Conventional “spaghetti” tags and tags originally designed for “sport fishing” and called Betyp tags, were used during a tuna tagging program conducted on board Dakar baitboats in 1999. With the aim of comparing the recapture rate of both types of tags, additional information obtained from previous tagging trips are used in a Bayesian context to set up an informative prior for conventional tags. We show in this study (1) how to account for the sampling uncertainty in the construction of the Beta prior with a likelihood method, and (2) how a simulation-based alternative can be useful for performing the probability density function of the difference between two posterior recapture rates. On the light of the resulting simulated difference we found that Betyp tags have a very strong negative effect on the return rate of bigeye tuna (−19.6% on average). For skipjack the strength of evidence concerning the decrease in recapture rate due to the implementation of Betyp tags (−3.2%) was supported only at a 10% level. The Bayesian approach is compared with the conventional “frequentist” approach and with a likelihood method allowing for the integration of previous information. The results obtained from different approaches indicate that the choice of the method, as well as the choice of the prior, does not modify the conclusion of the study. Potential causes for explaining the lowest efficiency of Betyp tags are discussed.

Suppose that it is desired to determine the frequency, known to lie in a range 0 < σ ≦ ω ≦ ΩT <∞, of a periodic effect modulating the rate of occurrence of a Poisson point process. It is shown that if ΩT increases sufficiently slowly with T, the length of the observation period, then the frequency corresponding to the maximum of the Bartlett periodogram over this range provides a consistent estimate of the unknown frequency. The asymptotic covariance matrix is found for the maximum likelihood estimates of the parameters in a cyclic Poisson model, and the close analogue with frequency estimation for Gaussian processes is emphasized. It is pointed out, however, that in the point-process case periodogram estimates will be efficient only for very special models.