Reproductive performance of Nile Tilapia, Oreochromis niloticus, gift strain as a function of water temperature

• Grant number: 20/06349-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2020
• End date: June 30, 2021
• Field of knowledge: Agronomical Sciences - Fishery Resources and Fishery Engineering - Aquaculture
• Principal Investigator: Rafael Vilhena Reis Neto
• Grantee: Fabio Augusto Paiva Camisa Nova
• Host Institution: Universidade Estadual Paulista (UNESP). Campus de Registro. Registro , SP, Brazil

Abstract

The proposal aims to evaluate the influence of water temperature on the reproductive performance of GIFT Tilapia females on a particular farm in three reproductive cycles. Initially, a database will be used with information on environmental variables and reproductive parameters collected in the 2010/2011 and 2011/2012 cycles. For a second stage, the same information will be used, but collected in the reproductive cycle of 2020/2021. The breeders evaluated in 2010/2011 and 2011/2012 are from the 4th generation of GIFT in Brazil and were acquired in 2008, for the collection of the 2020/2021 cycle, females of the 2nd generation of the original animals will be evaluated. During the reproductive period, animals are stored in tanks with a male/female ratio of 1: 3. The collection of eggs from the mothers' mouths, which are identified by microchips, occurs every five days. The collected eggs are measured in beakers of 100mL to estimate of the number of eggs, and, later, are incubated artificially in incubators installed in a water recirculation system with controlled temperature and oxygen. The dissolved oxygen levels and water temperature are measured daily in each tank in the morning. The spawning interval, volume and number of eggs and hatching rate will be analyzed. Data obtained will be submitted to Cox Regression analysis to test the effects of the year of collection, water temperature levels and the interaction of these two factors. Later, regression models will be adjusted between the response variables and the effects determined as significant by Cox Regression.