We performed predictions using a Bayesian stock-recruit model. Our model predicts the number of Sockeye Salmon recruits returning to each river from a given brood year (BY) that reared in freshwater for a duration of FA years, migrated to the ocean in year OY, and resided in the marine environment for a duration of MA years. The number of recruits is estimated separately for each river within the Bristol Bay watershed. For each unique river, BY, FA, MA combination, the number of recruits is estimated as a function of the number of spawners (S) for that river and BY, an estimated spawner fecundity parameter (f) where e^f represents the number of eggs produced per spawner, the observed proportion of recruits (p) from a given BY and subbasin combination with a given life-history pattern (ie unique combination of BY, FA, MA), estimated freshwater survival () for each river and BY, estimated marine survival () for fish that entered the ocean in a given OY and a residual error term (). Freshwater survival is represented on the logit scale as a linear function of estimated average freshwater survival in a given river (fw), a slope coefficient (fw) and a freshwater covariate (F), which is indexed by brood year and represents river conditions in the year of fry emergence (BY + 1). Marine survival is represented on the logit scale as a linear function of estimated average marine survival (mar), a slope coefficient (mar), freshwater covariate (M), which represents ocean conditions in the year of ocean entry, and an annual random effect () indexed by ocean entry year. Predictions from our model for a given return year are the sum of estimated recruits that return in a given RY = BY + 1 + FA + MA. For full model description with equations, please attached supporting documentation.

Data Recruits and spawners data for our model are the values provided in the Salmon Prize 2026 Bristol Bay data package. The proportion of recruits from each FA, MA combination in a given BY (p) is the observed ratio of recruits from a given FA,MA combination out of total recruits for that BY. For recent brood years with incomplete returns as of 2025, p was assumed to be equal to the mean from the previous 5 brood years with complete returns. The marine covariate we used in our final model is the North Pacific Current Bifurcation Index (Edwards et al. 2024), and the freshwater covariate was precipitation measured between March and June. The mean precipitation of the Dillingham Census Area and Lake and Peninsula Borough for the March to June period was used in the model (NOAA National Centers for Environmental information). Model Estimation Parameters for our model were estimated using Hamiltonian Monte Carlo sampling in the stan statistics package. For details of our model code and priors, please see the uploaded stan file. While we did not perform an extensive systematic covariate selection process, several different possible freshwater and marine covariate combinations were assessed based on mean absolute prediction error (MAPE) of retrospective predictions for 2021-2025. The LLM Claude Sonnet was used for R and stan coding assistance during implementation of the model. Will upload stan code file in supporting documentation References Edwards AM, Tai TC, Watson J, Peña MA, Hilborn A, Hannah CG, Rooper CN, Flynn KL (2024). “pacea: An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management.” https://github.com/pbs-assess/pacea,%20https://zenodo.org/doi/10.5281/zenodo.13840804. NOAA National Centers for Environmental information, Climate at a Glance: County Time Series, published June 2026, retrieved on June 30, 2026 from https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/county/time-series