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

Dong Liang, Genevieve Nesslage, Michael Wilberg and Thomas Miller ()
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Dong Liang: University of Maryland Center for Environmental Science
Genevieve Nesslage: University of Maryland Center for Environmental Science
Michael Wilberg: University of Maryland Center for Environmental Science
Thomas Miller: University of Maryland Center for Environmental Science

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 4, No 4, 497 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13253-017-0295-4

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