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Tracking of marine predators to protect Southern Ocean ecosystems

Abstract

Southern Ocean ecosystems are under pressure from resource exploitation and climate change1,2. Mitigation requires the identification and protection of Areas of Ecological Significance (AESs), which have so far not been determined at the ocean-basin scale. Here, using assemblage-level tracking of marine predators, we identify AESs for this globally important region and assess current threats and protection levels. Integration of more than 4,000 tracks from 17 bird and mammal species reveals AESs around sub-Antarctic islands in the Atlantic and Indian Oceans and over the Antarctic continental shelf. Fishing pressure is disproportionately concentrated inside AESs, and climate change over the next century is predicted to impose pressure on these areas, particularly around the Antarctic continent. At present, 7.1% of the ocean south of 40°S is under formal protection, including 29% of the total AESs. The establishment and regular revision of networks of protection that encompass AESs are needed to provide long-term mitigation of growing pressures on Southern Ocean ecosystems.

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Fig. 1: AESs in the Southern Ocean.
Fig. 2: Fishing effort in the Southern Ocean.
Fig. 3: Spatial protection of Southern Ocean AESs.
Fig. 4: Projected change in the distribution of AESs under RCP8.5.

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Data availability

The tracking data are available through our companion paper16.

Code availability

Computer code is available at https://github.com/SCAR/RAATD.

References

  1. Stark, J. S., Raymond, T., Deppeler, S. L. & Morrison, A. K. Antarctic Seas. World Seas: an Environmental Evaluation (Elsevier, 2019).

  2. Chown, S. L. & Brooks, C. M. The state and future of Antarctic environments in a global context. Annu. Rev. Environ. Resour. 44, 1–30 (2019).

    Google Scholar 

  3. Ainley, D. G. & Blight, L. K. Ecological repercussions of historical fish extraction from the Southern Ocean. Fish Fish. 10, 13–38 (2009).

    Google Scholar 

  4. Agnew, D. J., Hill, S. L., Beddington, J. R., Purchase, L. V. & Wakeford, R. C. Sustainability and management of southwest Atlantic squid fisheries. Bull. Mar. Sci. 76, 579–594 (2005).

    Google Scholar 

  5. Kock, K. H., Reid, K., Croxall, J. & Nicol, S. Fisheries in the Southern Ocean: an ecosystem approach. Phil. Trans. R. Soc. B. 362, 2333–2349 (2007).

    Google Scholar 

  6. Nicol, S., Foster, J. & Kawaguchi, S. The fishery for Antarctic krill—recent developments. Fish Fish. 13, 30–40 (2012).

    Google Scholar 

  7. Swart, N. C., Gille, S. T., Fyfe, J. C. & Gillett, N. P. Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci. 11, 836–841 (2018).

    ADS  CAS  Google Scholar 

  8. Convention on Biological Diversity. Decisions Adopted by the Conference of the Parties to the Convention on Biological Diversity at its Ninth Meeting. Report No. UNEP/CBD/COP/9/29 (CBD, 2008).

  9. Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).

    ADS  CAS  Google Scholar 

  10. Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).

    Google Scholar 

  11. Constable, A. J. et al. Developing priority variables (“ecosystem Essential Ocean Variables”—eEOVs) for observing dynamics and change in Southern Ocean ecosystems. J. Mar. Syst. 161, 26–41 (2016).

    Google Scholar 

  12. Reid, K., Croxall, J. P., Briggs, D. R. & Murphy, E. J. Antarctic ecosystem monitoring: quantifying the response of ecosystem indicators to variability in Antarctic krill. ICES J. Mar. Sci. 62, 366–373 (2005).

    Google Scholar 

  13. Cury, P. M. et al. Global seabird response to forage fish depletion—one-third for the birds. Science 334, 1703–1706 (2011).

    ADS  CAS  Google Scholar 

  14. Nicol, S. et al. Ocean circulation off east Antarctica affects ecosystem structure and sea-ice extent. Nature 406, 504–507 (2000).

    ADS  CAS  Google Scholar 

  15. Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).

    Google Scholar 

  16. Ropert-Coudert, Y. et al. The Retrospective Analysis of Antarctic Tracking Data project. Sci. Data https://doi.org/10.1038/s41597-020-0406-x (2020).

  17. Hindell, M. A. et al. in The Kerguelen Plateau: Marine Ecosystem and Fisheries (eds Duhamel, G. & Welsford, D.) 203–215 (Societe Francaise d’Ichtyologie, 2011).

  18. Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).

    ADS  CAS  Google Scholar 

  19. Hindell, M. A. et al. Decadal changes in habitat characteristics influence population trajectories of southern elephant seals. Glob. Chang. Biol. 23, 5136–5150 (2017).

    ADS  Google Scholar 

  20. Sallée, J.-B., Speer, K. G. & Rintoul, S. R. Zonally asymmetric response of the Southern Ocean mixed-layer depth to the Southern Annular Mode. Nat. Geosci. 3, 273–279 (2010).

    ADS  Google Scholar 

  21. Davies, R. G., Irlich, U. M., Chown, S. L. & Gaston, K. J. Ambient, productive and wind energy, and ocean extent predict global species richness of procellariiform seabirds. Glob. Ecol. Biogeogr. 19, 98–110 (2010).

    Google Scholar 

  22. Ardyna, M. et al. Delineating environmental control of phytoplankton biomass and phenology in the Southern Ocean. Geophys. Res. Lett. 44, 5016–5024 (2017).

    ADS  Google Scholar 

  23. Ropert-Coudert, Y. et al. in Biogeographic Atlas of the Southern Ocean (eds De Broyer, C. et al.) 364–387 (Scientific Committee on Antarctic Research, 2014).

  24. Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).

    ADS  CAS  Google Scholar 

  25. Nicol, S. & Raymond, B. in Antarctic Ecosystems: an Extreme Environment in a Changing World (eds Rogers, A. D. et al.) 243–254 (Wiley, 2012).

  26. Constable, A. J. et al. Climate change and Southern Ocean ecosystems I: how changes in physical habitats directly affect marine biota. Glob. Chang. Biol. 20, 3004–3025 (2014).

    ADS  Google Scholar 

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

    ADS  CAS  Google Scholar 

  28. Dayton, P. K., Thrush, S. F., Agardy, M. T. & Hofman, R. J. Environmental effects of marine fishing. Aquat. Conserv. 5, 205–232 (1995).

    Google Scholar 

  29. Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).

    ADS  CAS  Google Scholar 

  30. Mormede, S., Dunn, A., Parker, S. & Hanchet, S. Using spatial population models to investigate the potential effects of the Ross Sea region Marine Protected Area on the Antarctic toothfish population. Fish. Res. 190, 164–174 (2017).

    Google Scholar 

  31. Massom, R. A. & Stammerjohn, S. E. Antarctic sea ice change and variability—physical and ecological implications. Polar Sci. 4, 149–186 (2010).

    ADS  Google Scholar 

  32. Vaughan, D. et al. in Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) 317–382 (Cambridge University Press, 2013).

  33. Game, E. T. et al. Pelagic protected areas: the missing dimension in ocean conservation. Trends Ecol. Evol. 24, 360–369 (2009).

    Google Scholar 

  34. Harrison, A.-L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).

    Google Scholar 

  35. Hilborn, R. Policy: marine biodiversity needs more than protection. Nature 535, 224–226 (2016).

    ADS  CAS  Google Scholar 

  36. Phillips, R. A. et al. The conservation status and priorities for albatrosses and large petrels. Biol. Conserv. 201, 169–183 (2016).

    Google Scholar 

  37. Constable, A. J., De LaMare, W. K., Agnew, D. J., Everson, I. & Miller, D. Managing fisheries to conserve the Antarctic marine ecosystem: practical implementation of the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR). ICES J. Mar. Sci. 57, 778–791 (2000).

    Google Scholar 

  38. Sala, E. et al. Assessing real progress towards effective ocean protection. Mar. Policy 91, 11–13 (2018).

    Google Scholar 

  39. Roberts, C. M. et al. Marine reserves can mitigate and promote adaptation to climate change. Proc. Natl Acad. Sci. USA 114, 6167–6175 (2017).

    ADS  CAS  Google Scholar 

  40. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    ADS  CAS  Google Scholar 

  41. Peters, G. P. et al. The challenge to keep global warming below 2 °C. Nat. Clim. Chang. 3, 4–6 (2013).

    ADS  Google Scholar 

  42. 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. Lond. B 279, 2515–2523 (2012).

    Google Scholar 

  43. Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Chang. 9, 142–147 (2019).

    ADS  Google Scholar 

  44. Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).

    ADS  CAS  Google Scholar 

  45. Weimerskirch, H., Louzao, M., de Grissac, S. & Delord, K. Changes in wind pattern alter albatross distribution and life-history traits. Science 335, 211–214 (2012).

    ADS  CAS  Google Scholar 

  46. Cristofari, R. et al. Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nat. Clim. Chang. 8, 245–251 (2018).

    ADS  Google Scholar 

  47. Southwell, C. et al. Recent studies overestimate colonization and extinction events for Adelie penguin breeding colonies. Auk 134, 39–50 (2017).

    Google Scholar 

  48. Jacquet, J., Blood-Patterson, E., Brooks, C. & Ainley, D. ‘ Rational use ’ in Antarctic waters. Mar. Policy 63, 28–34 (2016).

    Google Scholar 

  49. Grémillet, D. et al. Persisting worldwide seabird-fishery competition despite seabird community decline. Curr. Biol. 28, 4009–4013 (2018).

    Google Scholar 

  50. Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).

    CAS  Google Scholar 

  51. Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572, 461–466 (2019).

    ADS  CAS  Google Scholar 

  52. Raymond, B. et al. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography 38, 121–129 (2015).

    Google Scholar 

  53. Reisinger, R. R. et al. Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean. Divers. Distrib. 24, 535–550 (2018).

    Google Scholar 

  54. R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2018).

  55. Jonsen, I. D. et al. Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100, e02566 (2019).

    CAS  Google Scholar 

  56. Aarts, G., MacKenzie, M., McConnell, B., Fedak, M. & Matthiopoulos, J. Estimating space-use and habitat preference from wildlife telemetry data. Ecography 31, 140–160 (2008).

    Google Scholar 

  57. Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    CAS  Google Scholar 

  58. Pya, N. & Wood, S. N. Shape constrained additive models. Stat. Comput. 25, 543–559 (2015).

    MathSciNet  MATH  Google Scholar 

  59. Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).

    Google Scholar 

  60. Rintoul, S. R. The global influence of localized dynamics in the Southern Ocean. Nature 558, 209–218 (2018).

    ADS  CAS  Google Scholar 

  61. World Meteorological Organization. Guide to Climatological Practices (WMO No. 100) (World Meteorological Organization, 2011).

  62. Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21, 1301–1315 (2007).

    Google Scholar 

  63. He, J. et al. Impact of ocean eddy resolution on the sensitivity of precipitation to CO2 increase. Geophys. Res. Lett. 45, 7194–7203 (2018).

    ADS  Google Scholar 

  64. Williams, J. W., Jackson, S. T. & Kutzbach, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).

    ADS  CAS  Google Scholar 

  65. Cavanagh, R. D. et al. A synergistic approach for evaluating climate model output for ecological applications. Front. Mar. Sci. 4, 308 (2017).

    Google Scholar 

Download references

Acknowledgements

This contribution is part of the Retrospective Analysis of Antarctic Tracking Data (RAATD), a project of the Expert Group on Birds and Marine Mammals of the Scientific Committee on Antarctic Research (SCAR; www.scar.org). The RAATD project would not have been possible without the many scientists, students and field assistants who helped collect data in the field or process them, including S. Adlard, A. Agüera, M. Biuw, M.-A. Blanchet, J. Clarke, P. Cock, H. Cox, M. Connan, A. R. Carlini, S. Corsolini, M. Cottin, J. D. Le Croquant, G. A. Danieri, D. Davies, B. Dilley, R. Downie, M. Dunn, B. M. Dyer, S. Focardi, H. O. Gillett, S. Haaland, L. Jonsen-Humble, H. Kane, B. A. Krafft, C. Kroeger, C. A. E. Lemon, G. Mabille, M. Marczak, T. McIntyre, S. McKooy, J. A. Mennucci, T. Nordstad, C. Oosthuizen, R. Orben, T. Photopoulou, B. Picard, O. Prud’homme, T. Raclot, S. Ramdohr, D. H. Raymond, L. Riekkola, G. Richard, G. Robertson, T. Rogers, K. Ropert-Kato, S. Schoombie, T. N. Snakes, E. Soininen, A. Specht, K. Stevens, J. N. Swærd, C. Tosh, S. G. Trivelpiece, O. S. G. Trolli, T. Truly, L. Upfold, M. Le Vaillant, Y. Watanabe, M. Wege, C. Wheeler, T. O. Whitehead, M. Widmann, A. G. Wood, N. Youdjou and I. Zimmer. We also thank the large number of fieldworkers without whom these data would not have been collected. D. G. Ainley and A. Constable commented on an earlier version of the manuscript. Support and funding were provided by supranational committees and organizations including SCAR, BirdLife International and the European Commission (IUCN BEST program), as well as various national institutions (see also author affiliations) and foundations, including but not limited to: Argentina (Dirección Nacional del Antártico); Australia (Australian Antarctic program; Australian Research Council; Sea World Research and Rescue Foundation; Australian Integrated Marine Observing System (IMOS) (IMOS is a national collaborative research infrastructure, supported by the Australian Government and operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent)); Belgium (Belgian Science Policy Office/Lifewatch), Brazil (Brazilian Antarctic Programme; National Council for Scientific and Technological Development (CNPq); Ministry of Science, Technology, Innovation and Communications (MCTIC); Ministry of the Environment; CAPES); France (Agence Nationale de la Recherche; Centre National d’Etudes Spatiales; Centre National de la Recherche Scientifique; the French Foundation for Research on Biodiversity (FRB; www.fondationbiodiversite.fr) in the context of the CESAB project ‘RAATD’; Fondation Total; Institut Paul-Emile Victor; Programme Zone Atelier de Recherches sur l’Environnement Antarctique et Subantarctique; Terres Australes et Antarctiques Françaises); Germany (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Deutsche Forschungsgemeinschaft, Hanse-Wissenschaftskolleg (Institute for Advanced Study)); Italy (Programma Nazionale di Ricerche in Antartide (PNRA)); Japan (Japanese Antarctic Research Expedition; JSPS Kakenhi grant 20310016; NIPR visiting professor fellowship for M.A.H.); Monaco (Fondation Prince Albert II de Monaco); New Zealand (Ministry for Primary Industries BRAG; Pew Charitable Trusts); Norway (Norwegian Antarctic Research Expeditions; Norwegian Research Council); Portugal (Foundation for Science and Technology); South Africa (Department of Environmental Affairs; National Research Foundation; South African National Antarctic Programme); UK (Darwin Plus; Ecosystems Programme at the British Antarctic Survey; Natural Environment Research Council; WWF); and USA (US AMLR Program of NOAA Fisheries; National Science Foundation Office of Polar Programs).

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Authors and Affiliations

Authors

Contributions

M.A.H. conceived and led the project. R.A., B.A., G.B., J.B., M.N.B., L.B., H.B., C.-A.B., P.B., J.-B.C., R.C., D.P.C., R.J.M.C., L.D.R., P.J.N.d.B., K.D., S.D., M.D., L.E., M.F., A.F., N.G., M.G., K.T.G., C.G., S.D.G., R.H., J.T.H., M.A.H., L.A.H., A.K., K.R.K., R.K., G.L.K., K.M.K., K.L., A.D.L., C.L., M.-A.L., P.O’B.L., A.B.M., M.E.I.M., B.I.M., C.R.M., M.M., K.W.N., E.S.N., S.O., R.A.P., P.P., J.P., K.P., N.R., Y.R.-C., P.G.R., M.S., A.S.B., C.S., I.S., A. Takahashi, A. Tarroux, L.G.T., P.N.T., W.T., E.W., H.W., B.W. and J.C.X. collected and contributed data. V.A.-G., H.B., J.-B.C., S.L.C., B.D., M.A.H., L.A.H., K.J., A.K., I.D.J., M.-A.L., D.N., B.R., R.R.R., Y.R.-C., D.T., L.G.T., P.N.T., A.P.V. and S.W. processed and analysed the data. M.A.H., H.B., J.-B.C., D.P.C., S.L.C., B.D., L.A.H., I.D.J., M.-A.L., B.R., R.R.R., Y.R.-C., L.G.T., P.N.T., A.P.V., S.W. and S.L.C. drafted the paper. All authors edited and proofread the paper.

Corresponding author

Correspondence to Mark A. Hindell.

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Competing interests

H.B., J.-B.C., D.P.C., B.D., M.A.H., L.A.H., I.D.J., M.-A.L., M.M., B.R., R.R.R., Y.R.-C., P.G.R., A. Takahashi, D.T., L.G.T., P.N.T., A.P.V. S.W. and J.C.X. are members of the SCAR Expert Group on Birds and Marine Mammals. S.L.C. is President of SCAR.

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Extended data figures and tables

Extended Data Fig. 1 Overview of the modelling process.

a, Habitat importance for a given life-history stage (for example, chick-rearing) of a given species (for example, king penguin (Apatagonicus)) is calculated using two models (grey boxes): the habitat-selection model (box 1) and the habitat accessibility model (box 2). b, These stage-specific, species-specific predictions of habitat importance are combined to calculate the mean habitat importance for multiple species (for example, king penguin and Antarctic fur seal (Arctocephalus gazella)). In the habitat accessibility model (box 2 in a) the distance to colony can be weighted by relative colony size or not. The unweighted version is shown here.

Extended Data Fig. 2 Maps showing the 19 environmental covariates that were used to model the habitat selection of marine predators in the Southern Ocean.

Grey lines indicate major oceanographic fronts. CHLA, chlorophyll a concentration; CURR, geostrophic current velocity; DEPTH, depth; DEPTHg, depth gradient; dSHELF, distance to shelf; EKE, eddy kinetic energy; ICE, sea-ice concentration; ICEA, accessibility through sea ice; ICEsd, standard deviation of sea-ice concentration; SAL, salinity difference; SHFLUX, surface heat flux; SHFLUXsd, standard deviation of surface heat flux; SSHa, sea surface height anomaly; SSHsd, sea surface height standard deviation; SST, sea surface temperature; SSTg, sea surface temperature gradient; VMIX, vertical velocity; VMIXsd, standard deviation of vertical velocity; WIND, surface wind speed. Sources and units of measurement are defined in Supplementary Table 2.

Extended Data Fig. 3 Habitat-importance scores for 16 marine predator species in the Southern Ocean.

The maps show predicted habitat importance for each species. Predictions for macaroni penguins (Eudyptes chrysocome) and royal penguins (Eudyptes schlegeli) are combined. Black circles show all known colony locations for the 14 colony-breeding species, which we used to predict the models across the whole Southern Ocean.

Extended Data Fig. 4 Covariate importance.

Relative importance of 19 environmental variables that were used as predictors in 40 boosted regression tree models of the habitat selection of Southern Ocean marine predators. Higher values of variable relative importance indicate that the variable has higher predictive power. Points show the values for each model and box plots (in grey, behind) show the distribution of values. Variables are ordered (top to bottom) by decreasing median importance. The three panels show the results for three different groups of species that were identified by hierarchical cluster analysis (see ‘Species grouping’ in Methods, and Extended Data Fig. 7). Full covariate names are provided in Supplementary Table 2. Box plots as in Fig. 4.

Extended Data Fig. 5 Varied relationships between covariates and habitat selection across species.

Scatter plot smoothed curves (black lines) of the relationship between predictions of the species habitat-selection models (boosted regression trees) (vertical axis) and the values of covariates used as predictors in our boosted regression tree models (horizontal axis). The smoothed curves were drawn by fitting generalized additive models for large datasets with a thin plate regression spline basis, as LOESS (locally estimated scatter plot) smoothing was not computationally feasible. Full covariate names and units are provided in Supplementary Table 2. Higher habitat-selection values indicate higher probabilities of use, irrespective of availability in this case. A smooth curve is shown for each species. Because each species had one to five predictions, for different life-history stages, we took the mean habitat-selection estimate per cell for each species. Rug marks on the horizontal axis indicate the distributions of the data points.

Extended Data Fig. 6 Potential environmental stressors in the Southern Ocean.

ac, Maps showing the change (mean in 1987–1998 compared to mean in 2007–2017) in sea-ice duration (days) (a), SST (°C) (b) and wind speed (m s−1) (c). Contour lines (black) indicate AESs. df, Kernel density plots show the distribution of values of each of ac inside (red) and outside (grey) AESs. Horizontal lines represent zero change. Two-tailed permutation tests indicate significant differences in each case, and the number of grid cells included in the test is given in each case (n).

Extended Data Fig. 7 Change in the distribution of AESs under RCP4.5.

a, Cells that were AES in the original results are shown in blue (remain as AES) or orange (become non-AES in the future). The gradation from orange to blue shows the proportion of climate models that indicate loss (orange) or retention (blue) of AESs. Similarly, the gradation from white to green shows the proportion of models that indicate that non-AES cells will remain as non-AES (white) or become AES (green). Orange and magenta outlines show current and proposed MPAs, respectively. b, Percentage change in the area of AESs according to the eight different climate models (black points), and the mean of these (red points). Box plots as in Fig. 4.

Extended Data Fig. 8 Comparison of unweighted and weighted overall habitat importance.

a, Overall habitat importance, calculated without accounting for colony sizes. b, Overall habitat importance if colony sizes are taken into account. Black points indicate colony locations for the 14 colony-breeding species; white contours indicate AESs. See Methods and Supplementary Information for details.

Extended Data Fig. 9 Dendrogram of hierarchical cluster analysis showing species groups in the dataset.

We performed UPGMA hierarchical cluster analysis on the Manhattan distance among species, calculated from the habitat-importance scores. The results show two clear species groups: Antarctic (blue) and sub-Antarctic (magenta). Humpback whales and southern elephant seals (orange) did not fall into either group and we assigned them to both groups for subsequent analyses. The cophenetic correlation coefficient between the distance matrix and the dendrogram was 0.86, which means that the dendrogram is a good representation of the Manhattan distance values among the species. Values can range from 0 (no correlation) to 1 (perfect correlation).

Extended Data Fig. 10 Mean habitat importance of Antarctic and sub-Antarctic species.

a, b, To account for regional differences in species richness we defined two species groups (Methods and Extended Data Fig. 5) and calculated the mean habitat importance for these two groups separately. These two mean habitat-importance layers (a and b) were then combined into a single overall habitat-importance layer by choosing the maximum value in each cell. Black points indicate the colony locations of colony-breeding species in each species group.

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This file contains Supplementary Methods, Supplementary Tables 1-3, Supplementary Figures 1-13 and the Analysis Flowchart.

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Hindell, M.A., Reisinger, R.R., Ropert-Coudert, Y. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020). https://doi.org/10.1038/s41586-020-2126-y

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