Bibliographic citations
Gozzer, R., (2015). Diversidad genética y estructura poblacional del perico (Coryphaena hippurus, Linnaeus 1758) en el Pacífico Peruano [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/1912
Gozzer, R., Diversidad genética y estructura poblacional del perico (Coryphaena hippurus, Linnaeus 1758) en el Pacífico Peruano [Tesis]. : Universidad Nacional Agraria La Molina; 2015. https://hdl.handle.net/20.500.12996/1912
@misc{renati/242985,
title = "Diversidad genética y estructura poblacional del perico (Coryphaena hippurus, Linnaeus 1758) en el Pacífico Peruano",
author = "Gozzer Wuest, Renato",
publisher = "Universidad Nacional Agraria La Molina",
year = "2015"
}
Mahi mahi (Coryphaena hippurus) is an opportunistic and cosmopolitan top predator, which presents fast growth and maturity, as well as high fertility. Annual landings in Peru between 2008 and 2012 remained around 50% of the world total. Despite the importance of this fishery both for Peru’s food safety as for its exports, it still does not have a management plan and fish stocks have not yet been scientifically defined, which is the basis to identify and establish the population(s) to be managed in the first place. In this study, 241 tissue samples were collected in seven localities of the Peruvian coast (Sechura, Huanchaco, Chimbote, Casma, Végueta, South of Ica and South of Tacna), between January and February of 2014. To measure genetic diversity and population structure from these samples, five highly polymorphic microsatellite loci were amplified and genotyped for each individual, and indices of genetic diversity and population structure were calculated. The results showed high genetic diversity, indicating that in Peru mahi mahi has good genetic health. The pairwise Fst analysis of populations and analysis of molecular variance - AMOVAs showed a subtle but significant population structure (Fst = 0.009 and p-value = 0.000). Tacna Sur showed significant levels of differentiation with respect to other locations (0.0093 < Fst < 0.0287 y p-value = 0.000). Bayesian analysis of population structure suggested allocation into two clusters. The principal component analysis (PCA) failed in the grouping components, showing no evidence of the existence of more than one subpopulation.
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