Bayesian spatial multispecies modelling to assess pelagic fish stocks from acoustic- and trawl-survey data

A Bayesian spatial model was constructed to estimate the abundance of multiple fish species in a pelagic environment. Acoustic-and trawl-survey data were combined with environmental data to predict the spatial distribution of (i) the acoustic backscattering of fish, (ii) the relative proportion of each species, and (iii) their mean length in the Gulf of Finland in the northeastern Baltic Sea. By combining the three spatial model layers, the spatial distribution of the biomass of each species was estimated. The model consists of a linear predictor on environmental variables and a spatial random effect given by a Gaussian process. A Bayesian approach is a natural choice for the task because it provides a theoretically justified means of summarizing the uncertainties from various model layers. In the study area, three species dominate pelagic waters: sprat (Sprattus sprattus), herring (Clupea harengus), and three-spined stickleback (Gasterosteus aculeatus). Results are presented for each model layer and for estimated total biomass for each species in 2 x 2 km lattices. The posterior mean and central 95% credible intervals of total biomass were sprat 45.7 kt (27.7-71.6), herring 24.6 kt (9.7-41.3), and three-spined stickleback 1.9 kt (0.9-3.2).