Abstract: Wildlife movement ecology often focuses on breeders, whose territorial attachments facilitate trapping and following individuals over time. This leads to incomplete understanding of movements of individuals not actively breeding due to age, breeding failure, subordinance, and other factors. These individuals are often present in breeding populations and contribute to processes such as competition and pathogen spread. Therefore, excluding them from movement ecology studies could bias or mask important spatial dynamics. Loafing areas offer an alternative to breeding sites for capturing and tracking individuals. Such sites may allow for sampling individuals regardless of breeding status, while also avoiding disturbance of sensitive breeding areas. However, little is known about the breeding status of individuals attending loafing sites, or how their movements compare to those of breeders captured at nests. We captured a seabird, the brown skua, attending either nests or loafing areas (‘clubs’) at a multi-species seabird breeding site on Amsterdam Island (southern Indian Ocean). We outfitted skuas with GPS-UHF transmitters and inferred breeding statuses of individuals captured at clubs using movement patterns of breeders captured at nests. We then compared space use and activity patterns between breeders and nonbreeders. Both breeding and nonbreeding skuas attended clubs. Nonbreeders ranged more widely, were more active, and overlapped more with other seabirds and marine mammals than did breeders. Moreover, some nonbreeders occupied fixed territories and displayed more restricted movements than those without territories. Nonbreeders became less active over the breeding season, while activity of breeders remained stable. Nonbreeding skuas were exposed to the agent of avian cholera at similar rates to breeders but were more likely to forage in breeding areas of the endangered endemic Amsterdam albatross, increasing opportunities for interspecific pathogen transmission. Our results show that inference based only on breeders fails to capture important aspects of population-wide movement patterns. Capturing nonbreeders as well as breeders would help to improve population-level representation of movement patterns, elucidate and predict effects of external changes and conservation interventions (e.g. rat eradication) on movement patterns and pathogen spread, and develop strategies to manage outbreaks of diseases such as highly pathogenic avian influenza. Read the free Plain Language Summary for this article on the Journal blog.

Abstract: Climate change is most strongly felt in the polar regions of the world, with significant impacts on the species that live there. The arrival of parasites and pathogens from more temperate areas may become a significant problem for these populations, but current observations of parasite presence often lack a historical reference of prior absence. Observations in the high Arctic of the seabird tick Ixodes uriae suggested that this species expanded poleward in the last two decades in relation to climate change. As this tick can have a direct impact on the breeding success of its seabird hosts and vectors several pathogens, including Lyme disease spirochaetes, understanding its invasion dynamics is essential for predicting its impact on polar seabird populations. Here, we use population genetic data and host serology to test the hypothesis that I. uriae recently expanded into Svalbard. Both black-legged kittiwakes (Rissa tridactyla) and thick-billed murres (Uria lomvia) were sampled for ticks and blood in Kongsfjorden, Spitsbergen. Ticks were genotyped using microsatellite markers and population genetic analyses were performed using data from 14 reference populations from across the tick's northern distribution. In contrast to predictions, the Spitsbergen population showed high genetic diversity and significant differentiation from reference populations, suggesting long-term isolation. Host serology also demonstrated a high exposure rate to Lyme disease spirochaetes (Bbsl). Targeted PCR and sequencing confirmed the presence of Borrelia garinii in a Spitsbergen tick, demonstrating the presence of Lyme disease bacteria in the high Arctic for the first time. Taken together, results contradict the notion that I. uriae has recently expanded into the high Arctic. Rather, this tick has likely been present for some time, maintaining relatively high population sizes and an endemic transmission cycle of Bbsl. Close future observations of population infestation/infection rates will now be necessary to relate epidemiological changes to ongoing climate modifications.

Abstract: The 2016 Kaikoura earthquake, New Zealand, ruptured more than a dozen faults, making it difficult to prescribe a model fault for analyzing the event by inversion. To model this earthquake from teleseismic records, we used a potency density tensor inversion, which projects multiple fault slips onto a single model fault plane, reducing the non-uniqueness due to the uncertainty in selecting the faults’ orientations. The resulting distribution of potency-rate density tensors is consistent with observed surface ruptures. In its initial stage, the rupture propagated northeastward primarily at shallow depths. Later, the rupture propagated northeastward at greater depths beneath a gap in reported surface ruptures. The main rupture phase started in the northeastern part of the Kekerengu fault after 50 s and propagated bilaterally to the northeast and southwest. The non-double-couple component grew to a large fraction of the source elements as the rupture went through the junction of the Jordan Thrust and the Papatea fault, which suggests that the rupture branched into both faults as it back-propagated toward the southwest. The potency density tensor inversion sheds new light on the irregular evolution of this earthquake, which produced a fault rupture pattern of unprecedented complexity. Our source model of the 2016 Kaikoura earthquake (e.g., back-rupture propagation) could prompt research to determine a more realistic model with segmented faults using near-field data.