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Return rates of Atlantic salmon (Salmo salar) from the sea to European rivers have declined in recent decades. The first months at sea are critical for growth and survival; recent evidence suggests that reduced food availability may be a contributory factor to the observed declines. Here, zooplankton abundance data are used to derive a measure of prey energy available to forage fish prey of salmon during early marine migration. This zooplankton prey energy has significantly and dramatically declined over much of the northeast Atlantic, and specifically within key salmon migration domains, over the past 60 years. Marine return rates from a set of southern European populations are found to exhibit clustering not entirely predictable from geographical proximity. Variability in grouped return rates from these populations is correlated with zooplankton energy on a range of scales, demonstrating the potential use of zooplankton energy as an indicator of salmon marine survival. Comparison with environmental variables derived from ocean model reanalysis data suggests zooplankton energy is regulated by a combination of climate change impacts on ecosystem productivity and multi-decadal variability in water mass influence along the migration routes. [ABSTRACT FROM AUTHOR]

For effective fishery management, estimated stock sizes, along with their uncertainties, should be accurate, precise, and unbiased. Atlantic salmon Salmo salar stock assessment in England and Wales (and elsewhere across the Atlantic) estimate returning salmon stocks by applying a measure of rod exploitation rate (RER), derived from less abundant fishery-independent stock estimates, to abundant fishery-dependent data. Currently, RER estimates are generated for individual principal salmon rivers based on available local data and assumptions. We propose a single, consistent, transparent, and statistically robust method to estimate salmon stocks that transfers strength of information from "data-rich" rivers, i.e. those with fisheries-independent data, to "data-poor" rivers without such data. We proposed, fitted, simplified, and then validated a Beta–Binomial model of RER, including covariates representing angler and fish behaviours, river flow, and random effects to control for nuisance effects. Our "best" model revealed covariate effects in line with our hypotheses and generalized to data not used to train it. We used this model to extrapolate stock estimates from 12 data-rich to 52 data-poor rivers, together with their uncertainties. The resulting river-specific salmon stock estimates were judged to be useful and can be used as key inputs to river-specific, national, and international salmon stock assessments. [ABSTRACT FROM AUTHOR]