The effects of high detection probabilities on model selection in paired release-recapture studies in the era of electronic tagging studies

Animal Biotelemetry

John R Skalski¹  Adam G Seaburg¹  Rebecca A Buchanan¹ 

[1] School of Aquatic & Fishery Sciences, University of Washington, 1325 Fourth Avenue, Suite 1820, Seattle, WA, USA

关键词: Model selection;    Parsimony;    Radio tags;    Acoustic tags;    Tagging studies;   

Others  :  791087
DOI  :  10.1186/2050-3385-1-12

 received in 2013-04-03, accepted in 2013-07-19,  发布年份 2013

【 摘 要 】

Background

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Skalski et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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