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

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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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Multibeam omnidirectional sonars allow the monitoring of pelagic fish schools surrounding the platform and are currently used by fishermen. Multibeam processing methods offer improved abilities for raw data storage. For the detection of fish schools associated with drifting Fish Aggregating Devices (FADs), the Simrad SP90 sonar has been used. Digital systems have been developed for the acquisition and processing of volume backscattering echoes and position data. Sampling method was defined according to two modes: during searching periods for FADs and associated schools, and during school monitoring in drifting mode. Validation of several FAD-associated schooling species detection was made by simultaneous visual observations or/and cross-checks with echosounder recordings. The characteristics of the targeted fish species' schooling behaviour are fundamental in order to avoid misleading acoustic data interpretation. The sonar detection threshold is the result of a compromise between fish number, size, species and the nearest neighbour distance (NND) of individuals per dynamic structure (school and shoal). In agreement with the tuna schooling dynamics, their NND can sometimes be too large for detecting their presence notwithstanding their number. The sonar data should be analysed and interpreted in a holistic approach, in combination with the behaviour pattern and the dynamics of all species around the drifting FADs. An autonomous sonar buoy prototype equipped with 360° scanning sonar coupled to video cameras will increase our understanding of tuna behaviour around drifting or anchored objects. A similar methodology can be applied to various platforms, either anchored or in a permanent position, promoting the monitoring of fish schools around artificial reefs or open sea aquaculture farms, across estuaries, channels and straits, which is undoubtedly essential for fisheries management.

Time series analysis techniques (ARIMA models), artificial neural networks (ANNs) and Bayesian dynamic models were used to forecast annual loliginid and ommastrephid landings recorded from the most important fishing ports in the Northern Aegean Sea (1984Sea ( -1999. The techniques were evaluated based on their efficiency to forecast and their ability to utilise auxiliary environmental information. Applying a "stepwise modelling" technique, namely by adding stepwise predictors and comparing the quality of fit, certain inferences concerning the importance of the predictors were made.The ARIMA models predicted the test data very precisely (high R 2 ), especially if the target time series contained a strong autoregressive character, after they were first differenced to obtain stationarity (R 2 > 0.96). The disadvantage of the ARIMA, as with most statistical models, is their assumption that the relationships and system parameters remain the same across the observation and forecasting periods.The influence of temperature on catches was mainly investigated by applying neural models, which predicted the monthly landings with high precision (R 2 = 0.89), even when incorporating in the model exclusively monthly SST descriptors. Similarly, ANN models of annual landings containing monthly mean temperatures provided high precision (R 2 = 0.87) and valuable inference concerning the possible effect of the SST in certain months.Bayesian dynamic models also provided a high precision (R 2 = 0.96). They combined the information of both environmental and landing time series, namely the monthly mean temperatures and the monthly seasonality of the landings. The impact factors estimated from the model have the form of time series representing the temperature effect.The results reveal that both the monthly and the annual landings can be predicted and that the Bayesian model is the best performer overall, characterised by a higher number of stable forecasts, and forecasts with higher precision and accuracy, than the other methods. It is evident, from application of the "stepwise modelling" technique, that the incorporation of temperature descriptors can significantly improve the model performance.