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Marine Biology

, Volume 162, Issue 11, pp 2279–2289 | Cite as

Year-round distribution suggests spatial segregation of Cory’s shearwaters, based on individual experience

  • Rogério V. Missagia
  • Jaime A. Ramos
  • Maite Louzao
  • Karine Delord
  • Henri Weimerskirch
  • Vitor H. Paiva
Original Paper

Abstract

Recent studies have shown that pelagic seabirds with little breeding experience are wide ranging individuals exploring different foraging grounds and occupying generally more pelagic habitats than more experienced birds. This study compared the spatial dynamic of the year-round distribution and behaviour between two different population components: experienced (Exp; >2 years of successful breeding) and inexperienced (Inexp; ≤2 successful years) Cory’s shearwaters (Calonectris borealis) individuals from Berlenga Island, offshore the Portuguese coast. Our aim was to verify the occurrence of variations in the at-sea activity, foraging habitats and isotopic niches of Exp (N = 11) and Inexp (N = 11) individuals, during their breeding and non-breeding phases. Our results confirmed differences in the migratory routes and foraging grounds during the annual cycle between these two population components: Inexp birds exhibited a more pelagic behaviour than Exp birds, with extensive migratory routes, marked by several stopovers, and a higher number of non-breeding areas. Exp individuals migrated through shorter routes, and wintered in fewer locations. Exp individuals foraged on coastal, shallow and cold water areas and showed higher carbon and nitrogen isotopic values, while Inexp birds foraged more on pelagic, windy and frontal zones and exhibited lower carbon and nitrogen isotopic values. Our results suggest that experience plays a relevant role in explaining the spatial distribution and behaviour of pelagic seabirds such as Cory’s shearwaters. Future research with larger sample sizes should focus on comparing the behaviour of juvenile, immature, first-time breeders and breeders with increasing experience and age.

Keywords

Migratory Route Isotopic Niche Pelagic Seabird Inexperienced Bird Outgoing Route 
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.

Notes

Acknowledgments

We would like to thank the Instituto da Conservação da Natureza e Florestas (ICNF) for their logistical support (lodging), especially the wardens of the Reserva Natural das Berlengas, Paulo Crisóstomo and Eduardo Mourato, for their companionship. We also thank Filipe Ceia and Lucas Krüger for help during fieldwork. GLS devices were financed by the EU INTERREG project FAME: The Future of the Atlantic Marine Environment and by former projects from Centre d’Etudes Biologiques de Chizé. R.M. acknowledges the study grant given by the EMMC-EMAE consortium and the European Commission. V.H.P. acknowledges the postdoctoral grants given by Fundação para a Ciência e Tecnologia (FCT; SFRH/BPD/63825/2009 and SFRH/BPD/85024/2012). The experimental approach was conducted with permission from the Portuguese Government—‘Instituto de Conservação da Natureza e Florestas (ICNF)’—with permit No. 89/2011/CAPT. All methods used in this study comply with the Portuguese laws Nos. 140/99, 49/2005, 316/89 and 180/2008.

Supplementary material

227_2015_2762_MOESM1_ESM.docx (128 kb)
Supplementary material 1 (DOCX 128 kb)

References

  1. Afán I, Navarro J, Cardador L et al (2014) Foraging movements and habitat niche of two closely related seabirds breeding in sympatry. Mar Biol 161:657–668. doi: 10.1007/s00227-013-2368-4 CrossRefGoogle Scholar
  2. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688. doi: 10.1111/j.1365-2699.2006.01584.x CrossRefGoogle Scholar
  3. Bates D, Maechler M, Bolker B, Walker S (2013) lme4: linear mixed-effects models using Eigen and S4 classes. R package version 10-4. https://cran.r-project.org/web/packages/lme4/lme4.pdf
  4. Calenge C (2006) The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol Model 197:516–519. doi: 10.1016/j.ecolmodel.2006.03.017 CrossRefGoogle Scholar
  5. Catry P, Dias MP, Phillips RA, Granadeiro JP (2011) different means to the same end: long-distance migrant seabirds from two colonies differ in behaviour, despite common wintering grounds. PLoS One 6:e26079. doi: 10.1371/journal.pone.0026079 CrossRefGoogle Scholar
  6. Catry P, Dias MP, Phillips RA, Granadeiro JP (2013) Carry-over effects from breeding modulate the annual cycle of a long-distance migrant: an experimental demonstration. Ecology 94:1230–1235CrossRefGoogle Scholar
  7. Dias MP, Granadeiro JP, Phillips RA et al (2011) Breaking the routine: individual Cory’s shearwaters shift winter destinations between hemispheres and across ocean basins. Proc R Soc B Biol Sci 278:1786–1793. doi: 10.1098/rspb.2010.2114 CrossRefGoogle Scholar
  8. Edrén SMC, Wisz MS, Teilmann J et al (2010) Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy. Ecography 33:698–708. doi: 10.1111/j.1600-0587.2009.05901.x CrossRefGoogle Scholar
  9. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. doi: 10.1111/j.2006.0906-7590.04596.x CrossRefGoogle Scholar
  10. Forslund P, Pärt T (1995) Age and reproduction in birds—hypotheses and tests. Trends Ecol Evol 10:374–378. doi: 10.1016/S0169-5347(00)89141-7 CrossRefGoogle Scholar
  11. Froy H, Lewis S, Catry P et al (2015) Age-related variation in foraging behaviour in the wandering albatross at South Georgia: no evidence for senescence. PLoS One. doi: 10.1371/journal.pone.0116415 Google Scholar
  12. Gonzalez-Solis J, Croxall JP, Oro D, Ruiz X (2007) Trans-equatorial migration and mixing in the wintering areas of a pelagic seabird. Front Ecol Environ 5:297–301. doi:10.1890/1540-9295(2007)5[297:TMAMIT]2.0.CO;2CrossRefGoogle Scholar
  13. González-Solís J, Felicísimo ÁM, Fox JW et al (2009) Influence of sea surface winds on shearwater migration detours. Mar Ecol Prog Ser 391:221–230. doi: 10.3354/meps08128 CrossRefGoogle Scholar
  14. Grémillet D, Lewis S, Drapeau L (2008) Spatial match–mismatch in the Benguela upwelling zone: should we expect chlorophyll and sea-surface temperature to predict marine predator distributions? J Appl Ecol. doi: 10.1111/j.1365-2664.2007.01447.x Google Scholar
  15. Guisan A, Graham CH, Elith J (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340. doi: 10.1111/j.1472-4642.2007.00342.x CrossRefGoogle Scholar
  16. Haug FD, Paiva VH, Werner AC, Ramos JA (2015) Foraging by experienced and inexperienced Cory’s shearwater along a 3-year period of ameliorating foraging conditions. Mar Biol. doi: 10.1007/s00227-015-2612-1 Google Scholar
  17. Igual JM, Forero MG, Tavecchia G et al (2005) Short-term effects of data-loggers on Cory’s shearwater (Calonectris diomedea). Mar Biol 146:619–624. doi: 10.1007/s00227-004-1461-0 CrossRefGoogle Scholar
  18. Jackson AL, Inger R, Parnell AC, Bearhop S (2011) Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J Anim Ecol 80:595–602. doi: 10.1111/j.1365-2656.2011.01806.x CrossRefGoogle Scholar
  19. Jones MGW, Ryan PG (2014) Effects of pre-laying attendance and body condition on long-term reproductive success in Wandering Albatrosses. Emu 114:137–145. doi: 10.1071/MU12054 Google Scholar
  20. Kubetzki U, Garthe S, Fifield D et al (2009) Individual migratory schedules and wintering areas of northern gannets. Mar Ecol Prog Ser 391:257–265. doi: 10.3354/meps08254 CrossRefGoogle Scholar
  21. Lecoq M, Catry P, Granadeiro JP (2011) Population trends of Cory’s shearwaters Calonectris diomedea borealis breeding at Berlengas Islands, Portugal. Airo 20:36–41Google Scholar
  22. Le Vaillant M, Le Bohec C, Prud’Homme O et al (2013) How age and sex drive the foraging behaviour in the king penguin. Mar Biol 160:1147–1156. doi: 10.1007/s00227-013-2167-y CrossRefGoogle Scholar
  23. Longhurst AR (2010) Ecological geography of the sea. Academic Press, San DiegoGoogle Scholar
  24. Louzao M, Delord K, Garcia D et al (2012) Protecting persistent dynamic oceanographic features: transboundary conservation efforts are needed for the critically endangered balearic shearwater. PLoS One. doi: 10.1371/journal.pone.0035728 Google Scholar
  25. Mackley EK, Phillips RA, Silk J et al (2010) Free as a bird? Activity patterns of albatrosses during the nonbreeding period. Mar Ecol Prog Ser 406:291–303. doi: 10.3354/meps08532 CrossRefGoogle Scholar
  26. Minagawa M, Wada E (1984) Stepwise enrichment of N-15 along food-chains—Further evidence and the relation between δ-N-15 and animal age. Geochim Cosmochim Acta 48:1135–1140. doi: 10.1016/0016-7037(84)90204-7 CrossRefGoogle Scholar
  27. Monaghan P, Charmantier A, Nussey DH, Ricklefs RE (2008) The evolutionary ecology of senescence. Funct Ecol 22:371–378. doi: 10.1111/j.1365-2435.2008.01418.x CrossRefGoogle Scholar
  28. Mougin J-L, Jouanin C, Roux F (2000) Démographie du puffin cendré Calonectris diomedea de Selvagem Grande. Rev Ecol Terre Vie 55:275–290Google Scholar
  29. Navarro J, González-Solís J (2009) Environmental determinants of foraging strategies in Cory’s shearwaters Calonectris diomedea. Mar Ecol Prog Ser 378:259–267. doi: 10.3354/meps07880 CrossRefGoogle Scholar
  30. Navarro J, Coll M, Somes CJ, Olson RJ (2013) Trophic niche of squids Insights from isotopic data in marine systems worldwide. Deep Sea Res II. doi: 10.1016/j.dsr2.2013.01.031 Google Scholar
  31. Nevoux M, Weimerskirch H, Barbraud C (2007) Environmental variation and experience-related differences in the demography of the long-lived black-browed albatross. J Anim Ecol 76:159–167. doi: 10.1111/j.1365-2656.2006.01191.x CrossRefGoogle Scholar
  32. Paiva VH, Geraldes P, Ramírez I et al (2010a) Oceanographic characteristics of areas used by Cory’s shearwaters during short and long foraging trips in the North Atlantic. Mar Biol 157:1385–1399. doi: 10.1007/s00227-010-1417-5 CrossRefGoogle Scholar
  33. Paiva VH, Geraldes P, Ramírez I et al (2010b) Foraging plasticity in a pelagic seabird species along a marine productivity gradient. Mar Ecol Prog Ser 398:259–274. doi: 10.3354/meps08319 CrossRefGoogle Scholar
  34. Pardo D, Barbraud C, Authier M, Weimerskirch H (2013) Evidence for an age-dependent influence of environmental variations on a long-lived seabird’s life-history traits. Ecology 94:208–220. doi: 10.1890/12-0215.1 CrossRefGoogle Scholar
  35. Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source partitioning using stable isotopes: coping with too much variation. PLoS One 5:e9672. doi: 10.1371/journal.pone.0009672 CrossRefGoogle Scholar
  36. Peron C, Grémillet D (2013) Tracking through life stages: adult, immature and juvenile autumn migration in a long-lived seabird. PLoS One. doi: 10.1371/journal.pone.0072713 Google Scholar
  37. Phillips RA, Xavier JC, Croxall JP (2003) Effects of satellite transmitters on albatrosses and petrels. Auk 120:1082–1090. doi:10.1642/0004-8038(2003)120[1082:EOSTOA]2.0.CO;2CrossRefGoogle Scholar
  38. Phillips RA, Silk J, Croxall JP et al (2004) Accuracy of geolocation estimates for flying seabirds. Mar Ecol Prog Ser 266:265–272. doi: 10.3354/meps266265 CrossRefGoogle Scholar
  39. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. doi: 10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  40. Quillfeldt P, McGill R, Furness RW (2005) Diet and foraging areas of Southern Ocean seabirds and their prey inferred from stable isotopes: review and case study of Wilson’s storm-petrel. Mar Ecol Prog Ser 295:295–304. doi: 10.3354/meps295295 CrossRefGoogle Scholar
  41. Quillfeldt P, Masello JF, Navarro J, Phillips RA (2013) Year-round distribution suggests spatial segregation of two small petrel species in the South Atlantic. J Biogeogr 40:430–441. doi: 10.1111/jbi.12008 CrossRefGoogle Scholar
  42. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
  43. Ramírez I, Paiva VH, Menezes D et al (2013) Year-round distribution and habitat preferences of the Bugio petrel. Mar Ecol Prog Ser 476:269–284. doi: 10.3354/meps10083 CrossRefGoogle Scholar
  44. Ramos JA, Moniz Z, Sola E, Monteiro LR (2003) Reproductive measures and chick provisioning of Cory’s Shearwater Calonectris diomedea borealis in the Azores. Bird Study 50:47–54CrossRefGoogle Scholar
  45. Ramos R, Gonzalez-Solis J, Ruiz X (2009) Linking isotopic and migratory patterns in a pelagic seabird. Oecologia 160:97–105. doi: 10.1007/s00442-008-1273-x CrossRefGoogle Scholar
  46. Riotte-Lambert L, Weimerskirch H (2013) Do naive juvenile seabirds forage differently from adults? Proc R Soc B. doi: 10.1098/rspb.2013.1434 Google Scholar
  47. Somes CJ, Schmittner A, Galbraith ED et al (2010) Simulating the global distribution of nitrogen isotopes in the ocean. Glob Biogeochem Cycles. doi: 10.1029/2009GB003767 Google Scholar
  48. Thiebot JB, Lescroel A, Pinaud D et al (2011) Larger foraging range but similar habitat selection in non-breeding versus breeding sub-Antarctic penguins. Antarct Sci 23:117–126. doi: 10.1017/S0954102010000957 CrossRefGoogle Scholar
  49. Verbruggen H, Tyberghein L, Belton GS et al (2013) Improving transferability of introduced species’ distribution models: new tools to forecast the spread of a highly invasive seaweed. PLoS One. doi: 10.1371/journal.pone.0068337 Google Scholar
  50. Votier SC, Grecian WJ, Patrick S, Newton J (2010) Inter-colony movements, at-sea behaviour and foraging in an immature seabird: results from GPS-PPT tracking, radio-tracking and stable isotope analysis. Mar Biol 158:355–362. doi: 10.1007/s00227-010-1563-9 CrossRefGoogle Scholar
  51. Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342. doi: 10.1890/10-1171.1 CrossRefGoogle Scholar
  52. Weimerskirch H, Gault A, Cherel Y (2005) Prey distribution and patchiness: factors in foraging success and efficiency of wandering albatrosses. Ecology 86:2611–2622. doi: 10.1890/04-1866 CrossRefGoogle Scholar
  53. Weimerskirch H, Cherel Y, Delord K et al (2014) Lifetime foraging patterns of the wandering albatross: life on the move! J Exp Mar Biol Ecol 450:68–78. doi: 10.1016/j.jembe.2013.10.021 CrossRefGoogle Scholar
  54. Zimmer I, Ropert-Coudert Y, Kato A et al (2011) Does Foraging Performance Change with Age in Female Little Penguins (Eudyptula minor)? PLoS One. doi: 10.1371/journal.pone.0016098 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Rogério V. Missagia
    • 1
  • Jaime A. Ramos
    • 1
  • Maite Louzao
    • 2
    • 3
  • Karine Delord
    • 4
  • Henri Weimerskirch
    • 4
  • Vitor H. Paiva
    • 1
  1. 1.MARE – Marine and Environmental Sciences Centre, Department of Life SciencesUniversity of CoimbraCoimbraPortugal
  2. 2.CO XixónInstituto Español de OceanografíaXixónSpain
  3. 3.AZTI FundazioaPasaiaSpain
  4. 4.Centre d’Etudes Biologiques de ChizéUMR 7372 CNRS – Université de la RochelleVilliers en BoisFrance

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