Seabirds face a diverse array of threats and are considered to comprise one of the most threatened avian groups globally. Development of appropriate conservation action requires a knowledge of the marine distribution of seabirds, furnished either by tracking the movements of individuals, or from at-sea surveys. Obtaining information on the distribution of the smallest seabird species, the storm-petrels Hydrobatidae, is challenging, but the recent development of <1 g GPS tracking tags now enables high-precision tracking and this study reports the first multi-year high-precision tracking of European Storm-petrels Hydrobates pelagicus from their largest UK breeding colony. A total of 42 successful tag deployments were made over four breeding seasons during incubation, brooding and post-brood phases, and there was no evidence of adverse impacts on adult body mass or nest survival rates. Foraging trips lasted between one and three days and ranged up to 397 km from the colony (median = 159 km). Foraging range and total distance covered were positively correlated with trip duration but did not differ across breeding stages. Storm-petrels did not feed to the west of the colony at the edge of the continental shelf where high concentrations have been reported in previous decades from boat surveys, but rather, foraging was restricted to shallow waters south of the colony, consistent across individuals, breeding stages and years. Two areas were identified that exceed the threshold criteria for marine Important Bird Area status and should be considered for statutory protection. The home range estimated across all three breeding stages overlapped with 206 active hydrocarbon wells and 14 operating platforms which represent potential threats as sources of surface pollution or through attraction of birds to gas flares. Improved understanding of the foraging distribution of storm-petrels from this protected colony greatly assists the identification of potential threats and informs appropriate marine spatial planning.

Introduction: Cognitive bias is often cited as an explanation for diagnostic errors. Of the numerous cognitive biases currently discussed in the literature, availability bias, defined as the current case reminds you of a recent similar example is most well-known. Despite the ubiquity of cognitive biases in medical and popular literature, there is surprisingly little evidence to substantiate these claims. The present study sought to measure the influence of availability bias and identify contributing factors that may increase susceptibility to the influence of a recent similar case. Methods: To investigate the role of prior examples and category priming on diagnostic error at different levels of expertise, we devised a 2 phase experiment. The experimental intervention was in a validation phase preceding the test, where participants were asked to verify a diagnosis which was either i) representative of Diagnosis A, and similar to a test case, ii) representative of Diagnosis A and dissimilar to a test case, iii) representative of Diagnosis B and similar to a test case. The test phase consisted of 8 written cases, each with two approximately equally likely diagnoses(A or B). Each participant verified 2 cases from each condition, for a total of 6. They then diagnosed all 8 test cases; the remaining 2 test cases had no prior example. All cases were counterbalanced across conditions. Comparison between Condition i) and ii) and no prior showed effect of prior exemplar; comparison between iii) and no prior showed effect of category priming. Because cases were designed so that both Diagnosis A and B were likely, overall accuracy was measured as the sum of proportion of cases in which either was selected. Subjects were emergency medicine staff (n=40), residents (n=39) and medical students (n=32) from McMaster University, University of Washington, and Harvard Medical School. Results: Overall, staff had an accuracy (A + B) of 98%, residents 98% and students 85% (F=35.6,p<.0001). For residents and staff there was no effect of condition (all mean accuracies 97% to 100%); for students there was a clear effect of category priming, with accuracy of 84% for i), 87% for ii) and 94% for iii) but only 73% for the no prime condition (Interaction F= 3.54, p<.002) Conclusion: Although prior research has shown substantial biasing effects of availability, primarily in cases requiring visual diagnosis, the present study has shown such effects only for novices (medical students). Possible explanations need to be explored. Nevertheless, our study shows that with increasing expertise, availability may not be a source of error.