCA (NW IFCA) has been formed by the amalgamation of the North West and Cumbria Sea Fisheries Committees. The new district covered by the NW IFCA extends from the Welsh administrative boundary in the Dee Estuary in the south to the administrative boundary in the Solway Firth in the north, and encompasses the total area of the constituent local authorities. The seaward extent from the shore is to the 6 mile limit line. 17 Morecambe Bay is the second-largest embayment in the UK, after the Wash. It is a large, very shallow, predominantly sandy bay bordered on the south by the channel of the Lune estuary and on the north by Walney Channel. At low tide vast areas of intertidal sandflats are exposed, with small areas of mudflat, particularly in the upper reaches of the associated estuaries, which constitute the largest single area of continuous intertidal mudflats and sandflats in the UK. The sediments of the bay are mobile and support a range of community types, from those typical of open coasts (mobile, well-sorted fine sands), grading through sheltered sandy sediments to low-salinity sands and muds in the upper reaches. Apart from the areas of intertidal flats and subtidal sandbanks, Morecambe Bay supports exceptionally large beds of mussels Mytilus edulis on exposed ‘scars’ of boulder and cobble, and small areas of reefs with fucoid algal communities. Of particular note is the rich community of sponges and other associated fauna on tide-swept pebbles and cobbles at the southern end of Walney Channel. Morecambe Bay is an important location for low levels of commercial fishing and for

recreational angling. Many species of fish, including flatfish, bass, cod and whitebait

are caught in the Bay or landed at its ports. Shellfish are also important and the

mussels and shrimps are caught for both local consumption and for export to

Europe. Cockles can also be an important part of the shellfisheries, but in recent

years their low abundance has affected the viability of the fishery. Within the

commercial fishery a diversity of techniques are used by the fishermen. Some work

on the shore when the tide is out, while others fish from boats. Throughout the Bay a

mixture of traditional and modern fishing methods are used. 18

In the North West, the EMS review process identified significant risks from all bottom towed gear on reef features: boulder and cobbles, bedrock reef and honeycomb worm reefs (Sabellaria alveolata). This includes bottom trawls and dredges. Seagrass (found in north Morecambe Bay) is also a feature considered to be at risk from fishing activity and requires protection from bait collection and hand gathering fisheries as well as bottom towed gear. Specific features within the Morecambe Bay EMS are boulder and cobble reef, Sabellaria alveolata reef and seagrass beds. In Lune Deep EMS there are boulder and cobble reef and bedrock reef features. NWIFCA introduced Byelaw 6 to protect reef features from bottom towed gear and seagrass from bottom towed gear and hand worked fisheries. The following case study evaluates the spatial impact of the potting fishery from Barrow and Fleetwood ports on Lune Deep SAC.

17 http://jncc.defra.gov.uk/protectedsites/sacselection/sac.asp?EUCode=UK0013027 18 http://www.ukmarinesac.org.uk/pdfs/casestudy-morecambebay.pdf

FISHERIES FOOTPRINT QUESTIONNAIRE

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From the ports of Barrow and Fleetwood, around 7 vessels undertake potting with a

vessel size of 6-10m, on 8-12 hour fishing trips. Gear sizes vary from smaller vessels

(7m) using 25-30 mixed pots (50x75cm up to 60x70x45cm) on 8 hour fishing trips,

up to larger vessels (10m) using around 200-400 pots in 300 yard strings of up to 30,

using larger parlour pots (115x60cm) on 12 hour fishing trips (80% of time actively

fishing) and a soak time of 1-2 days (depending on catch). Potting generally occurs

uniformly over suitable ground, with pots moved around depending on weather and

catch success. If an area produces poor results, potters will move the string to

another area and let the area ‘rest’ for a few weeks before trying again. Potters for

lobsters and crabs tend to work on rough ground inhabited by lobsters and crabs,

whereas whelks tend to be found on sandy muddy bottoms, therefore pots are

usually set on clear grounds. Potters for lobsters and crabs tend to fish edges of scar

grounds or over the entire scar.

In the case study example (presented in Annex 10) the situation has been simplified

with respect of the fishing gear used with only configuration of pots used and the

case study has been explicitly set over a particular area with clear fixed boundaries

(Lune Deep SAC). This is an example only, and does not represent the current

actual situation for potting in Lune Deep SAC and should not be used or

quoted elsewhere.

For this example case study we have used the smaller vessels only with pots of

0.5m x 0.7m, a fleet size of only 7 vessels and 30 pots set and hauled per day

fishing. The seasonality of the fishery and activity by month has been used to

provide an estimate of 253 days fishing per year.

For this example, the entire fishery is assumed to operate solely on the Lune Deep EMS as a hypothetical case but provides a good working example. It has also been assumed that potting can occur across the whole EMS, which in reality is not feasible due to environmental conditions. The following results were obtained. The current fleet (7 vessels) would exert a fishing pressure P of 0.00232 over a single year. In single boat terms this would represent a near negligible fishing pressure P of 0.000332 per vessel per year or 0.0000013 per vessel day. This is a clear example of where the fishing effort of a small fleet when quantified can be shown to be having a very small or negligible effect on a particular feature.

11.3 Sussex IFCA net fishery (Kingmere MCZ)

The following case study uses the fisheries footprint equation to assess the impact of using static nets (gill nets, trammels, entangling) in the Kingmere MCZ, which covers an area of 47.84 km2, and is situated between 3 and 6 nm offshore of the Sussex Coast. The MCZ includes the Kingmere Rocks and Worthing Lumps of sandstone outcrops and nearby boulders, which support a wide variety of marine life including a breeding site for black bream, bryozoans, coralline algae, sea squirts, sponges as well as native oyster and mussel beds. The sandstone outcrops forming Kingmere

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Rocks are particularly pronounced, rising 2-3m from the seabed, with areas between the rocks composed of mixed sediments and boulders. The Worthing Lumps consists of two separate north-facing chalk cliff exposures of up to 3m in height, separated by an area of mixed sediments approximately 250m wide. Kingmere MCZ is designated for three features: ‘moderate energy infralittoral rock and thin mixed sediments’, ‘subtidal chalk’ and ‘black seabream (Spondyliosoma cantharus)’, all of which are assessed as ‘recover to favourable condition’. The moderate energy intertidal rock is covered with a thin veneer of mixed sediments, which creates a complex mosaic of habitats that are particularly important as a breeding and nesting area for black seabream. Protection of the nesting area is therefore deemed important to ensure protection of the species due to its economic value as well as for conservation gains. It is estimated that commercial fishing activities currently occurring within and around the MCZ generate approximately £304,000 per year in landings and recreational activities (primarily angling) generate a further £723,435-£1,225,627 per year (Fletcher et al., 2012). Both static and mobile gear fisheries exist in the Kingmere MCZ area, with potting the dominant fishing method along with set netting and trawling (Finding Sanctuary, Irish Seas Conservation Zones, Net Gain & Balanced Seas, 2012). Target species include bass, black bream, brill, cod, Dover sole, plaice, turbot, common cuttlefish, crab and lobster (Vause & Clarke, 2011). Despite their presence, harvesting of native oyster Ostrea edulis is prohibited through an IFCA byelaw, and no known harvesting of blue mussel Mytilus edulis occurs in the MCZ area. The Kingmere Rocks and Worthing Lumps areas are of particular importance, providing habitat and food for a range of sessile and mobile benthic species, supporting the commercial fisheries. The greatest recreational use of the site is by sea anglers. The main target species are bass, cod, mackerel, whiting, plaice, Dover sole, conger eel, and of particular popularity, the black bream which can be caught at the site from May-October (Fletcher et al., 2012). The Balanced Seas processes led to a recognition of the value of protecting black bream in the area, and there was provisional buy-in from those stakeholders involved in Balanced Seas to cease activities over the bream nesting period (Apr-Jun). The trawling sector agreed to restrict their activities permanently (with the exception of passage through the site) to protect the sandstone reef habitat. Both private and charter boat anglers agreed to voluntarily close recreation angling over bream nesting sites during the black bream breeding season (Finding Sanctuary, Irish Sea Conservation Zones, Net Gain & Balanced Seas, 2012). However post designation consultation with the wider community has found a high level of opposition to proposed restrictions, particularly from private / charter anglers (E. Pettifer, pers. comm.). Seasearch Site Surveys (SSS) black bream nest surveys are conducted on behalf of the aggregates industry but only occur within set repeat survey areas, some of which fall within the site, but not over the whole site. Data on these surveys has been collected since 200219. The site is within the jurisdiction of the Sussex IFCA, and all district-wide byelaws will apply to this site. The Sussex has comprehensive fisheries data on all fishing activity

19

For example: http://www.seasearch.org.uk/downloads/KingmereSurveyReport.pdf

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going back to 2001, and Kingmere MCZ has been mapped with sidescan sonar since 2001, which is regularly updated. Static nets (gill nets, trammels, entangling) are deployed using vessels between 6-11m, with a fleet of around 150 vessels, typically undertaking fishing trips of around 8 hours. The average length of each net is on average 100m long, using 15-25kg anchors, with 4-6 nets making up a fleet and 3-8 fleets hauled cleared and reset on a daily basis. It was noted that fishing occurred in discrete areas (the northern and western edges and the Paleo Channel) of the MCZ and any fishing pressure would likely to be concentrated on these areas. The number of netters included in this case study comprises the estimated total amount of netters within the district to provide the highest risk scenario possible. It is estimated however, (Erin Petifer, Sussex IFCA, pers. comm.) that a maximum of 10 netting boats operate within Kingmere MCZ and this would be a more realistic estimate to include in an actual assessment. We have assumed the following parameters for the nets set around Kingmere:

each fleet that is hauled has a potential 2m lateral drift or drag that potentially impacts the bottom substrate.

all net interactions are given an interaction level of 20% to represent their low potential for interaction with the bottom substrate

the 15-25kg anchors have a typical footprint of about 450mm x 450mm. Each of these has a proportion of 100% and it is assumed there are two per 500m line set.

Based on the seasonality and patterns of fishing detailed in the questionnaire, a total of 140 days fishing per vessel per year has been assumed. The following results for Kingmere MCZ were obtained. (See Annex 11). The current fleet (150 vessels) would exert a surface fishing pressure P of 0.4838 over a single year. In single vessel terms this would represent a fishing pressure P 0.00322 per vessel per year or 0.000023 per vessel day.

11.4 GIS Example

As an exercise to investigate how fisheries footprint data and calculations may be progressed an example dataset has been processed geospatially to calculate the interactions and possible fisheries footprint. Interactions were investigated using a 100m grid spacing, which was considered the smallest scale at which interactions could be assessed due to the resolution of the datasets involved and also this area encompasses the area affected by a single haul. Seabed habitat data were extracted from GIS data available from EMODnet for an area which included the Wash and North Norfolk Coast SAC which is within the Eastern IFCA region.

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Using these data, the seabed habitats which were classified as the EMS sub-feature of ‘Subtidal mixed sediments’ were selected (the site-specific extent of this sub-feature is 80.93 sq km). Fisheries sightings data (shrimp beam trawl) from 2007 was used as this dataset showed a reasonable level of activity for which interactions could be assessed and were distributed within the Wash and North Norfolk area. The fisheries activity data were then intersected with the distribution of the Subtidal Mixed Sediments using a 100m resolution and the area where the fisheries and conservation features interacted were mapped. Figure 16 below shows the stages of data processing and the output of this.

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Figure 16: Stages of data processing for the GIS example.

Seabed Habitats

Fisheries Activity/Sightings Data

Intersecting the fisheries activity data with subtidal mixed sediments BSH

The location of areas of interaction between fisheries and BSH

The results from this exercise show the shrimp beam trawl fisheries to interact in only two locations for 2007 with each of these locations showing only a single interaction (multiple sightings within the same area were accounted for). Using the fisheries footprint equation to access these interactions then gives the following results The area impacted (in terms of surface abrasion pressure only) by each interaction is the beam trawl width (5m) multiplied by the length (1.5 knots x 1852) and duration of trawls (2 hours) and then multiplied by the average number of hauls per day (3) which equates to an area of 0.08334 sq km for each interaction. With only two interactions identified, the P can therefore be calculated as:

P = (0.08334 *2)/80.93 = 0.00206

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Using the GIS to identify where interactions occur relies on both accurate distribution of habitats and also upon accurate and representative fisheries activity data. It is likely that the fisheries activity data employed within this GIS scenario is thought to be an underestimate of the real activity on this specific sub-feature (D. Steadman, pers. comm.) and fisheries sightings data does incorporate many assumptions and caveats which should be considered. It should also be noted that these calculations represent, in effect, a footprint value for two sighted instances of shrimp trawling activity, whereas assessments are required to assess the fishery as a whole. In the absence of VMS data, the use of the precautionary principle (and the heterogeneous nature of shrimp trawling) may require that the multiplier in the equation be the total observed number of vessels in the site, rather than the observed number of sightings on the feature, as illustrated in the worked example in Annex 8, and summarised in section 11.1. If this were the case (i.e. P = [0.08334 * 29]/80.93 = 0.03), the P value is still small, but only applies to a single day’s fishing. Nevertheless, this scenario illustrates how fisheries activity and habitat distribution data can be used spatially and incorporated into a methodical approach for utilising the fisheries impact equation. Utilising the equation in mathematical scenarios uses assumptions and caveats which are mostly based upon theoretical maximums or precautionary estimates and therefore the outputs from the mathematically derived scenarios could be taken as a theoretical maximum. The results from the GIS scenario however, could be seen as a best available evidence situation with the results from both considered reflecting a minimum level of impact, and therefore providing the lower value within an to be an appropriate envelope of likely impact.

11.5 Feedback on the Fisheries Footprint Approach from IFCAs

11.5.1 Eastern IFCA

The Eastern IFCA were presented on the Project Steering Group for the project and led on the designing of the original specification, and provided the following statement: Attempting to characterise inshore fishing activities utilising a footprint approach has been central to the Authority’s Habitats Regulation Assessment work in The Wash & North Norfolk Coast Special of Conservation, as directed under Defra’s Revised Approach to managing fisheries in MPAs. As of April 2015, we have begun to engage with industry representatives in explaining and presenting this approach and the values behind it and they are encouraged by the results. We will continue to apply the approach for other MPAs in our district and fully believe in its potential to demonstrate the relative scale of fisheries pressure (especially in large, complex sites) and to, eventually, allow for direct comparison between fisheries and other industries active in MPAs.

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11.5.2 North Western IFCA

Feedback from the North Western IFCA noted that, as the data used in the case study was an example and based on general information for the district, the resulting figures would not be able to be used in an assessment directly. However, it was felt that the process used in the example would be useful to feed in the accurate information for this EMS once gathered, and using the approach would certainly be considered in all of the fisheries in EMS assessments where applicable once accurate up to date activity information was collected. It was thought to be extremely useful to have a tool such as this that could be used as standard across IFCAs in assessing fishing footprint. 11.5.3 Sussex IFCA

Both the footprint tool developed and the summary information contained in the literature review and in the report in general are considered useful. In particular, all the information provided on the fishing impact estimation parameters and best practice examples are very interesting and useful.

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12. Recommendations

This report has reviewed the fisheries footprint equation and discussed its feasibility

in light of existing research, the data sources available and their limitations and

assumptions, and suggested modifications that might help to address some of its

shortcomings. Case studies have also been worked up to illustrate how the equation

can be used with example data from IFCAs. However, there are still circumstances

where using the fisheries footprint equation as a tool for fisheries management

action requires further consideration and modification.

The case studies described in Section 11 have been used to illustrate the fishing

footprint approach, using a range of scenarios from three different IFCAs, using only

the summary data readily available. Fishing pressure “P” has been calculated at

different levels, such as for the entire fleet in a year, a single “typical” vessel or

person in a year or a single “typical” vessel per day. However, to test the approach

further it is recommended that the equation is tested more fully with a greater variety

of data types and using the modifications suggested within this report, for example:

Using a more sophisticated modelling approach, as well as development of a software tool, to better account for the variety of gear and operational parameters that have been simplified in the case studies, and to remove human error from calculation of fishing pressure (P)

Using a geospatial analysis approach to help identify interactions between fisheries and gears more accurately, and to address the temporal and spatial variability in fishing effort

Incorporating sensitivity information of features to gear types, for example using response curves to weight the severity of each interaction identified within the geospatial modelling approach

Using a variety of existing input datasets as identified in the literature review, and contrasting the results

Using an adaptive approach to incorporate better quality data when it becomes more readily available, such as using “gold standard” VMS data

Where weaknesses in data quality and/or availability are identified when applying the methodology, research should be undertaken to identify data gathering mechanisms for high quality data such as:

Information on sensitivity of features to various gear types

Positional information for some fisheries where it is currently lacking e.g. hand-gathered fisheries through the use of personal GPS devices.

Information on temporal and spatial variation of inshore fisheries e.g. through iVMS data

Improved precision of the spatial extents of MPA features

Information on levels of ghost fishing, where lost or abandoned fishing gear continues to exert fishing pressure

From a wider perspective, it is also recommended that research be initiated to work on setting standardised thresholds of fishing pressure to unify assessments across the country. Additionally a mechanism should be explored for how an evidence register might be maintained so that all relevant bodies can draw easily upon the

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relevant information. Consideration should also be given to how this approach will be integrated with approaches employed for offshore fisheries, and to test integration of the current approach with spatial footprint approaches for other sectors active in the marine environment.

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13. Acronyms

CCAMLR Commission for the Conservation of Antarctic Marine Living Resources

DEFRA Department for Environment, Food and Rural Affairs

EC European Commission

EEZ Exclusive Economic Zone

EIFCA Eastern Inshore Fisheries and Conservation Authority

EMS European Marine Site

ESFJC Eastern Sea Fisheries Joint Committee

EU European Union

EUNIS European Union Nature Information System

FAO Food and Agriculture Organisation of the United Nations

FOCI Features of Conservation Importance

GIS Geographic Information System

GPS Global Positioning System

HRA Habitats Regulations Assessment

ICES International Council for the Exploration of the Sea

IFCA Inshore Fishery Conservation Authority

JNCC Advisor to the Government on Nature Conservation

MaCAA Marine and Coastal Access Act

MarLIN Marine Life Information Network

MCZ Marine Conservation Zone

MEDIN Marine Environmental Data & Information Network

MESH Mapping European Seabed Habitats

MMO Marine Management Organisation

MPA Marine Protected Area

MSC Marine Stewardship Council

NIFCA Northumberland Inshore Fisheries Conservation Authority

NRW Natural Resources Wales

OSPAR Oslo/Paris convention (for the Protection of the Marine Environment of the North-East Atlantic)

REC Regional Environmental Characterisation

SAC Special Area of Conservation

SFC Sea Fisheries Committee

SPUE Sightings Per-Unit-Effort

SPA Special Protection Areas

UK BAP United Kingdom Biodiversity Action Plan

VIRIS Visserij Registratie en Informatie Systeem

VME Vulnerable Marine Ecosystem

VMS Vessel Monitoring System

VOIP Voice Over IP

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