Killer whale (Orcinus orca) is frequently encountered in coastal and high productive pelagic waters, near the shelf break. In the south-western Atlantic Ocean, spatial and temporal occurrence patterns are poorly known. However, the monitoring of the interaction between killer whales and longline fishery suggests that the species is frequent in this region. We analysed the killer whale presence within the Uruguayan pelagic longline fishing zone. Data were collected from 1996 to 2007, during 2189 fishing events, by vessel skippers and on-board observers. We estimated the sighting rate (SR = sightings days/fishing days * 100) for different time scales and in 1 × 1 degree grids. Generalized linear models were used to evaluate the effect of spatial, temporal, environmental and operational variables on the species presence. Killer whales were sighted in 100 fishing days (SR = 4.5%), this occurrence being explained by distance from shore and sea surface temperature, varying among months and fishing boats. Although sightings occurred year round, they were more frequent in autumn and winter, at 150–400 nautical miles (nm) from shore (mean = 250 nm) and in waters with temperatures ranging from 19 to 24°C (mean = 22°C). Sets took place between 19°–40°S and 21°–54°W, while killer whales occurred mostly from 34°–37°S and 48°–53°W. In this region, the high productive Brazil—Malvinas Confluence Zone is located, and concentrates fishing effort and also killer whales.

Ecological risk assessment is a useful methodology for assisting the management of fisheries from an ecosystem perspective. Atlantic tuna fisheries, managed by the International Commission for the Conservation of Atlantic Tunas (ICCAT), are economically important and interact with several bycatch species. In spite of these interactions, no comprehensive ecological risk assessment has been conducted for bycatch species caught in ICCAT fisheries. In this paper, we followed a two stage approach with the objective of assessing the relative risk of species being negatively impacted by Atlantic tuna fisheries. An analysis of the ICCAT bycatch species list (which includes all species reported to have interacted with different tuna fishing gears operating in the Atlantic) revealed that most of these species are caught in longline fisheries, followed by gillnets and purse seines. According to the IUCN red list, 7 species of the ICCAT bycatch list (3 coastal sharks, 3 sea turtles and one seabird) are categorized as critically endangered. In our study, and based on their life history characteristics, marine mammals and coastal sharks caught in ICCAT fisheries showed the highest intrinsic vulnerability values. A productivity susceptibility analysis for the European Union (EU) tropical tuna purse seine fleet and the United States (US) pelagic longline fleet revealed two groups with high relative risk scores. The first one included pelagic and coastal sharks, characterized by relatively low productivities, and the second one included teleosts, characterized by higher productivities but high susceptibility to purse seine and longline gears. Some alternative approaches to conduct productivity susceptibility analyses in the context of ecological risk assessments are discussed.

Oceanic juvenile loggerhead sea turtles, Caretta caretta, foraging in Azorean waters, are incidentally caught in drifting longlines that target swordfish, Xiphias gladius, and blue shark, Prionace glauca. Data were recorded during a longline gear modification experiment conducted in 2000. The results show that the captures are not uniformly distributed in relation to fishing effort, with 81% of the loggerheads (N = 232) caught in one-third of the sets (N = 93). The highest turtle catch rates were around the eastern group of islands, an area characterized by the presence of mesoscale eddies induced by bathymetric features. High catch rates coincided with the presence of the thermohaline front, between August and October, and when targeting blue shark. To investigate the causes of these differences we fitted a general linear model to evaluate the influence of mean depth, minimum depth, sea surface temperature (SST), soak area, soak time, moon phase and wind force on turtle and target species catch rates. These results revealed that mean depth and SST were most important for loggerhead catch rates, while minimum depth was the only significant variable for swordfish and mean depth, SST, wind force, moon phase and soak time influenced blue shark catches. The distribution of loggerhead sea turtles can be predicted by monitoring fishing areas in relation to bathymetry and SST. The impact of longline fishing could be greatly and quickly reduced by regulating the blue shark fishery and by increasing fishermen awareness for sea turtle conservation.