Effects of Aging Uncertainty on the Estimation of Growth Functions of Major Tuna Species
Author:
Lu Dongqi1, Lin Qinqin1, Zhu Jiangfeng123, Zhang Fan1ORCID
Affiliation:
1. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China 2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China 3. Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
Abstract
Fishery stock assessment requires accurate specification of the growth function of target species, and aging uncertainty is an important factor that affects the estimation of growth parameters. In this study, we used simulations to study the effects of two types of aging uncertainty, aging error and sampled age range, on the parameter estimation of the Von Bertalanffy growth function, including asymptotic length (L∞), growth coefficient (k), and theoretical age in the year at zero length (t0) of five important tuna species. We found that the uncertainty of the estimated growth curves increased with increasing aging errors. When aging errors were fixed among ages, the effects of age range on estimation error of growth parameters were different among species and growth parameters. When the aging error increased with age, the estimation uncertainty of L∞ and k was the greatest when only young age groups were sampled, while the estimation uncertainty of t0 was the greatest when only old age groups were sampled. Therefore, reducing the aging error and sampling individuals with a wider age range are important for increasing the accuracy and decreasing the uncertainty of the estimated growth function, which will further reduce the uncertainty in fishery stock assessment.
Funder
the National Natural Science Foundation of China Project on the Survey and Monitor-Evaluation of Global Fishery Resources sponsored by the Ministry of Agriculture and Rural Affairs
Subject
Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics
Reference58 articles.
1. Clear, N., Francis, M., Tsuji, S., Krusic-Golub, K., Itoh, T., Tzeng, W.N., Sutton, C., Findlay, J., Hirai, A., and Shiao, J.C. (2002, January 11–14). A manual for age determination of southern bluefin tuna Thunnus maccoyii, Otolith sampling, preparation and interpretation. Proceedings of the Direct Age Estimation Workshop CCSBT, Victoria, Australia. 2. Shimose, T., and Farley, J.H. (2015). Biology and Ecology of Bluefin Tuna, CRC Press. 3. Dortel, E., Massiot-Granier, F., Rivot, E., Million, J., Hallier, J.P., Morize, E., Munaron, J.-M., Bousquet, N., and Chassot, E. (2013). Accounting for age uncertainty in growth modeling, the case study of yellowfin tuna (Thunnus albacares) of the Indian Ocean. PLoS ONE, 8. 4. Fast versus slow growing tuna species: Age, growth, and implications for population dynamics and fisheries management;Murua;Rev. Fish Biol. Fish.,2017 5. Modelling fish growth: Model selection, multi-model inference and model selection uncertainty;Katsanevakis;Fish. Res.,2006
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