Skip to main content
Log in

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

  • Published:
Journal of Agricultural, Biological, and Environmental Statistics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Peel.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Key Words

Navigation