Geostatistical tools for assessing sampling designs applied to a Portuguese bottom trawl survey field experience

Ernesto Jardim; Paulo J. Ribeiro Jr.

doi:10.3989/scimar.2008.72n4623

Authors

Ernesto Jardim Instituto Nacional de Investigação Agrária e das Pescas
Paulo J. Ribeiro Jr. Laboratório de Estatística e Geoinformação, Universidade Federal do Paraná

DOI:

https://doi.org/10.3989/scimar.2008.72n4623

Keywords:

model-based geostatistics, hake, sampling design, bottom trawl survey

Abstract

We present a bottom trawl survey (BTS) field experience carried out off the Portuguese Continental shelf to test two sampling designs proposals previously analysed by simulation which implement a hybrid random-systematic and a systematic sampling strategy. We used a common base regular grid covering the survey area and overlapped it with the existent random design to build the hybrid design while the systematic design added a set of regular locations at smaller distances creating four denser sampling areas. We use hake (Merluccius merluccius) abundance and model-based geostatistics to compute measures such as mean abundance, µ, and the 95% percentile, p95, which summarise the areal behaviour; coverage of the prediction confidence interval, ξ, to assess the adequacy of the model; and a modified generalised cross validation index, p, to evaluate prediction precision. The hybrid design showed a lower coefficient of variation for µ (11.89% against 13.25%); a slightly higher coefficient of variation for p95 (11.31% against 11.09%); similar ξ (0.94); and lower π (16.32 against 18.82). We conclude that the hybrid design performs better, our procedure for building it can be used to adjust BTS designs to modern geostatistical techniques, and the statistics used constitute valuable tools for assessing BTS performance.

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References

Anonymous. – 2002. Report of the International Bottom Trawl Survey Working Group. ICES CM 2002/D:03.

Anonymous. – 2003. Report of the International Bottom Trawl Survey Working Group. ICES CM 2003/D:05.

Anonymous. – 2004. Report of the International Bottom Trawl Survey Working Group. ICES CM 2004/D:05.

Anonymous. – 2005a. Report of the International Bottom Trawl Survey Working Group. ICES CM 2005/D:05.

Anonymous. – 2005b. Report of the Workshop on Survey Design and Data Analysis. ICES CM 2005/ACFM:11.

Anonymous. – 2006. Report of the International Bottom Trawl Survey Working Group. ICES CM 2006/D:05.

Ault, J., G. Diaz, S. Smith, J. Luo and J. Serafy. – 1999. An Efficient Sampling Survey Design to Estimate Pink Shrimp Population Abundance in Biscayne Bay, Florida. North. Am. J. Fish. Manage., 19: 696-712. doi:10.1577/1548-8675(1999)019<0696:AESSDT>2.0.CO;2

Cerviño, S. and F. Saborido-Rey. – 2006. Using bootstrap to investigate the effects of varying tow lengths and catch sampling schemes in fish survey. Fish. Res., 79: 294-302. doi:10.1016/j.fishres.2006.03.021

Chiles, J.P. and P. Delfiner. – 1999. Geostatistics: Modeling Spatial Uncertainty. Wiley, New York.

Christensen, O., P. Diggle and P. Ribeiro Jr. – 2001. Analysing positive-valued spatial data: the transformed Gaussian model. In: P. Monestiez, D. Allard and Froidevaux (eds.), GeoENV III - Geostatistics for Environmental Applications, pp. 287-298.

Cochran, W. – 1960. Sampling Techniques. John Wiley and Sons, New York.

Cooper, A., A. Rosenberg, B. Stefánson and M. Mangel. – 2004. Examining the importance of consistency in multi-vessel trawl survey design based on the U.S. west coast groundfish bottom trawl survey. Fish. Res., 70: 239-250. doi:10.1016/j.fishres.2004.08.006

Cressie, N. – 1993. Statistics for spatial data. John Wiley and Sons, New York.

Diggle, P. and S. Lophaven. – 2006. Bayesian geostatistical design. Scand. J. Stat., 33: 55-64. doi:10.1111/j.1467-9469.2005.00469.x

Diggle, P. and P. Ribeiro Jr. – 2007. Model-based Geostatistics. Springer, New York.

Diggle, P., J. Tawn and R. Moyeed. – 1998. Model-based geostatistics (with discussion). J. R. Stat. Soc. Ser. C-Appl. Stat., 47: 299-350. doi:10.1111/1467-9876.00113

Folmer, O. and M. Pennington. – 2000. A statistical evaluation of the design and precision of the shrimp trawl survey off West Greenland. Fish. Res., 49: 165-178. doi:10.1016/S0165-7836(00)00196-X

Hastie, T., R. Tibshirani and J. Friedman. – 2001. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer, New York.

Hata, D. and J. Berkson. – 2004. Factors Affecting Horseshoe Crab Limulus polyphemus Trawl Survey Design. Trans. Am. Fish. Soc., 133: 292-299. doi:10.1577/02-145

Isaaks, E. and M. Srivastava. – 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York.

Jardim, E. and P. Ribeiro Jr. – 2007. Geostatistical Assessment of Sampling Designs for Portuguese Bottom Trawl Surveys. Fish. Res., 85: 239-247. doi:10.1016/j.fishres.2007.02.014

Kimura, D.K. and D.A. Somerton. – 2006. Review of Statistical Aspects of Survey Sampling for Marine Fisheries. Rev. Fish. Sci., 14, 3: 245-283. doi:10.1080/10641260600621761

Mendes, J.M., K.F. Turkman and E. Jardim. – 2007. A Bayesian hierarchical model for over-dispersed count data: a case study for abundance of hake recruits. Environmetrics, 18, 1: 27-53. doi:10.1002/env.800

Müller, W. – 2001. Collecting Spatial Data - Optimum Design of Experiments for Random Fields. Physica-Verlag, Heidelberg, 2nd ed.

Petitgas, P. – 2001. Geostatistics in fisheries survey design and stock assessment models, variance and applications. Fish Fish., 2: 231-249. doi:10.1046/j.1467-2960.2001.00047.x

Ripley, B. – 1981. Spatial Statistics. Wiley, New Jersey.

Schnute, J. and R. Haigh. – 2003. A simulation model for designing groundfish trawl surveys. Can. J. Fish. Aquat. Sci., 60: 640-656. doi:10.1139/f03-041

Simard, Y., P. Legendre, G. Lavoie and D. Marcotte. – 1992. Mapping, Estimating Biomass, and Optimizing Sampling Programs for Spatially Autocorrelated Data: Case Study of the Northern Shrimp (Pandalus borealis). Can. J. Fish. Aquat. Sci., 49: 32-45.

Smith, S. and S. Gavaris. – 1993. Improving the Precision of Abundance Estimates of Eastern Scotian Shelf Atlantic Cod from Bottom Trawl Surveys. North Am. J. Fish. Mange., 13: 35-47. doi:10.1577/1548-8675(1993)013<0035:ITPOAE>2.3.CO;2

Sousa, P., M. Azevedo and M. Gomes. – 2005. Demersal assemblages off Portugal: Mapping, seasonal, and temporal patterns. Fish. Res., 75: 120-137. doi:10.1016/j.fishres.2005.03.012

Sousa, P., R. Lemos, M. Gomes and M. Azevedo. – 2007. Analysis of horse mackerel, blue whiting, and hake catch data from Portuguese surveys (1989-1999) using an integrated GLM approach. Aquat. Living Resour., 20: 105-116. doi:10.1051/alr:2007021

Stelzenmüller, V., S. Ehrich and G. Zauke. – 2005. Impact of additional small-scale survey data on the geostatistical analyses of demersal fish species in the North Sea. Sci. Mar., 69: 587-602.

Thompson, S.K. – 1992. Sampling. Wiley, New York.

Wieland, K. and M. Storr-Paulsen. – 2006. Effect of tow duration on catch rate and size composition of Northern shrimp (Pandallus borealis) and Greenland halibut (Reinhardtius hippoglossoides) in the West Greenland Bottom Trawl Survey. Fish. Res., 78: 276-285. doi:10.1016/j.fishres.2006.01.004

Zhu, Z. and M. Stein. – 2006. Spatial Sampling Design for Prediction With Estimated Parameters. J. Agric. Biol. Environ. Stat., 11: 24-44. doi:10.1198/108571106X99751

Zimmerman, D. – 2006. Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction. Environmetrics, 17: 635-652. doi:10.1002/env.769

Zimmermann, M., M. Wilkins, K. Weinberg, R. Lauth and F. Shaw. – 2003. Influence of improved performance monitoring on the consistency of a bottom trawl survey. ICES J. Mar. Sci., 60: 818-826. doi:10.1016/S1054-3139(03)00043-2