摘要：Acoustic-tag studies with their high to very high detection rates defy traditional statistical wisdom regarding analysis of tagging studies. Conventional wisdom has been to use a parsimonious model with the fewest parameters that adequately describes the data to estimate survival parameters in release-recapture studies in order to find a reasonable trade-off between precision and accuracy. This quest has generated considerable debate in the statistical community on how to best accomplish this task. Among the debated options are likelihood ratio tests, Bayesian information criterion, Akaike information criterion, and model averaging. Our Monte Carlo simulation studies of paired release-recapture, acoustic-tag investigations indicate precision is the same if a fully parameterized or a reduced parameter model is used for data analysis if detection probabilities are very high. In addition, the fully parameterized model is robust to heterogeneous survival and detection processes, while a reduced parameter model may be sensitive to misspecification. Use fully parameterized, paired release-recapture models when detection probabilities are very high (≥0.90) to analyze acoustic-tagging data in order to retain both robustness and precision, and without the subjectivity and ambiguity introduced by the choice and application of model selection techniques.
关键词：Tagging studies ; Acoustic tags ; Radio tags ; Parsimony ; Model selection
