ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on June 30, 2008
ICES Journal of Marine Science: Journal du Conseil 2008 65(7):1248-1259; doi:10.1093/icesjms/fsn109
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The optimized interpolation of fish positions and speeds in an array of fixed acoustic receivers
1 Département de Biologie, Université Laval, QC, Canada, G1K 7P4
2 Ministère des Ressources naturelles et de la Faune du Québec, Direction de l'aménagement de la faune de l’Estrie, de Montréal et de la Montérégie, 201 Place Charles Le Moyne, Longueuil, QC, Canada, J4K 2T5
3 Ministère des Ressources naturelles et de la Faune, Direction de l'aménagement de la faune du Saguenay-Lac-Saint-Jean, 3950 Boul. Harvey, Jonquière, QC, Canada, G7X 8L6
4 Atlantic Salmon Federation, PO Box 5200, St Andrews, NB, Canada, E5B 3S8
Correspondence to R. D. Hedger: tel: +1 418 656 2681; fax:+1 418 656 2043; e-mail: richard.hedger.1{at}ulaval.ca
Hedger, R. D., Martin, F., Dodson, J, J., Hatin, D., Caron, F., and Whoriskey, F. G. 2008. The optimized interpolation of fish positions and speeds in an array of fixed acoustic receivers. – ICES Journal of Marine Science, 65: 1248–1259.The principal method for interpolating the positions and speeds of tagged fish within an array of fixed acoustic receivers is the weighted-mean method, which uses a box-kernel estimator, one of the simplest smoothing options available. This study aimed to determine the relative error of alternative, non-parametric regression methods for estimating these parameters. It was achieved by predicting the positions and speeds of three paths made through a dense array of fixed acoustic receivers within a coastal embayment (Gaspé Bay, Québec, Canada) by a boat with a GPS trailing an ultrasonic transmitter. Transmitter positions and speeds were estimated from the receiver data using kernel estimators, with box and normal kernels and the kernel size determined arbitrarily, and by several non-parametric methods, i.e. a kernel estimator, a smoothing spline, and local polynomial regression, with the kernel size or smoothing span determined by cross-validation. Prediction error of the kernel estimator was highly dependent upon kernel size, and a normal kernel produced less error than the box kernel. Of the methods using cross-validation, local polynomial regression produced least error, suggesting it as the optimal method for interpolation. Prediction error was also strongly dependent on array density. The local polynomial regression method was used to determine the movement patterns of a sample of tagged Atlantic salmon (Salmo salar) smolt and kelt, and American eel (Anguilla rostrata). Analysis of the estimates from local polynomial regression suggested that this was a suitable method for monitoring patterns of fish movement.
Keywords: acoustic telemetry, American eel, Atlantic salmon, optimized interpolation, smolt and kelt
Received 21 November 2007; accepted 11 May 2008; advance access publication 30 June 2008.