Fish schools of sardine, anchovy, and horse mackerel can be discriminated from each other, under given conditions, using a set of parameters extracted from echointegration data. Trawl sampling and hydroacoustic data were collected in 1992 and 1993 in the Thermaikos Gulf by using a towed dual-beam 120 kHz transducer. The parameters extracted from the available schools were used to train multi-layered feed-forward artificial neural networks. Various applied networks easily generated associations between school descriptors and species identity, providing a powerful tool for classification. The expertise of the trained network was tested with data from identified schools not used in training. The use of neural networks cannot replace classical statistical procedures, but offers an alternative when there are significant overlaps in the school characteristics and the parametric assumptions are not satisfied.1996 International Council for the Exploration of the Sea
Small pelagic fish are known to aggregate into schools and clusters of schools. It is commonly assumed that the number of such schools and clusters, as well as their size and densities, will vary with the stock abundance. We have carried out a PCA based meta-analysis, using series of acoustic survey data from five different locations in Europe to examine this assumption. The study concluded that there was no discernible relationship between stock abundance and the number of schools seen, or on the clustering of those schools. The study also showed that the number and structure of the school clusters was strongly correlated with the number of schools seen. An increased number of schools in an area tended to be linked with denser clusters (more schools per kilometre) and a higher occupation of the survey area by those clusters. There was also a weaker tendency to find more clusters. It is not clear whether these relationships and the absence of a link to abundance are due to density independence in aggregation patterns or whether such density dependence is only functional at relatively low stock abundance levels.
International audienceData exploitation, acquired by medium-frequency omnidirectional multibeam sonar, enables original studies in fisheries research but is seldom used despite the fact that such equipment is found on most fishing vessels and a number of research vessels. This is the only system for real-time monitoring of fish schools within a horizontal omnidirectional plane about a vessel or a buoy. Between 1996 and 2001, we used two standard omnidirectional sonars and developed new methodologies for exploiting their specific acoustic data according to two main sampling schemes: 'prospecting', including fishing and searching operations, and 'drifting', as with an instrumental buoy system or aboard a stationary vessel. We present a complete method for continuous data acquisition from aboard a research vessel or commercial boat, with automated data extraction by picture analysis and a data processing method. Two cases of data analysis are considered: the first on a school-by-school basis, the 'single school' mode; the second taking into account all fish schools detected within the sonar sampling volume, the 'cluster' mode. Elementary sonar information is divided into five categories that comprise 24 survey and sonar parameters and 55 school, cluster and fisher behaviour descriptors. We review the applications of these categories and discuss perspectives for their use in fisheries science. If the sonar system enables the evaluation of the effects of vessel avoidance on fish school biomass assessment, no accurate abundance estimate can be provided by a simple sonar echo-integration process. Omnidirectional sonar data can be used to analyse collectively the fish schools' swimming speed, kinematics in terms of diffusion and migration, aggregative dynamics as school splitting and merging indexes, spatial characteristics of clusters such as school density, 2D structure and fisher behaviour. The prospect of integrating such data into a fish school database, including multifrequency echo-sounder and lateral multibeam (3D) sonar data combined with a species recognition method, will enable a complete view of fish school behaviour and consequently the adoption of accurate fisheries management methods
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