Ecosystem Approach to Fisheries
Free Access
Review
Issue
Aquat. Living Resour.
Volume 22, Number 4, October-December 2009
Ecosystem Approach to Fisheries
Page(s) 433 - 445
DOI https://doi.org/10.1051/alr/2009027
Published online 01 September 2009
  • Axenrot T., Didrikas T., Danielsson C., Hansson S., 2004, Diel patterns in pelagic fish behaviour and distribution observed from a stationary, bottom-mounted, and upward-facing transducer. ICES J. Mar. Sci. 61, 1100–1104. [CrossRef] [Google Scholar]
  • Berger L., Poncelet C., Trenkel V.M., 2009, A method for reducing uncertainty in estimates of fish-school frequency response using data from multifrequency and multibeam echosounders. ICES J. Mar. Sci. 66, 1155–1161. [CrossRef] [Google Scholar]
  • Bertrand A., Barbieri M.A., Gerlotto, F., Leiva F., Córova J., 2006, Determinism and plasticity of fish schooling behaviour as exemplified by the South Pacific jack mackerel Trachurus murphyi. Mar. Ecol. Prog. Ser. 311, 145–156. [CrossRef] [Google Scholar]
  • Bertrand A., Gerlotto F., Bertrand S., Gutiérrez M., Alza L., Chipollini A., Díaz E., Espinoza P., Ledesma J., Quesquén R., Peraltilla S., Chavez F., 2008, Schooling behaviour and environmental forcing in relation to achoveta distribution: An analysis across multiple spatial scales. Prog. Oceanogr. 79, 264–277. [CrossRef] [Google Scholar]
  • Bourguignon S., Berger L., Scalabrin C., Fablet R., Mazauric V., 2009, Methodological developments for improved bottom detection with the ME70 multibeam echosounder. ICES J. Mar. Sci. 66, 1015–1022. [CrossRef] [Google Scholar]
  • Brehmer P., Georgakarakos S., Josse E., Trygonis V., Dalen J., 2007, Adaptation of fisheries sonar for monitoring schools of large pelagic fish: dependence of schooling behaviour on fish finding efficiency. Aquat. Living Resour. 20, 377–384. [CrossRef] [EDP Sciences] [Google Scholar]
  • Burgos J.M., Horne J.K., 2007, Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES J. Mar. Sci. 64, 160–168. [Google Scholar]
  • Burgos J.M., Horne J.K., 2008, Characterization and classification of acoustically detected fish spatial distributions. ICES J. Mar. Sci. 65, 1235–1247. [CrossRef] [Google Scholar]
  • Castillo J., Barbieri M.A., Gonzalez A., 1996, Relationships between sea surface temperature, salinity and pelagic fish distribution off northern Chile. ICES J. Mar. Sci. 53, 139–146. [CrossRef] [MathSciNet] [Google Scholar]
  • Coetzee J., 2000, Use of a shoal analysis and patch estimation system (SHAPES) to characterise sardine schools. Aquat. Living Resour. 13, 1–10. [CrossRef] [EDP Sciences] [Google Scholar]
  • Cutter Jr. G.R., Demer D.A., 2007, Accounting for scattering directivity and fish behaviour in multibeam-echsounder surveys. ICES J. Mar. Sci. 64, 1664–1674. [CrossRef] [Google Scholar]
  • Dalen J., Nedreaas K., Pedersen R., 2003, A comparative acoustic-abundance estimation of pelagic redfish (Sebastes mentella) from hull-mounted and deep-towed acoustic systems. ICES J. Mar. Sci. 60, 472–479. [CrossRef] [Google Scholar]
  • De Robertis A., Hjellvik V., Williamson N.J., Wilson C.D., 2008, Silent ships do not always encounter more fish: comparison of acoustic backscatter recorded by a noise-reduced and a conventional research vessel. ICES J. Mar. Sci. 65, 623–635. [CrossRef] [Google Scholar]
  • Demer D.A., Renfree J.S., 2008, Variations in echosounder-transducer performance with water temperature. ICES J. Mar. Sci. 65, 1021–1035. [CrossRef] [Google Scholar]
  • Diner N., Le Men R., 1983, Evaluation acoustique des stocks de poisson pélagiques dans la partie sud du golfe de Gascogne en avril-mai 83. ICES CM 1983/H: 44. [Google Scholar]
  • Diner N., 1999, Correction of school geometry and density: an approach based on acoustic image simulation. ICES Coop. Res. Rep. 238, 27–51. [Google Scholar]
  • Diner N., 2001, Correction on school geometry and density: approach based on acoustic image simulation. Aquat. Living Resour. 14, 211–222. [CrossRef] [EDP Sciences] [Google Scholar]
  • Doray M., Josse E., Gervain P., Reynal L., Chantrel J., 2007, Joint use of echosounding, fishing and video techniques to assess the structure of fish aggregations around moored Fish Aggregating Devices in Martinique (Lesser Antilles). Aquat. Living Resour. 20, 357–366. [CrossRef] [EDP Sciences] [Google Scholar]
  • Doucet A., 1998, On sequential simulation-based methods for Bayesian filtering. Signal Processing Department, University, Cambridge. [Google Scholar]
  • Fabi G., Sala A., 2002, An assessment of biomass and diel activity of fish at an artificial reef (Adriatic Sea) using a stationary hydroacoustic technique. ICES J. Mar. Sci. 59, 411–420. [CrossRef] [Google Scholar]
  • Fablet R., Lefort R., Karoui I., Berger L., Massé J., Scalabrin C., Boucher J.M., 2009, Classifying fish schools and estimating their species proportions in fishery-acoustic surveys. ICES J. Mar. Sci. 66, 1136–1142. [CrossRef] [Google Scholar]
  • Fässler S.M.M., Santos R., García-Nuñez N., Fernandes P.G., 2007, Multifrequency backscattering properties of Atlantic herring (Clupea harengus) and Norway pout (Trisopterus esmarkii). Can. J. Fish. Aquat. Sci. 64, 362–374. [CrossRef] [Google Scholar]
  • Fernandes P.G., Brierley A.S., Simmonds E.J., Millards N.W., McPhail S.D., Armstrong F., Stevenson P., Squires M., 2000, Fish do not avoid survey vessels. Nature 404, 35–36. [CrossRef] [PubMed] [Google Scholar]
  • Fernandes P.G., Stevenson P., Brierley A.S., Armstrong F., Simmonds E.J., 2003, Autonomous underwater vehicles: future platforms for fisheries acoustics. ICES J. Mar. Sci. 60, 684–691. [CrossRef] [Google Scholar]
  • Foote K.G., 2006, Optimizing two targets for calibrating a broadband multibeam sonar. Oceans 2006 Conference, Boston MA, Vol. 1–4, pp. 1499–1502. [Google Scholar]
  • Fréon P., Cury P., Shannon L., Roy C., 2005, Sustainable exploitation of small pelagic fish stocks challenged by environmental and ecosystem changes: a review. Bull. Mar. Sci. 76, 385–462. [Google Scholar]
  • Gerlotto F., 1993, Identification and spatial stratification of tropical fish concentrations using acoustic populations. Aquat. Living Resour. 6, 243–254. [CrossRef] [EDP Sciences] [Google Scholar]
  • Gerlotto F., Soria M., Fréon P., 1999, From two dimensions to three: the use of multibeam sonar for a new approach in fisheries acoustics. Can. J. Fish. Aquat. Sci. 56, 6–12. [CrossRef] [Google Scholar]
  • Gerlotto F., Paramo J., 2003, The three-dimensional morphology and internal structure of clupeid schools as observed using vertical scanning multibeam sonar. Aquat. Living Resour. 16, 113–122. [CrossRef] [EDP Sciences] [Google Scholar]
  • Gerlotto F., Castillo J., Saavedra A., Barbieri M.A., Espejo M., Cotel P., 2004, Three-dimensional structure and avoidance behaviour of anchovy and common sardine schools in central southern Chile. ICES J. Mar. Sci. 61, 1120–1126. [CrossRef] [Google Scholar]
  • Gorska N., Ona E., Korneliussen R., 2005, Acoustic backscattering by Atlantic mackerel as being representative of fish that lack a swimbladder, backscattering by individual fish. ICES J. Mar. Sci. 62, 984–995. [CrossRef] [Google Scholar]
  • Hamitouche-Djabou C., Togni S., Lecornu L., 1999, SBI Viewer: 3D fish schools and sea bottom analysis and visualisation software. User's Guide. AVITIS Contract FAIR CT 96-1717, Development release 3–10 June, 1999 Dpt ITI, ENST-Bretagne, Brest. [Google Scholar]
  • Hammond T.R., Swartzman G.L., 2001, A general procedure for estimating the composition of fish school clusters using standard acoustic survey data. ICES J. Mar. Sci. 58, 1115–1132. [CrossRef] [Google Scholar]
  • Handegard N.O., Tjøstheim, D., 2005, When fish meet a travelling vessel: examining the behaviour of gadoids using a free-floating buoy and acoustic split-beam tracking. Can. J. Fish. Aquat. Sci. 62, 2409–2422. [CrossRef] [Google Scholar]
  • Haralambous J., Georgakarakos S., 1996, Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53, 173–180. [CrossRef] [Google Scholar]
  • ICES, 2005, Description of the ICES HAC standard data exchange format, Version 1.60. ICES Coop. Res. Rep. 278. [Google Scholar]
  • Jech J.M., Michaels W.L., 2006, A multifrequency method to classify and evaluate fisheries acoustics data. Can. J. Fish. Aquat. Sci. 63, 2225–2235. [CrossRef] [Google Scholar]
  • Kloser R.J., 1996, Improved precision of acoustic surveys of benthopelagic fish by means of deep-towed transducer. ICES J. Mar. Sci. 53, 407–413. [CrossRef] [Google Scholar]
  • Kloser R.J., Ryan T., Sakov A., Williams A., Koslow J.A., 2002, Species identification in deep water using multiple acoustic frequencies. Can. J. Fish. Aquat. Sci. 59, 1065–1077. [CrossRef] [Google Scholar]
  • Korneliussen R., Ona E., 2003, Synthetic echograms generated from relative frequency response. ICES J. Mar. Sci. 60, 636–640. [CrossRef] [Google Scholar]
  • Korneliussen R.J., Diner N., Ona E., Berger L., Fernandes P.G., 2008, Proposals for the collection of multifrequency acoustic data. ICES J. Mar. Sci. 65, 982–994. [CrossRef] [Google Scholar]
  • Koslow, J.A. 2009, The role of acoustics in ecosystem-based fishery management. ICES J. Mar. Sci. 66, 966–973. [Google Scholar]
  • Lecornu L., Burdin V., Scalabrin C., Hamitouche C., 1998, Fish school analysis from multibeam sonar image processing. Oceans 98 Conference, Nice, pp. 587–591. [Google Scholar]
  • Lurton X., 2000, Swath bathymetry using phase difference: Theoretical analysis of acoustical measurement precision. IEEE J. Ocean. Engine. 25, 351–363. [CrossRef] [Google Scholar]
  • Lurton, X., 2002, An introduction to underwater acoustics: Principles and applications. Springer Verlag. [Google Scholar]
  • MacLennan D.N., Copland P.J., Armstrong E., Simmonds E.J., 2004, Experiments on the discrimination of fish and seabed echoes. ICES J. Mar. Sci. 61, 201–210. [CrossRef] [Google Scholar]
  • Massé J., Rouxel C., 1991, Improvement in acoustic assessments by discrimination of pelagic shoals with INES/MOVIES system. ICES Annual Science Conference, CM 1991/B:26. [Google Scholar]
  • Massé J., 1996, Acoustic observations in the Bay of Biscay: schooling, vertical distribution, species assemblages and behaviour. Sci. Mar. 60 (Suppl. 2), 227–234. [Google Scholar]
  • Massé J., Koutsikopoulos C., Patty W., 1996, The structure and spatial distribution of pelagic fish schools in multispecies clusters: an acoustic study. ICES J. Mar. Sci. 53, 155–160. [CrossRef] [Google Scholar]
  • Massé J., Gerlotto F., 2003, Introducing nature in fisheries research: the use of underwater acoustics for an ecosystem approach of fish population. Aquat. Living Resour. 16, 107–112. [CrossRef] [Google Scholar]
  • Moreno G., Josse E., Brehmer P., Nøttestadd L., 2007, Echotrace classification and spatial distribution of pelagic fish aggregations around drifting fish aggregating devices (DFAD). Aquat. Living Resour. 20, 343–356. [CrossRef] [EDP Sciences] [Google Scholar]
  • Ona E., Mitson R.B., 1996, Acoustic sampling and signal processing near the seabed: the deadzone revisited. ICES J. Mar. Sci. 53, 677–690. [CrossRef] [Google Scholar]
  • Ona E., Godø O.R., Handegard N.O., Hjellvik V., Patel R., Pedersen G., 2007, Silent research vessels are not quiet. J. Acoust. Soc. Am. 121, 145–150. [Google Scholar]
  • Ona E., Mazauric V., Andersen L.N., 2009, Calibration methods for two scientific multibeam systems. ICES J. Mar. Sci. 66, 1326–1334. [CrossRef] [Google Scholar]
  • Opderbecke J., Laframboise J.-M., 2007, AUVs for oceanographic science at IFREMER, project progress and operational feedback. Oceans 2007 Conference, Vancouver, Vol. 1–5, pp. 356–360. [Google Scholar]
  • Orlowski A., 2005, Experimental verification of the acoustic characteristics of the clupeoid diel cycle in the Baltic. ICES J. Mar. Sci. 62, 1180–1190. [CrossRef] [Google Scholar]
  • Patel R., Handegard N.O., Godø O.R., 2004, Behaviour of herring (Clupea harengus L.) towards an approaching autonomous underwater vehicle. ICES J. Mar. Sci. 61, 1044–1049. [CrossRef] [Google Scholar]
  • Petitgas P., Reid D., Carrera P., Iglesias M., Georgakarakos S., Liorzou B., Massé, J., 2001, On the relation between schools, clusters of schools, and abundance in pelagic fish stocks. ICES J. Mar. Sci. 58, 1150–2001. [CrossRef] [Google Scholar]
  • Petitgas P., Massé J., Beillois P., Lebarbier E., Le Cann, A., 2003, Sampling variance of species identification in fisheries-acoustic surveys based on automated procedures associating acoustic images and trawl hauls. ICES J. Mar. Sci. 60, 437–445. [CrossRef] [Google Scholar]
  • Petitgas P., Massé J., Bourriau P., Bellois P., Bergeron J.-P., Delmas D., Herbland A., Koueta N., Froidefond M., Santos, M., 2006, Hydro-plankton characteristics and their relationship with sardine and anchovy distributions on the French shelf of the Bay of Biscay. Sci. Mar. 70S1, 161–172. [Google Scholar]
  • Planque B., Lazure P., Jégou A.-M., 2004, Detecting hydrological landscapes over the Bay of Biscay continental shelf in spring. Climate Res. 28, 41–52. [CrossRef] [Google Scholar]
  • Rigaud V., 2007, Innovation and operation with robotized underwater systems. J. Field Robotics 24, 449–459. [CrossRef] [Google Scholar]
  • Scalabrin C., Massé J., 1993, Acoustic detection of the spatial and temporal distribution of fish shoals in the Bay of Biscay. Aquat. Living Resour. 6, 269–283. [CrossRef] [EDP Sciences] [Google Scholar]
  • Scalabrin C., Diner N., Weill A., Hillion A., Mouchot M.C., 1996, Narrowband acoustic identification of monospecific fish schools. ICES J. Mar. Sci. 53, 181–188. [CrossRef] [Google Scholar]
  • Scalabrin C., Diner N., Veron G., Choqueuse D., Sanchez F., 2005, Autonomous bottom moored acoustic observatory for fisheries resources monitoring. Oceans 2005 Conference, Brest, Vol. 1–2. [Google Scholar]
  • Scalabrin C., Marfia C., Boucher J., 2009, How much fish is hidden in surface and bottom acoustic blind zones? ICES J. Mar. Sci. 66, 1355–1363. [Google Scholar]
  • Simmonds E.J., MacLennan D.N., 2005, Fisheries acoustics. Theory and practice. 2nd edition. Blackwell, Oxford. [Google Scholar]
  • Soria M., Bahri T., Gerlotto F., 2003, Effect of external factors (environment and survey vessel) on fish school characteristics observed by echosounder and multibeam sonar in the Mediterranean Sea. Aquat. Living Resour. 16, 145–157. [CrossRef] [EDP Sciences] [Google Scholar]
  • Totland A., Johansen G.O., Godø O.R., Ona E., Torkelsen T., 2009, Quantifying and reducing the surface blind zone and the seabed dead zone using new technology. ICES. Mar. Sci. 66, 1370–1376. [CrossRef] [Google Scholar]
  • Trenkel V.M., Mazauric V., Berger L., 2008, The new multibeam fisheries echosounder ME70: description and expected contribution to fisheries research. ICES J. Mar. Sci. 65, 645–655. [CrossRef] [Google Scholar]
  • Trevorrow M.V., 2005, The use of moored inverted echo sounders for monitoring meso-zooplankton and fish near the ocean surface. Can. J. Fish. Aquat. Sci. 62, 1004–1018. [CrossRef] [Google Scholar]
  • Trygonis V., Georgakarakos S., Simmonds E.J., 2009, An operational system for automatic school identification on multi-beam sonar echoes. ICES J. Mar. Sci. 66, 935–949. [CrossRef] [Google Scholar]
  • Villalobos H., 2008, Évolution de l'écosystème pélagique du golfe de Gascogne pendant la période 1990-2003. Conséquences sur la capturabilité des espèces. PhD, Université de Bretagne Occidentale. [Google Scholar]
  • Weill A., Scalabrin C., Diner N., 1993, MOVIES-B: an acoustic detection description software. Application to shoal species' classification. Aquat. Living Resour. 6, 255–267. [Google Scholar]
  • Zwolinski J., Morais A., Marques V., Stratoudakis Y., Fernandes P.G., 2007, Diel variation in the vertical distribution and schooling behaviour of sardine (Sardina pilchardus) off Portugal. ICES J. Mar. Sci. 64, 963–972. [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.