#tunaspecies

Highlighted weight - size functions correspond to the annual equations representing overall mean for the population. Size measures are straight fork length (SFL), curved fork length (CFL), length 1st dorsal spine (LD1), head length straight line from snout to operculum (HeadL), preopercular length straight line from the snout to preoperculum (PreOP). Weight measures are round weight: weight of the whole fish (RWT); gutted weight: weight without guts and gonads (GWT); gutted and gilled: weight without guts, gonads and gills (GGWT); gutted gilled and tailed: weight without guts, gonads, gills and tail (GGTWT); and dressed weight: weight of fish gutted, head and tail off (DWT). All size units are centimeters (cm) and weight in kilograms (kg).

Tropical tuna purse-seine fishing vessels contribute to abandoned, lost or discarded (ALD) fishing equipment by deploying large numbers of drifting Fish Aggregating Devices (dFADs). Here we analysed more than 80,000 dFAD trajectories (56,263 tracking buoys) in the Indian and Atlantic oceans from 2012 to 2018. We found that more than 40% of dFAD trajectories ultimately drifted away from fishing grounds, becoming ALD. About 20% of these lost dFADs passed within 50 km of major ports, indicating that port-based programmes could be effective in collecting ALD dFADs at sea. We also identified areas within the fishing grounds where most dFADs exit and where high-seas recovery could be valuable. For example, most dFADs leaving Indian Ocean fishing grounds along their eastern border at ~70° E, particularly in October–December, do not return to fishing grounds. Despite considerable logistical challenges, at-sea dFAD recovery offers promising options for reducing the ecological footprints of purse-seine fisheries.

Overfishing has reduced numerous fish populations to remnants of their historical levels, yet we have a poor understanding of what impact this has had on their evolutionary potential and resilience. This information is crucial to predict future demographic changes and thus promote sustainable fisheries management. Studies of historical marine ecology offer an opportunity to learn and heed these past lessons. In particular, genetic/genomic studies can infer past history from contemporary samples, or directly test archaeological and archival samples for losses in genetic diversity, population restructuring, or adaptive responses to natural factors e.g., climate, or anthropogenic ones e.g., fisheries-induced evolution (FIE). A decade ago, the first to investigate temporal demographic changes in the key species Atlantic bluefin tuna (Thunnus thynnus, hereafter BFT) using archival early-20th century samples and microsatellite markers. Here, we build on this work by genotyping archival and archaeological samples to extend investigations into the pre-industrial era, when fishing may have also had the potential to impact BFT.

Declines in abundance signal an urgent need for reductions to exploitation levels while recoveries of some populations demonstrate the role of accurate assessment and effective management in reversing stock collapse. A fundamental step in the effective management of a fishery is to identify the appropriate management unit (stock). This is unfortunately wrought with difficulties; fish populations rarely maintain geographically discrete distributions throughout their life cycle and many fisheries exploit mixed aggregations. Populations that are sufficiently isolated during reproduction are genetically discrete and can be definitively distinguished using genetic markers. However, populations that have recently diverged may not show detectable variation in selectively neutral genetic markers. In addition, low levels of gene flow between neighbouring populations may preclude genetic discreteness while being insufficient to ensure replenishment of one by the other in the event of stock collapse. A single stock may contain multiple biologically relevant units that differ in life history traits or behaviour with varying degrees of mixing between them. Fisheries management must ensure that stock definitions used in assessment correspond to biologically relevant units. A flexible approach to stock identification is needed, which recognises the broad spectrum of stock structure scenarios that exist.