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A Model-Based Approach to Designing a Fishery-Independent Survey

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Abstract

This paper uses Generalized Additive Models to evaluate model-based designs for wildlife abundance surveys where substantial pre-existing data are available. This is often the case in fisheries with historical catch and effort data. Compared to conventional stratified design or design-based designs, our model-based designs can be both efficient and flexible, for example in allowing uneven sampling due to survey logistics, and providing a general framework to answer specific design questions. As an example, we describe the design and preliminary implementation of a trawl survey for eleven fish species along the continental slope off South-East Australia.

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References

  • Arbia, G., and Lafratta, G. (2002), “Anisotropic Spatial Sampling Designs for Urban Pollution,” Journal of the Royal Statistical Society Series C, 51, 223–234.

    Article  MathSciNet  MATH  Google Scholar 

  • Breslow, N., and Clayton, D. (1993), “Approximate Inference in Generalized Linear Mixed Models,” Journal of the American Statistical Association, 88, 9–25.

    MATH  Google Scholar 

  • Britt, L., and Martin, M. (2001), “Data Report: 1999 Gulf of Alaska Bottom Trawl Survey. US Department of Commerce,” NOAA Technical Memorandum NMFS-AFSC-121.

  • Brus, D. J., and deGruijter, J. J. (1997), “Random Sampling or Geostatistical Modelling? Choosing Between Design-Based and Model-Based Sampling Strategies for Soil (with Discussion),” Geoderma, 80, 1–44.

    Article  Google Scholar 

  • Brus, D. J., and Heuvelink, G. (2007), “Optimization of Sample Patterns for Universal Kriging of Environmental Variables,” Geoderma, 138, 86–95.

    Article  Google Scholar 

  • Candy, S. G. (2004), “Modelling Catch and Effort Data Using Generalised Linear Models, the Tweedie Distribution, Random Vessel Effects and Random Stratum-by-Year Effects,” CCAMLR Science, 11, 59–80.

    MathSciNet  Google Scholar 

  • Cotter, A. (2001), “Intercalibration of North Sea International Bottom Trawl Surveys by Fitting Year-Class Curves,” ICES Journal of Marine Science, 58, 622–632.

    Article  Google Scholar 

  • Curtis, A. (1999), “Optimal Design of Focused Experiments and Surveys,” Geophysical Journal International, 139, 205–215.

    Article  Google Scholar 

  • Diggle, P., and Lophaven, S. (2006), “Bayesian Geostatistical Design,” Scandinavian Journal of Statistics, 33, 53–64.

    Article  MathSciNet  MATH  Google Scholar 

  • Doubleday, W. (1981), Manual on Groundfish Surveys in the Northwest Atlantic. Northwest Atlantic Fisheries Organization.

    Google Scholar 

  • Dunn, P. K. (2009), “Improving Comparisons Between Models for CPUE,” Fisheries Research, 97, 148–149.

    Article  Google Scholar 

  • Dunn, P. K., and Smyth, G. K. (1996), “Randomized Quantile Residuals,” Journal of Computational and Graphical Statistics, 5, 236–244.

    Google Scholar 

  • — (2001), “Tweedie Family Densities: Methods of Evaluation,” in Proceedings of the 16th International Workshop on Statistical Modelling, Odense, Denmark, pp. 155–162.

    Google Scholar 

  • — (2005), “Series Evaluation of Tweedie Exponential Dispersion Model Densities,” Statistics and Computing, 15, 267–280.

    Article  MathSciNet  Google Scholar 

  • Gao, H. Y., Wang, J. H., and Zhao, P. D. (1996), “The Updated Kriging Variance and Optimal Sample Design,” Mathematical Geology, 28, 295–313.

    Article  MathSciNet  MATH  Google Scholar 

  • Hastie, T., and Tibshirani, R. (1986), “Generalized Additive Models (with Discussion),” Statistical Science, 1, 297–318.

    Article  MathSciNet  Google Scholar 

  • — (1990), Generalized Additive Models, London: Chapman and Hall.

    MATH  Google Scholar 

  • Jolly, G., and Hampton, I. (1990), “A Stratified Random Transect Design for Acoustic Surveys of Fish Stocks,” Canadian Journal of Fisheries and Aquatic Sciences, 47, 1282–1291.

    Article  Google Scholar 

  • Jørgensen, B. (1987), “Exponential Dispersion Models,” Journal of the Royal Statistical Society Series B, 49, 127–162.

    Google Scholar 

  • — (1997), Theory of Dispersion Models, London: Chapman and Hall.

    Google Scholar 

  • Michaelsen, J., Schimel, D. S., Friedl, M. A., Davis, F. W., and Dubayah, R. C. (1994), “Regression Tree Analysis of Satellite and Terrain Data to Guide Vegetation Sampling and Surveys,” Journal of Vegetation Science, 5, 673–686.

    Article  Google Scholar 

  • Minami, M., Lennert-Cody, C. E., Gao, W., and Roman-Verdesoto, M. (2007), “Modeling Shark Bycatch: The Zero-Inflated Negative Binomial Regression Model with Smoothing,” Fisheries Research, 84, 210–221.

    Article  Google Scholar 

  • Oehlert, G. (1992), “A Note on the Delta Method,” The American Statistician, 46, 27–29.

    MathSciNet  Google Scholar 

  • Pennington, M. (1986), “Some Statistical Techniques for Estimating Abundance Indices from Trawl Surveys,” Fishery Bulletin, 84, 519–525.

    Google Scholar 

  • Petitgas, P. (2001), “Geostatistics in Fisheries Survey Design and Stock Assessment: Models, Variance and Applications,” Fish and Fisheries, 2, 231–249.

    Article  Google Scholar 

  • R Core Development Team (2007), “R: A Language and Environment for Statistical Computing,” Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org.

  • Rivoirard, J., Simmonds, J., Foote, K., Fernandes, P., and Bez, N. (2000), Geostatistics for Estimating Fish Abundance, Oxford: Blackwell Science.

    Book  Google Scholar 

  • Schnute, J., and Haigh, R. (2003), “A Simulation Model for Designing Groundfish Trawl Surveys,” Canadian Journal of Fisheries and Aquatic Sciences, 60, 640–656.

    Article  Google Scholar 

  • Shono, H. (2008), “Application of the Tweedie Distribution to Zero-Catch Data in CPUE Analysis,” Fisheries Research, 93, 154–162.

    Article  Google Scholar 

  • Silverman, B. (1985), “Some Aspects of the Spline Smoothing Approach to Non-parametric Regression Curve Fitting,” Journal of the Royal Statistical Society. Series B, 47, 1–52.

    MATH  Google Scholar 

  • Smith, A., and Smith, D. (2001), “A Complex Quota-managed Fishery: Science and Management in Australia’s South-East Fishery. Introduction and Overview,” Marine and Freshwater Research, 52, 353–360.

    Article  Google Scholar 

  • Smyth, G. K., and Verbyla, A. P. (1999), “Adjusted Likelihood Methods for Modelling Dispersion in Generalized Linear Models,” Environmetrics, 10, 695–709.

    Article  Google Scholar 

  • van den Berg, J., Curtis, A., and Trampert, J. (2003), “Optimal Nonlinear Bayesian Experimental Design: An Application to Amplitude Versus Offset Experiments,” Geophysical Journal International, 155, 411–421.

    Article  Google Scholar 

  • van Groenigen, J. W., and Stein, A. (1998), “Constrained Optimization of Spatial Sampling Using Continuous Simulated Annealing,” Journal of Environmental Quality, 27, 1078–1086.

    Article  Google Scholar 

  • Williams, A., Kloser, R., Barker, B., Bax, N., and Butler, A. (2005), “A Seascape Perspective for Managing Deep Sea Habitats,” in “Deep Sea”: Conference on the Governance and Management of Deep-Sea Fisheries, Vol. FAO Fisheries Proceedings of 3, Rome: FAO, pp. 89–97.

    Google Scholar 

  • Wood, S. (2006), Generalized Additive Models: An Introduction with R, London/Boca Raton: Chapman and Hall/CRC Press.

    MATH  Google Scholar 

  • — (2011), “Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73, 3–36. doi:10.1111/j.1467-9868.2010.00749.x

    Article  MathSciNet  Google Scholar 

  • Xiao, Y. (2004), “Use of Individual Types of Fishing Effort in Analyzing Catch and Effort Data by Use of a Generalized Linear Model,” Fisheries Research, 70, 311–318.

    Article  Google Scholar 

  • Zhu, Z. Y., and Stein, M. L. (2006), “Spatial Sampling Design for Prediction with Estimated Parameters,” Journal of Agricultural Biological and Environmental Statistics, 11, 24–44.

    Article  Google Scholar 

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Correspondence to D. Peel.

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Peel, D., Bravington, M.V., Kelly, N. et al. A Model-Based Approach to Designing a Fishery-Independent Survey. JABES 18, 1–21 (2013). https://doi.org/10.1007/s13253-012-0114-x

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  • DOI: https://doi.org/10.1007/s13253-012-0114-x

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