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Fisheries observer programs represent the most reliable way to collect data on fisheries bycatch. However, their limited coverage leads to important data gaps that preclude bycatch mitigation at the basin scale. Habitat models developed from available fisheries observer programs offer a potential solution to fill these gaps. We focus on tropical tuna purse seine fisheries (TTPSF) that span across the tropics and extensively rely on floating objects (FOBs) for catching tuna schools, leading to the bycatch of other species associated with these objects. Bycatch under floating objects is dominated by five species, including the vulnerable silky shark Carcharhinus falciformis and four bony fishes (oceanic triggerfish Canthidermis maculata, rainbow runner Elagatis bipinnulata, wahoo Acanthocybium solandri, and dolphinfish Coryphaena hippurus). Our objective was to predict possible bycatch hotspots associated with FOBs for these five species across two tropical oceans. We used bycatch data collected from observer programs onboard purse seiners in the Atlantic and Indian oceans. We developed a generalized additive model per species and per ocean relating bycatch to a set of environmental covariates (depth, chlorophyll-a concentration, sea surface temperature, mixed layer depth, surface salinity, total kinetic energy and the density of floating objects) and temporal covariates (year and month). We extrapolated modeled relationships across each ocean within the range of environmental covariates associated with the bycatch data and derived quarterly predictions. We then detected bycatch hotspots as the 90th percentiles of predictions. In the Atlantic Ocean, bycatch hotspots were predicted throughout tropical and subtropical waters with little overlap between species. By contrast in the Indian Ocean, major overlapping hotspots were predicted in the Arabian Sea throughout most of the year for four species, including the silky shark. Our modeling approach provides a new analytical way to fill data gaps in fisheries bycatch. Even with the lack of evaluation inherent to extrapolations, our modeling effort represents the first step to assist bycatch mitigation in TTPSF and is applicable beyond these fisheries.

Recently, declining populations of several pelagic shark species have led to global conservation concerns surrounding this group. As a result, a series of species-specific banning measures have been implemented by Regional Fishery Management Organizations (RFMOs) in charge of tuna fisheries, which include retention bans, finning bans and trading bans. There are both positive and negative aspects to most management measures, but generally, the positive aspects outweigh the negatives, ensuring the measure is beneficial to the resource and its users in the long term. Banning measures are a good first step towards the conservation of pelagic shark species, especially since they improve conservation awareness among fishers, managers and the public. Measures that impose total bans, however, can lead to negative impacts that may jeopardize the populations they were intended to protect. The majority of pelagic shark catches are incidental and most sharks die before they reach the vessel or after they are released. The legislation set out by RFMOs only prevents retention but not the actual capture or the mortality that may occur as a result. Managers should be fully aware that the development and implementation of mitigation measures are critical for a more effective conservation strategy.