2 resultados para Data uncertainty

em eResearch Archive - Queensland Department of Agriculture


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Report on evidence of shrinkage of live coral trout during professional fishing operations on the Great Barrier Reef in 2000. Excel data includes the following fields: Column A. Fish (fish number from 1 -24) Column B. Bin (1-8, container the fish was held in during the experiment) Column C. Measure (1-7, number of the measurement of each fish) Column D. Observer (1 or 2, making the measurement) Column E. Time 2 Column F. Time (time of the day the measurement was made) Column G. FL (Fork Length) Column H. TL (Total Length) Column I. Difference (difference in length between measures) Column J. Order Column K. Temperature (surface water temp under the boat)

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The Northern Demersal Scalefish Fishery has historically comprised a small fleet (≤10 vessels year−1) operating over a relatively large area off the northwest coast of Australia. This multispecies fishery primarily harvests two species of snapper: goldband snapper, Pristipomoides multidens and red emperor, Lutjanus sebae. A key input to age-structured assessments of these stocks has been the annual time-series of the catch rate. We used an approach that combined Generalized Linear Models, spatio-temporal imputation, and computer-intensive methods to standardize the fishery catch rates and report uncertainty in the indices. These analyses, which represent one of the first attempts to standardize fish trap catch rates, were also augmented to gain additional insights into the effects of targeting, historical effort creep, and spatio-temporal resolution of catch and effort data on trap fishery dynamics. Results from monthly reported catches (i.e. 1993 on) were compared with those reported daily from more recently (i.e. 2008 on) enhanced catch and effort logbooks. Model effects of catches of one species on the catch rates of another became more conspicuous when the daily data were analysed and produced estimates with greater precision. The rate of putative effort creep estimated for standardized catch rates was much lower than estimated for nominal catch rates. These results therefore demonstrate how important additional insights into fishery and fish population dynamics can be elucidated from such “pre-assessment” analyses.