Estimating fish abundance from acoustic surveys: Calculating variance due to acoustic backscatter and length distribution error

Canadian Journal of Fisheries and Aquatic Sciences

Volumn 66, Issue 12, 2009, Pages 2081-2095

  Zwolinski, Juan ^a,c   Fernandes, Paul G ^b   Marques, Vítor ^a   Stratoudakis, Yorgos ^a  

^a INSTITUTO PORTUGUÊS DO MAR E DA ATMOSFERA   (Portugal)

^b MARINE LABORATORY   (United Kingdom)

^c SOUTHWEST FISHERIES SCIENCE CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABUNDANCE; ACOUSTIC SURVEY; BACKSCATTER; BIOMASS; CONFIDENCE INTERVAL; POPULATION ESTIMATION; POPULATION MODELING; TRAWLING;

EURASIA; EUROPE; IBERIAN PENINSULA; SOUTHERN EUROPE;

EID: 70849130509     PISSN: 0706652X     EISSN: None     Source Type: Journal    
DOI: 10.1139/F09-138     Document Type: Article

References (59)

1. Aitchison, J. 1955. On the distribution of positive random variables having a discrete probability mass at the origin. J. Am. Stat. Assoc. 50(271): 901-908. doi:10.2307/2281175.
2. Barry, S., and Welsh, A. 2002. Generalized additive modelling and zero inflated count data. Ecol. Model. 157(2-3): 179-188. doi:10.1016/S0304-3800(02)00194-1.
3. Borchers, D.L., Buckland, S., Priede, I., and Ahmadi, S. 1997. Improving the precision of the daily egg production method using generalized additive models. Can. J. Fish. Aquat. Sci. 54(12): 2727-2742. doi:10.1139/cjfas-54-12-2727.
4. Carrera, P., and Porteiro, C. 2003. Stock dynamic of Iberian sardine (Sardina pilchardus, W.) and its implication on the fishery off Galicia (NW Spain). Sci. Mar. 67: 245-258.
5. Chilès, J.-P., and Delfiner, P. 1999. Geostatistics - modelling spatial uncertainty. 1st ed. Wiley Series in Probability and Statistics, John Wiley & Sons Inc., New York.
6. Cochran, W. 1977. Sampling techniques. 3rd ed. John Wiley & Sons Inc., New York.
7. Conners, M.E., and Schwager, S.J. 2002. The use of adaptive cluster sampling for hydroacoustics surveys. ICES J. Mar. Sci. 59(6): 1314-1325. doi:10.1006/jmsc.2002.1306.
8. Cressie, N. 1991. Statistics for spatial data. 1st ed. John Wiley & Sons Inc., New York.
9. Demer, D. 2004. An estimate of error for the CCAMLR 2000 survey estimate of krill biomass. Deep Sea Res. Part II Top. Stud. Oceanogr. 51: 1237-1251.
10. Foote, K. 1983. Linearity of fisheries acoustics, with addition theorems. J. Acoust. Soc. Am. 73(6): 1932-1940. doi:10.1121/1.389583.
11. Foote, K., Knudsen, H., Vestnes, G., MacLennan, D., and Simmonds, E. 1987. Calibration of acoustic instruments for fish density estimation: a practical guide. ICES Coop. Res. Rep. No. 144.
12. Fréon, P., and Misund, O. 1999. Dynamics of pelagic fish distribution and behaviour: effects on fisheries and stock assessment. 1st ed. Fishing News Books, Oxford, UK.
13. Fréon, P., Cury, P., Shannon, L., and Claude, R. 2005. Sustainable exploitation of small pelagic fish stocks challenged by environmental and ecosystem changes: a review. Bull. Mar. Sci. 76: 385-462.
14. Gimona, A., and Fernandes, P. 2003. A conditional simulation of acoustic survey data: advantages and potential pitfalls. Aquat. Living Resour. 16(3): 123-129. doi:10.1016/S0990-7440(03) 00028-7.
15. Guisan, A., and Zimmermann, N. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135(2-3): 147-186. doi:10.1016/S0304-3800(00)00354-9.
16. Harrell, F.E., Jr., Lee, K.L., and Mark, D.B. 1996. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4): 361-387. doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.PMID:8668867.
17. International Council for the Exploration of the Sea. 1993. Report of the Workshop on the Applicabilty of Spatial Techniques to Acoustic Survey Data. ICES Coop. Res. Rep. No. 195.
18. International Council for the Exploration of the Sea. 1998. Report of the Planning Group for Acoustic Surveys in ICES Sub-areas VIII and IX. ICES CM 1998/G:2.
19. International Council for the Exploration of the Sea. 2001. Report of the Working Group on the Assessment of Mackerel, Horse Mackerel, Sardine, and Anchovy. ICES CM 2001/ACFM:6.
20. International Council for the Exploration of the Sea. 2004. Report of the Working Group on Survey Design and Data Analysis. ICES CM 2004/D:05.
21. International Council for the Exploration of the Sea. 2005. Report of the Working Group on Survey Design and Data Analysis. ICES CM 2005/B:07.
22. International Council for the Exploration of the Sea. 2006a. Report of the Working Group on Acoustic and Egg Surveys for Sardine and Anchovy in ICES Areas VIII and IX. ICES CM 2006/ LRC:01.
23. International Council for the Exploration of the Sea. 2006b. Report of the Working Group on the Assessment of Mackerel, Horse Mackerel, Sardine, and Anchovy. ICES CM 2006/ACFM:36.
24. International Council for the Exploration of the Sea. 2008. Report of the Working Group on Fisheries Acoustic Science and Technology (WGFAST). ICES CM 2008/FTC:08.
25. Jolly, G.M., and Hampton, I. 1990. A stratified random transect design for acoustic surveys of fish stocks. Can. J. Fish. Aquat. Sci. 47: 1282-1291. doi:10.1139/f90-147.
26. Kappenman, R. 1999. Trawl survey based abundance estimation using data sets with unusually large catches. ICES J. Mar. Sci. 56(1): 28-35. doi:10.1006/jmsc.1998.0422.
27. Mackinson, S., Nøttestad, L., Guénette, S., Pitcher, T., Misund, O., and Ferno, A. 1999. Cross-scale observations on distribution and behavioural dynamics of ocean feeding Norwegian spring-spawning herring (Clupea harengus L.). ICES J. Mar. Sci. 56(5): 613-626. doi:10.1006/jmsc.1999.0513.
28. MacLennan, D.N., and MacKenzie, I.G. 1988. Precision of acoustic fish stock estimates. Can. J. Fish. Aquat. Sci. 45: 605-616. doi:10.1139/f88-074.
29. McCullagh, P., and Nelder, J. 1997. Generalized linear models. 2nd ed. Monographs on Statistics and Applied Probability, Chapman & Hall, London.
30. Mood, A.M., Graybill, F.A., and Boes, D.C. 1974. Introduction to the theory of statistics. 3rd ed. McGraw-Hill, Singapore.
31. Myers, R., and Pepin, P. 1990. The robustness of log-normal based estimators of abundance. Biometrics, 46(4): 1185-1192. doi:10.2307/2532460.
32. Nakken, O., and Dommasnes, A. 1975. The application of an echo integration system in investigations of the stock strength of the Barents Sea capelin. ICES CM 1975/B:25.
33. O'Driscoll, R.L. 2004. Estimating uncertainty associated with acoustic surveys of spawning hoki (Macruronus novaezelandiae) in Cook Strait, New Zealand. ICES J. Mar. Sci. 61(1): 84-97. doi:10.1016/j.icesjms.2003.09.003.
34. Peliz, A.J, and Fiúza, A.F.G. 1999. Temporal and spatial variability of CZCS-derived phytoplankton pigment concentrations off the Western Iberian Peninsula. Int. J. Remote Sens. 20: 1363-1403. doi:10.1080/014311699212786.
35. Pennington, M. 1983. Efficient estimators of abundance, for fish and plankton surveys. Biometrics, 39(1): 281-286. doi:10.2307/2530830.
36. Pennington, M. 1996. Estimating the mean and variance from highly skewed marine survey data. Fish. Bull. (Washington, D.C.), 95: 498-505.
37. Petitgas, P. 1993. Use of a disjunctive kriging to model areas of high pelagic fish density in acoustic fisheries surveys. Aquat. Living Resour. 6(3): 201-209. doi:10.1051/alr:1993021.
38. Petitgas, P., Massé, J., Beillois, P., Lebarbier, E., and 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(3): 437-445. doi:10.1016/S1054-3139(03)00026-2.
39. R Development Core Team. 2005. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
40. Rivoirard, J., Simmonds, J., Foote, K., Fernandes, P., and Bez, N. 2000. Geostatistics for estimating fish abundance. 1st ed. Black-well Science, Oxford, UK.
41. Rose, G., Gauthier, S., and Lawson, G. 2000. Acoustic surveys in the full monte: simulating uncertainty. Aquat. Living Resour. 13: 367-372. doi:10.1016/S0990-7440(00)01074-3.
42. Silva, A., Carrera, P., Massé, J., Uriarte, A., Santos, M.B., Oliveira, P.B., Soares, E., Porteiro, C., and Stratoudakis, Y. 2008. Geographic variability of sardine growth across the northeastern Atlantic and the Mediterranean Sea. Fish. Res. 90(1-3): 56-69. doi:10.1016/j.fishres.2007.09.011.
43. Simmonds, E., and Fryer, J. 1996. Which are better, random or systematic acoustic surveys? A simulation using North Sea herring as an example. ICES J. Mar. Sci. 53(1): 39-50. doi:10.1006/jmsc.1996.0004.
44. Simmonds, E.J., and MacLennan, D.N. 2005. Fisheries acoustics: theory and practice. 2nd ed. Fish and Fisheries Series, Black-well, Oxford, UK.
45. Smith, S. 1990. Use of statistical models for the estimation of abundance from groundfish trawl data. Can. J. Fish. Aquat. Sci. 47(5): 894-903. doi:10.1139/f90-103.
46. Smith, S.J., Perry, R.I., and Fanning, L.P. 1991. Relationships between water mass characteristics and estimates of fish population abundance from trawl surveys. Environ. Monit. Assess. 17(2-3): 227-245. doi:10.1007/BF00399305.
47. Stratoudakis, Y., Bernal, M., Borchers, D., and Borges, M.F. 2003. Changes in the distribution of sardine eggs and larvae off Portugal, 1985-2000. Fish. Oceanogr. 12(1): 49-60. doi:10.1046/j. 1365-2419.2003.00222.x.
48. Su, L., Tom, B.D.M., and Farewell, V.T. 2009. Bias in 2-part mixed models for longitudinal semicontinuous data. Biostatistics, 10(2): 374-389. doi:10.1093/biostatistics/kxn044.PMID:19136448.
49. Syrjala, S. 2000. Critique on the use of the delta distribution for the analysis of trawl survey data. ICES J. Mar. Sci. 57(4): 831-842. doi:10.1006/jmsc.2000.0571.
50. Thompson, S., and Seber, G. 1996. Adaptive sampling. 1st ed. Wiley Series in Probability and Statistics, John Wiley & Sons Inc., New York.
51. Venables, W., and Dichmont, C. 2004. GLMS, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fish. Res. 70(2-3): 319-337. doi:10.1016/j.fishres.2004.08.011.
52. Venables, W.N., and Ripley, B.D. 2002. Modern applied statistics with S. 4th ed. Springer, New York.
53. Walline, P. 2007. Geostatistical simulations of eastern Bering Sea walleye pollock spatial distributions, to estimate sampling precision. ICES J. Mar. Sci. 64(3): 559-569. doi:10.1093/icesjms/fsl045.
54. Weill, A., Scalabrin, C., and Diner, N. 1993. MOVIES-B: an acoustic detection description software. Application to shoal species classification. Aquat. Living Resour. 6(3): 255-267. doi:10.1051/alr:1993026.
55. Welsh, A., Cunningham, R., Donnelly, C., and Lindenmayer, D. 1996. Modelling the abundance of rare species: statistical models for counts with extra zeros. Ecol. Model. 88(1-3): 297-308. doi:10.1016/0304-3800(95)00113-1.
56. Wood, S. 2006. Generalized additive models: an introduction with R. 1st ed. Texts in Statistical Sciences, Chapman & Hall/CRC, London, UK.
57. Wood, S.N., and Augustin, N.H. 2002. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157(2-3): 157-177. doi:10.1016/S0304-3800(02)00193-X.
58. Wood, S., and Horwood, J. 1995. Spatial distribution functions and abundances inferred from sparse noisy plankton data: an application of constrained thin plate splines. J. Plankton Res. 17(6): 1189-1208. doi:10.1093/plankt/17.6.1189.
59. Zwolinski, J., Morais, A., Marques, V., Stratoudakis, Y., and Fernandes, P. 2007. Diel variation in the vertical distribution and schooling behaviour of sardine (Sardina pilchardus) off Portugal. ICES J. Mar. Sci. 64(5): 963-972. doi:10.1093/icesjms/fsm075.