Mapping fishing effort: by Through the Gaps

Fisheries Research 189 (2017) 67–76

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Mapping ﬁshing effort: Combining ﬁshermen’s knowledge with satellite monitoring data in English waters R. Enever a,∗ , S. Lewin b , A. Reese c , T. Hooper d a

Natural England, Sterling House, Exeter, UK University of Plymouth, Faculty of Science and Environment, Plymouth, UK Independent Statistical Consultant, Forston, Dorchester, UK d Seas Life Ltd., St. Germans, Cornwall, UK b c

a r t i c l e

i n f o

Article history: Received 22 April 2016 Received in revised form 11 January 2017 Accepted 16 January 2017 Handled by George A. Rose Keywords: Fishermen’s knowledge Marine protected areas Vessel Monitoring System Fishing effort Static fishing Mobile fishing UK fisheries Marine Conservation Zones Conservation

a b s t r a c t We describe and analyse data on fishing effort collected by interviewing 1914 fishermen between 2007 and 2010. Combining socio-spatial data collected through a voluntary mapping project called “FisherMap” with UK and European vessel satellite monitoring data provides high resolution, national-scale maps of distribution and relative intensity of fishing for six gear types. The effort maps show, for the first time, a large scale and holistic approach to mapping fishing effort by including the under-reported, yet significant, inshore fishing fleet (85% of registered vessels,<15 m). The data from this study have been used to facilitate the planning, management advice and subsequent designation of 38 inshore Marine Conservation Zones. The authors conclude that, effective management of the inshore marine environment requires up-todate, high resolution and holistic maps of fishing effort that can be obtained only through validated interpretation of inshore vessel monitoring system data. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Fishing activities are complex and vary greatly across different ports, seasons and gear types. Vessel Monitoring Systems (VMS) are required in the UK only on vessels ≥12 m long (EC No. 1224/2009), which represent 15% of the UK fleet size (MMO, 2014). Prior to 2002, VMS were limited to vessels ≥15 m (10% of UK fleet size). As most fishing vessels are not required to carry VMS there is a paucity of data describing the spatial distribution and intensity of effort by smaller, generally “inshore” fishing vessels at the resolution required for setting Marine Protected Area (MPA) conservation objectives (Hinz et al., 2013). Effective conservation and management of the marine environment requires knowledge of the spatial distribution of commercial fishing effort (FAO, 2003; Sinclair and Valdimarsson, 2003; Jennings, 2007). High resolution spatial data on fisheries are needed for future MPA designations, and for the effective management of

∗ Corresponding author. E-mail addresses: robert.enever@naturalengland.org.uk, rob.enever@ﬁshtekmarine.com (R. Enever). http://dx.doi.org/10.1016/j.ﬁshres.2017.01.009 0165-7836/© 2017 Elsevier B.V. All rights reserved.

existing sites (Aswani and Lauer, 2006; Witt and Godley, 2007). Marine spatial planners and other industries using the marine environment also need detailed fisheries activity data to assess the potential impacts of proposals on fishing livelihoods, such as assessing potential displacement effects or looking for co-location potential between different uses (Douvere, 2008; Eastwood et al., 2007; Stelzenmüller et al., 2008; Ventin, 2014). Whether an MPA can deliver its ecological and social benefits depends on effective management of human activities. Without appropriate management, English MPAs cannot contribute to the wider, international protected areas network to which the UK Government is committed (The Conservative Party, 2015). Such activities may damage or disturb the protected habitats and species (herein referred to as features) for which the site was designated (Jennings and Kaiser, 1998). Understanding the spatial distribution of activities relative to site features is therefore fundamental in setting MPA conservation objectives for the site. As of 2016, 240 (87%) of the UK’s 275 MPAs lie within 6 nm of the UK coastline. Of these, 47 (17%) are less than 10 km2 . In order for MPA management advice (e.g. adaptive management or restricted access) to be effective, the resolution of reporting fishing activity needs to reflect the scale of the sites and of the

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mapped features within them. National-scale data on fishing effort are currently compiled from information provided by skippers, who are required to report their fishing locations in large rectangles (∼56 km × 69 km). Such data are spatially too coarse for high resolution analyses (Hinz et al., 2013; Lee et al., 2010). Also, the absence of data for the smaller inshore fleets presents a considerable challenge to MPA managers who are required to deliver MPAs in favourable condition whilst facilitating practicable, feature-based management for the UK fishing industry. While VMS technologies are not used on a large proportion (85%) of the UK fishing fleet, other methods of describing the distribution and intensity of fishing activities are needed. Previous efforts to describe fishing activity have been based on log book data (e.g. Kaiser et al., 2000), aerial surveys (e.g. De Juan and Demestre, 2012), ‘distance from port’ rules, seabed maps as proxies for possible fishing grounds, and interviews with fishers (Breen et al., 2015). Until recently, UK fishing effort studies have focussed on individual gear types, regions or vessel size. The current study uses data from a voluntary mapping project, called “FisherMap” comprising 1914 interviews with fishermen between 2007 and 2010, combined with UK and European VMS data to provide high resolution, national scale maps of fishing distribution and intensity for six gear types.

2. Materials and methods 2.1. Mapping fishing effort for vessels <15 m 2.1.1. FisherMap The FisherMap study aimed to map the extent and intensity of fishing activities around the coast of England to inform the new Marine Conservation Zone recommendations under the UK’s Marine and Coastal Access Act (MCAA, 2009; des Clers et al., 2008). Data were collected by four regional project teams across England (“Net Gain”, “Balanced Seas”, “Finding Sanctuary” and “Irish Sea Conservation Zone’s”) (Fig. 1). The FisherMap methodology was adapted from two participatory mapping projects piloted with commercial fishermen in the Thames Estuary (des Clers et al., 2001; des Clers, 2004a,b), and from the Pacific Marine Analysis and Research Association (Ardron et al., 2005) methods on marine planning. The FisherMap interviews were carried out by a team of 15 full time liaison officers who asked individual fishermen to draw the areas where they had fished that year on maps (Plate 1) and complete a questionnaire describing their activity, i.e. fishing methods, gear type, species targeted, and months of the year of operations (des Clers et al., 2008). Interviews typically lasted between one and two hours. Liaison officers generally had a background as experienced commercial fishermen which enhanced credibility for the project and gave better mutual understanding of the technical information. The pattern of engagement varied with the port, season, weather and state of the tide. Initial meetings were held with local or sectoral association leaders and vessel owners to explain the nature of the project and the interview process. Once a general agreement had been reached within a port, individual fishermen were approached on the quayside and an interview organised. Each interview involved a questionnaire and large format maps. The questionnaire gathered basic data about the size and power of vessel used and the home port. Each gear type was described by the months of the year that it was operated and the species targeted, and linked to one or more polygons drawn on clear acetate overlays to indicate the areas. The acetates were A2 size taped to a background chart. Background charts were produced using commercial maritime data using similar colour schemes and symbology to mimic nautical charts. They were printed in large format at four

different scales for the region, so the fishermen could select the most appropriate for the scale of use (Plate 1). Each chart had a unique reference printed at the top of the sheet, and four reference points repeated on each acetate to ensure accurate geo-referencing, and linkage to the correct interview. The acetates were digitised and linked to the other data within the database. 2.1.2. Interview sampling strategy The FisherMap survey categorised fishing gear into 6 broad types (demersal gears, dredging gears, pelagic gears, nets, lines, and pots & traps). FisherMap interviews were stratified by gear type, home port and vessel size so as to ensure representation across the fleets. The current study used only interviews for <15 m vessels as effort for vessels ≥15 m is captured from VMS data. Data from Cornwall were collected separately by the Cornish Fish Producers Organisation (CFPO) whose methodology had a broader gear classification, did not identify individual vessels, and used lower spatial resolution for vessel presence (1 km2 ). 2.1.3. Data validation When regional scale maps had been produced, fishermen and representative bodies were invited to validate them to confirm the data collection methods adequately captured their fleet’s activities and that no errors had been made during the digitisation process. Validation was undertaken part way through the data collection period, so that sampling could be adjusted to cover areas or fleets which, in the views of stakeholders, were poorly represented. Holding group data validation meetings with all participants provided an important opportunity for combined outputs to be discussed, verified and corrected. 2.1.4. Raising sampled interview data to fleet level Interview data as samples have limited use, so multipliers were used to raise the data to fleet level in order to provide more meaningful management information that was comparable between the four regional projects. Distribution data for sampled vessels were raised to fleet level using landing records between 2007 and 2010, extracted from the Defra iFISH database. This gave estimates, by regional project and gear type, of fleet activity. Vessels <15 m were identified uniquely using a combination of Registry of Shipping and Seamen (RSS), Port Letter and Number (PLN), length, nationality and Administration Port data. The vessel’s administration port was then used to assign a record to the appropriate regional project’s fishing fleet. Mean annual vessel numbers taken during the study period, for each gear type, were divided by the interview sample size to generate the fleet raising factors (Table 1). iFISH data for pair trawlers were carefully treated to prevent double counting and the 84 vessels with unique RSS numbers and ‘Unknown’ administration port were excluded from estimates of fleet size. Furthermore, the Marine Management Organisation’s (MMO) landings records have an unknown number of vessels assigned to group categories for unidentified boats <10 m and between 10 m and 15 m. These records were also excluded from the estimate of fleet size. All <15 m vessels whose administrative port was registered outside of England were also excluded from the estimations of fleet size on the basis that interview data from FisherMap for such registered vessels was negligible and thus not a significant component of the English fishing fleets. 2.2. Mapping fishing effort for vessels ≥15 m Numbers of vessels ≥15 m actively fishing were derived from VMS data between 2007 and 2010 using standardised methods described by Lee et al. (2010). Unprocessed VMS point data were filtered using a simple rule that a speed between 1 and 6 knots

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Fig. 1. The FisherMap study area: Showing English territorial waters (landwards of 12 nm) and the UK offshore waters adjacent to England (Seawards of 12 nm) for the 4 Regional Projects.

Plate 1. A FisherMap interview taking place on board a ﬁshing vessel (a) and subsequent ﬁsherman’s knowledge being drawn onto acetate and digitised into GIS (b).

indicated fishing activity; the gear type was inferred by linking the VMS data to logbook data using the vessel identifier and time. Unique vessel counts associated with vessels assessed as fishing were summed and allocated to 3-min grid cells (area ∼21 km2 at 47◦ N to 14 km2 at 64◦ N). Non-EU vessels were excluded from these analyses as they do not record their gear and so are precluded from estimates of fishing effort.

2.2.1. Vessel counts as a proxy for fishing effort FisherMap data records only where and when vessels <15 m were fishing, whereas VMS data for ≥15 m vessels links to national logbook data for more sophisticated effort metrics such as fishing hours. To produce a single, meaningful, fishing-effort map covering all vessel sizes, we combined vessel-count data by gear type from FisherMap and VMS datasets to provide a proxy for fishing effort in each area. This approach treats vessels equally whether they fished

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Table 1 Interview sampling effort, ﬂeet size and raising factors used by gear type and regional project. Regional project (administration port)

Net Gain (Grimsby, Lowestoft, North Shields, Scarborough)

Gear type

Interviews (ﬂeet size)

Raising factor Interviews (ﬂeet size)

Raising factor

Demersal gears Dredging gears Pelagic gears Pots and Traps Lines Nets

108 (518) 28 (57) 0 (19) 246 (605) 111 (222) 168 (389)

4.80 2.04 N.a 2.46 2.00 2.32

1.62 1.00 N.a 1.34 1.52 2.22

6.13 5.52 1.00 3.29 3.86 4.90

2.78 1.00 1.00 1.70 N.a 1.31

Total interviews (ﬂeet size) 661 (1810)

Balanced Seas (Hastings)

93 (151) 86 (55) 82 (0) 140 (187) 54 (82) 172 (381)

Finding Sanctuary (Brixham, Newlyn, Plymouth, Poole)

627 (856)

for a few hours or for several months in one 3-min grid cell. It is therefore important to validate the assumption that vessel-count data performs well as a proxy for fishing effort. The assumption was tested using effort data from the official statistics held in the Department for Environment, Fisheries and Rural Affairs’ (Defra) Fishing Activities Database (FAD) for demersal trawling and dredging only. Effort data for pots and traps, nets, pelagic fishing and lines could not be used in this analysis as either effort data calculation methods varied between gear types within the gear groups or VMS data was not reliable for determining fishing effort. Data were provided on number of hours fished and respective numbers of vessels in each 3-min grid cell for demersal trawlers and dredgers (vessel counts). There were 9699 cells in total but no activity was recorded in 1085 of the cells. Demersal trawling was recorded in 8258 cells and dredging in 1724. Effort (hours fished) was observed to increase monotonically with intensity (number of vessels). Visually, the best linear fit suggested a square-root transformation of effort and accounted for 0.75 of the variation for demersal and 0.80 for dredging, based on this sole predictor. This is also consistent with normalizing Poisson errors. However, diagnostic plots showed complementary biases in the tails and skewed residuals, and the variance increased with the mean. Therefore, effort was regressed on intensity (the only available predictor) using generalized linear modelling (McCullagh and Nelder, 1983) by the glm command in Stata (StataCorp., 2011) with logit link and either Poisson or negative binomial errors. The best fit model was selected by Akaike’s Information Criteria, AIC (Fig. 2a and b). 2.3. Combining <15 m FisherMap data with ≥15 m VMS data Effort data for the <15 m (Length Over All – LOA) fleet were broken down by area on a sampling grid with a cell size of 3 min latitude or longitude. These 3-min grid cells are precise subdivisions of ICES statistical rectangles (ISR), with each ISR containing 200 sampling grid cells. The number of <15 m vessels that had reported using a specific gear class within that grid cell was counted for each of the six FisherMap gear types. For grid cells with coextensive surveys of fishing effort (for example in Cornwall and Devon where a CFPO survey was integrated with Finding Sanctuary’s FisherMap survey) each vessel’s unique identification (its Port Landing Number or PLN) was consulted, to ensure that there was no double counting of effort. These values were then expressed as proportions of the total gear specific effort within each ISR using the formula below: ProportionofISReffort(PIE) = Nij/˙{Nij}cell1tocell200. where Nij represents the number of vessels recorded using gear i in cell j. Each cell’s PIE value was then multiplied by the mean annual number of <15 m UK vessels that had reported using that gear type

55 (337) 42 (232) 34 (16) 167 (550) 104 (401) 142 (696)

Irish Sea Conservation Zone (Fleetwood)

32 (89) 6 (6) 5 (2) 10 (17) 0 (6) 29 (38)

544 (2232)

82 (158)

Total interviews (ﬂeet size)

288 (1095) 162 (350) 121 (37) 563 (1359) 269 (711) 511 (1504) 1914 (5056)

Table 2 Exposure thresholds (High, Moderate and Low) by gear type. Gear type

Demersal gears Dredging gears Pelagic gears Nets Lines Pots and traps

Exposure (vessels nm−2 y−1 ) High

Moderate

Low

>4.5200 >1.6300 >0.3569 >2.3300 >3.2400 >2.5700

1.4000−4.5200 0.3300−1.6300 0.1695−0.3569 0.3500−2.3300 0.3900−3.2400 0.4600−2.5700

>0 <1.4000 >0 <0.3300 >0 <0.1695 >0 <0.3500 >0 <0.3900 >0 <0.4600

within that ISR between 2007 and 2011. This value represented the fleet normalised effort for that grid cell. The information on UK effort within the ISR was derived from Cefas’ iFish Landings Database. Effort data for the ≥15 m fleet did not require any pro rata adjustments in order to model fleet scale effort due to the mandatory recording of fishing effort for these vessels. The MMO supplied the regional projects with activity data derived from their own analysis of VMS data which were allocated to the same spatial resolution and gear classes as the <15 m data. Once the two sets of effort data were in the same units for each cell (vessels nm−2 y−1 ), they were summed to give estimates of all gear class specific effort within UK waters (Fig. 3). 2.3.1. Standardising vessel exposure The area of a 3-min grid cell varies with latitude, from a minimum of 4.98 nm2 in the north of the study area to a maximum of 6.03 nm2 in the south. Exposure was standardised by dividing the total vessel numbers by the area of the grid to give vessel counts per square nautical mile (vessel density). Where grid cells overlapped the coastline, the fishable area of a grid cell was restricted to only that below mean low water. 2.4. Setting exposure thresholds Arbitrary thresholds for high, medium or low levels of fishing activity were chosen relative to the vessel counts per square nautical mile observed in each grid cell for each gear group. All 14,205 grid cells were sorted by vessel density and the quartiles determined. Grid cells below the lower quartile were assigned low exposure; between the quartiles, moderate exposure and above the upper quartiles were high exposure (Table 2, Fig. 3h). 3. Results 3.1. FisherMap The 1914 fishermen operating out of English ports and interviewed between 2007 and 2010 comprised an estimated 38%

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Fig. 2. Relationship between raised ﬁshing effort (time) and vessel counts per 3-min grid cells for (a) demersal gears and (b) dredging gears. Vessel counts per 3-min grid cells are summarized by conventional box plots showing the median and quartile values, “whiskers”, and individually plotted outliers. The smooth dotted lines are logistic curves for the ﬁtted model.

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Fig. 3. The creation of a spatial model of gear speciﬁc ecological pressure from the aggregation of multiple indices of ﬁshing effort.

of English <15 m ﬁshers (Table 1). Interview sample sizes varied between gear type and regional project. Fleet coverage varied between the regional projects, ranging from 24% of the <15 m ﬂeet

by Finding Sanctuary to 73% for Balanced Seas, and gear-type interview coverage ranged from 26% for bottom trawls to 100% for mid water trawls.

R. Enever et al. / Fisheries Research 189 (2017) 67–76 Table 3 Area ﬁshed and unﬁshed by gear type. Gear group

Unﬁshed (nm−2 y−1 )

Fished (nm−2 y−1 )

% ﬁshed

Demersal gears Pots and traps Nets Lines Dredging gears Pelagic gears

4097 43,163 43,549 50,784 54,510 57,284

69,897 30,831 30,444 23,210 19,483 16,709

94.5 41.7 41.1 31.4 26.3 22.6

3.2. Using vessel counts as a proxy for fishing effort The statistical relationship identified as the best fit for both dredging and demersal trawling effort data, scaling by 1 + using the maximum effort as a pragmatic asymptote, was a logistic curve with Poisson errors. The marginal distributions of numbers of vessels in each cell are skewed: for both demersal and dredging gear types, 67% of observed grid cells had six or fewer vessels per square nautical mile between 2007 and 2010. (AIC values for null/effort + neg.bin./effort + Poisson: Demersal −2.37/ + 0.24/–3.15 and Dredging −1.30/ + 0.42/–2.71) Fig. 2a and b respectively show the observed effort as boxplots (Stata ibid) for demersal trawling and dredging respectively, against each number of vessels. The predicted means from the logistic fit are shown as a bar to the right of each box. The large numbers of “outlying” values (in the sense defined for boxplots), particularly for small numbers of vessels, highlight the skewed conditional distributions. Fitted values are close to the sample means over the main range of dredging, but greatly overestimate the effort for demersal trawling when the number of vessels exceeds 50. However, this involves less than 1.5% of observations and may reflect that vessels gather in high densities mainly when not working – for example sheltering from weather. 3.3. Setting exposure thresholds Gear specific thresholds of relative fishing effort (High, Moderate, Low) varied between gear types. ‘High’ fishing effort for demersal gears was over 4.52 vessels per nm2 per year and for pelagic gears was over 0.357 vessels per nm2 per year. Exposure thresholds set for each gear type are shown in Table 2 and applied, by gear type, to the map symbology of each 3-min grid cell displayed in the effort maps (Fig. 3). 3.4. Relative fishing effort maps Fig. 4(a–f) represent relative fishing effort from the current study using a 3 min grid histogram of vessel counts. The use of a sampling grid to audit fishing effort reduces the impact of topological artefacts (such as minimally overlapping polygons) skewing the results, but does inevitably introduce the modifiable unit area problem (Openshaw, 1983).Spatial distribution of effort varies between gear types. Use of demersal gear is most widely distributed and fished 94.5% of the study area between 2007 and 2010. Pelagic gears were used on the least ground, covering 22.6% of the study area (Table 3). Fishing exposure intensity is presented as high (yellow), moderate (green), low (blue) and no reported fishing (no shading) by gear type (Fig. 4). Demersal trawling and netting hotspots were identified inshore along the east coast of England and in the western end of the English Channel (Fig. 4a and d). There were hotspots of dredge activity in known clam, cockle and scallop grounds located in The Wash, off the south east coast of England, Irish Sea and in the Solent (Fig. 4b). Pelagic fishing was most prevalent in the western English Channel (Fig. 4c) and netters were prevalent close inshore around the southwest of England and off East Anglia (Fig. 4d). Vessels undertaking

line fishing were mostly observed to fish in waters off Devon, Cornwall and East Anglia (Fig. 4e). Pots and traps were widely used in English inshore waters with higher concentrations of effort shown in known fisheries off the southwest and eastern coasts of England (Fig. 4f).

4. Discussion The results of this study show that data collected from a large scale, participatory mapping project of inshore fishing activity can be combined with vessel monitoring data to provide high resolution, national scale maps of fishing distribution and intensity for all vessel size classes and nationalities. In the continued absence of high resolution, up-to-date position data for the majority (85%) of fishing vessels operating in UK waters, the methods described in this paper provides one approach for developing the evidence required for effective marine spatial planning and MPA management. The FisherMap project has, on a national scale demonstrated the geographical preferences of inshore fisherman. The spatial and temporal deployment of the inshore fleets relative to their target species, as reported during the interview process, has provided new insight into this unknown sector and in detail that cannot be captured by existing inshore datasets (e.g. sightings or landings data). Furthermore, FisherMap has shown that it is possible to capture this knowledge in a rigorous and consistent manner. The process ensures valuable engagement with fishermen and an opportunity to stimulate discussion on changes to the use of marine space. Involving fishermen in the development of effort maps provide fishers with the overview necessary for them to defend their spatial usage against competing industries. It is not possible to assign a level of confidence to the data presented in the maps as the validity of the outputs relies heavily on the accuracy of the information submitted by the fishermen. For demersal and dredge gear types we were able to validate using vessel count data from FisherMap as a suitable proxy of fishing effort (Fig. 2). However, for static gears the assumption that vessel counts are a suitable proxy for fishing effort remains untested. Participating fishermen and representative bodies were invited to validate the maps, and the resultant maps concur extremely well with existing and well known fishing grounds. The fishers that participated in the studies were self-selecting and therefore likely to be positively engaged with the process. However, it is possible that an unknown proportion of the fisher data could have been falsely represented for fear of future management reprisals and perceived future economic disadvantage. Equally, to prevent any disclosure of personal fishing ground to other fishers, fishermen may have chosen not to map some fishing grounds. These potential sources of uncertainty must be considered before drawing firm conclusions from the maps produced in this study. The raising of the sample data to the fleet level (Table 1) is a source of error for some gear types in some areas. There were instances where FisherMap’s record of gear use is not consistent with the gear use reported in the iFish landings records, leading to difficulties in the application of a pro rata scaling factor to some fleet sectors. This appears to be a particular issue with regards to pelagic trawls in Southern England (Finding Sanctuary and Balanced Seas) where both projects report more vessels using these gears than have been logged in the iFish landings database (Table 1). FisherMap’s gear ontology was the result of a dialogue between small vessel fishers and project researchers who were concerned with capturing the diversity of gear use within their region and understanding the ecological impact of that gear use. This was an issue with the recording of ring netting, which is effectively a pelagic

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Fig. 4. The spatial distribution of gear speciďŹ c ďŹ shing pressure within English maritime territory. Pressure modelled using the methodology presented in Fig. 3 and represented using a 3 nautical mile grid.

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trawl or seine that is recorded in iFish as a type of static netting practice. Due to the potential for this gear type being recorded as unspecified gill and entangling nets within iFish, further research would be needed in order to understand what fraction of iFish’s generic gillnet landings are actually the result of mobile pelagic fishing with ring nets. Whilst the use of iFish landings records has probably led to the underrepresentation of this gear type within the exposure model, the highly dispersed nature of the small vessel fleet prevented the collection of a complete audit of all activity within that fleet sector. The findings of this study represent a snapshot in time (the study period). The data collected and their subsequent value are likely to diminish with time. Whilst both industry and science benefits from capturing fishermen’s knowledge, the costs of data collection and associated data limitations are likely to prevent this method being repeated in the UK. The inability to triangulate FisherMap data or derive objective assessments of confidence highlight a key limitation of this work: that data captured through a participatory reporting process are no substitute for real-time, high resolution VMS data. The FisherMap methodology has great potential for use in developing countries that lack sophisticated vessel monitoring systems, or are undertaking a protected area or spatial planning exercise. A number of techniques for gathering local knowledge have been described (e.g. Scholz et al., 2004; Aswani and Lauer, 2006) which incorporate fisher knowledge and GIS tools. A recent study by Breen et al. (2015) mapped fishing activity for 5 gear types limited to 20 nm off the coasts of England and Wales. The authors used sightings data gathered from fisheries protection vessels and land-based and aerial surveys over a three year period. The sightings data identify hotspots of fishing activity by gear type, but coverage and density of records are, in many places, insufficient to meet the needs of protected site management. For example, the mean density of valid sighting records was approximately 1 per 2 nm2 of sea area, per year (for all gear types combined). Notwithstanding this, aggregating sightings data over several years increases the confidence in the resultant maps (Breen et al., 2015). As with our study, such maps are valuable for identifying hotspots of fishing effort to support broad-scale proxy assessments of habitat condition. Habitat condition not only informs the management objectives for MPAs, but is also a reporting requirement reporting under the Marine and Coastal Access Act (MCAA, 2009), Habitats Directive (EEC, 1992 Art. 17) and the Marine Strategy Framework Directive (EEC, 2008). Moreover, the Breen et al. (2015) study can be cost-effectively repeated on an annual basis. However, recent changes in data collection possibilities since Breen’s work are likely to reduce the frequency, coverage and therefore value of sightings data. The UK Royal Navy, who previously collected fisheries data throughout the 24 h each day, will now limit data collection to 9 h each day (Hansard, 2015). Further, the UK Marine Management Organisation has reduced surveillance flights by 97.5% from historic levels of 1000 h a year (2000/2001–2010/2011) to just 25 h in 2015/16. Since 2012, the number of MPAs in the UK has risen sharply with the introduction of the new Marine Conservation Zones and the UK government’s ongoing commitment to create a “Blue Belt” of protection around the UK coastline (The Conservative Party, 2015). At the time of writing, 17.4% of UK waters, and 29.1% of English inshore waters lie within an MPA. Whilst most MPAs (>99% by area) in UK waters allow for selected fishing activities to continue, fishers feel threatened by the squeeze on their grounds, be it from increasing numbers of MPAs or from a number of other competing demands such as offshore wind farm developments (Caveen et al., 2014). Managers and conservation advisors need better evidence relating to the fishing activities; both to alleviate the fishers’ concern

around squeeze and to ensure conservation objectives and productive fishery goals are met. Evidence needs to be current and of sufficient resolution to facilitate pragmatic, risk averse advice. Inshore VMS technology has been trialled successfully in the Lyme Bay and Torbay MPA in 2011 as a means to allow fine scale management of fishing activity around the protected reef structures within the site (MMO et al., 2012). This trial enabled fishers to access grounds within the protected sites whilst ensuring the protected feature of the site was left untouched. If inshore VMS is rolled out to all vessels it will ultimately provide the necessary and cost effective means of real time fisheries management, and provide a dataset that can be used to confidently inform effective marine spatial planning and MPA management.

Acknowledgments The authors would to thank the Department for Environment, Food & Rural Affairs for funding the FisherMap project, all of the fishery liaison officers from the four Regional Projects for collecting the data and the fishermen and their representatives who generously gave up their time to contribute to this project. We would also like to thank Natural England staff, Ian Saunders (for his provision of the MPA statistics) Tim Hill, Melanie Parker, Heidi Pardoe, and Duncan Vaughan, (for their editorial input) and Stefan Reade from the Marine Management Organisation for supplying up-to-date fisheries statistics.

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