No CrossRef data available.
Article contents
Selecting the number of transects in multispecies acoustic surveys in northern Chile using a surface occupation index
Published online by Cambridge University Press: 23 October 2009
Abstract
An empirical approach was used to determine the sample size of transects in acoustic surveys to estimate the abundance of three pelagic species in northern Chile. Relationships between the coefficient of variation of fish density and modified degree of coverage were established, where the modified degree of coverage is proportional to the distance sailed and the surface occupation index (Co) of the species and inversely related to the square root of the study area. From this relationship, an equation was obtained to estimate the number of transects required in order to obtain a predefined level of precision, given a known level of occupation. The surface occupation index corrects the degree of coverage and explains to a large degree the differences in the estimated coefficients of variation among the different species. Results showed that sample size declined with an increase in the surface occupation index of the species, and the magnitude of that reduction was appreciably greater for higher levels of precision. The number of transects must be limited to sample sizes with a minimum transect separation in order to assure independence between transect densities. The empirical procedure used for the estimation of the number of transects can be applied to other species situations if the information is available from previous surveys, since the approach only requires repeated echo integrator surveys.
- Type
- Research Article
- Information
- Copyright
- © EDP Sciences, IFREMER, IRD, 2009
References
Aglen A., 1989, Empirical results on precision-effort relationships for acoustic surveys. ICES CM 1989/B: 30.
Barange, M., Coetzee, J.C., Twatwa, N.M., 2005, Strategies of space occupation by anchovy and sardine in the southern Benguela: the role of stock size and intra-species competition. ICES J. Mar. Sci.
62, 645-654.
CrossRef
Barange, M., Hampton, I., 1997, Spatial structure of co-occurring anchovy and sardine populations from acoustic data: implications for survey design. Fish. Oceanogr.
6, 94-108.
CrossRef
Castillo J., Blanco J., Braun M., Reyes H., Robotham H, 1994, Evaluación directa del stock de sardina española, anchoveta y jurel (regiones I a IV). Tech. Rep. Fish. Fund. Instituto de Fomento Pesquero, Chile.
Castillo, J., Robotham, H., 2004, Spatial structure and geometry of schools of sardine (Sardinops sagax) in relation to abundance, fishing effort, and catch in northern Chile. ICES J. Mar. Sci.
61, 1113-1119.
CrossRef
Cochran W.G., 1977, Sampling techniques, 3rd edn. J. Wiley & Sons, New York.
Cressie N.A.C., 1993, Statistics for spatial data. Revised edn. John Wiley & Sons, New York.
Foote K., Knudsen H., Vestnes G., MacLennan D., Simmonds J., 1987, Calibration of acoustic instruments for fish density estimation: a practical guide. ICES Coop. Res. Rep. 144.
Francis, R., 1985, Two acoustics surveys of pelagic fish in Hawke Bay, New Zealand, 1980. N.Z. J. Mar. Freshw. Res.
19, 375-389.
CrossRef
Gerlotto, F., Stequert. B., 1983, Une méthode de simulation pour étudier la distribution des densités de poissons : Application à deux de ces réels. FAO Fish. Rep.
300, 278-292.
Griffith, D., 2005, Effective geographic sample size in the presence of spatial autocorrelation. Ann. Assoc. Am. Geogr.
95, 740-760.
CrossRef
Hampton, I., 1996, Acoustic and egg-production estimates od South African anchovy biomass over a decade: comparisons, accuracy, and utility. ICES J. Mar. Sci.
53, 493-500.
CrossRef
Jolly, G.M., Hampton, J., 1990, A stratified random transect for acoustic survey of fish stocks. Can. J. Fish. Aquat. Sci.
47, 1282-1291.
CrossRef
ICES 2005, Report of the workshop on survey design and data analysis (WKSAD), 9-13 May 2005, Sète. ICES CM 2005/B:07.
Kimura, D., Lemberg, K. N.A., 1981, Variability of line intercept density estimates (a simulation study of the variance of hydroacoustic biomass estimates). Can. J. Fish. Aquat. Sci.
38, 1141-1152.
CrossRef
Kish L., 1965, Survey sampling. Wiley, New York.
Liu, G., Liang, K., 1997, Sample size calculations for studies with correlated observations. Biometrics
93, 937-947.
CrossRef
MacLennan, D.N., Mackenzie, I.G., 1988, Precision of acoustics fish stock estimates. Can. J. Fish. Aquat. Sci.
45, 605-616.
CrossRef
Matheron G., 1971, The theory of regionalized variables and their applications. Cah. Cent. Morphol. Math. Fontainebleau. cCullagh P., Nelder J., 1995, Generalized linear models, 2nd. Chapman & Hall, London.
Petitgas P., 1991, Contributions géostatistiques à la biologie des pêches maritimes. École Nationale Supérieure des Mines Paris, Thèse Dr Géostatistique.
Petitgas, P., 1993, Geostatiscs for fish stock assessments: a review and acoustic application. ICES J. Mar. Sci.
50, 285-298.
CrossRef
Petitgas P., Prampart A., 1995, EVA Estimation variance. A geostatistical software for structure characterization and variance computation. Editions ORSTOM, Paris.
Schabenberger O., Gotway C.A., 2005, Statistical methods for spatial data analysis. Chapman & Hall, CRC.
Self, S., Mauritsen, R., 1988, Power/sample size calculations for generalized linear models. Biometrics
44, 79-86.
CrossRef
Simmonds E.J., Williamson N., Gerlotto F., Aglen A., 1992, Acoustic survey design and analysis procedure: A comprehensive review of current practice. ICES Coop. Res. Rep. 187.
Williamson, N.J., 1982, Cluster sampling estimation of the variance of abundance estimates derived from quantitative echo sounder surveys. Can. J. Fish. Aquat. Sci.
30, 229-231.
CrossRef