期刊
ICES JOURNAL OF MARINE SCIENCE
卷 72, 期 2, 页码 508-514出版社
OXFORD UNIV PRESS
DOI: 10.1093/icesjms/fsu155
关键词
gonadosomatic index; logistic multinomial model; maturity ogive; maturity staging; reproduction
资金
- Conicyt-Fondecyt [3130425]
- IFOP-Chile
The proportion of mature fish at age or length is one of the most important population attributes in assessing reproductive potential. This proportion is usually named the maturity ogive. The most crucial step in estimating this proportion deals with maturity staging assessed by macroscopic or histology analysis. Macroscopic analysis is relatively inexpensive but usually introduces large amount of error. Histology is the most accurate method for maturity staging but is expensive and time consuming. Here, we propose using the gonadosomatic index (GSI) as an alternative way to estimate the maturity ogives. Alogistic multinomial model was implemented to separate immature, mature-active, and mature-inactive fish, based only on their value of GSI. We evaluated the performance of the GSI-based method by comparing the results with ogives estimated from macroscopic and histological staging using the extensive database available for Chilean hake (Merluccius gayi gayi). Maturity ogives from GSI analysis were evaluated at the start and end of the reproductive season. Results showed that, in all cases analysed, maturity ogives from GSI were closer to the ogives based on histology than those from macroscopic staging. Comparing across periods, those maturity ogives computed at the start of the reproductive season give estimates very similar to those from histological staging. To have unbiased estimates of maturity ogives from GSI analysis, we recommend using data from the start of the reproductive season to minimise the frequency of spent fish. In addition, the assumption of the isometry between gonad and gutted weight across maturity stages needs to be tested before the use of this GSI method. The analyses presented here provide a promising method to estimate maturity ogives when histological staging data are lacking or when macroscopic analysis is suspected to have large amounts of errors.
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