Fisheries Science

, Volume 75, Issue 2, pp 285–294 | Cite as

Deep water longline selectivity for black spot seabream (Pagellus bogaraveo) in the Strait of Gibraltar

  • Ivone Alejandra CzerwinskiEmail author
  • Karim Erzini
  • Juan Carlos Gutiérrez-Estrada
  • José Antonio Hernando
Original Article Fisheries


Species and size selectivity of the deep water longline traditionally used in commercial fishing of the black spot seabream (Pagellus bogaraveo) were studied in the Strait of Gibraltar with four sizes of hooks. Black spot seabream contributed up to 88% of the catch by number. Catch and by-catch rates differed for the different hooks and fishing trials. Significant differences in average fish length between all hooks, except in one case, were found. The comparison of two experimental fishing trials within 4 years indicates a displacement towards smaller sizes in the size frequency distributions. The results of this study show that the fishing gear can be size selective depending on hook size. The fitted selectivity models for each experiments were very different despite having two hooks in common. This is probably due to the very different catch size distributions in the two periods, which suggests that the population size structure changed significantly between 2000/2001 and 2004/2005.


Hook Longline Pagellus bogaraveo Selectivity 



This work has been partly financed by the Agro alimentary and Fishery Research and Formation Institute (IFAPA) (project: C03-007-2003-110), the General Direction of Fishery and Aquaculture of the Council of Andalucía and the Provincial Deputation of Cadiz. The University of Cadiz provided the necessary facilities for a stay at the Universidade do Algarve. We would like to express our gratitude to our colleagues Dr. Mila C. Soriguer, Dr. Cristina Zabala, Dr. Eva Velasco, Mª Carmen Gómez Cama, Remedios Cabrera, Javier Llorente and Jose M. García Rebollo for the assistance they willingly provided during the samplings. We also thank two anonymous reviewers for their helpful comments and suggestions for improving the manuscript.


  1. 1.
    Jiménez-Toribio R, García-del-Hoyo JJ (2006) Evidence of market price leadership in the Spanish red seabream value chain: implications for fisheries management. Fish Res 81:51–59CrossRefGoogle Scholar
  2. 2.
    Manzano Harriero C, Martín Martín J, Merino Martinez E (2001) Actuaciones en investigacion pesquera y acuícola 1997–2000. Consejería de Agriculura y Pesca, Junta de Andalucía, SevillaGoogle Scholar
  3. 3.
    Clark JR (1960) Report on selectivity of fishing gear. In: Fishing effort, the effects of fishing on resources and the selectivity of fishing gear, vol 2. ICNAF Special Publication. ICNAF, Dartmouth, pp 27–36Google Scholar
  4. 4.
    Hilborn R, Walters CJ (1992) Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Chapman & Hall, LondonGoogle Scholar
  5. 5.
    Beverton R, Holt S (1993) On the dynamics of exploited fish populations. Chapman & Hall, LondonGoogle Scholar
  6. 6.
    Quinn TJ, Deriso RB (1999) Quantitative fish dynamics. Oxford University Press, New YorkGoogle Scholar
  7. 7.
    Brock VE (1999) On the nature of the selective fishing action of longline gear. Pac Sci 16:3–14Google Scholar
  8. 8.
    Cortez Zaragoza E, Dalzell P, Pauly P (1989) Hook selectivity of yellowfin tuna (Thunnus albacares) caught off Darigayos Cove, La Union, Philippìnes. J Appl Ichthyol 1:12–17CrossRefGoogle Scholar
  9. 9.
    Erzini K, Gonçalves JMS, Bentes L, Lino PG, Cruz J (1996) Species and size selectivity in a Portuguese multispecies artisanal long-line fishery. ICES J Mar Sci 53:811–819CrossRefGoogle Scholar
  10. 10.
    Erzini K, Gonçalves JMS, Bentes L, Lino PG, Ribeiro J (1998) Species and size selectivity in a ‘red’ seabream longline ‘metier’ in the Algarve (southern Portugal). Aquat Living Resour 11:1–11CrossRefGoogle Scholar
  11. 11.
    Kanda K, Koike A, Takeuchi S, Ogura M (1978) Selectivity of the hook for mackerel, Scomber japonicus houttuyn, pole fishing. J Tokyo Univ Fish 64:109–114Google Scholar
  12. 12.
    Koike A, Takeuchi S, Ogura M, Kanda K, Arihara C (1968) Selection curve of the hook of long-line. J Tokyo Univ Fish 55:77–82Google Scholar
  13. 13.
    Koike A, Kanda K (1978) Selectivity of the hook of pod smelt, Hypomesus olidus. J Tokyo Univ Fish 64:115–123Google Scholar
  14. 14.
    Millar RB, Holst R (1997) Estimation of gillnet and hook selectivity using log-linear models. ICES J Mar Sci 54:471–477CrossRefGoogle Scholar
  15. 15.
    Pope JA, Margetts AR, Hamley JM, Akyuz EF (1975) Manual of methods for fish stock assessment. Part III. Selectivity of fishing gear. FAO Fisheries Technical Paper No 41. FAO, RomeGoogle Scholar
  16. 16.
    Ralston S (1982) Influence of hook size in the Hawaiian deep-sea handline fishery. Can J Fish Aquat Sci 39:1297–1302Google Scholar
  17. 17.
    Ralston S (1990) Size selection of snappers (Lutjanidae) by hook and line gear. Can J Fish Aquat Sci 47:696–700CrossRefGoogle Scholar
  18. 18.
    Sousa F, Isidro E, Erzini K (1999) Semi-pelagic longline selectivity for two demersal species from the Azores: the black spot seabream (Pagellus bogaraveo) and the bluemouth rockfish (Helicolenus dactylopterus dactylopterus). Fish Res 41:25–35CrossRefGoogle Scholar
  19. 19.
    Takeuchi S, Koike A (1969) The effect of size and shape of hook and the catching efficiency and selection curve of long-line. J Tokyo Univ Fish 55:119–124Google Scholar
  20. 20.
    Otway NM, Craig JR (1993) Effects of hook size on the catches of undersized snapper Pagrus auratus. Mar Ecol Prog Ser 93:9–15CrossRefGoogle Scholar
  21. 21.
    Woll AK, Boje J, Holst R, Gundersen AC (2001) Catch rates and hook and bait selectivity in longline fishery for Greenland halibut (Reinhardtius hippoglossoides, Walbaum) at East Greenland. Fish Res 51:237–246CrossRefGoogle Scholar
  22. 22.
    Kirkwood GP, Walker TI (1986) Gill net mesh selectivities for gummy shark, Mustelus antarcticus, taken in southeastern Australian waters. Aust J Mar Fresh Res 37:689–697CrossRefGoogle Scholar
  23. 23.
    Wulff A (1986) Mathematical model for selectivity of gill nets. Arch Fish Wiss 37:101–106Google Scholar
  24. 24.
    Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172CrossRefGoogle Scholar
  25. 25.
    Ventura S, Silva M, Pérez-Bendito D, Hervás C (1995) Artificial neural networks for estimation of kinetic analytical parameters. Anal Chem 67:1521–1525CrossRefGoogle Scholar
  26. 26.
    Kitanidis PK, Bras RL (1980) Real time forecasting with a conceptual hydrological model 2. Applications and results. Water Resour Res 16:1034–1044CrossRefGoogle Scholar
  27. 27.
    Griñó R (1992) Neural networks for univariate time series forecasting and their application to water demand prediction. Neural Netw World 2:437–450Google Scholar
  28. 28.
    Legates DR, McCabe GJ Jr (1999) Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241CrossRefGoogle Scholar
  29. 29.
    Bjordal A (1989) Recent advances in longline gear—catching performance selectivity and conservation aspects. In: Proc of the World Symp on Fish Gear and Fish Boat Design. Newfoundland and Labrador Institute of Fisheries and Marine Technology, St. John's, pp 19–24Google Scholar
  30. 30.
    Rago PJ, Sosebee KA, Brodziak JKT, Murawski SA, Anderson ED (1998) Implications of recent increases in catches on the dynamics of Northwest Atlantic spiny dogfish (Squalus acanthias). Fish Res 39:165–181CrossRefGoogle Scholar
  31. 31.
    Stevens JD, Bonfil R, Dulvy NK, Walker PA (2000) The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J Mar Sci 57:476–494CrossRefGoogle Scholar
  32. 32.
    Bergstad OA, Hoines AS (2001) Effects of exploitation on age and size structure of sandeel, Ammodytes marinus, populations in the North Sea. Arch Fish Mar Res 49:3–18Google Scholar
  33. 33.
    Balik I, Cubuk H, Ozkok R, Uysal R (2004) Size composition, growth characteristics, stock analysis of the pikeperch, Sander lucioperca (L. 1758), population in Lake Egirdir. Turk J Vet Anim Sci 28:715–722Google Scholar
  34. 34.
    Ault JS, Smith SG, Bohnsack JA (2005) Evaluation of average length as an estimator of exploitation status for the Florida coral-reef fish community. ICES J Mar Sci 62:417–423CrossRefGoogle Scholar
  35. 35.
    Rochet MJ, Trenkel VM (2003) Which community indicators can measure the impact of fishing? A review and proposals. Can J Fish Aquat Sci 60:86–99CrossRefGoogle Scholar
  36. 36.
    Gil J (2006) Biología y pesca del voraz [Pagellus bogaraveo (Brünich, 1768)] en el estrecho de Gibraltar. PhD thesis. Universidad de Cádiz, CadizGoogle Scholar
  37. 37.
    Arimoto T, Ogura M, Inoue Y (1982) Catch variation with immersion time of gear in coastal set-line. Bull Jpn Soc Sci Fish 49:705–709Google Scholar
  38. 38.
    Anonymous (1983) Circle hooks outfish traditional halibut hooks. Mar Fish Rev 45:10–12Google Scholar
  39. 39.
    Skeide R, Bjordal A, Løkkeborg S (1986) Testing of a new hook design (E-Z-Baiter) through comparative longline fishing trials. ICES CM B:25Google Scholar
  40. 40.
    Løkkeborg S, Bjordal A (1992) Species and size selectivity in long-line fishing: a review. Fish Res 13:311–322CrossRefGoogle Scholar
  41. 41.
    Bjordal A, Løkkeborg S (1996) Longlining. Blackwell, OxfordGoogle Scholar
  42. 42.
    Bertrand J (1988) Selectivity of hooks en the handline fishery of the Saya de Malha Banks (Indian Ocean). Fish Res 6:249–255CrossRefGoogle Scholar
  43. 43.
    Erzini K, Salgado M, Castro M (2006) Dynamics of black spot seabream (Pagellus bogaraveo) mean length: evaluating the influence of life history parameters, recruitment, size selectivity and exploitation rates. J Appl Ichthyol 22:183–188CrossRefGoogle Scholar
  44. 44.
    Garrick M, Cunnane C, Nash JE (1978) A criterion of efficiency for rainfall-runoff models. J Hydrol 36:375–381CrossRefGoogle Scholar
  45. 45.
    Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’Donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995–9005CrossRefGoogle Scholar
  46. 46.
    Stergiou KI, Christou ED, Petrakis G (1997) Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods. Fish Res 29:55–95CrossRefGoogle Scholar
  47. 47.
    Pulido-Calvo I, Portela MM (2007) Application of neural approaches to one step daily flow forecasting in Portuguese watersheds. J Hydrol 332:1–15CrossRefGoogle Scholar
  48. 48.
    Velo-Suárez L, Gutiérrez-Estrada JC (2007) Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain). Harmful Algae 6:361–471CrossRefGoogle Scholar

Copyright information

© The Japanese Society of Fisheries Science 2009

Authors and Affiliations

  • Ivone Alejandra Czerwinski
    • 1
    Email author
  • Karim Erzini
    • 2
  • Juan Carlos Gutiérrez-Estrada
    • 3
  • José Antonio Hernando
    • 1
  1. 1.Biology Department, Marine and Environmental Faculty, Campus of Puerto RealCadiz UniversityCadizSpain
  2. 2.Centre of Marine Sciences (CCMAR), Campus of GambelasUniversity of AlgarveFaroPortugal
  3. 3.Agroforestry Sciences Department, Polytechnic University College, Campus of La RábidaUniversity of HuelvaHuelvaSpain

Personalised recommendations