Volume 54

Visual Assessment of Queen Conch (Strombus gigas) Stocks in the Turks and Caicos Islands: Cross-checking Yield Estimates


Authors
Clerveaux, W.; Danylchuk, A.J.
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Date: November, 2001


Pages: 250-258


Event: Proceedings of the Fifty Fourth Annual Gulf and Caribbean Fisheries Institute


City: Providenciales Turks & Caicos Islands


Country: Turks and Caicos Islands

Abstract

Once abundant throughout it's range, queen conch is currently threatened in many regions. In response to concerns that it's status is the result of overfishing, fisheries legislation has been enacted in many countries to help regulate harvest rates.\To further ensure the sustainability of this important resource, queen conch was placed on Appendix n of the Convention on the International Trade of Endangered Species (CITES) in 1992. To satisfy CITES requirements, a modified version of the Schaefer model was used in the Turks and Caicos Islands during the 1993/1994 licensing year to model historical catch and effort data, estima te catch per unit effort (CPUE), and provide the basis for an annual Total Allowable Catch (TAC).However, since 1994 the model has consistently underestimated CPUE. To crosscheck yield estimates derived from the modified Schaefer model, we conducted a visual assessment of queen conch stocks across the Caicos Bank. Surveys were carried out in traditionally fished areas using replica te belt transects al randomly selected sites. All queen conch encountered were enumerated and placed into size/age categories based on siphonallength and lip thickness. In total, 170 sites were surveyed between October 2000 and August 2001, and nearly 4000 queen conch enumerated. Our results indicate that the yield estimate derived using the modified Schaefer model is very close (i.e., within <1 %) to those generated using the biomass estimate from our visual surveys, and within 10% of those estimated using the standard Schaefer and Fox surplus production models. As such, we conclude that the modified Schaefer model is producing reasonable yield estimates, but the catch and effort data need to be examined closely to determine why the model is underestimating CPUE.

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