Factors affecting the fishing impact on cartilaginous fishes in southeastern Spainwestern Mediterranean Sea

  1. Manuel Mendoza 1
  2. Diego Garrido 1
  3. Jose M. Bellido 1
  1. 1 Instituto Español de Oceanografía, Centro Oceanográfico de Murcia
Revue:
Scientia Marina

ISSN: 0214-8358

Année de publication: 2014

Volumen: 78

Número: 1

Pages: 67-76

Type: Article

DOI: 10.3989/SCIMAR.04025.21A DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Scientia Marina

Résumé

We propose a global index of impact based on the relative vulnerability of the local population of every species and the further application of regression trees globally optimized with evolutionary algorithms to study the fishing impact on the cartilaginous fish in southeastern Spain. The fishing impact is much higher in areas of less than 40 m depth within 11 km of the Cape Palos marine reserve. The impact also depends on the state of the sea and the kind of habitat. Deep-sea habitats associated with hard substrata and sandy beds show the highest impact, and sublittoral muds and habitats associated with circalittoral rocks with moderate energy show the lowest impact. The fishing impact changes throughout the moon cycle, showing different day-scale patterns associated with different habitats and different species compositions. Finally, we show that the global optimization of the regression trees can be essential to find some important patterns and that these trees are a useful tool for determining which areas are considered to be more important in terms of protection, taking into account specifically the vulnerability of the local populations.

Références bibliographiques

  • Baum J.K., Myers R.A., Kehler D.G., Worm B., Harley S.J., Do-herty P.A. 2003. Collapse and conservation of shark popula-tions in the Northwest Atlantic. Science 299: 389-392. http://dx.doi.org/10.1126/science.1079777
  • Bigelow K.A., Boggs C.H., He X. 1999. Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery. Fish. Oceanogr. 8: 178-198. http://dx.doi.org/10.1046/j.1365-2419.1999.00105.x
  • Borregaard M.K., Rahbek, C. 2010. Causality of the relationship between geographic distribution and species abundance. Quart. Rev. Biol. 85: 3-25. http://dx.doi.org/10.1086/650265
  • Casey J.M. Myers R.A. 1998. Near extinction of a large, widely distributed fish. Science 281: 690-692.http://dx.doi.org/10.1126/science.281.5377.690
  • Castro J.I., Woodley C.M., Brudek, R.L. 1999. A preliminary evalu-ation of the status of shark species. FAO Fisheries Technical Paper 380.
  • Castro W.L., Pitcher T.J., Pauly, D. 2005. A fuzzy logic expert system to estimate intrinsic extinction vulnerability of marine fishes to fishing. Biol. Conserv. 124: 97-111. http://dx.doi.org/10.1016/j.biocon.2005.01.017
  • Damalas D., Megalofonou P., Apostolopoulou, M. 2007.Environ-mental, spatial, temporal and operational effects on swordfish (Xiphias gladius) catch rates of eastern Mediterranean Sea long-line fisheries. Fish. Res. 84: 233-246.http://dx.doi.org/10.1016/j.fishres.2006.11.001
  • Davidson A.D., Boyer A.G., Ki H., Pompa-Mansilla S., Hamilton M.J., Costa D.P., Ceballos G., Brown J.H. 2012. Drivers and hotspots of extinction risk in marine mammals. PNAS 109: 3395-3400.http://dx.doi.org/10.1073/pnas.1121469109
  • Davies C.E., Moss D., O’Hill, M. 2004. EUNIS habitat classifica-tion Revised 2004. European Environment Agency, European topic centre on nature protection and biodiversity. http://eunis.eea.europa.eu/upload/EUNIS_2004_report.pdf
  • Dulvy N.K., Forrest R.E. 2010. Life histories, population dynamics and extinction risks in chondrichthyans. In: Carrier J., Musick J., Heithaus M. (eds), Sharks and their Relatives II. Biodiver-sity, Adaptive Physiological Conservation. CRC Press, Boca Raton, pp. 639-679. http://dx.doi.org/10.1201/9781420080483-c17
  • Dulvy N.K., Reynolds J.D. 2002. Predicting extinction vulnerability in skates. Conserv. Biol. 16: 440-450. http://dx.doi.org/10.1046/j.1523-1739.2002.00416.x
  • Dulvy N.K., Sadovy Y., Reynolds J.D. 2003. Extinction vulnerabil-ity in marine populations. Fish. Fish. 4: 25-64. http://dx.doi.org/10.1046/j.1467-2979.2003.00105.x
  • Dulvy N.K., Ellis J.R., Goodwin N.B., Grant A., Reynolds J.D., Jennings S. 2004. Methods of assessing extinction risk in marine fishes. Fish. Fish. 5: 255-276.http://dx.doi.org/10.1111/j.1467-2679.2004.00158.x
  • European Commission 2006. Sensitive and Essential Fish Habitats in the Mediterranean Sea. Rome, pp. 6-10.
  • Ferretti F., Myers R.A., Serena F., Lotze, H.K. 2008. Loss of large predatory sharks from the Mediterranean Sea. Conserv. Biol. 22: 952-964.http://dx.doi.org/10.1111/j.1523-1739.2008.00938.x
  • Frankham R., Ballou J.D., Briscoe D.A. 2002. Introduction to con-servation genetics. Cambridge Univ. Press. http://dx.doi.org/10.1017/CBO9780511808999
  • Frisk M.G., Miller T.J., Dulvy N.K. 2005. Life histories and vul-nerability to exploitation of cartilaginous fish: Inferences from elasticity, perturbation and phylogenetic analyses. J. Northw. Atl. Fish. Sci. 35: 27-45. http://dx.doi.org/10.2960/J.v35.m514
  • Froese R., Pauly D. 2011. FishBase. World Wide Web electronic publication. www.fishbase.org, version.
  • Gouraguine A., Hidalgo M., Moranta J., Bailey D., Ordines F., Gui-jarro B., Valls M., Barberá C., De Mesa A. 2011. Cartilaginous fish spatial segregation in the western Mediterranean. Sci. Mar. 75: 653-664. http://dx.doi.org/10.3989/scimar.2011.75n4653
  • Grubinger T., Zeileis A., Pfeiffer K.P. 2011. evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees. In: Working Paper 2011-xx. Working Papers in Econom-ics and Statistics, Research Platform Empirical and Experimen-tal Economics, Universitt Innsbruck. http://EconPapers.RePEc.org/RePEc:inn:wpaper:2011-xx
  • Hazin F., Burgess G., Carvalho F. 2008. A Shark Attack Outbreak Off Recife, Pernambuco, Brazil: 1992–2006. B. Mar. Sci. 82: 199-212.
  • Hergstrom K., Niall R. 1990. Presence–absence sampling of two spotted spider mite (Acari: Tetranychidae) in pear orchards. J. Econ. Entomol. 83: 2032-2035.
  • Hernandez-Milian G., Goetz S., Varela C., Rodriguez J., Romon J., Fuertes J.R., Ulloa E., Tregenza N.J.C., Smerdon A., Otero M.G., Tato V., Wang J., Santos M.B., López A., Lago R., Por-tela, J., Pierce G.J. 2008. Results of a short study of interactions of cetaceans and longline fisheries in Atlantic waters: environ-mental correlates of catches and depredation events. Hydrobio-logia 612: 251-268. http://dx.doi.org/10.1007/s10750-008-9501-2
  • Isaac N.J.B., Cowlishaw G. 2004. How species respond to multiple extinction threats. P. Royal Soc. B-Biol. Sci. 271: 1135-1141.
  • Jennings S., Kaiser M. 1998. The effects of fishing on marine eco-systems. Adv. Mar. Biol. 34: 201-352. http://dx.doi.org/10.1016/S0065-2881(08)60212-6
  • Jennings S., Greenstreet S.P.R., Reynolds J.D. 1999. Structural change in an exploited fish community: a consequence of dif-ferential fishing effects on species with contrasting life histo-ries. J. Anim. Ecol. 68: 617-627. http://dx.doi.org/10.1046/j.1365-2656.1999.00312.x
  • Kaiser M.J., Collie J.S., Hall S.J., Jennings S. Poiner I.R. 2003. Im-pacts of fishing gear on marine benthic habitats. In: Sinclair M., Valdimarsson G. (eds), Respons. Fish. Mar. Ecos. pp. 197-217. Rome, FAO.
  • Krzysztof J.C., Pedrycz W., Swiniarski R.W., Kurgan L.A. 2007. Data Mining: A Knowledge Discovery Approach. Springer-Verlag New York, Inc. Secaucus, New Jersey.Madsen N. 2007. Selectivity of fishing gears used in the Baltic Sea cod fishery. Rev Fish. Biol. Fish. 4: 517-544. http://dx.doi.org/10.1007/s11160-007-9053-y
  • Massutí E., Moranta J. 2003. Demersal assemblages and depth distribution ofcartilaginous fish from the continental shelf and slope off the Balearic Islands westernMediterranean. – ICES J. Mar. Sci. 60: 753-766. http://dx.doi.org/10.1016/S1054-3139(03)00089-4
  • McDowall R.M. 1969. Lunar Rhythms in Aquatic Animals: A General Review. Tuatara 17: 3.
  • McEachran J.D., Musick J.A. 1975. Distribution and relative abun-dance of seven species of skates Pisces: Rajidae which occur between Nova Scotia and Cape Hatteras.U.S. Fish. B. 73: 110-136.
  • Mendoza M. 2007. Decision Trees: a Machine Learning Methodol-ogy for characterizing Morphological Patterns resulting from Ecological Adaptations. In: MacLeod N. (ed), Automated Object Identification in Systematics: Theory, Approaches and Applications pp. 261-276. Systematics Association’s Special Volume Series, UK. http://dx.doi.org/10.1201/9781420008074.ch15
  • Mendoza M., García T., Baro J. 2010. Using classification trees to study the effects of fisheries management plans on the yield of Merluccius merluccius Linnaeus, 1758 in the Alboran Sea Western Mediterranean. Fish. Res.102: 191-198. http://dx.doi.org/10.1016/j.fishres.2009.11.012
  • Mendoza M., Pennino M.G., Bellido J.M. 2011. Tree-Based Ma-chine Learning Analysis for Fisheries Research. In: Intilli J.S. (ed), Fishery Management pp. 25-35. Fish, Fishing and Fisher-ies Series. Nova Science Publishers.
  • Millsap B.A., Gore. J.A., Runde D.E., Cerulean S.I. 1990. Setting priorities for the conservation of fish and wildlife species in Florida. Wildlife Monographs.Moore H.B. 1958. Marine Ecology Wiley, New York.
  • Nielsen S.E., Johnson C.J., Heard D.C.Boyce M.S. 2005. Can models of presence-absence be used to scale abundance? Two case studies considering extremes in life history. Ecography 28: 197-208. http://dx.doi.org/10.1111/j.0906-7590.2005.04002.x
  • Pallares P., Garcia-Mamolar J.M. 1985. Efectos de las fases de la luna sobre los rendimientos de la flota atunera tropical espa-ola. International Commission for the Conservation of Atlantic Tunas ICCAT 23: 228-236.
  • Pauly D., Christensen V., Guénette S., Pitcher T., Sumaila U.R., Walters C., Watson R., Zeller D. 2002. Toward sustainability in world fisheries. Nature 418: 689-695. http://dx.doi.org/10.1038/nature01017
  • Pauly D., Alder J., Bennett E., Christensen V., Tyedmers P., Watson R. 2003. The future for fisheries. Science 302: 1359-1361. http://dx.doi.org/10.1126/science.1088667
  • Pennino M., Mu-oz F., Conesa D., López-Quílez A.Bellido J.M. 2013. Modelling sensitive cartilaginous fish habitats. J. Sea Res. V: 1-25.
  • Pérez-Ortiz M, Colmenarejo R., Fernández-Caballero J. C., Hervás-Martínez C. 2013. Can Machine Learning Techniques Help to Improve the Common Fisheries Policy? IWANN (2) 2013: 278-286.Quinlan J.R. 1985. Induction of decision trees. Mach. Learn. 1: 81-106. http://dx.doi.org/10.1007/BF00116251 http://dx.doi.org/10.1023/A:1022643204877
  • Reynolds J.D., Jenings S., Dulvy N.K. 2001. Life histories of fishes and population responses to exploitation. In: Reynolds J.D., Mace G.M., Redford K.H., Robinson J.G. (eds), Conservation of Exploited Species pp. 147-168. Cambridge University Press, Cambridge.
  • Roel B.A. 1987. Demersal communities of the west coast of South Africa. South Afric. J. Mar. Sci. 5: 575–584. http://dx.doi.org/10.2989/025776187784522135
  • Rogan J., Franklin J., Stow D., Miller J., Roberts D.A., Woodcock C. 2008. Mapping land cover modifications over large areas: A comparison of machine learning techniques, Remote Sens. Environ. 112: 2272-2283.http://dx.doi.org/10.1016/j.rse.2007.10.004
  • Sadovy Y., Cheung W.L. 2003. Near extinction of a highly fecund fish: the one that nearly got away. Fish. Fish. 4: 86-99.http://dx.doi.org/10.1046/j.1467-2979.2003.00104.x
  • Stevens J.D., Bonfil R., Dulvy N.K., Walker P.A. 2000. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57: 476-494. http://dx.doi.org/10.1006/jmsc.2000.0724
  • Venier L.A., Fahrig L. 1998. Intraspecific abundance-distribution relationships. Oikos 82: 483-490. http://dx.doi.org/10.2307/3546369
  • Watling L., Norse E.A. 1998. Disturbance of the seabed by mobile fishing gear: comparison to forest clearcutting. Conserv. Biol. 12: 1180-1197. http://dx.doi.org/10.1046/j.1523-1739.1998.0120061180.x
  • Wilson S.K., Fisher R., Pratchett M.S., Graham N.A.J., Dulvy N.K., Turner R.A., Cakacaka A., Polunin N.V.C. 2010. Habitat degra-dation and fishing effects on the size structure of coral reef fish communities. Ecol. Appl. 20: 442-451. http://dx.doi.org/10.1890/08-2205.1