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The species composition and number of sharks used by the shark fin trade were estimated from a partial set of daily auction records for the world's largest shark fin trading centre in Hong Kong for the period October 1999 to March 2001. More than 10 000 lot descriptions of shark type, fin position, fin size and fin weight were translated and statistically modeled using Bayesian Markov Chain Monte Carlo methods (WinBUGS). These methods allowed a robust estimation of missing information in individual auction records, as well as of entire auctions for which no data are available, through a hierarchical model with uninformative priors. The model provides estimates of the complete data set for the sampled period, including the total auctioned weights of fins by shark type and fin position. Separate studies, undertaken in Hong Kong to genetically map trade names to species names, are being used to align the estimates with particular taxa. This paper demonstrates how the traded quantity estimates can be converted to the weight and number of sharks represented based on preliminary conversion factors from the literature and from this research. A potentially more robust Bayesian conversion algorithm, involving fin size-classes and stochastic relationships between fin lengths and fin weights, is outlined for future implementation.

Estimates of Shark Species Composition and Numbers Associated with the Shark Fin Trade Based on Hong Kong Auction Data