Climate-driven range shifts of the king penguin in a fragmented ecosystem

Matters Arising to this article was published on 28 January 2019

Abstract

Range shift is the primary short-term species response to rapid climate change, but it is often hampered by natural or anthropogenic habitat fragmentation. Different critical areas of a species’ niche may be exposed to heterogeneous environmental changes and modelling species response under such complex spatial and ecological scenarios presents well-known challenges. Here, we use a biophysical ecological niche model validated through population genomics and palaeodemography to reconstruct past range shifts and identify future vulnerable areas and potential refugia of the king penguin in the Southern Ocean. Integrating genomic and demographic data at the whole-species level with specific biophysical constraints, we present a refined framework for predicting the effect of climate change on species relying on spatially and ecologically distinct areas to complete their life cycle (for example, migratory animals, marine pelagic organisms and central-place foragers) and, in general, on species living in fragmented ecosystems.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Past and future breeding range of the king penguin.
Fig. 2: King and emperor penguins’ past demography in response to Quaternary climate change.
Fig. 3: Convergent and divergent effects of climate change.

References

  1. 1.

    Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).

    CAS  Google Scholar 

  2. 2.

    Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–224 (2015).

    Google Scholar 

  3. 3.

    Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).

    CAS  Google Scholar 

  4. 4.

    Garcia, R. A., Cabeza, M., Rahbek, C. & Araújo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).

    Google Scholar 

  5. 5.

    Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).

    CAS  Google Scholar 

  6. 6.

    Gouveia, S. F. et al. Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Glob. Change Biol. 22, 2003–2012 (2016).

    Google Scholar 

  7. 7.

    Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).

    CAS  Google Scholar 

  8. 8.

    Saraux, C. et al. Reliability of flipper-banded penguins as indicators of climate change. Nature 469, 203–206 (2011).

    CAS  Google Scholar 

  9. 9.

    Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).

    Google Scholar 

  10. 10.

    Thuiller, W. et al. A road map for integrating eco-evolutionary processes into biodiversity models. Ecol. Lett. 16, 94–105 (2013).

    Google Scholar 

  11. 11.

    Elith, J., Kearney, M. & Phillips, S. The art of modelling range‐shifting species. Methods Ecol. Evol. 1, 330–342 (2010).

    Google Scholar 

  12. 12.

    Fordham, D. A. et al. Population dynamics can be more important than physiological limits for determining range shifts under climate change. Glob. Change Biol. 19, 3224–3237 (2013).

    Google Scholar 

  13. 13.

    Fordham, D. A., Brook, B. W., Moritz, C. & Nogués-Bravo, D. Better forecasts of range dynamics using genetic data. Trends Ecol. Evol. 29, 436–443 (2014).

    Google Scholar 

  14. 14.

    Fordham, D. A. et al. Predicting and mitigating future biodiversity loss using long-term ecological proxies. Nat. Clim. Change 6, 909–916 (2016).

    Google Scholar 

  15. 15.

    Alter, S. E. et al. Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100. Mol. Ecol. 24, 1510–1522 (2015).

    CAS  Google Scholar 

  16. 16.

    Kearney, M., Porter, W. P., Williams, C., Ritchie, S. & Hoffmann, A. A. Integrating biophysical models and evolutionary theory to predict climatic impacts on species’ ranges: the dengue mosquito in Australia. Funct. Ecol. 23, 528–538 (2009).

    Google Scholar 

  17. 17.

    Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    CAS  Google Scholar 

  18. 18.

    Bost, C. A. et al. Large-scale climatic anomalies affect marine predator foraging behaviour and demography. Nat. Commun. 6, 8220 (2015).

    CAS  Google Scholar 

  19. 19.

    Trucchi, E. et al. King penguin demography since the last glaciation inferred from genome-wide data. Proc. R. Soc. B 281, 20140528 (2014).

    Google Scholar 

  20. 20.

    Péron, C., Weimerskirch, H. & Bost, C.-A. Projected poleward shift of king penguins’ (Aptenodytes patagonicus) foraging range at the Crozet Islands, southern Indian Ocean. Proc. R. Soc. B 279, 2515–2523 (2012).

    Google Scholar 

  21. 21.

    Le Bohec, C. et al. King penguin population threatened by Southern Ocean warming. Proc. Natl Acad. Sci. USA 105, 2493–2497 (2008).

    Google Scholar 

  22. 22.

    Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter. Ecography 32, 34–45 (2009).

    Google Scholar 

  23. 23.

    Clucas, G. V. et al. Dispersal in the sub-Antarctic: king penguins show remarkably little population genetic differentiation across their range. BMC Evol. Biol. 16, 211 (2016).

    Google Scholar 

  24. 24.

    Barrat, A. Quelques aspects de la biologie et de l’écologie du manchot royal Aptenodytes patagonicus des îles Crozet. Com. Natl Fr. Rech. Antarct. 40, 9–51 (1976).

    Google Scholar 

  25. 25.

    Heupink, T. H., van den Hoff, J. & Lambert, D. M. King penguin population on Macquarie Island recovers ancient DNA diversity after heavy exploitation in historic times. Biol. Lett. 8, 586–589 (2012).

    Google Scholar 

  26. 26.

    Pistorius, P. A., Baylis, A., Crofts, S. & Pütz, K. Population development and historical occurrence of king penguins at the Falkland Islands. Antarct. Sci. 24, 435–440 (2012).

    Google Scholar 

  27. 27.

    Kusch, A. & Marín, M. Sobre la distribución del Pingüino Rey Aptenodytes Patagonicus (Aves: Spheniscidae) en Chile. An. Inst. Patagonia 40, 157–163 (2012).

    Google Scholar 

  28. 28.

    Wallberg, A. et al. A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera. Nat. Genet. 46, 1081–1088 (2014).

    CAS  Google Scholar 

  29. 29.

    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeog. 12, 361–371 (2003).

    Google Scholar 

  30. 30.

    Bost, C.-A. et al. The importance of oceanographic fronts to marine birds and mammals of the southern oceans. J. Mar. Syst. 78, 363–376 (2009).

    Google Scholar 

  31. 31.

    Wolff, E. W. et al. Southern Ocean sea-ice extent, productivity and iron flux over the past eight glacial cycles. Nature 440, 491–496 (2006).

    CAS  Google Scholar 

  32. 32.

    Kohfeld, K. E. et al. Southern Hemisphere westerly wind changes during the Last Glacial Maximum: paleo-data synthesis. Quat. Sci. Rev. 68, 76–95 (2013).

    Google Scholar 

  33. 33.

    Gersonde, R., Crosta, X., Abelmann, A. & Armand, L. Sea-surface temperature and sea ice distribution of the Southern Ocean at the EPILOG Last Glacial Maximum: a circum-Antarctic view based on siliceous microfossil records. Quat. Sci. Rev. 24, 869–896 (2005).

    Google Scholar 

  34. 34.

    Hodgson, D. A. et al. Terrestrial and submarine evidence for the extent and timing of the Last Glacial Maximum and the onset of deglaciation on the maritime-Antarctic and sub-Antarctic islands. Quat. Sci. Rev. 100, 137–158 (2014).

    Google Scholar 

  35. 35.

    Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet. 47, 555–559 (2015).

    CAS  Google Scholar 

  36. 36.

    Cristofari, R. et al. Full circumpolar migration ensures evolutionary unity in the Emperor penguin. Nat. Commun. 7, 11842 (2016).

    CAS  Google Scholar 

  37. 37.

    Borboroglu, P. G. & Boersma, P. D. Penguins: Natural History and Conservation (University of Washington Press, Seattle & London, 2013).

    Google Scholar 

  38. 38.

    Austin, J. J. et al. The origins of the enigmatic Falkland Islands wolf. Nat. Commun. 4, 1552 (2013).

    Google Scholar 

  39. 39.

    Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).

    CAS  Google Scholar 

  40. 40.

    Carr, M.-E. et al. A comparison of global estimates of marine primary production from ocean color. Deep Sea Res. II 53, 741–770 (2006).

    Google Scholar 

  41. 41.

    Froneman, P. W., Laubscher, R. K. & McQuaid, C. D. Size-fractionated primary production in the south Atlantic and Atlantic sectors of the Southern Ocean. J. Plankton Res. 23, 611–622 (2001).

    CAS  Google Scholar 

  42. 42.

    Pütz, K. & Cherel, Y. The diving behaviour of brooding king penguins (Aptenodytes patagonicus) from the Falkland Islands: variation in dive profiles and synchronous underwater swimming provide new insights into their foraging strategies. Mar. Biol. 147, 281–290 (2005).

    Google Scholar 

  43. 43.

    Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).

    CAS  Google Scholar 

  44. 44.

    Norberg, J., Urban, M. C., Vellend, M., Klausmeier, C. A. & Loeuille, N. Eco-evolutionary responses of biodiversity to climate change. Nat. Clim. Change 2, 747–751 (2012).

    Google Scholar 

  45. 45.

    Hope, A. G., Waltari, E., Payer, D. C., Cook, J. A. & Talbot, S. L. Future distribution of tundra refugia in northern Alaska. Nat. Clim. Change 3, 931–938 (2013).

    Google Scholar 

  46. 46.

    Roberge, J. M. & Angelstam, P. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85 (2004).

    Google Scholar 

  47. 47.

    Jackson, J. B. C. Ecological extinction and evolution in the brave new ocean. Proc. Natl Acad. Sci. USA 105, 11458–11465 (2008).

    CAS  Google Scholar 

  48. 48.

    Kuhlbrodt, T. et al. An integrated assessment of changes in the thermohaline circulation. Clim. Change 96, 489–537 (2009).

    CAS  Google Scholar 

  49. 49.

    Travis, J. M. Climate change and habitat destruction: a deadly anthropogenic cocktail. Proc. R. Soc. B 270, 467–473 (2003).

    CAS  Google Scholar 

  50. 50.

    Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).

    Google Scholar 

  51. 51.

    Augustin, L. et al. Eight glacial cycles from an Antarctic ice core. Nature 429, 623–628 (2004).

    CAS  Google Scholar 

  52. 52.

    Li, C. et al. Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment. Gigascience 3, 27 (2014).

    Google Scholar 

  53. 53.

    Zhou, Q. et al. Complex evolutionary trajectories of sex chromosomes across bird taxa. Science 346, 1246338 (2014).

    Google Scholar 

  54. 54.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  Google Scholar 

  55. 55.

    Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics 15, 356 (2014).

    Google Scholar 

  56. 56.

    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    CAS  Google Scholar 

  57. 57.

    Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

  58. 58.

    Hanson-Smith, V., Kolaczkowski, B. & Thornton, J. W. Robustness of ancestral sequence reconstruction to phylogenetic uncertainty. Molecular Biol. Evol. 27, 1988–1999 (2010).

  59. 59.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Google Scholar 

  60. 60.

    Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50 (2005).

    CAS  Google Scholar 

  61. 61.

    Reich, D., Thangaraj, K., Patterson, N., Price, A. L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    CAS  Google Scholar 

  62. 62.

    Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).

    CAS  Google Scholar 

  63. 63.

    Fumagalli, M., Vieira, F. G., Linderoth, T. & Nielsen, R. ngsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics 30, 1486–1487 (2014).

  64. 64.

    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).

    CAS  Google Scholar 

  65. 65.

    Skotte, L., Korneliussen, T. S. SpringerAmpamp; Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).

    CAS  Google Scholar 

  66. 66.

    Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197, 573–589 (2014).

    Google Scholar 

  67. 67.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Google Scholar 

  68. 68.

    Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).

    CAS  Google Scholar 

  69. 69.

    Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).

    Google Scholar 

  70. 70.

    Saether, B. E. et al. Generation time and temporal scaling of bird population dynamics. Nature 436, 99–102 (2005).

    CAS  Google Scholar 

  71. 71.

    Millar, C. D. et al. Mutation and evolutionary rates in Adélie penguins from the Antarctic. PLoS Genet. 4, e1000209 (2008).

    Google Scholar 

  72. 72.

    Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).

    CAS  Google Scholar 

  73. 73.

    Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–927 (2014).

    CAS  Google Scholar 

  74. 74.

    Staab, P. R., Zhu, S., Metzler, D. & Lunter, G. scrm: efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics 31, 1680–1682 (2015).

    CAS  Google Scholar 

  75. 75.

    Rambaut, A. & Grass, N. C. Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13, 235–238 (1997).

    CAS  Google Scholar 

  76. 76.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Met. Soc. 93, 485–498 (2012).

    Google Scholar 

  77. 77.

    Meijers, A. J. S. The Southern Ocean in the Coupled Model Intercomparison Project phase 5. Phil. Trans. R. Soc. A 372, 20130296 (2014).

    CAS  Google Scholar 

  78. 78.

    Moore, J. K., Abbott, M. R. & Richman, J. G. Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data. J. Geophys. Res. 104, 3059–3073 (1999).

    Google Scholar 

  79. 79.

    Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).

    Google Scholar 

  80. 80.

    Adams, N. J. & Klages, N. T. Seasonal variation in the diet of the king penguin (Aptenodytes patagonicus) at sub Antarctic Marion Island. J. Zool. 212, 303–324 (1987).

    Google Scholar 

  81. 81.

    Koudil, M., Charrassin, J.-B., Le Maho, Y. & Bost, C.-A. Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector. Comptes Rendus Acad. Sci. 323, 377–384 (2000).

  82. 82.

    Pütz, K. Spatial and temporal variability in the foraging areas of breeding king penguins. Condor 104, 528–538 (2002).

    Google Scholar 

  83. 83.

    Moore, G. J., Robertson, G. & Wienecke, B. Food requirements of breeding king penguins at Heard Island and potential overlap with commercial fisheries. Polar Biol. 20, 293–302 (1998).

    Google Scholar 

  84. 84.

    Wienecke, B. & Robertson, G. Foraging areas of king penguins from Macquarie Island in relation to a marine protected area. Environ. Manag. 29, 662–672 (2002).

    Google Scholar 

  85. 85.

    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).

    CAS  Google Scholar 

  86. 86.

    Turner, J., Bracegirdle, T. J., Phillips, T., Marshall, G. J. & Hosking, J. S. An initial assessment of Antarctic sea ice extent in the CMIP5 models. J. Clim. 26, 1473–1484 (2013).

    Google Scholar 

  87. 87.

    Xu, S. et al. Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes. Adv. Atmos. Sci. 30, 658–673 (2013).

    Google Scholar 

  88. 88.

    Shu, Q., Song, Z. & Qiao, F. Assessment of sea ice simulations in the CMIP5 models. Cryosphere 9, 399–409 (2015).

    Google Scholar 

  89. 89.

    Goberville, E., Beaugrand, G., Hautekèete, N. C., Piquot, Y. & Luczak, C. Uncertainties in the projection of species distributions related to general circulation models. Ecol. Evol. 5, 1100–1116 (2015).

    Google Scholar 

  90. 90.

    Raybaud, V. et al. Decline in kelp in west Europe and climate. PloS One 8, e66044 (2013).

    CAS  Google Scholar 

  91. 91.

    Cabré, A., Marinov, I., Bernardello, R. & Bianchi, D. Oxygen minimum zones in the tropical Pacific across CMIP5 models: mean state differences and climate change trends. Biogeosciences 12, 5429–5454 (2015).

Download references

Acknowledgements

This work was conducted within the framework of the Programme 137 of the Institut Polaire Français Paul-Emile Victor (IPEV; CLB), with additional support from the French National Research Agency (ANR) ‘PICASO’ grant (ANR-2010-BLAN-1728-01; Y.L.M.), Marie Curie Intra European Fellowships (FP7-PEOPLE-IEF-2008, European Commission; project no. 235962 to C.L.B. and FP7-PEOPLE-IEF-2010, European Commission; project no. 252252 to E.T.), the Centre Scientifique de Monaco through the budget allocated to the Laboratoire International Associé 647 BioSensib (CSM/CNRS-University of Strasbourg; C.L.B., Y.L.M.), the Centre National de la Recherche Scientifique (Programme Zone Atelier de Recherches sur l’Environnement Antarctique et Subantarctique), South African National Antarctic Programme (P.P.) and the IPEV Programme 109 (Y.C.). Logistic and field costs of research were supported by the IPEV Programme 137 (C.L.B.), the South African Department of Environmental Affairs and the National Research Foundation (P.P.). This work was performed on the Abel Cluster, owned by the University of Oslo and the Norwegian Metacenter for High Performance Computing (NOTUR), and operated by the Department for Research Computing at USIT, the University of Oslo. We are very grateful to M. Skage, A. Tooming-Klunderud, M. Selander-Hansen and the Norwegian Sequencing Center for their very valuable help in the laboratory, as well as L. Nederbragt and M. Matschiner for their assistance with the Abel cluster, and M. Fumagalli and T. Korneliussen for their precious advice regarding ngsTools and ANGSD. We thank G. Bertorelle, L. Fusani, A. Mazzarella and D. Fordham for useful comments and advice. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information

Affiliations

Authors

Contributions

C.L.B. and E.T. conceived and supervised the study. C.L.B., F.B., Y.C. and P.P. collected the samples. R.C. performed DNA extraction, library preparation, and prepared and performed the genomic and demographic analyses and the climate modelling. X.L. and E.T. participated in the genomic and demographic analyses. V.R. and C.L.B. participated in climate modelling. N.C.S. hosted the project. R.C., C.L.B. and E.T. wrote the manuscript. F.B., N.C.S., P.P., Y.C., Y.L.M. and V.R. commented the manuscript.

Corresponding authors

Correspondence to Céline Le Bohec or Emiliano Trucchi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1–5, Supplementary Figures 1–9, Supplementary Tables 1–4, Supplementary References

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cristofari, R., Liu, X., Bonadonna, F. et al. Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nature Clim Change 8, 245–251 (2018). https://doi.org/10.1038/s41558-018-0084-2

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing