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International audience; Analyzing the long-term changes in ecosystems and disentangling the influence of overfishing and environment require historical data integration. Fisheries-independent data are available only since the mid-1980s in the Celtic Sea and thus provide a short-term and truncated vision of fishing impacts. We conducted a catch-based study over the 1950–2015 period. We successively (i) reconstituted catch time-series, including unreported catch estimates, and studied them through a trophic-spectrum approach and ecosystem indicators based on trophic level, size and species diversity; (ii) estimated biomass trends of the main Celtic Sea target species from catch and effort time-series, using production models that detect potential shifts in productivity; and (iii) explored linkages between species productivity potential and both fishing and environment using long-term time-series of large-scale climatic indices, locally observed hydroclimatic variables and plankton data from the Continuous Plankton Recorder. Our results highlight that fishing has caused a drastic depletion of the main exploited species in the Celtic Sea since World War II. The biomass reduction of larger demersal species may reach more than 80 %; additionally, this depletion occurred earlier than previously thought and may have initiated a trophic cascade in the ecosystem. North Atlantic hydroclimatic variability affected species productivity, worsening the early stock depletion while potentially mitigating fishing impact in the 1990s. The common pattern in productivity changes among analyzed species highlights an abrupt transition whose timing matches that of the ecosystem shifts identified in several Atlantic ecosystems. Finally, we show that the recent fishing pressure reduction led by the European Common Fisheries Policy initiated a partial recovery of stocks and ecosystem status over the last decade.

Bayesian observation error (OEM), process error (PEM) and state-space (SSM) implementations of a Fox biomass dynamics model are compared using a simulation-estimation approach and by applying them to data for the octopus fishery off Mauritania. Estimation performance is evaluated in terms of bias, precision, and reliability measured by the extreme tail-area probability and the mean highest posterior density interval. The PEM generally performs poorest of the three methods in terms of the these performance metrics. In contrast, the OEM is precise, but under-represents uncertainty. The OEM is outperformed by the SSM in terms of its ability to provide posterior distributions which adequately capture parameter uncertainty. It is key to consider the above four metrics when comparing estimation performance in a Bayesian context. Finally, although model performance measures are useful, there is still a need to examine goodness of fit statistics in actual applications.