Marine Biology

, Volume 158, Issue 2, pp 329–339 | Cite as

Ocean surface winds drive local-scale movements within long-distance migrations of seabirds

  • María MateosEmail author
  • Gonzalo M. Arroyo
Original Paper


Long-distance migration is a major part of the life cycle of many seabirds. The main processes driving local movements within those long-distance migratory movements are essentially unknown. Here, we studied detailed patterns of the movements with respect to distance from land of the most abundant seabird species migrating across the northernmost part of the Strait of Gibraltar and analysed how ocean surface winds influence those patterns. We did this by using visual and S-band radar surveys. Our results show that seabirds followed lines of travel that were located nearer the coast than randomly expected. Re-sampling techniques and comparison with additional data from ship-based counts corroborated this pattern, which was not substantially affected by the decrease in detection at distances of up to 3,000 m. Wind direction and speed covaried with local patterns of flight trajectories in a general manner. All the seabirds responded to headwinds by approaching the coast in proportion to the magnitude of wind intensity. Such a change in flight patterns could be a strategy to reduce the effect of headwinds, by approaching the coast where wind intensity was reduced by orographic factors. Under tailwind conditions, seabirds tended to fly further from the coast, profiting from increasing winds further from shore. Our results imply that modification of off-shore distance in relation to conditions of ocean surface winds may be an energetically advantageous strategy for migrating seabirds. Off-shore distances were also dependent on global and local migratory behaviour of different species, but not on flight type.


Radar Flock Size Radar Operator Ocean Surface Wind Visual Observer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was conducted within a collaboration agreement between the Migres Foundation and the University of Cádiz. The radar facilities were supplied by Ceowind Capital Energy Off-shore Company. María Mateos was granted with an FPU fellowship by the Junta de Andalucía. We thank Migres Foundation technical staff for their help in the fieldwork and Dr. Bruno Bruderer, Dr. Mark Desholm, Dr. Jacob González-Solís, Andy Paterson and two anonymous referees for their comments and suggestions on previous versions of this manuscript.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Biology Department, Faculty of Marine and Environmental SciencesUniversity of CadizPuerto Real, CadizSpain
  2. 2.Fundación Migres, Complejo Huerta GrandePelayo, Algeciras, CadizSpain

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