Estimation of growth curves is a critical component of fish stock assessments. Two widely used otolith sampling methods, the age–length key (ALK) sampling method and the random otolith sampling (ROS) method, have problems that limit their utility for estimating growth curves. First, growth curves based on the ALK method are biased in that otolith samples obtained with the ALK are not selected via simple random sampling. Second, the precision and accuracy of growth curves based on the ROS method are often compromised because random sampling frequently results in a small number of older fish samples. In this study, bias in growth curves based on ALK data that were re‐sampled from a simulated data set describing king mackerel Scomberomorus cavalla was corrected with a new reweighting technique. This technique reweighted the growth curves with the length‐frequency distribution of randomly resampled fork length data. The resulting growth curves were compared with growth curves obtained from ROS data to determine which method (reweighted ALK sampling or ROS) was more appropriate for selecting otolith samples for the estimation of growth curves. The results showed that the reweighted growth curves constructed from ALK samples were more precise and accurate than growth curves obtained from ROS data for all sample sizes examined because the reweighted ALK growth curves (1) had less variability in the estimated growth parameters, (2) decreased the probability of drawing wrong conclusions about a fish stock, and (3) provided greater accuracy and precision in predicting mean lengths at age. Results from this study and a previous study support the view that the ALK sampling method is more efficient than the ROS method when otolith samples are used for the determination of king mackerel age composition and growth curves.
The aims of this study were to (1) compare the efficiency of two widely used otolith sampling methods: the random otolith sampling method (ROS) and the age-length key sampling method (ALK); (2) explore whether a new otolith sampling method, the reweighting method (REW), provides more efficiency than either the ROS or ALK methods; and (3) incorporate the concept of effective sample size into sampling design. The REW differs from the ALK in that the REW has larger sampling intervals and reweights the estimated age-frequency distribution with the length-frequency distribution instead of building an age-length key. Bootstrapping approaches were used to analyze the precision of age-frequency distributions derived with these three methods (1) when otolith samples were sampled directly from a simulated population of king mackerel Scomberomorus cavalla and (2) when king mackerel samples were collected via two-stage cluster sampling, which is the sampling method commonly used in the field. The results showed that when samples were selected directly from the simulated king mackerel population the age-frequency distributions derived from the REW samples had slightly better precision than those estimated from the ROS and ALK samples. However, when cluster sampling was used the efficiency of the REW and ALK greatly exceeded that of the ROS. This is because the nonindependence among fish samples in individual trips makes the effective sample sizes much smaller than the actual sample sizes. The problem of nonindependence of otolith samples can be avoided when the ALK or REW is used. Overcoming the nonindependence problem of the ROS requires a number of otolith samples that can exceed the number needed for the ALK or REW by several times. Thus, the ROS is the least cost-effective among the three sampling methods examined, while the REW provides the best combination of efficiency and flexibility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.