Judy Shamoun-Baranes

Wind energy development leads to an increased mortality of birds due to collisions with wind turbines, especially in raptors. To assess the sensitivity of different bird species to wind turbine collisions, predict collision risk and identify effective mitigation measures, detailed information on flight behaviour is essential, but thus far unavailable for many species. Here, we evaluated the sensitivity of breeding birds of six raptor species—Montagu's Harrier (MoH), Hen Harrier, Marsh Harrier, Common Buzzard (CB), Red Kite (RK) and Short‐toed Eagle (STE)—to wind turbine collisions by integrating three aspects of flight behaviour, that is time spent in flight, proportion of flights at risk height and distance travelled from the nest location. We used GPS‐tracking data collected in 15 study areas in 6 European countries, comprising 280 individuals, 74,501 individual tracking days and 5786 h of high‐frequency flight tracks, providing accurate flight height data. The average time spent in flight ranged from 0.5 (CB) to 3.4 h day ⁻¹ (MoH) in females and from 1.0 (CB) to 7.4 h day ⁻¹ (MoH) in males. The proportion of flights at risk height (32–200 m above ground level) varied between 12.4% (MoH) and 56.4% (RK). The highest sensitivity index (time spent at risk height per year) was found in RK (1.7–7.4 times higher than in the other species). The mean 90% quantile of distance from nest ranged from 0.7 (CB) to 9.8 km (STE) in females, and from 1.0 (CB) to 5.6 km (MoH) in males. With increasing distance from nest, sensitivity (time spent at risk height per km ² ) decreased with species‐specific slopes. Practical implication: These results indicate that flight behaviour explains substantial differences in the sensitivity to wind turbine collisions among raptor species. Our integrative approach to assess sensitivity based on flight behaviour regardless of current exposure allows to prioritise species for the selection of wind farm sites and conservation measures, and can be complemented with other aspects, for example extinction risk. Our findings on spatial sensitivity with respect to nest‐turbine distances may inform the siting of wind turbines to minimise collision risk.

Countries have committed to expanding the marine protected area (MPA) network to 30% of the world's ocean by 2030, more than tripling current coverage, and focusing on areas important for biodiversity. For such expansion to translate into effective conservation of marine biodiversity, it must rely on the best available knowledge. Here, we analyze tracking data on > 13,000 individuals (44 species, eight families) to map the distribution of highly mobile seabirds in the Atlantic Ocean and adjacent seas throughout the year. We identify regions important year‐round or seasonally. The High Seas are as important as national waters for highly mobile seabirds but are even less protected. Current Atlantic MPAs hold < 14% of tracked species density, but their expansion to 30% of the ocean could protect up to two thirds. We share this new knowledge layer to support national and international efforts and policies to strategically expand the Atlantic MPA network.

With global ambitions to decarbonize the energy system, wind power capacity will continue to increase dramatically worldwide. This raises concerns about environmental impacts of wind energy infrastructure and operations, particularly for collision of aerial wildlife. Using a network of weather surveillance radars, we quantified numbers, timing and spatial extent of nightly and annual large-scale bird movements over Western Europe. We also mapped onshore wind turbines and calculated potential energy production using wind speed and distribution data. Integrating bird movement patterns, turbine characteristics and energy production, we estimated the number of birds that are potentially at risk of collision because they fly in proximity to wind turbines and at heights of rotating blades. To demonstrate potential for designing measures to mitigate risk to aerial biodiversity, we derive curtailment scenarios and compare costs and benefits for energy production and conserving biodiversity and show that surprisingly efficient trade-offs may be possible. Our findings contribute to broader efforts for minimizing impacts from wind energy production on migratory bird populations while endeavouring to ensure adequate energy supply.

Many migratory bird populations are declining in the face of habitat degradation and climate change, making it important to identify which stages of their annual cycle are most affected in order to guide conservation measures. The Bewick's Swan Cygnus columbianus bewickii , an Arctic‐breeding waterfowl species, has suffered a dramatic population decline (from approximately 30 000 individuals in 1995 to around 13 000 in 2020) due to low reproductive output. However, it is unknown which component of reproduction (breeding propensity, nesting success and post‐hatching survival) is the underlying cause. We analysed GPS tracking and accelerometer data from over 60 adult female Bewick's Swans collected over 8 years, to derive breeding propensity (i.e. the decision to breed or not) and nesting success (i.e. completing incubation) at the individual level, and assess how these metrics are influenced by onset of spring and migration timing. The odds of breeding increased by 3.9% (estimate = −0.04, se = 0.02, P = 0.04) for each day that the swans arrived earlier at the breeding grounds. The odds of successfully nesting increased by 5.8% (estimate = −0.06, se = 0.03, P = 0.06) per day of earlier snowmelt anomaly, suggesting a slight positive effect of climate warming on nesting. However, the proportion of breeding females was consistently low throughout the study period, suggesting that other factors may be driving the long‐term population decline of the species by negatively impacting breeding propensity. Our study shows that distinguishing between different phases of the reproductive cycle is critical for a better understanding of the causes of migratory population declines in a changing world.

Plastic pollution is a global threat to biodiversity. While physical factors are well known, the role of animals in the spread of plastic is less understood. Here, we compared the role of two closely related gull species with different migratory behaviour, dispersive patterns, and habitat use—the yellow-legged gull (Larus michahellis) and the lesser black-backed gull (Larus fuscus)— as biovectors of plastics to a coastal Ramsar site in southwestern Spain. We quantified and characterized plastic present in pellets of both species. We combined pellet analyses with individual movements tracked by GPS and census data to estimate the amount plastic imported, in terms of mass and number of items. 89.9% of pellets contained plastics, mostly as microplastics (< 5 mm), or other anthropogenic debris, with significant differences in plastic polymers and morphotypes between species. Differences in habitat selection between both species explained much of this variation: yellow-legged gulls were associated to urban habitats whereas lesser black-backed gulls relied heavily on landfills. We estimated that during winter ~270 kg of plastics (over 8.1 million plastic particles) were transported by both species into the protected area. Lesser black backed gulls transported plastics from further away and a greater quantity than yellow-legged gull, and plastic load was spatially heterogeneous. These findings show that the two gull species act differently as biovectors of plastic pollution, linking human-modified environments with ecologically important wetlands, creating plastic hotspots far from the source areas. Understanding these pathways of plastic transport is essential for developing management strategies that address waste control.

Learning behavioral taxonomies from animal-borne sensors is challenging because labels are scarce, classes are highly imbalanced, and behaviors may be absent from the annotated set. We study generalized behavior discovery in short multivariate motion snippets from gulls, where each sample is a sequence with 3-axis IMU acceleration (20 Hz) and GPS speed, spanning nine expert-annotated behavior categories. We propose a semi-supervised discovery pipeline that (i) learns an embedding function from the labeled subset, (ii) performs label-guided clustering over embeddings of both labeled and unlabeled samples to form candidate behavior groups, and (iii) decides whether a discovered group is truly novel using a containment score. Our key contribution is a KDE + HDR (highest-density region) containment score that measures how much a discovered cluster distribution is contained within, or contains, each known-class distribution; the best-match containment score serves as an interpretable novelty statistic. In experiments where an entire behavior is withheld from supervision and appears only in the unlabeled pool, the method recovers a distinct cluster and the containment score flags novelty via low overlap, while a negative-control setting with no novel behavior yields consistently higher overlaps. These results suggest that HDR-based containment provides a practical, quantitative test for generalized class discovery in ecological motion time series under limited annotation and severe class imbalance.

Developing robust strategies for human–wildlife coexistence is hampered by our limited understanding of how humans impact animal space use. It is challenging to measure the relative effects of landscape modification and human mobility on wildlife, since these factors are typically confounded. The extreme change in human mobility levels that occurred during COVID-19 lockdowns provided an opportunity to disentangle these impacts. Many gull species are considered urban adapters, capable of roosting, foraging and breeding near humans in highly modified environments. We predicted that lockdown-induced changes in human mobility would affect gulls’ selection for urban and beach habitats because of altered disturbance levels and food availability. We analysed GPS tracking data from 113 individual gulls over multiple years (2015–2022), across three species in western Europe (herring gull Larus argentatus, lesser black-backed gull L. fuscus and yellow-legged gull L. michahellis). We found that, during lockdowns, selection for urban areas increased in two of ten colonies and selection for beaches increased in one colony and decreased in two others. This heterogeneous pattern likely reflects differences in how gull populations respond to opportunities and challenges presented by human-modified landscapes. Understanding this context dependence is emerging as a priority for coordinated efforts to promote sustainable human–wildlife coexistence.