Data from: Towards a new understanding of migration timing: slower spring than autumn migration in geese reflects different decision rules for stopover use and departure

According to migration theory and several empirical studies, long-distance migrants are more time-limited during spring migration and should therefore migrate faster in spring than in autumn. Competition for the best breeding sites is supposed to be the main driver, but timing of migration is often also influenced by environmental factors such as food availability and wind conditions. Using GPS tags, we tracked 65 greater white-fronted geese Anser albifrons migrating between western Europe and the Russian Arctic during spring and autumn migration over six different years. Contrary to theory, our birds took considerably longer for spring migration (83 days) than autumn migration (42 days). This difference in duration was mainly determined by time spent at stopovers. Timing and space use during migration suggest that the birds were using different strategies in the two seasons: In spring they spread out in a wide front to acquire extra energy stores in many successive stopover sites (to fuel capital breeding), which is in accordance with previous results that white-fronted geese follow the green wave of spring growth. In autumn they filled up their stores close to the breeding grounds and waited for supportive wind conditions to quickly move to their wintering grounds. Selection for supportive winds was stronger in autumn, when general wind conditions were less favourable than in spring, leading to similar flight speeds in the two seasons. In combination with less stopover time in autumn this led to faster autumn than spring migration. White-fronted geese thus differ from theory that spring migration is faster than autumn migration. We expect our findings of different decision rules between the two migratory seasons to apply more generally, in particular in large birds in which capital breeding is common, and in birds that meet other environmental conditions along their migration route in autumn than in spring

Hotspots in the grid : Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa

Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero carbon energy. We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species' specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5 x 5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines. Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey. Synthesis and applications. We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts