Lagrangian Analysis of Satellite Data for the Pacific Cod Biomass Estimation in the West Bering Sea Zone

Based on satellite altimetry data on the velocity of geostrophic currents for each day from 2000 to 2021, the trajectories of passive tracers distributed over a grid in the Bering Sea were calculated. The Lagrangian indicators, the length of the propagation paths of these particles (L) and the Lyapunov exponent (Λ), have been calculated for the month in the past prior to reaching the sites of scientific bottom trawling (BT). It is shown that the "random forest" machine learning method, among other bagging and boosting methods, best predicts the logarithm of the pacific cod density in scientific BT (t/km) with Lagrangian indicators, describing more than 51% of the variance in the validation set. The standard vector autoregressive spatiotemporal model for calculating the dynamics of cod biomass in the West Bering Sea zone describes 6% less variance in identical testing, and the generalized additive model describes 20% less variance. All tested models in their optimal configurations included a significantly positive effect of L and a nonlinear effect of Λ in addition to the known dome-shaped effect of the depth of the BT site and the threshold effect of temperature of water at the bottom.