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Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys

  • Dong Liang
  • Genevieve Nesslage
  • Michael Wilberg
  • Thomas Miller
Article

Abstract

Abundance and standard error estimates in surveys of fishery resources typically employ classical design-based approaches, ignoring the influences of non-design factors such as varying catchability. We developed a Bayesian approach for estimating abundance and associated errors in a fishery survey by incorporating sampling and non-sampling variabilities. First, a zero-inflated spatial model was used to quantify variance components due to non-sampling factors; second, the model was used to calibrate the estimated abundance index and its variance using pseudo empirical likelihood. The approach was applied to a winter dredge survey conducted to estimate the abundance of blue crabs (Callinectes sapidus) in the Chesapeake Bay. We explored the properties of the calibration estimators through a limited simulation study. The variance estimator calibrated on posterior sample performed well, and the mean estimator had comparable performance to design-based approach with slightly higher bias and lower (about 15% reduction) mean squared error. The results suggest that application of this approach can improve estimation of abundance indices using data from design-based fishery surveys.

Keywords

Auxiliary information Empirical likelihood Integrated Nested Laplace Approximation (INLA) Model-assisted approach Survey design Index standardization Variance estimation 

Notes

Acknowledgements

We thank the Maryland Department of Natural Resources (MDNR) and the Virginia Institute of Marine Science for conducting the Chesapeake Bay blue crab winter dredge survey and G. Davis (MDNR), L. Fegley (MDNR), S. Iverson (Virginia Marine Resource Commission), and A. Sharov (MDNR) for providing the data. Two anonymous reviewers provided helpful comments. The work was funded by Grant CBSAC4 from the Chesapeake Bay Trust. This is contribution number 5383 of the University of Maryland Center for Environmental Science.

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Copyright information

© International Biometric Society 2017

Authors and Affiliations

  • Dong Liang
    • 1
  • Genevieve Nesslage
    • 2
  • Michael Wilberg
    • 2
  • Thomas Miller
    • 2
  1. 1.Environmental Statistics Collaborative, Chesapeake Biological LaboratoryUniversity of Maryland Center for Environmental ScienceSolomonsUSA
  2. 2.Chesapeake Biological LaboratoryUniversity of Maryland Center for Environmental ScienceSolomonsUSA

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