Fish Silage as Replacement of Fishmeal in Red Tilapia Feeds

We estimated the effect on growth and nutrient efficiency of replacing fishmeal with silage incorporated with rice bran in diets for fingerling red tilapia (Oreochromis mossambicus × Oreochromis niloticus × Oreochromis aureus) over 12 weeks. Isonitrogenous (300 g kg−1 protein dry matter basis) and isoenergetic (4450 Kcal gross energy kg−1) feed formulations with increasing levels of tilapia silage as a replacement for fishmeal were prepared: Diet 1 with no silage (0 g Kg−1), Diet 2 (250 g Kg−1), Diet 3 (500 g Kg−1), and Diet 4 (750 g Kg−1). Feed intake was similar among Diets 1, 2, and 3, while Diet 4 had a significantly lower intake. There was no significant difference (P > 0.05) in weight gain or specific growth rate (SGR), feed conversion ratio (FCR), and protein efficiency ratio (PER), among fish fed Diets 1, 2, and 3. Fish fed with Diet 4 had significantly lower weight gain; SGR and PER and significantly higher FCR. Organoleptic properties of the fish were not affected by the diets. The results of this study indicate that less expensive dried fish silage with rice bran is an alternative protein source for tilapia feed up to 50% of fishmeal replacement.

Investigation of an emerging bacterial disease in wild Queensland groper, marine fish and stingrays with production of diagnostic tools to reduce the spread of disease to other states of Australia : final report

This project describes how Streptococcus agalactiae can be transmitted experimentally in Queensland grouper. The implications of this research furthers the relatedness between Australian S. agalactiae strains from animals and humans. Additionally, this research has developed diagnostic tools for Australian State Veterinary Laboratories and Universities, which will assist in State and National aquatic animal disease detection, surveillance, disease monitoring and reporting

Hazard Function Models to Estimate Mortality Rates Affecting Fish Populations with Application to the Sea Mullet (Mugil cephalus) Fishery on the Queensland Coast (Australia)

Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .

Physical Oceanographic Influences on Queensland Reef Fish and Scallops : Final Report FRDC 2013/020

The project examined coastal and physical oceanographic influences on the catch rates of coral trout (Plectropomus leopardus) and saucer scallops (Amusium balloti) in Queensland. The research was undertaken to explain variation observed in the catches, and to improve quantitative assessment of the stocks and management advice. 3.1 OBJECTIVES 1. Review recent advances in the study of physical oceanographic influences on fisheries catch data, and describe the major physical oceanographic features that are likely to influence Queensland reef fish and saucer scallops. 2. Collate Queensland’s physical oceanographic data and fisheries (i.e. reef fish and saucer scallops) data. 3. Develop stochastic population models for reef fish and saucer scallops, which can link physical oceanographic features (e.g. sea surface temperature anomalies) to catch rates, biological parameters (e.g. growth, reproduction, natural mortality) and ecological aspects (e.g. spatial distribution).