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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on February 25, 2009
ICES Journal of Marine Science: Journal du Conseil 2009 66(4):708-719; doi:10.1093/icesjms/fsp022
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© United States Government, NOAA 2009.

Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic

M. Aaron MacNeil1,2, John K. Carlson2 and Lawrence R. Beerkircher3

1 National Research Council, Panama City Laboratory, 3500 Delwood Beach Road, Panama City Beach, FL 32408, USA
2 NOAA National Marine Fisheries Service, Panama City Laboratory, 3500 Delwood Beach Road, Panama City Beach, FL 32408, USA
3 NOAA National Marine Fisheries Service, Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, FL 33149, USA

Correspondence to M. A. MacNeil: Present address: Australian Institute of Marine Science, PMB 3 Townsville MC, Townsville, QLD, Australia: tel: +1 850 234 6541 ext. 257; fax: +1 850 235 3559; e-mail: macneil{at}glau.ca.

MacNeil, M. A., Carlson, J. K., and Beerkircher, L. R. 2009. Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic. – ICES Journal of Marine Science, 66: 708–719.

A suite of modelling approaches was employed to analyse shark depredation rates from the US Atlantic pelagic longline fishery. As depredation events are relatively rare, there are a large number of zeroes in pelagic longline data and conventional generalized linear models (GLMs) may be ineffective as tools for statistical inference. GLMs (Poisson and negative binomial), two-part (delta-lognormal and truncated negative binomial, T-NB), and mixture models (zero-inflated Poisson, ZIP, and zero-inflated negative binomial, ZINB) were used to understand the factors that contributed most to the occurrence of depredation events that included a small proportion of whale damage. Of the six distribution forms used, only the ZIP and T-NB models performed adequately in describing depredation data, and the T-NB and ZINB models outperformed the ZIP models in bootstrap cross-validation estimates of prediction error. Candidate T-NB and ZINB model results showed that encounter probabilities were more strongly related to large-scale covariates (space, season) and that depredation counts were correlated with small-scale characteristics of the fishery (temperature, catch composition). Moreover, there was little evidence of historical trends in depredation rates. The results show that the factors contributing to most depredation events are those already controlled by ships' captains and, beyond novel technologies to repel sharks, there may be little more to do to reduce depredation loss in the fishery within current economic and operational constraints.

Keywords: bycatch, fisheries, zero-inflated models

Received 19 June 2008; accepted 24 January 2009; advance access publication 25 February 2009.


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