48 resultados para Fish meal


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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

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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 .

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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).