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Using vessel monitoring system (VMS) data to assess the impact of marine protection boundaries on blue ling fishing northwest of the British Isles

Published online by Cambridge University Press:  12 August 2014

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Abstract

In 2009, the European Commission set restricted fishing areas northwest of the British Isles to protect deep-sea vulnerable marine ecosystems and fish stocks. Two protection areas which, historically, have been targeted by fisheries directed at blue ling (Molva dypterygia), were defined. The study aims to assess the effectiveness of restricting fishing activity within the protection areas during the blue ling spawning period (March–May) and to determine whether the existing boundaries are fit for purpose. Estimations of the spatial apportionment of blue ling landings within and outside the protection areas are achieved by combining low-resolution data from fishing vessel logbook entries with higher-resolution vessel monitoring system (VMS) data. High-resolution spatial apportionment of blue ling landings is limited by a lack of high-resolution logbook data, and certain assumptions need to be made on whether vessels are engaging in fishing activity at any individual VMS data point, based on vessel speed and types of fishing gear available. Although current measures appear to have influenced fishing activity in the vicinity of the protection areas, more evidence is needed for a robust evaluation of their effectiveness in protecting blue ling. Recommendations are made for improvements in data collection methods and data availability for research in support of monitoring, assessment and delineation of marine protection boundaries.

Type
Research Article
Copyright
© EDP Sciences, IFREMER, IRD 2014

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References

Armstrong, M.P., Dean, M.J., Hoffman, W.S., Zemeckis, D.R., Nies, T.A., Pierce, D.E., Diodati, P.J., McKiernan, D.J., 2013, The application of small scale fishery closures to protect Atlantic cod spawning aggregations in the inshore Gulf of Maine. Fish. Res. 141, 6269. CrossRefGoogle Scholar
Augustin, N.H., Trenkel, V.M., Wood, S.N., Lorance, P., 2013, Space-time modelling of blue ling for fisheries stock management. Environmetrics 24, 109119. CrossRefGoogle Scholar
Bastardie, F., Nielsen, J.R., Ulrich, C., Egekvist, J., Degel, H., 2010, Detailed mapping of fishing effort and landings by coupling fishing logbooks with satellite-recorded vessel geo-location. Fish. Res. 106, 4153. CrossRefGoogle Scholar
Bertrand, S., Díaz, E., Lengaigne, M., 2008, Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data. Prog. Oceanogr. 79, 379389. CrossRefGoogle Scholar
Chang, S.-K., 2011, Application of a vessel monitoring system to advance sustainable fisheries management – Benefits received in Taiwan. Mar. Policy 35, 116121. CrossRefGoogle Scholar
Cohen D.M., Inada T., Iwamoto T., Scialabba N., 1990, Gadiform fishes of the world (Gadiformes). An annotated and illustrated catalogue of cods, hakes, grenadiers and other gadiform fishes known to date. FAO Fisheries Synopsis 125(10). Rome, FAO, 442 p.
Crawford, R., Vagle, S., Carmack, E., 2012, Water mass and bathymetric characteristics of polar cod habitat along the continental shelf and slope of the Beaufort and Chukchi seas. Polar Biol. 35, 179190. CrossRefGoogle Scholar
Davies, A.J., Roberts, J.M., Hall-Spencer, J., 2007, Preserving deep-sea natural heritage: Emerging issues in offshore conservation and management. Biol. Conserv. 138, 299312. CrossRefGoogle Scholar
EC, 2002a, Regulation No 2371/2002 of 20 December 2002 on the conservation and sustainable exploitation of fisheries resources under the Common Fisheries Policy. OJ L 358, 31.12.2002, 59–80.
EC, 2002b, Regulation No 2347/2002 of 16 December 2002 establishing specific access requirements and associated conditions applicable to fishing for deep-sea stocks. OJ L 351, 28.12.2002, 6–11.
EC, 2008, Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). OJ L 164, 25.6.2008, pp. 19–40.
Enguehard, R.A., Devillers, R., Hoeber, O., 2013, Comparing interactive and automated mapping systems for supporting fisheries enforcement activities-a case study on vessel monitoring systems (VMS). J. Coast. Conserv. 17, 105119. CrossRefGoogle Scholar
Fock, H.O., 2008, Fisheries in the context of marine spatial planning: Defining principal areas for fisheries in the German EEZ. Mar. Policy 32, 728739. CrossRefGoogle Scholar
Gerritsen, H., Lordan, C., 2011, Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. ICES J. Mar. Sci. 68, 245252. CrossRefGoogle Scholar
Gordon J.D.M., Hunter J.E., 1994, Study of deep-water fish stocks to the west of Scotland. Unpublished Rep., Dunstaffnage Marine Laboratory, Oban, Scotland.
Hall-Spencer, J.M., Tasker, M., Soffker, M., Christiansen, S., Rogers, S., Campbell, M., Hoydal, K., 2009, Design of Marine Protected Areas on high seas and territorial waters of Rockall Bank. Mar. Ecol. Prog. Ser. 397, 305308. CrossRefGoogle Scholar
Harley, S.J., Myers, R.A., Dunn, A., 2001, Is catch-per-unit-effort proportional to abundance? Can. J. Fish. Aquat. Sci. 58, 17601772. CrossRefGoogle Scholar
Hiddink, J.G., Hutton, T., Jennings, S., Kaiser, M.J., 2006, Predicting the effects of area closures and fishing effort restrictions on the production, biomass, and species richness of benthic invertebrate communities. ICES J. Mar. Sci. 63, 822830. CrossRefGoogle Scholar
Hintzen, N.T., Piet, G.J., Brunel, T., 2010, Improved estimation of trawling tracks using cubic Hermite spline interpolation of position registration data. Fish. Res. 101, 108115. CrossRefGoogle Scholar
Hintzen, N.T., Bastardie, F., Beare, D., Piet, G.J., Ulrich, C., Deporte, N., Egekvist, J., Degel, H., 2012, VMStools: Open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fish. Res. 115, 3143. CrossRefGoogle Scholar
ICES, 2004, Report of the ICES Advisory Committee on Fishery Management and Advisory Committee on Ecosystems 2004, ICES Advice 1(2), 1544 p.
ICES, 2006, Report of the ICES Advisory Committee on Fishery Management, Advisory Committee on the Marine Environment and Advisory Committee on Ecosystems, 2006. Widely distributed and migratory stocks, ICES Advice 2006, Book 9, 255 p.
ICES, 2008, Report of the ICES Advisory Committee, 2008. Widely distributed and migratory stocks, ICES Advice 2008, Book 9.
ICES, 2012a, General context of ICES advice. ICES Advice 2012, Book 1.
ICES, 2012b, Report of the ICES Advisory Committee 2012, Book 9. Widely distributed and migratory stocks.
Jennings, S., Lee, J., 2012, Defining fishing grounds with vessel monitoring system data. ICES J. Mar. Sci. 69, 5163. CrossRefGoogle Scholar
Joo, R., Bertrand, S., Chaigneau, A., Ñiquen, M., 2011, Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery. Ecol. Model. 222, 10481059. CrossRefGoogle Scholar
Jury, M.R., 2012, Physical oceanographic influences on Central Benguela fish catch. Earth Interactions 16, 115. CrossRefGoogle Scholar
Large, P.A., Diez, G., Drewery, J., Laurans, M., Pilling, G.M., Reid, D.G., Reinert, J., South, A.B., Vinnichenko, V.I., 2010, Spatial and temporal distribution of spawning aggregations of blue ling (Molva dypterygia) west and northwest of the British Isles. ICES J. Mar. Sci. 67, 49501. CrossRefGoogle Scholar
Lee, J., South, A.B., Jennings, S., 2010, Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES J. Mar. Sci. 67, 12601271. CrossRefGoogle Scholar
Lorance, P., Pawlowski, L., Trenkel, V.M., 2010, Standardizing blue ling landings per unit effort from industry haul-by-haul data using generalized additive models. ICES J. Mar. Sci. 67, 16501658. CrossRefGoogle Scholar
Marchal, P., De Oliveira, J.A.A., Lorance, P., Baulier, L., Pawlowski, L., 2013, What is the added value of including fleet dynamics processes in fisheries models? Can. J. Fish. Aquat. Sci. 70, 9921010. CrossRefGoogle Scholar
Mills, C.M., Townsend, S.E., Jennings, S., Eastwood, P.D., Houghton, C.A., 2007, Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES J. Mar. Sci. 64, 248255. CrossRefGoogle Scholar
Muus B.J., Nielsen J.G., 1999, Sea fish. Scandinavian Fishing Year Book. Hedehusene, Denmark.
Olafsdottir, A.H., Rose, G.A., 2012, Influences of temperature, bathymetry and fronts on spawning migration routes of Icelandic capelin (Mallotus villosus). Fish. Oceanogr. 21, 182198. CrossRefGoogle Scholar
Overholtz, W.J., Hare, J.A., Keith, C.M., 2011, Impacts of interannual environmental forcing and climate change on the distribution of Atlantic mackerel on the U.S. Northeast Continental Shelf. Mar. Coast. Fish. 3, 219232. CrossRefGoogle Scholar
Palmer, M.C., 2008, Calculation of distance traveled by fishing vessels using GPS positional data: A theoretical evaluation of the sources of error. Fish. Res. 89, 5764. CrossRefGoogle Scholar
Reed J.K., Shepard A.N., Koenig C., Sanlon K.N., Gilmore R.G., 2005, Mapping, habitat characterization, and fish surveys of the deep-water Oculina coral reef Marine Protected Area: a review of historical and current research. In: Freiwald A., Roberts M. (Eds.), Cold-water corals and ecosystems. Berlin/Heidelberg, Springer, pp. 443–465.
Ross, P.S., Barlow, J., Jefferson, T.A., Hickie, B.E., Lee, T., MacFarquhar, C., Parsons, E.C., Riehl, K.N., Rose, N.A., Slooten, E., Tsai, C.-Y., Wang, J., Wright, A.J., Yang, S.C., 2011, Ten guiding principles for the delineation of priority habitat for endangered small cetaceans. Mar. Policy 35, 483488. CrossRefGoogle Scholar
Ross, R.E., Howell, K.L., 2013, Use of predictive habitat modelling to assess the distribution and extent of the current protection of “listed” deep-sea habitats. Divers. Distrib. 19, 433445. CrossRefGoogle Scholar
Russo, T., Parisi, A., Prorgi, M., Boccoli, F., Cignini, I., Tordoni, M., Cataudella, S., 2011, When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks. Fish. Res. 111, 5364. CrossRefGoogle Scholar
Stelzenmuller, V., Rogers, S.I., Mills, C.M., 2008, Spatio-temporal patterns of fishing pressure on UK marine landscapes, and their implications for spatial planning and management. ICES J. Mar. Sci. 65, 10811091. CrossRefGoogle Scholar
Trenkel, V.M., Bravington, M.V., Lorance, P., 2012, A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality. Can. J. Fish. Aquat. Sci. 69, 18811893. CrossRefGoogle Scholar
UN, 1982, United Nations Convention on the Law of the Sea (UNCLOS).
UN, 1992, United Nations Conference on Environment and Development (UNCED), Rio de Janeiro, Brazil.
UN, 1995, United Nations conference on straddling fish stocks and highly migratory fish stocks.
UN, 2002, World Summit on Sustainable Development (WSSD). Johannesburg, South Africa.
Walker, E., Bez, N., 2010, A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data. Ecol. Model. 221, 20082017. CrossRefGoogle Scholar