Atlantic bluefin tuna (Thunnus thynnus) is considered
to be overfished, but the status of its populations has been debated, partly
because of uncertainties regarding the effects of mixing on fishing grounds.
A better understanding of spatial structure and mixing may help fisheries
managers to successfully rebuild populations to sustainable levels while maximizing
catches. We formulate a new seasonally and spatially explicit fisheries model
that is fitted to conventional and electronic tag data, historic catch-at-age
reconstructions, and otolith microchemistry stock-composition data to improve
the capacity to assess past, current, and future population sizes of Atlantic
bluefin tuna. We apply the model to estimate spatial and temporal mixing of
the eastern (Mediterranean) and western (Gulf of Mexico) populations, and
to reconstruct abundances from 1950 to 2008. We show that western and eastern
populations have been reduced to 17% and 33%, respectively,
of 1950 spawning stock biomass levels. Overfishing to below the biomass that
produces maximum sustainable yield occurred in the 1960s and the late 1990s
for western and eastern populations, respectively. The model predicts that
mixing depends on season, ontogeny, and location, and is highest in the western
Atlantic. Assuming that future catches are zero, western and eastern populations
are predicted to recover to levels at maximum sustainable yield by 2025 and
2015, respectively. However, the western population will not recover with
catches of 1750 and 12,900 tonnes (the “rebuilding quotas”) in
the western and eastern Atlantic, respectively, with or without closures in
the Gulf of Mexico. If future catches are double the rebuilding quotas, then
rebuilding of both populations will be compromised. If fishing were to continue
in the eastern Atlantic at the unregulated levels of 2007, both stocks would
continue to decline. Since populations mix on North Atlantic foraging grounds,
successful rebuilding policies will benefit from trans-Atlantic cooperation.
A simulation‐based approach known as management strategy evaluation (MSE) is increasingly used by resource managers to identify management procedures that are robust to uncertainties in system dynamics.
The majority of global fish populations are data limited and there is large uncertainty over their population and exploitation dynamics.
The Data‐Limited Methods Toolkit (DLMtool) is an R package that allows for rapid and flexible MSE specification. The package consolidates a large number of existing data‐limited management procedures and allows for rapid MSE testing of new approaches.
The DLMtool package has supported transparent and rigorous decision‐making for a number of data‐limited populations, identifying robust management procedures and revealing performance trade‐offs.
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