Bayesian arrival model for Atlantic salmon smolt counts powered by environmental covariates and expert knowledge

Abstract

Annual run size and timing of Atlantic salmon (Salmo salar) smolt migration was estimated using Bayesian model framework and data from 6 years of a video monitoring survey. The model has a modular structure. It separates subprocesses of departing, traveling, and observing, of which the first two together define the arrival distribution. The subprocesses utilize biological background and expert knowledge about the migratory behavior of smolts and about the probability to observe them from the video footage under varying environmental conditions. Daily mean temperature and discharge were used as environmental covariates. The model framework does not require assuming a simple distributional shape for the arrival dynamics and thus also allows for multimodal arrival distributions. Results indicate that 20%–43% of smolts passed the Utsjoki monitoring site unobserved during the years of study. Predictive studies were made to estimate daily run size in cases with missing counts either at the beginning or in the middle of the run, indicating good predictive performance.