Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T05:02:12.903Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling the spatio-temporal interplay between North Sea saithe (Pollachius virens) and multiple fleet segments for management evaluation

Published online by Cambridge University Press:  02 September 2014

Sarah Laura Simons ,
Ralf Döring  and
Axel Temming
Sarah Laura Simons*
Affiliation:
Johann Heinrich von Thünen Institute (Federal Research Institute for Rural Areas, Forestry and Fisheries), Institute of Sea Fisheries, Palmaille 9, 22767 Hamburg, Germany Institut für Hydrobiologie und Fischereiwissenschaft, Universität Hamburg, Olbersweg 24, 22767 Hamburg, Germany
Ralf Döring
Affiliation:
Johann Heinrich von Thünen Institute (Federal Research Institute for Rural Areas, Forestry and Fisheries), Institute of Sea Fisheries, Palmaille 9, 22767 Hamburg, Germany
Axel Temming
Affiliation:
Institut für Hydrobiologie und Fischereiwissenschaft, Universität Hamburg, Olbersweg 24, 22767 Hamburg, Germany
*
a Corresponding author: [email protected]
Get access

Abstract

There is growing interest in bio-economic models as tools for understanding pathways of fishery behaviour, in order to assess the impact on natural resources. Based on ‘FishRent’, a modelling approach is presented that integrates economics of the fleet, the impact of fishing on stock development and their spatio-temporal interplay. The simulation of species seasonal movements in combination with both observed values and stochastic recruitment allowed analysing the economic response of fleet segments to changes in stock distribution and development. Optimisation of net profits determines the effort adjustment and spatial allocation of fleet segments, which in turn affects the level of catch rates. Effort tended to concentrate where fish abundance was high, but also where fishing costs were low. In simulations with the current management plan spawning stock of North Sea saithe (Pollachius virens) declined below its precautionary reference point. In response fishing far from home ports became expensive and 40% of the initial effort was shifted to areas closer to home ports, but as areas of high fish concentrations were located by the modelled fleet segments catch rates remained high. Changes in seasonal/annual stock distribution, the stock decline and costs influenced the change in fishing effort distributions leading to overestimated catch per unit of effort values that masked the decline of stock abundance.

Keywords

Bio-economicsFisheries economicsFleet dynamicsSpatial structureStock assessmentPopulation dynamics
Type
Research Article
Information
Aquatic Living Resources , Volume 27 , Issue 1 , January 2014 , pp. 1 - 16
Copyright
© EDP Sciences, IFREMER, IRD 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrahams, M., Healey, M., 1993, Some consequences of variation in vessel density: a manipulative field experiment. Fish. Res. 15, 315–322. CrossRefGoogle Scholar
Alban, F., Le Floc’h, P., Boncoeur, J., 2004, The impact of economic and regulatory factors on the relative profitability of fishing boats: A case study of the seaweed harvesting fleet of Northwest Brittany (France). Aquat. Living Resour. 17, 185–193. CrossRefGoogle Scholar
Allen, P.M., McGlade, J.M., 1986, Dynamics of discovery and exploitation - the case of the Scotian Shelf groundfish fisheries. Can. J. Fish. Aquat. Sci. 43, 1187–1200. CrossRefGoogle Scholar
Anderson J., Guillen J., 2009, Annual Economic Report on the EU Fishing Fleet. .
Armannsson, H., Jonsson, S.T., Neilson, J.D., Marteinsdottir, G., 2007, Distribution and migration of saithe (Pollachius virens) around Iceland inferred from mark-recapture studies. ICES J. Mar. Sci. 64, 1006–1016. CrossRefGoogle Scholar
Baranov, F.I., 1918, On the question of the biological basis of fisheries. Nauchnge Issledovaniya Ikhtiol. Inst. Izvestiya 1, 81–128. Google Scholar
Béné, C., Doyen, L., Gabay, D., 2001, A viability analysis for a bio-economic model. Ecol. Econ. 36, 385–396. CrossRefGoogle Scholar
Bertelsen, E., 1942, Contributions to the biology of the coalfish (Gadus virens L.) in Faroe waters, with special regard to the youngest age groups. Medd. Komm. Danm. Fiskeri og Havunders 11, 1–69. Google Scholar
Beverton, R.J.H., Holt, S.J., 1957, On the dynamics of exploited fish populations. Fish. Invest. Lond., Ser. 2, 19–533. Google Scholar
Bockstael, N.E., Opaluch, J.J., 1983, Discrete modelling of supply response under uncertainty: the case of the fishery. J. Environ. Econ. Manage. 10, 125–137. CrossRefGoogle Scholar
Booth, A.J., 2000, Incorporating the spatial component of fisheries data into stock assessment models. ICES J. Mar. Sci. 57, 858–865. CrossRefGoogle Scholar
Botsford, L.W., Brumbaugh, D.R., Grimes, C., Kellner, J.B., Largier, J., O’Farrell, M.R., Ralston, S., Soulanille, E., Wespestad, V., 2009, Connectivity, sustainability, and yield: bridging the gap between conventional fisheries management and marine protected areas. Rev. Fish. Biol. Fish. 19, 69–95. CrossRefGoogle Scholar
Branch, T.A., Hilborn, R., et al., 2006, Fleet dynamics and fishermen behavior: lessons for fisheries managers. Can J. Fish. Aquat. Sci. 63, 1647–1668. CrossRefGoogle Scholar
Caddy, J., Carocci, F., 1999, The spatial allocation of fishing intensity by port-based inshore fleets: a GIS application. ICES J. Mar. Sci. 56, 388–403. CrossRefGoogle Scholar
Cambiè, G., Ourens, R., Vidal, D.F., Carabel, S., Freire, J., 2012, Economic performance of coastal fisheries in Galicia (NW Spain): case study of the Cíes Islands. Aquat. Living Resour. 25, 195–204. CrossRefGoogle Scholar
Casini, M., Cardinale, M., Hjelm, J., Vitale, F., 2005, Trends in CPUE and related changes in spatial distribution of demersal fish species in the Kattegat and Skagerrak, eastern North Sea, between 1981 and 2003. ICES J. Mar. Sci. 62, 671–682. CrossRefGoogle Scholar
Cheung, W.W.L., Dunne, J., Sarmiento, J.L., Pauly, D., 2011, Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. ICES J. Mar. Sci. 68, 1008–1018. CrossRefGoogle Scholar
Clay, D., Stobo, W.T., Beck, B., Hurley, P.C.F., 1989, Growth of juvenile pollock (Pollachius virens L.) along the Atlantic coast of Canada with inferences of inshore-offshore movements. J. Northwest Atl. Fish. Sci. 9, 37–43. CrossRefGoogle Scholar
Commission Decision, 2009, Adopting a multiannual Community programme for the collection, management and use of data in the fisheries sector for the period 2011-2013. Off. J. Eur. Union 2010/93/EU.
DeYoung, B., Rose, G.A., 1993, On recruitment and distribution of Atlantic cod (Gadus morhua) off Newfoundland. Can. J. Fish. Aquat. Sci. 50, 2729–2741. CrossRefGoogle Scholar
Dieckmann, U., O’Hara, B., Weisser, W., 1999, The evolutionary ecology of dispersal. Trends Ecol. Evol. 14, 88–90. CrossRefGoogle Scholar
Dorn, M.W., 1998, Fine-scale fishing strategies of factory trawlers in a midwater trawl fishery for Pacific hake (Merluccius productus). Can. J. Fish. Aquat. Sci. 55, 180–198. CrossRefGoogle Scholar
Dornbusch R., Fisher S., 1994, Macroeconomics. McGraw-Hill Inc., Chapter 10.
Drud A., 1991, CONOPT- a large scale GRG codeARKI Consulting Development A.S, Bagsvaerd, Denmark.
Eales, J., Wilen, J.E., 1986, An examination of fishing location choice in the pink shrimp fishery. Mar Resour. Econ. 2, 331–351. CrossRefGoogle Scholar
Eide, A., Skjold, F., Olsen, F., Flaaten, O., 2003, Harvest functions: The Norwegian bottom trawl cod fisheries. Mar. Resour. Econ. 18, 81–94. CrossRefGoogle Scholar
FAO, 1999, Yearbook of Fishery Statistics. .
Federal Ministry of Finance, 2009, Entwicklung der Energie- (vormals Mineralöl-) und Stromsteuersätze in der Bundesrepublik Deutschland, Bonn.
Federal Office for Agriculture and Food, 2005, 2006, 2007, Bericht über die Fischerei und die Marktsituation für Fischereierzeugnisse in der Bundesrepublik Deutschland. Monatsberichte .
Federal Statistical Office, 2013, Erzeugerpreise gewerblicher Produkte (Inlandsabsatz). Preise für leichtes Heizöl, schweres Heizöl, Motorenbenzin und Dieselkraftstoff. Lange Reihen ab 1976 bis August 2013. Artikelnummer: 5612402131085 .
Frost H., Andersen J.L., Hoff A., Thorgersen T., 2009, The EIAA Model: Methodology, definitions and model outline. Copenhagen, Institute of Food and Resource Economics. FOI Report No. 200.
Gascuel, D., Fonteneau, A., Foucher, E., 1993, Analysis of fishing power evolution using virtual population analysis: the case of purse seiners exploiting yellowfin (Thunnus albacares) in the Eastern Atlantic. Aquat. Living Resour. 6, 15–30. CrossRefGoogle Scholar
Gatewood, J.B., 1984, cooperation, competition, and synergy: information-sharing groups among Southeast Alaskan salmon seiners. Am. Ethnol. 11, 350–370. CrossRefGoogle Scholar
Gillis, D.M., 2003, Ideal free distributions in fleet dynamics: a behavioral perspective on vessel movement in fisheries analysis. Can. J. Zool. 81, 177–187. CrossRefGoogle Scholar
Gorfine, H.K., Dixon, C.D., 2001, Diver behaviour and its influence on assessments of a quota-managed abalone fishery. J. Shellfish Res. 20, 787–794. Google Scholar
Hanna, S.S., Smith, C.L., 1993, Attitudes of trawl vessel captains about work, resource use, and fishery management. N. Am. J. Fish. Manage. 13, 367–375. 2.3.CO;2>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, 1760–1772. CrossRefGoogle Scholar
Hilborn, R., 1985, Fleet dynamics and individual variation - why some people catch more fish than others. Can. J. Fish. Aquat. Sci. 42, 2–13. CrossRefGoogle Scholar
Hilborn, R., Kennedy, R.B., 1992, Spatial pattern in catch rates: a test of economic theory. Bull. Math. Biol. 54, 263–273. CrossRefGoogle Scholar
Hilborn, R., Ledbetter, M., 1979, Analysis of the British-Columbia salmon purse-seine fleet - Dynamics of movement. J. Fish Res. Board Can. 36, 384–391. CrossRefGoogle Scholar
Hilborn, R., Walters, C.J., 1992, Quantitative fisheries stock assessment: Choice, dynamics and uncertainty. Rev. Fish Biol. Fish. 2, 177–178. Google Scholar
Homrum, E.I., Hansen, B., Steingrund, P., Hatun, H., 2012, Growth, maturation, diet and distribution of saithe (Pollachius virens) in Faroese waters (NE Atlantic). Mar. Biol. Res. 8, 246–254. CrossRefGoogle Scholar
ICES, 2010, Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). ICES Document CM 2010/ACOM, 13.
ICES, 2011, Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). ICES Document CM 2011/ACOM, 560.
ICES, 2012, Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). ICES Document CM 2012/ACOM, 13.
ICES, 2013, Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). 24–30 April 2013. ICES Document CM 2013/ACOM:13.
Jacobson, L.D., Thomson, C.J., 1993, Opportunity costs and the decision to fish for northern anchovy. N. Am. J. Fish. Manage. 13, 27–34. 2.3.CO;2>CrossRefGoogle Scholar
Jones, B.W., Jonson, J., 1971, Coalfish tagging experiments at Iceland. Rit Fiskideildar 5, 1–27. Google Scholar
Jones M.C., Dye S.R., Fernandes J.A., Froelicher T.L., Pinnegar J.K., Warren R., Cheung W.W.L., 2013, Predicting the impact of climate change on threatened species in UK waters. Plos One 8, e54216.
Jones, M.C., Dye, S.R., Pinnegar, J.K., Warren, R., Cheung, W.W.L., 2012, Modelling commercial fish distributions: Prediction and assessment using different approaches. Ecol. Model. 225, 133–145. CrossRefGoogle Scholar
Kaschner K., Ready J., et al., 2008, AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, wwwaquamapsorg, Version 10, 2008.
Kerr, L.A., Cadrin, S.X., Secor, D.H., 2010, The role of spatial dynamics in the stability, resilience, and productivity of an estuarine fish population. Ecol. Appl. 20, 497–507. CrossRefGoogle ScholarPubMed
Lane, D.E., 1988, Investment decision making by fishermen. Can. J. Fish. Aquat. Sci. 45, 782–796. CrossRefGoogle Scholar
Lassen H., Medley P., 2001, Virtual population analysis: a practical manual for stock assessment. FAO.
Marchal, P., Andersen, B., Caillart, B.Eigaard, O., Guyader, O., et al., 2007, Impact of technological creep on fishing effort and fishing mortality, for a selection of European fleets. ICES J. Mar. Sci. 64, 192–209. Google Scholar
Maunder, M.N., Punt, A.E., 2004, Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159. CrossRefGoogle Scholar
Olsen, E., Aanes, S., Mehl, S., Holst, J.C., Aglen, A., Gjosaeter, H., 2010, Cod, haddock, saithe, herring, and capelin in the Barents Sea and adjacent waters: a review of the biological value of the area. ICES J. Mar. Sci. 67, 87–101. CrossRefGoogle Scholar
Opaluch, J.J., Bockstael, N.E., 1984, Behavioral modeling and fisheries management. Mar. Resour. Econ. 1, 105–115. CrossRefGoogle Scholar
Palmer, C.T., 1991, Kin-selection, reciprocal altruism, and information sharing among Maine lobstermen. Ethol. Sociobiol. 12, 221–235. CrossRefGoogle Scholar
Paloheimo J., Dickie L., 1964, Abundance and fishing success. Rapp. P.-V. Réun. Cons. Internat. Explor. Mer 155.
Pelletier, D., Magal, P., 1996, Dynamics of a migratory population under different fishing effort allocation schemes in time and space. Can. J. Fish. Aquat. Sci. 53, 1186–1199. CrossRefGoogle Scholar
Poos, J.J., Rijnsdorp, A.D., 2007, An “experiment” on effort allocation of fishing vessels: the role of interference competition and area specialization. Can. J. Fish. Aquat. Sci. 64, 304–313. CrossRefGoogle Scholar
Prince J., Hilborn R., 1998, Concentration profiles and invertebrate fisheries management. Can. Spec. Publ. Fish. Aquat. Sci. 187–198.
Reinsch H., 1976, Köhler und Steinköhler. A. Ziemsen Verlag Vittenberg Lutherstadt.
Robinson, C., Pascoe, S., 1998, Input controls, input substitution and profit maximisation in the English Channel beam trawl fishery. J. Agric. Econ. 49, 16–33. Google Scholar
Rose, G.A., Atkinson, B.A, Baidr, J., Bishop, C.A., Kulka, D.W., 1994, Changes in distribution of Atlantic cod and thermal variations in Newfoundland waters, 1980-1992. ICES Mar. Sci. Symp. 198, 542–552. Google Scholar
Rose, G.A, Kulka, D.W., 1999, Hyperaggregation of fish and fisheries: how catch-per-unit-effort increased as the northern cod (Gadus morhua) declined. Can. J. Fish. Aquat. Sci. 56(S1), 118–127. CrossRefGoogle Scholar
Salas, S., Gaertner, D., 2004, The behavioural dynamics of fishers: management implications. Fish Fish. 5, 153–167. CrossRefGoogle Scholar
Salz P., Buisman E., Frost, H., Accadia, P., Prellezo, R., Soma, K., 2011, Fishrent: bio-economic simulation and optimization model for fisheries. LEI Report 2011, n° 24.
Sampson D.B., 1990, Fishing Technology and Fleet Dynamics: Predictions from a bio-economic model. Portsmouth Polytechnic, Centre for Marine Resource Economics.
Sumaila, U.R., Teh, L., Watson, R., Tyedmers, P., Pauly, D., 2008, Fuel price increase, subsidies, overcapacity and resource sustainability. ICES J. Mar. Sci. 65, 832–840. CrossRefGoogle Scholar
Swain, D.P., Wade, E.J., 2003, Spatial distribution of catch and effort in a fishery for snow crab (Chionoecetes opilio): tests of predictions of the ideal free distribution. Can. J. Fish. Aquat. Sci. 60, 897–909. CrossRefGoogle Scholar
Tidd, A.N., Hutton, T., Kell, L.T., Padda, G., 2011, Exit and entry of fishing vessels: an evaluation of factors affecting investment decisions in the North Sea English beam trawl fleet. ICES J. Mar. Sci. 68, 961–971. CrossRefGoogle Scholar
Trenkel, V.M., Beecham, J.A., Blanchard, J.L., Edwards, C.T.T., Lorance, P., 2013, Testing CPUE-derived spatial occupancy as an indicator for stock abundance: application to deep-sea stocks. Aquat. Living Resour. 26, 319–332. CrossRefGoogle Scholar
Ulrich, C., Pascoe, S., Sparre, PJ., De Wilde, JW., Marchal, P., 2002, Influence of trends in fishing power on bioeconomics in the North Sea flatfish fishery regulated by catches or by effort quotas. Can. J. Fish. Aquat. Sci. 59, 829–843. CrossRefGoogle Scholar
van Dijk, D., Hendrix, E.M., Haijema, R., Groeneveld, R.A., van Ierland, E.C., 2014, On solving a bi-level stochastic dynamic programming model for analyzing fisheries policies: Fishermen behavior and optimal fish quota. Ecol. Model. 272, 68–75. CrossRefGoogle Scholar
von Bertalanffy, L., 1938, A quantitative theory of organic growth. Hum. Biol. 10, 181–213. Google Scholar
Wilen, J.E., 1979, Fisherman behavior and the design of efficient fisheries regulation programs. J. Fish. Board Can. 36, 855–858. CrossRefGoogle Scholar