The principle of competitive exclusion postulates that ecologically-similar species are expected to partition their use of resources, leading to niche divergence. The most likely mechanisms allowing such coexistence are considered to be segregation in a horizontal, vertical or temporal dimension, or, where these overlap, a difference in trophic niche. Here, by combining information obtained from tracking devices (geolocator-immersion and time depth recorders), stable isotope analyses of blood, and conventional morphometry, we provide a detailed investigation of the ecological mechanisms that explain the coexistence of four species of abundant, zooplanktivorous seabirds in Southern Ocean ecosystems (blue petrel Halobaena caerulea, Antarctic prion Pachyptila desolata, common diving petrel Pelecanoides urinatrix and South Georgian diving petrel P. georgicus). The results revealed a combination of horizontal, vertical and temporal foraging segregation during the breeding season. The stable isotope and morphological analyses reinforced this conclusion, indicating that each species occupied a distinct trophic space, and that this appears to reflect adaptations in terms of flight performance. In conclusion, the present study indicated that although there was a degree of overlap in some measures of foraging behaviour, overall the four taxa operated in very different ecological space despite breeding in close proximity. We therefore provide important insight into the mechanisms allowing these very large populations of ecologically-similar predators to coexist.

13 pages, 5 figures, 3 tables.We investigated ontogenetic and seasonal changes in the feeding ecology of 2 small pelagic fish species, the European anchovy Engraulis encrasicolus and the European pilchard Sardina pilchardus in the Gulf of Lions (NW Mediterranean). By analysing the stable isotopes δ13C and δ15N, we determined the seasonal variation in the food sources and in the trophic level of these species, and we examined dietary shifts during development. The results of these investigations provided estimates of the diets of both species. We compared the values observed during different seasons (summer, autumn and winter) and at different developmental stages (late-larvae, juveniles and adults) for both species, together with the values of potential groups of prey (microplankton, cladocerans, copepods and appendicularians). Late-larvae preferred to feed on microplankton, although differences in the diet appeared after metamorphosis. Cladocerans were usually the preferred prey when available (summer), and appendicularians were the preferred prey in autumn. During the winter, the diets seemed to be more heterogeneous. Different feeding behaviours between the late-larvae of the 2 species were the most likely reason for the slightly different trophic levels found in the present study. This research demonstrates that studies with stable isotopes can furnish an alternative and/or complementary method for determining the diet of small pelagic fishes over extended periods and provides comprehensive knowledge of the functioning of the pelagic ecosystem.D.C. was funded by a PhD contract within the European project SARDONE (FP6 - 44294). J.N. was supported by a postdoctoral contract of the Juan de la Cierva programme (MICINN-JDC, Spanish Ministry of Science and Innovation).Peer reviewe

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.