Free Access
Issue |
Aquat. Living Resour.
Volume 31, 2018
|
|
---|---|---|
Article Number | 12 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/alr/2017048 | |
Published online | 20 February 2018 |
- Álvarez-Borrego S. Physical, chemical, and biological oceanography of the Gulf of California, in: C.R. Brusca (Ed.), The Gulf of California: biodiversity and conservation, University of Arizona Press, Tucson, AZ, 2010, pp. 24–48. [Google Scholar]
- Anderson CIH, Horne JK, Boyle J. 2007. Classifying multi-frequency fisheries acoustic data using a robust probabilistic classification technique. JASA Express Lett 121: EL230–EL237. [Google Scholar]
- Benaglia T, Chauveau D, Hunter DR. 2009a. An em-like algorithm for semi-and nonparametric estimation in multivariate mixtures. J Comput Graphi Stat 18: 505–526. [CrossRef] [Google Scholar]
- Benaglia T, Chauveau D, Hunter DR, Young D. 2009b. mixtools: an R package for analyzing mixture models. J Stat Softw 32: 1–29. [CrossRef] [Google Scholar]
- Berger L, Durand C, Marchalot C, Diner N. 2005. Movies + user manual version 4.3, Tech. Rep. DNIS/ESI/DLE/DTI/00-051, IFREMER. [Google Scholar]
- Burgos JM, Horne JK. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES J Mar Sci 64: 160–168. [Google Scholar]
- Churnside JH, Demer DA, Mahmoudi B. 2003. A comparison of lidar and echosounder measurements of fish schools in the Gulf of Mexico. ICES J Mar Sci 60: 147–154. [CrossRef] [Google Scholar]
- Clayden J. 2017. mmand: mathematical morphology in any number of dimensions. R package version 1.5.0. URL https://CRAN.R-project.org/package=mmand [Google Scholar]
- De Robertis A, Higginbottom I. 2007. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J Mar Sci 64: 1282–1291. [CrossRef] [Google Scholar]
- Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc Ser B (Methodol.) 39: 1–38. [Google Scholar]
- Diner N. 2001. Correction on school geometry and density: approach based on acoustic image simulation. Aquat Living Resour 14: 211–222. [CrossRef] [Google Scholar]
- Domínguez-Contreras JF, Robinson CJ, Gómez-Gutierrez J. 2012. Hydroacoustical survey of near-surface distribution, abundance and biomass of small pelagic fish in the Gulf of California. Pac Sci 66: 311–326. [CrossRef] [Google Scholar]
- Eckmann R. 1998. Allocation of echo integrator output to small larval insect (Chaoborus sp.) and medium-sized (juvenile fish) targets. Fish Res 35: 107–113. [CrossRef] [Google Scholar]
- Fablet R, Gay P, Peraltilla S, Peña C, Castillo R, Bertrand A. 2012. Bags-of-features for fish school cluster characterization in pelagic ecosystems: application to the discrimination of juvenile and adult anchovy (Engraulis ringens) clusters off Peru. Can J Fish Aquat Sci 69: 1329–1339. [CrossRef] [Google Scholar]
- Fässler SM, Brunel T, Gastauer S, Burggraaf D. 2016. Acoustic data collected on pelagic fishing vessels throughout an annual cycle: operational framework, interpretation of observations, and future perspectives. Fish Res 178: 39–46. [CrossRef] [Google Scholar]
- Fernandes P, Korneliussen R, Lebourges-Dhaussy A, Massé J, Iglesias M, Diner N, Ona E, Knutsen T, Gajate J, Ponce R. 2006. The SIMFAMI project: species identification methods from acoustic multi-frequency information, Tech. rep., Final Report to the EC no. Q5RS- 2001-02054. [Google Scholar]
- Gasca R, Suárez E. 1991. Nota sobre los sifonóforos (Cnidaria: Siphonophora) del Golfo de California (agosto-septiembre, 1977). Cienc Pesq Mex 8: 119–125. [Google Scholar]
- Gastauer S, Scoulding B, Fässler SMM, Benden DPLD, Parsons M. 2016. Target strength estimates of red emperor (Lutjanus sebae) with Bayesian parameter calibration. Aquat Living Resour 29: 301. [CrossRef] [Google Scholar]
- Gonzalez RC, Woods RE. Digital image processing, 3rd Edition, Pearson Prentice Hall, Upper Saddle River, NJ, 2008. [Google Scholar]
- Gregg MC, Horne JK. 2009. Turbulence, acoustic backscatter, and pelagic nekton in Monterey Bay. J Phys Oceanogr 39: 1097–1114. [CrossRef] [Google Scholar]
- Helfman GS. 1986. Fish behaviour by day, night and twilight, in: T.J. Pitcher (Ed.), The behaviour of Teleost fishes, 1st Edition, Croom Helm Ltd., London, pp. 366–387. [CrossRef] [Google Scholar]
- ICES. 2005. Description of the ICES hac standard data exchange format, version 1.60, Tech. Rep. 278, ICES Cooperative Research Report. [Google Scholar]
- Jech JM, Michaels WL. 2006. A multifrequency method to classify and evaluate fisheries acoustics data. Can J Fish Aquat Sci 63: 2225–2235. [CrossRef] [Google Scholar]
- Kristensen K. 2017. readHAC: read Acoustic HAC Format, R package version 1.0. URL https://CRAN.R-project.org/package=readHAC [Google Scholar]
- Lawson GL, Barange M, Fréon P. 2001. Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES J Mar Sci 58: 275–287. [CrossRef] [Google Scholar]
- MacLennan DN, Fernandes PG, Dalen J. 2002. A consistent approach to definitions and symbols in fisheries acoustics. ICES J Mar Sci 59: 365–369. [CrossRef] [Google Scholar]
- Madureira LS, Everson I, Murphy EJ. 1993. Interpretation of acoustic data at two frequencies to discriminate between antarctic krill (Euphausia superba Dana) and other scatterers. J Plankton Res 15: 787–802. [CrossRef] [Google Scholar]
- McLachlan GJ, Basford KE. Mixture models: inference and applications to clustering, Vol. 84 of Statistics: textbooks and monographs, Marcel Dekker, New York, 1988. [Google Scholar]
- McLachlan GJ, Peel D. Finite mixture models, Wiley Series in Probability and Statistics, John Wiley & Sons, New York, 2000. [Google Scholar]
- Melvin GD, Gerlotto F, Lang C, Trillo P. 2016. Fishing vessels as scientific platforms: an introduction. Fish Res 178: 1–3. [CrossRef] [Google Scholar]
- Nevárez-Martínez MO, Martínez-Zavala M, Jacob-Cervantes ML, Cotero-Altamirano CE, Santos-Molina JP, Valdez-Pelayo A. Peces pelágicos menores, in: L.F.J. Beléndez-Moreno, E. Espino-Barr, G. Galindo-Cortes, M.T. Gaspar-Dillanes, L. Huidobro-Campos, E. Morales-Bojórquez (Eds.), Sustentabilidad y Pesca Responsable en México, Evaluación y Manejo, 1st Edition, SAGARPA − Instituto Nacional de Pesca, Mexico City, 2014, pp. 87–139. [Google Scholar]
- Parker-Stetter SL, Rudstam L, Sullivan P, Warner D. Standard operating procedures for fisheries acoustic surveys in the Great Lakes, Great Lakes Fisheries Commission Special Publication, Ann Arbor, MI, 2009. [Google Scholar]
- Peltonen H, Balk H. 2005. The acoustic target strength of herring (Clupea harengus l.) in the northern Baltic Sea. ICES J Mar Sci 62: 803–808. [CrossRef] [Google Scholar]
- Petitgas P, Diner N, Georgakarakos S, Reid D, Aukland R, Massé J, Scalabrin C, Iglesias M, Muiño R, Carrera-López P. 1998. Sensitivity analysis of school parameters to compare schools from different surveys: a review of the standardisation task of the EC-FAIR programme CLUSTER. ICES Documents CM 1998/J: 23. [Google Scholar]
- Quiñonez-Velázquez C, Nevárez-Martínez MO, Gluyas-Millán MG. 2000. Growth and hatching dates of juvenile Pacific sardine Sardinops caeruleus in the Gulf of California. Fish Res 48: 99–106. [CrossRef] [Google Scholar]
- . R Core Team. R: a language and environment for statistical computing, R foundation for statistical computing, Vienna, Austria, 2017. URL https://www.R-project.org/ [Google Scholar]
- Reid DG. Report on echo trace classification, ICES cooperative research report 238, ICES, Copenhagen, Denmark, 2000. [Google Scholar]
- Robinson CJ, Gómez-Aguirre S, Gómez-Gutiérrez J. 2007. Pacific sardine behaviour related to tidal current dynamics in Bahía Magdalena, México. J Fish Biol 71: 200–218. [CrossRef] [Google Scholar]
- Sato M, Horne JK, Parker-Stetter SL, Keister JE. 2015. Acoustic classification of coexisting taxa in a coastal ecosystem. Fish Res 172: 130–136. [CrossRef] [Google Scholar]
- Simmonds J, MacLennan D. Fisheries acoustics: theory and practice, Fish and aquatic resources series, 2nd Edition, Blackwell Science Ltd., Ames, Iowa, 2005. [Google Scholar]
- Stanton TK, Wiebe PH, Chu D, Benfield MC, Scanlon L, Martin L, Eastwood RL. 1994. On acoustic estimates of zooplankton biomass. ICES J Mar Sci 51: 505–512. [CrossRef] [Google Scholar]
- Stanton TK, Chu D, Wiebe PH. 1996. Acoustic scattering characteristics of several zooplankton groups. ICES J Mar Sci 53: 289–295. [CrossRef] [Google Scholar]
- Trevorrow MV, Mackas DL, Benfield MC. 2005. Comparison of multifrequency acoustic and in situ measurements of zooplankton abundances in Knight Inlet, British Columbia. J Acoust Soc Am 117: 3574–3588. [CrossRef] [PubMed] [Google Scholar]
- Villalobos H, López-Serrano A, Nevárez-Martínez MO. 2018. Volume backscattering strength samples and echograms (38 kHz) associated to small pelagic fish schools in the Gulf of California, SEANOE, Mexico, http://doi.org/10.17882/53034. [Google Scholar]
- Warren J, Stanton T, Benfield M, Wiebe P, Chu D, Sutor M. 2001. In situ measurements of acoustic target strengths of gas-bearing siphonophores. ICES J Mar Sci 58: 740–749. [CrossRef] [Google Scholar]
- Woillez M, Ressler PH, Wilson CD, Horne JK. 2012. Multifrequency species classification of acoustic-trawl survey data using semi-supervised learning with class discovery. J Acoust Soc Am 131: EL184–E L190. [CrossRef] [PubMed] [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.