EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 17 / No 3 (July-September 2004) Aquat. Living Resour., 17 3 (2004) 353-360 References
Free Access
Issue
Aquat. Living Resour.
Volume 17, Number 3, July-September 2004
The "Erika" Oil Spill: Environmental Contamination and Effects in the Bay of Biscay
Page(s) 353 - 360
DOI https://doi.org/10.1051/alr:2004044
Published online 15 July 2004
  • Barrett R.T., 2002, Atlantic puffin Fratercula arctica and common guillemot Uria aalge chick diet and growth as indicators of fish stocks in the Barents Sea. Mar. Ecol. Prog. Ser. 230, 275-287. [CrossRef] [Google Scholar]
  • Begg G.S., Reid J.B., 1997, Spatial variation in seabird density at a shallow sea tidal mixing front in the Irish Sea. ICES J. Mar. Sci. 54, 552-565. [CrossRef] [Google Scholar]
  • Briggs K.T., Tyler W.B., Lewis D.B., 1985a, Comparison of ship and aerial surveys of birds at sea. J. Wildl. Manage. 49, 405-411. [CrossRef] [Google Scholar]
  • Briggs K.T., Tyler W.B., Lewis D.B., 1985b, Aerial surveys for seabirds: Methodological experiments. J. Wildl. Manage. 49, 411-417. [Google Scholar]
  • Buckland S.T., Anderson D.R., Burnham K.P., Laake J.L., Borchers D.L., Thomas L., 2001, Introduction to Distance Sampling. Oxford University Press. [Google Scholar]
  • Burger A.E., Simpson M., 1986, Diving depths of Atlantic Puffins and common Murres. Auk 103, 828-829. [Google Scholar]
  • Cadiou B., Riffaut L., McCoy K., Cabelguen J., Fortin M., Gélinaud G., Le Roch A., Tirard C., Boulinier T., 2004, Ecological impact of the “Erika” oil spill: Determination of the geographic origin of the affected common guillemots. Aquat. Living Resour. 17, 369. [CrossRef] [EDP Sciences] [Google Scholar]
  • Carl Schoch G., Dethier M.N., 1996, Scaling up: The statistical linkage between organismal abundance and geomorphology on rocky shorelines. J. Exp. Mar. Biol. Ecol. 201, 37-72. [CrossRef] [Google Scholar]
  • Castège I., Hémery G., Roux N., d' Elbée J., Lalanne Y., d'Amico F., Mouchès C., 2004, Changes in abundance and at-sea distribution of seabirds in the Bay of Biscay prior to, and following the “Erika” oil spill. Aquat. Living Resour. 17, 361. [CrossRef] [EDP Sciences] [Google Scholar]
  • Clarke E.D., Spear L.B., McCraken M.L., Marques F.F.C., Borchers D.L., Buckland S.T., Ainley D.G., 2003, Validating the use of generalized additive models and at-sea surveys to estimate size and temporal trends of seabird populations. J. Appl. Ecol. 40, 278-292. [CrossRef] [Google Scholar]
  • Clua E., Grosvalet F., 2001, Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11-18. [CrossRef] [EDP Sciences] [Google Scholar]
  • Cressie N.A.C., 1993, Statistics for spatial data. Wiley. [Google Scholar]
  • Daunt F., Peters G., Scott B., Grémillet D., Wanless S., 2003, rapid-response recorders reveal interplay between marine physics and seabird behaviour. Mar. Ecol. Prog. Ser. 255, 283-288. [CrossRef] [Google Scholar]
  • Davoren G.K., Montevecchi W.A, Anderson J.T., 2002, Scale-dependent associations of predators and prey: Constraints imposed by flightlessness of common murres. Mar. Ecol. Prog. Ser. 245, 259-272. [CrossRef] [Google Scholar]
  • Davoren G.K., Montevecchi W.A., Anderson J.T., 2003, Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour. Mar. Ecol. Prog. Ser. 256, 229-242. [CrossRef] [Google Scholar]
  • Estrada-Pena A., 1999, Geostatistics and remote sensing using NOAA-AVHRR satellite imagery as predictive tools in tick distribution and habitat suitability estimations for Boophilus mocroplus (Acari: Ixodidae) in south america. Vet. Parasitol. 81, 73-82. [CrossRef] [PubMed] [Google Scholar]
  • Ettema C.H., Yeates G.W., 2003, Nested spatial biodiversity patterns of nematode genera in a New Zealand forest and pasture soil. Soil Biol. Biochem. 35, 339-342. [CrossRef] [Google Scholar]
  • Fauchald P., Erikstad K.E., Skarsfjord H., 2000, Scale-dependent predator-prey interactions: The hierarchical spatial distribution of seabirds and prey. Ecology 81, 773-783. [Google Scholar]
  • Fauchald P., Erikstad K.E., 2002, Scale dependent predator-prey interactions: The aggregative response of seabirds to prey under variable prey abundance and patchiness. Mar. Ecol. Prog. Ser. 231, 279-291. [CrossRef] [Google Scholar]
  • Fauchald P., Erikstad K.E., Systad G.H., 2002, Seabirds and marine oil incidents: Is it possible to predict the spatial distribution of pelagic seabirds? J. Appl. Ecol. 39, 349-360. [CrossRef] [Google Scholar]
  • Ford R.G., Bonnell M.L., Varoujean D.H., Page G.W., Sharp B.E., Heinemann D., Casey J.L., 1996, Total direct mortality of seabirds from the Exxon Valdez oil spill. Am. Fish. Soc. Symp.18, 684-711. [Google Scholar]
  • Fox A.D., Petersen I.K., Hedley S.L., 2003, Calculating density estimates for seabirds based upon aerial survey transect counts. Progress report on development techniques. NERI Report to the Danish Energy Agency. [Google Scholar]
  • Furness R.W., Tasker M.L., 2000, Seabird-fishery interactions: Quantifying the sensivity of seabirds to reduction in sandeel abundance, and identification of key areas for sensitive seabirds in the North sea. Mar. Ecol. Prog. Ser. 202, 253-264. [CrossRef] [Google Scholar]
  • Guiblin P., Rivoirard J., Simmonds E.J., 1995, Analyse structurale de données à distribution dissymétrique : exemple du hareng écossais. Ecole des Mines, Paris. Cah. Géostat. 5, 59-137. [Google Scholar]
  • Hémery G., Pasquet E., Yésou P., 1986, Data banks and population monitoring in France. In: Monbailliu X. (Ed.), MEDMARAVIS, Mediterranean Marine Avifauna, Populations Studies and Conservation, pp. 163-177. [Google Scholar]
  • Jelinski D.E., Krueger C.C., Duffus D.A., 2002, Geostatistical analyses of interactions between killer whales (Orcinus orca) and recreational whale-watching boats. Appl. Geogr. 22, 393-411. [CrossRef] [Google Scholar]
  • Joensen A.H., 1968, Wildfowl counts in Denmark in November 1967 and January 1968. Danish Rev. Game Biol. 8, 1-42. [Google Scholar]
  • Komdeur J., Bertelsen J., Cracknell G., 1992, Manual in counting birds from aircraft and ship. IWRB Spec. Publ. 19. IWRB, Slimbridge. [Google Scholar]
  • Laake J.L., Calambokidis J., Osmek S.D., Rugh D.J., 1997, Probability of detecting harbor porpoise from aerial surveys: Estimating g(0). J. Wildl. Manage. 61, 63-73. [CrossRef] [Google Scholar]
  • Lichtein J.W., Simons T.R., Shriner S.A., Franzreb K.E., 2002, Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr. 72, 445-463. [CrossRef] [Google Scholar]
  • Lloyd H., Cahill A., Jones M., Mardsen S., 2003, Estimating bird densities using distance sampling. In: Expedition field technique, pp. 34-51. [Google Scholar]
  • Marinoni O., 2003, Improving geological models using a combined ordinary-indicator kriging approach. Eng. Geol. 69, 37-45. [CrossRef] [Google Scholar]
  • Mehlum F., Hunt G.L., Klusek Z., Decker M.B., 1999, Scale-dependent correlations between the abundance of Brunnich's guillemots and their prey. J. Anim. Ecol. 68, 60-72. [CrossRef] [Google Scholar]
  • Mehlum F., Nordlund N., Isaksen K., 1998, The importance of the “Polar Front” as a foraging habitat for guillemots Uria spp. breeding at Bjornoya, Barents Sea. J. Mar. Syst. 14, 27-43. [CrossRef] [Google Scholar]
  • van der Meer J., Leopold M.F., 1995, Assessing the population size of the European storm petrel (Hydrobates pelagicus) using spatial autocorrelation between counts from segments of criss-cross ship transects. ICES J. Mar. Sci. 52, 809-818. [CrossRef] [Google Scholar]
  • Oliver M.A., Webster R., 1986, Combining nested and linear sampling for determining the scale and form of spatial variation of regionalized variables. Geogr. Anal. 18, 227-242. [CrossRef] [Google Scholar]
  • Paramo J., Roa R., 2003, Acoustic-geostatistical assessment and habitat-abundance relations of small pelagic fish from the Colombian Caribbean. Fish. Res. 60, 309-319. [CrossRef] [Google Scholar]
  • Piatt J.F., Lensink C.J., Butler W., Kendziorek M., Nysewander D.R., 1990, Immediate impact of the “Exxon Valdez” oil spill on marine birds. Auk 107, 387-397. [Google Scholar]
  • Quang P.X., Becker E.F., 1996, Line transect sampling under varying conditions with application to aerial surveys. Ecology 77, 1297-1302. [CrossRef] [Google Scholar]
  • Recorbet B., 1996, Composition, abondance, distribution et vulnérabilité aux pollutions par hydrocarbures de l'avifaune marine côtière du Sud Bretagne/Nord Gascogne. Thèse, École Pratique des Hautes Études, Montpellier. [Google Scholar]
  • Recorbet B., 1998, Phénologie, distribution et abondance de quelques oiseaux marins au large de l'estuaire de la Loire. Spatule 6, 3-116. [Google Scholar]
  • Rivoirard J., Simmonds J., Foote K G., Fernandes P., Bez N., 2000, Geostatistics for Estimating Fish Abundance, Blackwell Science Ltd, Osney Mead (Ed.), Oxford, pp. 1-5. [Google Scholar]
  • Reid J.B., Pollock C.M., Mavor R., 2001, Seabirds of the Atlantic Frontier, north and west of Scotland. Cont. Shelf Res. 21, 1029-1045. [CrossRef] [Google Scholar]
  • Schneider D.C., Piatt J.F., 1986, Scale-dependent correlation of seabirds with schooling fish in a coastal ecosystem. Mar. Ecol. Prog. Ser. 32, 237-246. [CrossRef] [Google Scholar]
  • Spear L., Nur N. Ainley, D.G., 1992, Estimating absolute densities of flying seabirds using analyses of relative movements. Auk 109, 385-389. [Google Scholar]
  • Stone C.J., Webb A., Tasker M.L., 1995, The distribution of auks and Procellariiformes in north-west European waters in relation to depth of sea. Bird Study 42, 50-56. [CrossRef] [Google Scholar]
  • Yoshida H., Shirakihara K., Kishino H., Shirakihara M., Takemura A., 1998, Finless porpoise abundance in Omura Bay, Japan: Estimation from aerial sighting surveys. J. Wildl. Manage. 62, 286-291. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.