Coho salmon escapement and trends in migration timing to a data-poor river: estimates from a Bayesian hierarchical model

Abstract

Data to inform fisheries management are often limited in remote or low economic value fisheries. Here, we use a Bayesian hierarchical modeling structure and two alternative migration timing models to estimate the annual escapement of coho salmon (Oncorhynchus kisutch) to the Chignik River (Alaska, USA) in years with little data, borrowing information from data-rich years to inform parameter estimates. Additionally, we examined trends in peak migration timing between 1922 and 2013 and relative to environmental conditions. Our analyses show that annual escapement estimates are prone to substantial errors unless daily escapement is enumerated for at least 7 days after peak migration date. Finally, peak migration date was negatively correlated with the strength of the Pacific Decadal Oscillation in May–August, and increased over time, though the significance of these associations was dependent on the specific form of the migration timing model used. The modeling approach we present here is easily adaptable to similar situations where data from alternative periods of time or spatial locations can be used to objectively inform local parameter estimates of population characteristics.