Quality issues in the use of otoliths for fish age estimation
A. K. Morison A E , J. Burnett B , W. J. McCurdy C and E. Moksness D
+ Author Affiliations
- Author Affiliations
A Marine and Freshwater Systems, Department of Primary Industries, PO Box 114, Queenscliff, Vic. 3225, Australia.
B National Marine Fisheries Service, Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543, USA.
C Aquatic Systems Branch, Agriculture Food and Environmental Science Division, Department of Agriculture and Rural Development for Northern Ireland, Newforge Lane, Belfast BT9 SPX, Northern Ireland, UK.
D Institute of Marine Research Arendal, Flødevigen Marine Research Station, N-4817 HIS, Norway.
E Corresponding author. Email: sandy.morison@dpi.vic.gov.au
Marine and Freshwater Research 56(5) 773-782 https://doi.org/10.1071/MF04217
Submitted: 17 August 2004 Accepted: 6 April 2005 Published: 24 July 2005
Abstract
Quality issues in fish age estimation, which historically have focused mainly on inadequacies in the validation process, are increasingly directed at ways to measure and control the errors or inconsistencies in the application of established and validated methods. The process of age estimation, as undertaken by human operators, involves a complex mix of pattern recognition and interpretation based on knowledge and experience. It is best characterised as a skill rather than an art. Such an approach promotes the use of well-recognised techniques designed to maintain and enhance skills that also assist in maintaining standards. The results of a questionnaire completed by representatives of over 50 ageing laboratories worldwide were used to assess current quality assurance and quality control practices. Results indicate a great diversity in attention to, and no clear consensus on desirable standards for, quality issues, including staff training, use of reference sets, reading protocols, and post-reading analyses. This is considered more likely to reflect variation in awareness of the importance of quality issues than variation in the need for quality assurance and quality control measures. Greater attention to a range of quality control processes is urged, particularly the more regular use of reference sets.
Extra keywords: bias, error, growth, mortality, precision, recruitment, stock assessment.
Acknowledgments
Thanks are owing to those who distributed and completed questionnaires, to Gavin Begg and members of the Steering Committee of the Third International Symposium on Fish Otolith Research and Application for the invitation to contribute a keynote address on this topic, and to past and present staff of the Central Ageing Facility, Queenscliff, Australia for sharing their experiences, knowledge, and insights.
References
Adams T. M. (2002). ‘Guide for the Estimation of Measurement Uncertainty in Testing.’ American Association for Laboratory Testing, Frederick, MD. Available online at http://www.a2la.org/guidance/est_mu_testing.pdf, verified June 2005.
Allison M. J. (1995). TQM comes to the laboratory. In ‘Proceedings of the Sixth International Symposium on the Harmonization of the Role of Laboratory Quality Assurance in Relation to Total Quality Management’. (Ed. M. Parkany.) pp. 86–92. (The Royal Society of Chemistry: Melbourne.)
Beamish, R. J. , and Fournier, D. A. (1981). A method for comparing the precision of a set of age determinations. Canadian Journal of Fisheries and Aquatic Sciences 38, 982–983.
Beamish R. J., and McFarlane G. A. (1983b). Current trends in age determination methodology. In ‘Age and Growth of Fish’. (Eds R. C. Summerfelt and G. E. Hall.) pp. 15–42. (Iowa State University: Ames, IA.)
Beamish R., and McFarlane G. A. (1995). A discussion of the importance of aging errors, and an application to walleye pollock: the world's largest fishery. In ‘Recent Developments In Fish Otolith Research’. (Eds D. H. Secor, J. M. Dean and S. E. Campana.) pp. 545–565. (University of South Carolina Press: Columbia, SC.)
Bedford, B. C. (1983). A method of preparing sections of large numbers of otoliths embedded in black polyester resin. Journal du Conseil, Conseil International pour l’Exploration de la Mer 41, 4–12.
Blumenthal T. K. (1995). Motivation and ‘marketing’ of analytical laboratory for TQM: pride of place. In ‘Proceedings of the Sixth International Symposium on the Harmonization of the Role of Laboratory Quality Assurance in Relation to Total Quality Management’. (Ed. M. Parkany.) pp. 1–7. (The Royal Society for Chemistry: Melbourne.)
Boehlert, G. W. (1985). Using objective criteria and multiple regression models for age determination in fishes. Fishery Bulletin 83, 103–117.
Hardie N. (1997). ‘The Consequences of Quality: A Critical Look at the Quality Movement.’ (Consensus Books: Sydney.)
Kalish J. M., Beamish R. J., Brothers E. B., Casselman J. M., Francis R. I. C. C., et al. (1995). Glossary for otolith studies. In ‘Recent Developments in Fish Otolith Research’. (Eds D. A. Secor, J. M. Dean and S. E. Campana.) pp. 723–729. (University of South Carolina Press: Columbia, SC.)
Kimura, D. K. (1977). Statistical assessment of the age-length key. Journal of the Fisheries Research Board of Canada 34, 317–324.
Macy W. K., III. (1995). The application of digital image processing to the aging of long-finned squid, Loligo pealei, using the statolith. In ‘Recent Developments In Fish Otolith Research’. (Eds D. H. Secor, J. M. Dean and S. E. Campana.) pp. 283–302. (University of South Carolina Press: Colombia, SC.)
McCurdy W. J., Hammer C., and Appelberg M. (1999). EFAN in the context of quality assurance and quality control. Working Document for the EFAN Annual Meeting 1999, Crete. EFAN Report 5 – 1999. European Fish Ageing Network, Institute of Marine Research, Bergen, Norway.
McGowan M. F., Prince E. D., and Lee D. W. (1987). An inexpensive microcomputer-based system for making rapid and precise counts and measurements of zonations in video displayed skeletal structures of fish. In ‘Age and Growth of Fish’. (Eds R. C. Summerfelt and G. E. Hall.) pp. 385–395. (The Iowa State Univeristy Press: Ames, IA.)
Morales-Nin B., and Panfili J. (2002). Chapter IIIA. Age estimation. In ‘Manual of Fish Sclerochronology’. (Eds J. Panfili, H. d. Pontual, H. Troadec and P. J. Wright.) pp. 89–110. (IFREMER-IRD coedition: Brest, France.)
Morales-Nin, B. , Lombarte, A. , and Japon, B. (1998). Approaches to otolith age determination: image signal treatment and age attribution. Scienta Marina 62, 247–256.
NATA (1995). Guidelines for quality control in the analytical laboratory, Australia Technical Note 23. National Association of Testing Authorities, Sydney.
Nicholson A. (1995). Customer satisfaction: a reality in analytical laboratories? In ‘Proceedings of the Sixth International Symposium on the Harmonization of the Role of Laboratory Quality Assurance in Relation to Total Quality Management’. (Ed. M. Parkany.) pp. 232–240. (The Royal Society for Chemistry: Melbourne.)
Pilling, G. M. , Grandcourt, E. M. , and Kirkwood, G. P. (2003). The utility of otolith weight as a predictor of age in the emperor Lethrinus mahsena and other tropical fish species. Fisheries Research 60, 493–506.
| Crossref | GoogleScholarGoogle Scholar |
Robertson S. G., and Morison A. K. (2003). Chapter 19. Age estimation of fish using a probabilistic neural network. In ‘Ecological Informatics: Understanding Ecology by Biologically-Inspired Computation’. (Ed. F. Recknagel.) pp. 369–382. (Springer-Verlag: New York.)
Schaalje, G. B. , Shaw, J. L. , and Belk, M. C. (2002). Using nonlinear hierarchical models for analyzing annulus-based size-at-age data. Canadian Journal of Fisheries and Aquatic Sciences 59, 1524–1532.
| Crossref | GoogleScholarGoogle Scholar |
Small G. J., and Hirschhorn G. (1987). Computer-assisted age and growth pattern recognition of fish scales using a digitizing tablet. In ‘Age and Growth of Fish’. (Eds R. C. Summerfelt and G. E. Hall.) pp. 397–410. (The Iowa State University Press: Ames, IA.)
Sych R. (1974). The sources of errors in ageing fish and considerations of the proofs of reliability. In ‘The Ageing of Fish’. (Ed. T. B. Bagenal.) pp. 78–86. (Unwin Brothers Ltd: London.)
Szedlmayer, S. T. , Szedlmayer, M. M. , and Sieracki, M. E. (1991). Automated enumeration by computer digitization of age-0 weakfish Cynoscion regalis scale annuli. Fishery Bulletin 89, 337–340.
Troadec H. (Ed.) (1998). Establishment of a digital image reference database. EFAN Report 3 – 1998. European Fish Ageing Network, Institute of Marine Research, Bergen, Norway.
Troadec H. (Ed.) (2000). Use of digital tools for exchanging annotated images of calcified structures. EFAN Report 8 – 2000. European Fish Ageing Network, Institute of Marine Research, Bergen, Norway.
Troadec H., and Benzinou A. (2002). Chapter VI. Computer-assisted age estimation. In ‘Manual of Fish Sclerochronology’. (Eds J. Panfili, H. d. Pontual, H. Troadec and P. J. Wright.) pp. 199–241. (IFREMER-IRD coedition: Brest, France.)
Tyler, A. V. , Beamish, R. J. , and McFarlane, G. A. (1989). Implications of age determination errors to yield estimates. Canadian Special Publication of Fisheries and Aquatic Science 108, 27–35.
Welleman H. C., and Storbeck F. (1995). Automatic ageing of plaice (Pleuronectes platessa L.) otoliths by means of image analysis. In ‘Recent Developments in Fish Otolith Research’. (Eds D. H. Secor, J. M. Dean and S. E. Campana.) pp. 271–282. (University of South Carolina Press: Columbia, SC.)
Worthington, D. G. , Doherty, P. J. , and Fowler, A. J. (1995). Variation in the relationship between otolith weight and age – implications for the estimation of age of two tropical damselfish (Pomacentrus moluccensis and P. wardi). Canadian Journal of Fisheries and Aquatic Sciences 52, 233–242.
Zar J. H. (1984). ‘Biostatistical Analysis.’ 2nd edn. (Prentice-Hall: Englewood Cliffs, NJ.)
