Sampling variance of species identification in fisheries-acoustic surveys based on automated procedures associating acoustic images and trawl hauls

During the acoustic surveys of fish stocks, a small number of echo traces are identified to species by fishing. During data analysis, the process of echogram scrutiny leads to allocating echo-trace backscattered energies to species. While the precision of survey estimates is generally based on the spatial variation in the energy, no variance term accounts for species identification and energy allocation. In this paper, the sampling variance of species identification is developed and automated procedures are used allowing energy allocation to be carried out by a non-expert. The procedures are based on the fact that at the sampling stage trawl hauls are linked with particular acoustic images. The procedures have two steps: the classification step corresponds to species identification and the aggregation step to energy allocation. Classification is performed on the identified images and results in defining groups of images and estimating in each the sampling variability of the species identification. Aggregation is performed on non-identified images and results in post-stratifying the data. The estimation (map, abundance and variance per species) is then derived automatically and is conditioned by the post-stratification. Two approaches are followed, one based on the echo-trace characteristics making full use of the echogram (acoustic-image classification) and the other on the spatial continuity of the species composition between trawl hauls (trawl-haul classification). These methods are described and compared. The species-identification variance term is also compared to the spatial variance.