Effective sustainable management of marine fisheries requires that assessed management units (that is, fish stocks) correspond to biological populations. This issue has long been discussed in the context of Atlantic bluefin tuna (ABFT, Thunnus thynnus) management, which currently considers two unmixed stocks but does not take into account how individuals born in each of the two main spawning grounds (Gulf of Mexico and Mediterranean Sea) mix in feeding aggregations throughout the Atlantic Ocean. Using thousands of genome‐wide molecular markers obtained from larvae and young of the year collected at the species’ main spawning grounds, we provide what is, to the best of our knowledge, the first direct genetic evidence for “natal homing” in ABFT. This has facilitated the development of an accurate, cost‐effective, and non‐invasive tool for tracing the genetic origin of ABFT that allows for the assignment of catches to their population of origin, which is crucial for ensuring that ABFT management is based on biologically meaningful stock units rather than simply on catch location.

We examined growth rates and reproductive characteristics of Sardinella aurita off Senegal and other coastal areas over a 20 year period (1995–2014) to determine how they relate to variations in environmental characteristics of coastal waters. Based on fish length‐frequency data and a coastal upwelling index, we found that S. aurita recruitment tends to occur during the periods of most intensive upwelling (March–April off Senegal). Peak reproduction corresponds to periods of low sea‐surface temperature (in February or March). The sex ratio was remarkably consistent during the 30 year study period and so was not affected by environmental changes. We hypothesise that S. aurita takes advantage of the higher zooplankton productivity that occurs in coastal waters when upwelling brings nutrient‐rich water to the surface (i.e., it increases its growth rate and accumulates energy reserves for spawning). Growth performance appears to be strongly dependent on environmental conditions. The timing of spawning seems to occur when food (zooplankton) is most available for supplying the energy requirements needed by adults for spawning and early development of larvae. Environmental changes seem to have a significant effect on S. aurita growth and reproduction, which endorses their high phenotypic plasticity.

Fisheries management is difficult especially in developing countries where there are little or no data available for stock assessment. Here, a simple model based on the length-based Bayesian biomass (LBB) is applied to length frequencies collected on the two sardinella species (Sardinella maderensis and S. aurita) collected in Senegalese waters (2004 to 2014) to diagnose these stocks and to support the development of fishery management options that may improve the livelihoods of artisanal fishermen. Annual mean length of both species of sardinella showed a large variation during the decadal study period. It is assumed that such variations are due to environmental changes. According to our results, based on the current exploitation rate, both sardinella species are overexploited. To reverse these bad stock status, three management indicators were estimated for both sardinella: (i) length at first capture (Total Length (TL); 24 and 27 cm for S. maderensis and S. aurita, respectively); (ii) the length at maximum possible yield per recruit (TL; 26 and 29 cm for S. maderensis and S. aurita, respectively); and (iii) the optimal length for the first capture (TL; ; 25 and 28 cm for S. maderensis and S. aurita, respectively). According to Senegalese maritime fishing code, which sets the small pelagic fish size of the first capture 18 cm, we urge the Senegalese governments to press ahead with much needed reforms of the fishing code. We recommend capturing sardinella at the size to provide a natural safeguard against any recruitment failure related to environmental variability and allow individuals to grow and ensure the longterm survival of populations and thus sustainable fisheries. The results suggest that LBB model could be a tool to assess data-poor fisheries allowing the possibility to include in the analysis several years of length-frequency data with a minimum of prerequisites.

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