Accuracy — Validation and Corroboration
Last modified on Aug 13, 2019
It is important to realize that estimates of fish age using scales are just estimates. These estimates can be viewed with more confidence if the data can be validated or corroborated. Age validation means to compare estimated ages with known (true) ages to indicate a degree of accuracy within the dataset.
Age estimation error consists of two major types: error that affects accuracy, or the closeness of the age estimate to the true value; and error that affects precision, or the reproducibility of repeated measurements on a given structure (Kalish et al., 1995). The degree of agreement among readers is a measure of precision of the estimations and not the accuracy of the technique. These two types of error are not necessarily linked. Thus, quality control monitoring needs to assess both types of error and is an important component of an aging program (Campana et al., 1995; Morison et al., 1998).
To estimate scale age, readers use pattern recognition. Natural variation in scale growth patterns can obscure differences in these patterns, interfering with a reader's ability to discern annuli and estimate scale age. Examination of accuracy rates for age estimation is important. However, most scales are collected from fish carrying no secondary marking that validate age, such as a coded-wire tag (CWT) or passive-integrated transponder (PIT) tag, thus few reliable methods exist to assess the true scale age. Campana (2001) suggested that structures aged and agreed upon by several readers could be used in place of known- age structures to assess accuracy. To improve accuracy of age data, Buckmeier (2002) recommended:
- Procedures and equipment be standardized;
- Acceptable levels of accuracy and bias be determined;
- Reference structures be obtained and shared; and
- Readers be trained and periodically tested.
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.
Buckmeier, D.L., 2002. Assessment of reader accuracy and recommendations to reduce subjectivity in age estimation. Fisheries 27: 10-14.
Campana, S., 2001. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. Journal of Fish Biology 59: 197-242.
Campana, S.E., Annand, M.C., and McMillan, J.I. 1995. Graphical and statistical methods for determining the consistency of age determinations. Transactions of the American Fisheries Society 124: 131-138.
Chang, W.Y. 1982. A statistical method for evaluating the reproducibility of age determination. Canadian Journal of Fisheries and Aquatic Sciences 39: 1208-1210.
Kalish, J.M., Beamish, R.J., Brothers, E.B., Casselman, J.M., Francis, R.I.C.C., Mosegaard, H., Panfili, J., Prince, E.D., Thresher, R.E., Wilson, C.A., and Wright, P.J. 1995. Glossary for otolith studies. In: Secor, D.H., Dean, J.M., Campana, S.E., Miller, A.B. (Eds.), Recent Developments in Fish Otolith Research. University of South Carolina Press, Columbia, South Carolina, pp. 723-729.
Morison, A.K., Robertson, S.G., and Smith, D.C. 1998. An integrated system for production fish aging: image analysis and quality assurance. North American Journal of Fisheries Management 18: 587-598.