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Ecological niche theory predicts sympatric species to show segregation in their spatio‐temporal habitat utilization or diet as a strategy to avoid competition. Similarly, within species individuals may specialize on specific dietary resources or foraging habitats. Such individual specialization seems to occur particularly in environments with predictable resource distribution and limited environmental variability. Still, little is known about how seasonal environmental variability affects segregation of resources within species and between closely related sympatric species. The aim of the study was to investigate the foraging behaviour of three closely related and sympatrically breeding fulmarine petrels (Antarctic petrels Thalassoica antarctica, cape petrels Daption capense and southern fulmars Fulmarus glacialoides) in a seasonally highly variable environment (Prydz Bay, Antarctica) with the aim of assessing inter‐ and intraspecific overlap in utilized habitat, timing of foraging and diet and to identify foraging habitat preferences. We used GPS loggers with wet/dry sensors to assess spatial habitat utilization over the entire breeding season. Trophic overlap was investigated using stable isotope analysis based on blood, feathers and egg membranes. Foraging locations were identified using wet/dry data recorded by the GPS loggers and expectation‐maximization binary clustering. Foraging habitat preferences were modelled using generalized additive models and model cross‐validation. During incubation and chick‐rearing, the utilization distribution of all three species overlapped significantly and species also overlapped in the timing of foraging during the day—partly during incubation and completely during chick‐rearing. Isotopic centroids showed no significant segregation between at least two species for feathers and egg membranes, and among all species during incubation (reflected by blood). Within species, there was no individual specialization in foraging sites or environmental space. Furthermore, no single environmental covariate predicted foraging activity along trip trajectories. Instead, best‐explanatory environmental covariates varied within and between individuals even across short temporal scales, reflecting a highly generalist behaviour of birds. Our results may be explained by optimal foraging theory. In the highly productive but spatio‐temporally variable Antarctic environment, being a generalist may be key to finding mobile prey—even though this increases the potential for competition within and among sympatric species. This article shows a striking case of closely related and sympatrically breeding species overlapping in their resource use across multiple dimensions. The very unusual results are best explained by high food availability in combination with spatio‐temporal environmental variability and mobile prey fields, requiring predators to respond opportunistically and show generalist foraging behaviour.

Human activity in the coastal zone is increasing worldwide, putting a number of seabird species under pressure. Norway is no exception to this development, and with > 35% of the NE Atlantic population of the currently declining European shag (Gulosus aristotelis) population, Norway has an international responsibility for the conservation of this species, and its important foraging habitats during breeding. We analysed tracking data from shags breeding in five colonies along the Norwegian coast spread over a latitudinal gradient of > 1700 km. We identified foraging locations and associated environmental characteristics. Using model cross-validation, we assessed the transferability of habitat models, both spatially (across colonies) and temporally (within colonies and across years), based on three modelling approaches: Training datasets consisted either of the data from one year at one colony, all years at one colony, or all years from all colonies except the testing colony. Across colonies, foraging activity was associated with shallow depths, proximity to colony, and the presence of kelp forests, while sea surface temperature and sea surface height contributed little to model fit. Transferability of habitat use across colonies was low when based on the training data from only one year and one colony and improved little when using several years of data from one colony for training the models. Transferability was very high for all colonies if the training dataset consisted of data from all years and all colonies except the one to be predicted. Our results highlight the importance of multi-year and multi-colony studies and show that it is possible to make sound finescale predictions of important foraging areas for breeding shags without the need to track birds in every colony. This facilitates much needed management of coastal marine ecosystems and the protection of the most important feeding areas for breeding shags. expectation-maximization binary clustering (EMBC), Norwegian coastal zone, kelp forest, bathymetry, foraging range, sea surface temperature, sea surface height, model transferability

Background Multiple methods have been developed to infer behavioral states from animal movement data, but rarely has their accuracy been assessed from independent evidence, especially for location data sampled with high temporal resolution. Here we evaluate the performance of behavioral segmentation methods using acoustic recordings that monitor prey capture attempts. Methods We recorded GPS locations and ultrasonic audio during the foraging trips of 11 Mexican fish-eating bats, Myotis vivesi , using miniature bio-loggers. We then applied five different segmentation algorithms (k-means clustering, expectation-maximization and binary clustering, first-passage time, hidden Markov models, and correlated velocity change point analysis) to infer two behavioral states, foraging and commuting, from the GPS data. To evaluate the inference, we independently identified characteristic patterns of biosonar calls (“feeding buzzes”) that occur during foraging in the audio recordings. We then compared segmentation methods on how well they correctly identified the two behaviors and if their estimates of foraging movement parameters matched those for locations with buzzes. Results While the five methods differed in the median percentage of buzzes occurring during predicted foraging events, or true positive rate (44–75%), a two-state hidden Markov model had the highest median balanced accuracy (67%). Hidden Markov models and first-passage time predicted foraging flight speeds and turn angles similar to those measured at locations with feeding buzzes and did not differ in the number or duration of predicted foraging events. Conclusion The hidden Markov model method performed best at identifying fish-eating bat foraging segments; however, first-passage time was not significantly different and gave similar parameter estimates. This is the first attempt to evaluate segmentation methodologies in echolocating bats and provides an evaluation framework that can be used on other species.

While interspecific differences in foraging behaviour have attracted much attention,less is known about how foraging behaviour differs between populations of the same species.Here we compared the foraging strategy of a pantropical seabird, the red-footed booby Sula sula,in 5 populations breeding in contrasted environmental conditions. The foraging strategy stronglydiffered between sites, from strictly diurnal short trips in Europa Island (Mozambique channel) tolong trips including up to 5 nights at sea in Genovesa Island (Galapagos archipelago). The ExpectationMaximisation binary Clustering (EMbC) algorithm was used to determine the differentbehaviours of individuals during their foraging trips (travelling, intensive foraging, resting andrelocating). During the day, the activity budget was similar for all the breeding colonies. Duringthe night, birds were primarily on the water, drifting with currents. At all sites, birds similarly performedintensive foraging in zones of area-restricted search (ARS), although the size and durationof ARS zones differed markedly. Red-footed boobies foraged over deep oceanic waters, withchlorophyll a concentrations varying between sites. Birds did not appear to target areas withhigher productivity. We suggest that range differences between populations may be linked toother factors such as intra- and interspecific competition.

Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time.We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within-group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation–maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets.We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.