Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Individual improvements and selective mortality shape lifelong migratory performance


This article has been updated


Billions of organisms, from bacteria to humans, migrate each year1 and research on their migration biology is expanding rapidly through ever more sophisticated remote sensing technologies2,3,4. However, little is known about how migratory performance develops through life for any organism. To date, age variation has been almost systematically simplified into a dichotomous comparison between recently born juveniles at their first migration versus adults of unknown age5,6,7. These comparisons have regularly highlighted better migratory performance by adults compared with juveniles6, but it is unknown whether such variation is gradual or abrupt and whether it is driven by improvements within the individual, by selective mortality of poor performers, or both. Here we exploit the opportunity offered by long-term monitoring of individuals through Global Positioning System (GPS) satellite tracking to combine within-individual and cross-sectional data on 364 migration episodes from 92 individuals of a raptorial bird, aged 1–27 years old. We show that the development of migratory behaviour follows a consistent trajectory, more gradual and prolonged than previously appreciated, and that this is promoted by both individual improvements and selective mortality, mainly operating in early life and during the pre-breeding migration. Individuals of different age used different travelling tactics and varied in their ability to exploit tailwinds or to cope with wind drift. All individuals seemed aligned along a race with their contemporary peers, whose outcome was largely determined by the ability to depart early, affecting their subsequent recruitment, reproduction and survival. Understanding how climate change and human action can affect the migration of younger animals may be the key to managing and forecasting the declines of many threatened migrants.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: A river of raptors.
Figure 2: Migration performance across and within individuals.
Figure 3: Age-related changes in average speed, duration and timing of pre-breeding migrations.
Figure 4: Age-related changes in average speed, duration and timing of post-breeding migrations.

Change history

  • 19 November 2014

    A minor change was made to the main text.


  1. 1

    Dingle, H. Migration: The Biology of Life on the Move (Oxford Univ. Press, 1996)

    Google Scholar 

  2. 2

    Wikelski, M. et al. Going wild: what a global small-animal tracking system could do for experimental biologists. J. Exp. Biol. 210, 181–186 (2007)

    PubMed  Google Scholar 

  3. 3

    Bowlin, M. S. et al. Grand challenges in migration biology. Integr. Comp. Biol. 50, 261–279 (2010)

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Milner-Gulland, E. J., Fryxell, J. M. & Sinclair, A. R. E. Animal Migration: A Synthesis (Oxford Univ. Press, 2011)

    Google Scholar 

  5. 5

    Berthold, P. Bird Migration: A General Survey (Oxford Univ. Press, 2001)

    Google Scholar 

  6. 6

    Newton, I. The Migration Ecology of Birds (Academic, 2008)

    Google Scholar 

  7. 7

    Rappole, J. H. The Avian Migrant: The Biology of Bird Migration (Columbia Univ. Press, 2013)

    Google Scholar 

  8. 8

    Alerstam, T., Hake, M. & Kjellén, N. Temporal and spatial patterns of repeated migratory journeys by ospreys. Anim. Behav. 71, 555–566 (2006)

    Google Scholar 

  9. 9

    Robinson, W. D. et al. Integrating concepts and technologies to advance the study of bird migration. Front. Ecol. Environ. 8, 354–361 (2010)

    Google Scholar 

  10. 10

    Hake, M., Kjellén, N. & Alerstam, T. Age dependent migration strategy in honey buzzards Pernis apivorus tracked by satellite. Oikos 103, 385–396 (2003)

    Google Scholar 

  11. 11

    Strandberg, R. et al. Complex timing of Marsh Harrier Circus aeroginosus migration due to pre- and post-migratory movements. Ardea 96, 159–171 (2008)

    Google Scholar 

  12. 12

    Thorup, K., Alerstam, T., Hake, M. & Kjellén, N. Bird orientation: compensation for wind drift in migrating raptors is age dependent. Proc. R. Soc. Lond. B 270, S8–S11 (2003)

    Google Scholar 

  13. 13

    Dodge, S. et al. Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 1471–2970 (2014)

    Google Scholar 

  14. 14

    Schifferli, A. Vom Zug schweizerischer und deutscher Schwarzer Milane Milvus migrans nach Ringfunden. Orn. Beob. 64, 34–51 (1967)

    Google Scholar 

  15. 15

    Sergio, F., Blas, J. & Hiraldo, F. Predictors of floater status in a long-lived bird: a cross sectional and longitudinal test of hypotheses. J. Anim. Ecol. 78, 109–118 (2009)

    PubMed  Google Scholar 

  16. 16

    Sergio, F. et al. Age-structured vital rates in a long-lived raptor: implications for population growth. Basic Appl. Ecol. 12, 107–115 (2011)

    Google Scholar 

  17. 17

    Zalles, J. I. & Bildstein, K. L. Raptor Watch: A Global Directory of Raptor Migration Sites (Birdlife International, 2000)

    Google Scholar 

  18. 18

    Bildstein, K. L. Migrating Raptors of the World: Their Ecology and Conservation (Cornell Univ. Press, 2006)

    Google Scholar 

  19. 19

    Kerlinger, P. Flight Strategies of Migrating Hawks (Univ. of Chicago Press, 1989)

    Google Scholar 

  20. 20

    Sergio, F. et al. Raptor nest decorations are a reliable threat against conspecifics. Science 331, 327–330 (2011)

    ADS  CAS  PubMed  Google Scholar 

  21. 21

    Hedenström, A., Alerstam, T., Green, M. & Gudmundsson, G. A. Adaptive variation of airspeed in relation to wind, altitude and climb rate by migrating birds in the Arctic. Behav. Ecol. Sociobiol. 52, 308–317 (2005)

    Google Scholar 

  22. 22

    Liechti, F. Birds: blowin’ by the wind? J. Ornithol. 147, 202–211 (2006)

    Google Scholar 

  23. 23

    Barry, R. G. & Chorley, R. J. Atmosphere, Weather and Climate (Routledge, 2010)

    Google Scholar 

  24. 24

    Sergio, F. & Penteriani, V. Public information and territory establishment in a loosely colonial raptor. Ecology 86, 340–346 (2005)

    Google Scholar 

  25. 25

    Wilcove, D. S. & Wikelski, M. Going, going, gone: is animal migration disappearing? PLoS Biol. 6, e188 (2008)

    PubMed  PubMed Central  Google Scholar 

  26. 26

    Blas, J., Sergio, F. & Hiraldo, F. Age-related reproduction in a long-lived raptor: a cross-sectional and longitudinal study. Ecography 32, 647–657 (2009)

    Google Scholar 

  27. 27

    Sergio, F. et al. Short and long-term consequences of individual and territory quality in a diurnal raptor. Oecologia 160, 507–514 (2009)

    ADS  PubMed  Google Scholar 

  28. 28

    Tanferna, A., López-Jiménez, L., Blas, J., Hiraldo, F. & Sergio, F. Different location sampling frequencies by satellite tags yield different estimates of migration performance: pooling data requires a common protocol. PLoS ONE 7, e49659 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Kenward, R. A Manual for Wildlife Radio Tagging (Academic, 2001)

    Google Scholar 

  30. 30

    Alerstam, T. in Biomechanics in Animal Behaviour (eds Domenici, P. & Blake, R. W. ) 105–123 (BIOS Scientific Publishers, 2000)

    Google Scholar 

  31. 31

    Klaassen, R. H. G., Hake, M., Strandberg, R. & Alerstam, T. Geographical and temporal flexibility in the response to crosswinds by migrating raptors. Proc. Biol. Sci. 278, 1339–1346 (2011)

    PubMed  Google Scholar 

  32. 32

    Klaassen, R. H. G., Strandberg, R., Hake, M. & Alerstam, T. Flexibility in daily travel routines causes regional variation in bird migration speed. Behav. Ecol. Sociobiol. 62, 1427–1432 (2008)

    Google Scholar 

  33. 33

    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)

    ADS  Google Scholar 

  34. 34

    Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005)

    ADS  Google Scholar 

  35. 35

    Bohrer, G. et al. Estimating updraft velocity components over large spatial scales: contrasting migration strategies of golden eagles and turkey vultures. Ecol. Lett. 15, 96–103 (2012)

    ADS  PubMed  Google Scholar 

  36. 36

    Stull, R. B. An Introduction to Boundary Layer Meteorology (Kluwer Academic, 1988)

    MATH  Google Scholar 

  37. 37

    Shannon, H. D., Young, G. S., Yates, M. A., Fuller, M. R. & Seegar, W. S. Measurement of thermal updraft intensity over complex terrain using American white pelicans and a simple boundary-layer forecast model. Boundary-Layer Meteorol. 104, 167–199 (2002)

    ADS  Google Scholar 

  38. 38

    Safi, K. et al. Flying with the wind: scale dependency of speed and direction measurements in modelling speed support in avian flight. Movement Ecol. 1, 4 (2013)

    Google Scholar 

  39. 39

    Batschelet, E. Circular Statistics in Biology (Academic, 1981)

    MATH  Google Scholar 

  40. 40

    Tabachnick, B. G. & Fidell, L. S. Using Multivariate Statistics (HarperCollins, 1996)

    Google Scholar 

  41. 41

    Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005)

    PubMed  Google Scholar 

  42. 42

    Despland, E., Rosenberg, J. & Simpson, S. J. Landscape structure and locust swarming: a satellite's eye view. Ecography 27, 381–391 (2004)

    Google Scholar 

  43. 43

    Schaub, M., Kania, W. & Köppen, U. Variation of primary production during winter induces synchrony in survival rates in migratory white storks Ciconia ciconia . J. Anim. Ecol. 74, 656–666 (2005)

    Google Scholar 

  44. 44

    Trierweiler, C. et al. A Palaearctic migratory raptor species tracks shifting prey availability within its wintering range in the Sahel. J. Anim. Ecol. 82, 107–120 (2013)

    PubMed  Google Scholar 

  45. 45

    Alerstam, T. Bird Migration (Cambridge Univ. Press, 1993)

    MATH  Google Scholar 

  46. 46

    Viñuela, J. & Bustamante, J. Effect of growth and hatching asynchrony on the fledging age of black and red kites. Auk 109, 748–757 (1992)

    Google Scholar 

  47. 47

    Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009)

    MATH  Google Scholar 

  48. 48

    Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 85, 935–956 (2010)

    PubMed  Google Scholar 

  49. 49

    Crawley, M. J. The R Book (Wiley, 2007)

    MATH  Google Scholar 

  50. 50

    Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol 4, 133–142 (2013)

    Google Scholar 

  51. 51

    R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2009)

Download references


We thank F. J. Chicano, F. G. Vilches, J. M. Giralt and M. Anjos for help in the field, I. Afán and D. Aragonés for support with GIS analyses, the personnel of the Reserva Biológica de Doñana for logistical help and accommodation, the LEM-EBD for molecular sexing, and Microwave Telemetry for technical support. Part of the study was funded by Natural Research Ltd and research projects CGL2008-01781, CGL2011-28103 and CGL2012-32544 of the Spanish Ministry of Science and Innovation/Economy and Competitiveness and FEDER funds, 511/2012 of the Spanish Ministry of Agriculture, Food and the Environment (Autonomous Organism of National Parks), JA-58 of the Consejería de Medio Ambiente de la Junta de Andalucía and by the Excellence Projects RNM 1790, RNM 3822 and RNM 7307 of the Junta de Andalucía. R.D.S. was supported by the Juan de la Cierva Programme and by the Severo Ochoa Programme for Centres of Excellence of the Spanish Ministry of Economy and Competitiveness (SEV-2012-0262). J.B. was supported by a Ramón y Cajal contract from the CSIC.

Author information




F.S., A.T., L.L.J., J.B. and F.H. conducted fieldwork. F.S., A.T., R.D.S., G.T. and D.P. prepared the database, extracted and processed the environmental data from internet sources and analysed the data. F.S. and F.H. obtained funding. R.D.S., A.T. and F.S. developed the Supplementary Videos. All authors took part in the conceptual planning of the study and in the preparation of the manuscript.

Corresponding author

Correspondence to Fabrizio Sergio.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Mean components of pre-breeding migration.

aj, Migration components varied cross-sectionally with age in timing (a, b), speed of progression (c, d), duration (e, f, g, h), route length (i) and longitudinal position of the route (j). Error bars represent 1 s.e.m.

Extended Data Figure 2 Mean components of post-breeding migration.

aj, Migration components varied cross-sectionally with age in timing (a, b), speed of progression (c, d), duration (e, f, g, h), route length (i) and longitudinal position of the route (j). Error bars represent 1 s.e.m.

Extended Data Table 1 Estimates of pre-breeding and post-breeding migration by 92 individual black kites of Doñana National Park, southwestern Spain
Extended Data Table 2 Summary of main results of the mixed models examining the effects of age and environmental variables on migration components
Extended Data Table 3 Cross-sectional effect of age and environmental variables on migration components in the pre-breeding migration
Extended Data Table 4 Cross-sectional effect of age and environmental variables on migration components in the post-breeding migration
Extended Data Table 5 Longitudinal effect of age, migration timing and environmental variables on migration components in the pre-breeding migration
Extended Data Table 6 Repeatability of migration components for different age classes
Extended Data Table 7 Mixed effects models testing the relationship between migration components and fitness
Extended Data Table 8 Longitudinal effect of age, migration timing and environmental variables on migration components in the post-breeding migration

Related audio

Supplementary information

Video 1: The speed of kites in the pre-breeding migration varied with age and was at the maximum in birds of age 3-6, intermediate for birds above 7 years old and lowest in 1-2 years olds

In this video simulation, individuals of all ages are imposed to depart for migration at the same time. They then travel with a speed proportional to the mean value for their age, class and progress along the average population route (see Methods). Under this scenario of equal departure timings, individuals of 3-6 years of age are the first to arrive to the breeding quarters, followed by kites older than seven years and then by 1-2 years old birds. This pattern changes radically when incorporating differences in timing of departure among age classes (see Supplementary Video 2). (WMV 2802 kb)

Video 2: When incorporating differences in timing of departure among age-classes, older birds always arrived at the breeding quarters before younger ones, independently of differences in speed performance.

In this simulation, differences in the timings of departure among age classes are proportional to the observed mean differences in timing. The birds then travel with a speed proportional to the mean value for their age, class and progress along the average route, as in Supplementary Video 1. Under this scenario, age-differences in departure times are so large that they fully dictate the order of arrival and older birds always arrive earlier than younger ones. Therefore, the higher speed of 3-6 years olds, shown in Supplementary Video 1, is swamped by the capability to depart early, which is typical of older, more experienced individuals. Note that the migration speeds in Video 1 and 2 have been arranged so that, in both cases, the overall video-duration is 50 seconds, while the relationship between migratory speed and the time lags in sequential departures are always proportional to their observed averages. (WMV 2755 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sergio, F., Tanferna, A., De Stephanis, R. et al. Individual improvements and selective mortality shape lifelong migratory performance. Nature 515, 410–413 (2014).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links