Short communication Accuracy of VMS data from Norwegian demersal stern trawlers for estimating trawled areas in the Barents Sea

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1 ICES Journal of Marine Science (2011), 68(8), doi: /icesjms/fsr091 Short communication Accuracy of VMS data from Norwegian demersal stern trawlers for estimating trawled areas in the Barents Sea K. L. Skaar 1 *, T. Jørgensen 1, B. K. H. Ulvestad 1, and A. Engås 1,2 1 Institute of Marine Research, PO Box 1870 Nordnes, 5817 Bergen, Norway 2 Department of Biology, University of Bergen, PO Box 7800, 5020 Bergen, Norway *Corresponding Author: tel: ; fax: ; kristian.skaar@imr.no. Skaar, K. L., Jørgensen, T., Ulvestad, B. K. H., and Engås, A Accuracy of VMS data from Norwegian demersal stern trawlers for estimating trawled areas in the Barents Sea. ICES Journal of Marine Science, 68: Received 29 October 2010; accepted 15 April 2011; advance access publication 8 June The accuracy of vessel monitoring system (VMS) data, used to determine fishing activity in the trawl fishery for gadoids in the Barents Sea, was studied by observer notes and Global Positioning System (GPS) data from two Norwegian vessels in October A speed rule of 2 5 knots correctly classified 75 80% of the fishing activity and 85 90% of the non-fishing activity. Linear interpolation between hourly VMS recordings underestimated trawl trajectories by 15%. The median haulwise difference between the VMS and the GPS trajectories was 500 m. The interpolated VMS data are appropriate for mapping the large-scale distribution of fishing effort and the area impacted, but to link fishing activities with small-scale mapping of benthos, more-frequent VMS-update times and more-refined interpolation techniques are required. Keywords: accuracy, Barents Sea, linear interpolation, stern trawler, VMS. Introduction Estimates of the geographic fleet distribution and trawling effort are needed to support studies of the impact of fishing gear on seabed communities. Traditionally, such data have been collected from logbooks, but recent studies have shown that data from vessel monitoring systems (VMS) can be used to calculate fishing effort with high spatio-temporal resolution (Deng et al., 2005; Murawski et al., 2005; Mills et al., 2007; Mullowney and Dawe, 2009; Lee et al., 2010; Gerritsen and Lordan, 2011). During the past decade, several countries have introduced VMS technology for surveillance and enforcement, and with the wide and growing use of this technology in many fisheries, there is considerable potential for using VMS data to analyse fishing activities. A VMS usually provides position recordings at intervals of 1 or 2 h(bertrand et al., 2005; Mills et al., 2007), a resolution perhaps not sufficiently high for studies of fishing impacts on benthic habitats (Hiddink et al., 2006a, b; Piet et al., 2007) or vessel behaviour during fishing operations (Deng et al., 2005; Murawski et al., 2005). To obtain a greater resolution, VMS positions may be interpolated linearly to estimate intervening track points (Eastwood et al., 2007; Witt and Godley, 2007; Stelzenmüller et al., 2008), and by taking the width of the gear into account, the trawled area can be estimated (Eastwood et al., 2007; Stelzenmüller et al., 2008). However, this technique assumes straight-line sailing of a vessel between successive VMS positions, but fishing vessels do not necessarily travel that way (Deng et al., 2005). Wrongly assuming straight-line trajectories will lead to underestimates of track length, and hence of the impacted area (Mills et al., 2007). When reconstructed tracklines based on straight-line interpolations are used in effort or impact studies, therefore, it is essential to quantify the extent of possible deviations for the type of fishery being investigated. It is also important that the VMS data on which the reconstruction is based actually reflect trawling and not other activities such as steaming or drifting. Although the VMS data provide a sequence of vessel positions, they do not per se indicate whether or not a vessel is fishing actively. However, by using the estimated mean speed between two consecutive records, or the instantaneous speed, which is often transmitted together with VMS positions, records where vessels are moving slower or faster than the range of normal towing speeds can be eliminated. This method requires accurate differentiation between fishing and non-fishing activities, but vessels may steam at reduced speed (similar to that while trawling) during bad weather, between hauling and shooting the gear, when turning, and when travelling near the shore. The fishing-speed range is usually chosen based on a speed frequency distribution determined independently from a sample of vessels in the fishery, e.g. based on GPS (Global Positioning System) data and observer notes (Murawski et al., 2005; Mills et al., 2007). It is important that the speed rule be verified to estimate its ability to correctly identify fishing activity. VMS was introduced on all Norwegian fishing vessels of length.24 m in July 2000 (.21 m from July 2008, and.15 m from July 2010). Since then, the Norwegian Directorate of Fisheries has received approximately hourly information giving the time (minute resolution), vessel position, permit number, heading, # 2011 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please journals.permissions@oup.com

2 1616 K. L. Skaar et al. and speed. The records also include VMS data provided by the flag state for foreign vessels fishing in the Norwegian EEZ. For Russia, the VMS data are updated every second hour. In this study, we use VMS information, observer notes, and GPS data from two commercial Norwegian stern trawlers targeting demersal gadoids in the Barents Sea to establish and verify the speed rule, to examine the accuracy of the VMS-based track-length estimates, and to calculate the lateral deviation between the VMS and the GPS tracklines. To assess the effect of VMS-update frequency in the fishery, calculations were made for update intervals of 1 and 2 h. The results form the basis of an investigation of large-scale geographic and temporal patterns in the distribution of fishing effort and fishing intensity for the relevant fleet operating in the Barents Sea and are also linked to mapping of the benthos in the area to assess the impact of trawling on the seabed. Material and methods Data The study is based on VMS data from October GPS data were collected from two stern trawlers (referred to as vessel 1 and vessel 2 for reasons of confidentiality) fishing for gadoids in the Barents Sea. The vessels are 64 and 67 m long and are typical of the Norwegian stern-trawler fleet fishing in the Barents Sea. The fleet consists of 40 vessels ranging in size from 24 to 69 m, with 90%.40 m and 25%.60 m. Most of the fleet target gadoids throughout the year. Both vessels were contracted by the Institute of Marine Research and carried scientific observers. They operated as if fishing commercially, with the choice of fishing grounds and haul duration left to skipper discretion. The vessels made one trip each, with durations of 18 and 17 d, shorter than normal because commercial trips often last for 3 5 weeks. Vessels 1 and 2 made totals of 37 and 44 hauls, respectively, with towing time ranging between 2 and 8 h for vessel 1 and between 0.8 and 8.7 h for vessel 2. The polling interval of the GPS speed and position data was 45 s for vessel 1 and 15 s for vessel 2; the accuracy of the GPS positions was +10 m. The observers noted the start and stop times of each haul, and the haul tracklines created from the GPS data were matched to these times. All hauls were made within the three main fishing areas exploited by the fleet; northeast of Hopen Island, around Bear Island, and off the coast of Finnmark in mainland Norway. The sizes of these areas, in square nautical miles, are 7500, 3600, and 1500, respectively. Within each area, totals of 24, 10, and 3 hauls were made by vessel 1 and 17, 22, and 5 hauls by vessel 2. To compare VMS update intervals of 1 and 2 h, calculations were also made for subsets of recorded VMS position fixes, consisting of sequences of every second recording. The first or second VMS recording after the start of each haul was selected randomly to start the sequence. The instantaneous vessel speeds transmitted by the VMS were of high quality; only 2% of potential observations were missing. However, because these data were not mandatory in the European Union until 2006 (Witt and Godley, 2007), and earlier studies of Norwegian VMS data reported frequently missing speed data before 2005 (Salthaug, 2006), we decided to construct derived speeds from the VMS positions and to investigate the speed rule using both data sources. The derived speed is the distance between consecutive VMS recordings divided by the update time interval. All data manipulations and analyses were performed in ArcGIS v.9 w (Environmental System Institute), using the Lambert azimuthal equal-area projection, and the ET GeoWizard v.9.8 (ET Spatial Techniques) extension software. Verification of the speed rule A speed range for fishing activities was established from the frequency distribution of GPS speeds belonging to fishing intervals registered by the observer. The frequency distributions of the GPS speed data showed that a speed range of 2 5 knots generally corresponded to fishing activity (4% of GPS speed records were outside this range while fishing, and 24% were in this range while not fishing). This range is also in agreement with the speed range reported by skippers. To determine the accuracy of this speed rule applied to VMS data, they were classified into fishing and non-fishing activities. This was first based on transmitted speed, then on derived speed. In the former case, the transmitted speed in the last of the two successive VMS position fixes defining a segment was used. The activities classified from the two sources of speed data were then compared with the observer notes, and the proportion of correct classifications (fishing, nonfishing) was calculated. Large trawlers are not allowed to fish within 6 nautical miles of the coast. Moreover, vessels may occasionally sail at reduced speed while in confined coastal waters. Position recordings from within this area were therefore removed from the dataset. Accuracy of the VMS track length The VMS-based trawl trajectories were linearly interpolated by connecting successive VMS position recordings. The start and/ or stop positions for a haul along this trajectory were identified using the relevant times recorded in the observer logbooks, by linear interpolation along the segments connecting the hourly or bi-hourly position fixes. Vessels frequently change course and speed before shooting the trawl and after the completion of a haul. The interpolated end segments may therefore be less accurate than the remainder of the track. To examine this, track-length accuracy was also analysed without the end pieces of the tracklines (i.e. using only complete VMS line segments). Hauls with one or more missing VMS recordings were excluded from the analysis. Lateral deviation To investigate the lateral deviation between the true (GPS) and estimated (VMS) haul trajectories, the perpendicular distances from each GPS point to the VMS line segments (Figure 1) were measured. This analysis ignored the time along the line segment, so the GPS point from which the distance was measured did not necessarily correspond in time with the perpendicular intersect on the VMS line segment. The haul was divided into straight-line segments, and the distance readings from each line segment were summed to calculate the mean deviations. In cases where it was not possible to measure the perpendicular line (e.g. the end segment in Figure 1), the distance was measured to the nearest endpoint of the corresponding VMS segment. As in the analysis of linear differences between VMS and GPS tracklines, the analyses of lateral differences between the two types of trackline were compared first with complete tracklines (interpolated start and stop times of the VMS trajectories), and later with the end pieces excluded (see above).

3 Estimating trawled seabed area with VMS data 1617 correct identification of fishing activity by vessel 2 dropped by 6%. Success rates using the derived speeds fell by 5 and 15% for the classification of fishing and non-fishing activities, respectively (Table 1). Accuracy of VMS track length Haul trajectories created from the 1-h VMS records were on average 14.6% shorter than those created from the GPS positions for vessel 1 and 15.1% less for vessel 2 (Figure 2). The corresponding results for 2-h VMS updates were 33.1 and 29.8%, respectively. There was a large variation between hauls, depending on the number and extent of the turns made. The interquartile range nearly doubled in changing from 1- to 2-h VMS updates (Figure 2). For both update frequencies, the underestimate in the VMS-based haul trajectories was only marginally affected by the inclusion or exclusion of the VMS end segments (Figure 2). Figure 1. Trawl trajectories indicated by GPS-based (blue line) and VMS-based positions (red line). For each GPS position, the lateral deviation is measured as the length of the perpendicular to the VMS trackline (examples shown as grey lines). In cases where it was not possible to measure the perpendicular line, the distance was measured to the nearest endpoint of the corresponding VMS segment (examples shown as green lines). The start and/or stop positions for a haul along the VMS trajectory were identified using the relevant times recorded in observer logbooks, by linear interpolation along the segments connecting the hourly or bi-hourly position fixes. Table 1. Proportion of correctly identified classification of activity (fishing or non-fishing), based on transmitted and derived speeds, where Dt update is the interval between successive VMS recordings. Transmitted VMS speed (%) Derived VMS speed (%) Vessel Dt update (h) Fishing Non-fishing Fishing Non-fishing Results Verification of the speed rule The analyses using a 1-h update frequency showed that a speed range of 2 5 knots correctly identified fishing activity in 75 80% of cases (Table 1). There was little difference between the results based on transmitted and derived speeds. Non-fishing activity was correctly classified in and 70 80% of cases when based on the transmitted and derived speeds, respectively, for the two vessels. When a 2-h update time was used, classification success based on transmitted speed was largely unchanged, except that the Lateral deviation For an update frequency of 1 h, the median haulwise mean lateral deviations between the GPS and the VMS trawl trajectories including the end segments were 438 and 476 m for vessels 1 and 2, respectively, and 420 and 418 m for those excluding the end segments (Figure 3). The greatest distances between VMS and GPS trajectories observed across all hauls were 3228 and 3475 m for vessels 1 and 2, respectively (Figure 3). The medians of the haul-by-haul maximum lateral deviations were 1756 and 1734 m (1478 and 1734 m when endpoints were excluded) for vessels 1 and 2, respectively. For a 2-h update frequency, the deviations including the endpoints nearly tripled. The median haul-by-haul mean deviations between GPS- and VMS-estimated trajectories were then 1709 and 1146 m for vessels 1 and 2, respectively (Figure 3). Corresponding estimates made without the VMS endpoints were 1618 and 1172 m. The greatest deviations between VMS and GPS trajectories observed across all hauls were 6826 and 8095 m for vessels 1 and 2, respectively (Figure 3). The medians of the maximum haul-by-haul deviation were 5180 and 3964 m (3366 and 3021 m when endpoints were excluded) for vessels 1 and 2, respectively. Discussion This is the first study of the accuracy of VMS data in estimating the distribution of fishing effort by Norwegian stern trawlers in the Barents Sea. The calculations are based on a limited set of observations, but the vessels studied and the fishing trips are typical of this fleet. Moreover, the hauls made during the trips observed were not confined to a restricted geographic area, but distributed across the main fishing areas of the fleet. The observed fishing patterns are therefore likely to reflect those of the fleet as a whole. The analysis showed that a speed rule classifying vessels as actively fishing when the VMS-estimated speed was 2 5 knots correctly identified 80% of fishing activity, comparable with the 88% correct classification achieved in the Irish mixed ottertrawl fishery, obtained using a speed rule of knots (Gerritsen and Lordan, 2011). However, it is markedly less than the 99% success rate reported by Mills et al. (2007) for beam trawlers in the North Sea, whose fishing speeds were considered to be in the range 2 8 knots. The difference between the last study and the current one is probably a consequence of dissimilar fishing patterns between the two fleets. In the Barents Sea, stern trawlers often take large catches that require long processing times, and the trawl cannot be shot again before a good part of the previous catch has

4 1618 K. L. Skaar et al. Figure 2. Ratio of haul-trajectory lengths estimated from VMS and GPS positions (VMS/GPS) for 1-h and 2-h VMS update times. Calculations were made separately for hauls including ( with EP ) and excluding ( no EP ) endpoints. Boxes indicate the first and third quartiles, bold horizontal lines the medians, whiskers show for each dataset the value of the lowest datum no more than 1.5 the interquartile range (IQR) from the lower quartile, and the largest datum no more than 1.5 IQR of the upper quartile. Open circles are extreme values. Hauls with one or more missing VMS-position fixes were excluded. been processed. In such circumstances, vessels often sail to the next fishing position, or conduct searches for good fish registrations, at low speed similar to that used during trawling. This explanation is supported by the observation that most of the VMS-based speeds misclassified as fishing were from periods when consecutive hauls were made in the vicinity. Moreover, the GPS speeds recorded during the fishing trip with observers on board showed that speeds in the 2 5 knots interval were used for 24% of the time when the vessel was engaged in non-fishing operations. When the VMS-transmitted speed was used, classification success was marginally affected by the VMS-update time, but for derived speeds, the classification success decreased by 5% between 1- and 2-h updates. This probably results from the greater underestimation of sailed distance between two consecutive VMS positions as update time increases, leading to more derived speeds that fall below the lower limit defining fishing. The success rate in classifying non-fishing activity was and 55 80% based on transmitted and derived speeds, respectively. In the latter case, classification success decreased markedly when the update time increased from 1 to 2 h. Other studies have obtained 95% success rates for 2-h update times (Mills et al., 2007; Gerritsen and Lordan, 2011). For derived-speed calculations, this discrepancy is probably attributable to frequent turning manoeuvres during towing, underestimating the distance sailed (and the average speed) between two consecutive VMS positions. The longer the time between updates, the larger would be the expected underestimation, as the results here show. For the Barents Sea trawler fleet, the transmitted speed performed better than the derived speed in correctly classifying both fishing and non-fishing activity. However, if transmitted speed is not available (as was often the case for VMS data recorded before 2006), the results suggest that derived speed could still be used, particularly for 1-h updates. The combination of rules on fishing speeds and directions added little to the classification success achieved by Mills et al. (2007), and unless unknown mechanisms operate, this approach would be unlikely to benefit the Barents Sea fishery either. Lee et al. (2010) also believe that further refinement of speed rules will not solve the misclassification problem. In the present study, the proportion of misclassifications was consistent between the two vessels, but the misclassification of fishing activity was relatively large (20 30%). The acceptable level of misclassification will depend on the purpose of the data, with errors being of less concern when large geographic and temporal scales are being considered. Estimates of trawl trajectory lengths based on linear interpolation of VMS recordings showed that they were underestimated by 15 and 30% for 1- and 2-h VMS update times, respectively. This underestimation is considerably less than the 53% observed for Dutch beam trawlers in the North Sea (Hintzen et al., 2010), and the 54% underestimation calculated for UK fishing vessels by Eastwood et al. (2007). It also contrasts favourably with a study of an Australian prawn fishery, in which the track length was underestimated by and 60 70% using 1- and 2-h update times, respectively (Deng et al., 2005). To match the accuracy of our study (with a 1-h update time), Deng et al. (2005) would have required a polling interval of 20 min. The extent of underestimation depends on the behaviour of the vessels through the sinuosity of their trawl tracks. This might depend on an individual skipper s preferred course, or it might be fishing behaviour typical of the entire fleet. The small difference between the two vessels in our study suggests that the differences between the studies mainly reflect a fleet-level effect. The two vessels were on average 500 m laterally displaced from their estimated VMS trajectories with a 1-h update time, and m with a 2-h update time. Corresponding values for maximum distances were 3500 and m for the 1- and 2-h update times, respectively. To the best of our knowledge, no directly comparable estimates have been published, although Hintzen et al. (2010), using 2-h update times, estimated the distance between actual and linearly interpolated VMS positions for Dutch beam trawlers. They reported median values,50 m and extreme values,100 m, an order of magnitude less than observed

5 Estimating trawled seabed area with VMS data 1619 Figure 3. Statistics of the haulwise mean sideways deviation (D mean ; top) and maximum deviation (D max ; bottom) between GPS and VMS trawl trajectories for vessels 1 and 2, with 1-h (left plot) and 2-h (right plot) update times. Calculations were done separately for hauls including ( with EP ) and excluding ( no EP ) endpoints. Boxes indicate first and third quartiles, bold horizontal lines the medians, whiskers show for each dataset the value of the smallest datum no more than 1.5 the interquartile range (IQR) from the lower quartile, and the largest datum no more than 1.5 IQR of the upper quartile. Open circles are extreme values. Hauls with one or more missing VMS position fixes were excluded. for stern trawlers in the Barents Sea. The type of fishing strategy may explain some of the difference. In the Barents Sea, trawl locations are usually based on hydroacoustic registrations of target species, so because fish are distributed patchily, this strategy requires frequent turns to keep the vessel within the area of high fish density. Sharp turns may result in large deviations between true and VMS-estimated path lengths. Estimates of lateral deviation are of particular interest in detailed studies of the effects of fishing on the seabed and bottom fauna, where the trawl track and seabed maps have to be matched accurately. The present results indicate that reconstructed trawl trajectories based on linear interpolations of VMS recordings are not sufficiently accurate to draw inferences on a fine scale, i.e. over distances of less than nautical mile. However, the results do offer useful guidelines on appropriate no-fishing zones that might be required around closed areas and MPAs. Recent studies have shown that it is possible to improve the accuracy of tracklines reconstructed from VMS data, using behavioural rules (Mills et al., 2007) and interpolation techniques that combine position, heading, and speed information (Hintzen et al., 2010). The latter is based on a cubic Hermite-spline interpolation and requires settings optimized for either lateral deviation or track length. The accuracy of the optimized parameter generally improved considerably compared with linear interpolation of VMS positions, whereas that of the other improved less or even worsened. As our VMS data are similar to those used in the other studies listed above, it is likely that more-accurate estimates could have been obtained using these more advanced techniques. However, because VMS datasets are mostly very large, and so need many computations, ordinary linear interpolation must be seen as one of the simplest and quickest techniques capable of dealing with discontinuous data.

6 1620 K. L. Skaar et al. Several methods with various degrees of complexity have been used to study the distribution of fishing effort based on VMS data. Lee et al. (2010) advocate a simple standardized point-summation method, whereby VMS records are allocated to geographic grids and points summed to calculate effort. They conclude that effective track construction cannot be done with a VMS-update time of 2 h or more, as used in EU monitoring programmes, but may be more appropriate when the interval is shorter, as in our study. For the Barents Sea trawler fleet, the analysis showed that estimation of track length based on linear interpolation performs considerably better than it did in similar studies (with the same update times) in the North Sea (Eastwood et al., 2007; Mills et al., 2007) and Australia (Deng et al., 2005). Therefore, the technique described here provides more-accurate estimates of the spatial and temporal distribution of fishing effort than grids or pointsummation of effort (Lee et al., 2010). However, if the VMS-based distribution of fishing effort is to be used for seabed management, better accuracy will be needed, for instance to link fishing activities to maps of benthos. Studies have shown that sensitive habitats can be patchily distributed at scales as small as m (Mortensen and Buhl-Mortensen, 2004). In this context, even more complex methods of interpolating VMS positions to reconstruct trawl trajectories (e.g. the cubic Hermite spline; Hintzen et al., 2010), are unlikely to provide sufficient accuracy, unless shorter update times are used. The analysis here demonstrates the importance of establishing and verifying the appropriate methodology for the specific fishery being examined. The analyses of track lengths and lateral deviations show that the accuracy of these measures is very different between North Sea beam trawlers (Dinmore et al., 2003; Mills et al., 2007; Hintzen et al., 2010) and Barents Sea stern trawlers. Clearly, there is need for further work to understand better the possibilities and limitations of using VMS data for management purposes in particular fisheries. Acknowledgements We thank the Norwegian Directorate of Fisheries for giving us access to the VMS data, and the skippers and crews of the two fishing vessels for their assistance during the collection of observer data. We are also grateful to three anonymous referees for their comments on an earlier version of the manuscript, and to guest editor Dave MacLennan for his helpful comments and advice during our revision. References Bertrand, S., Burgos, J. 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