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Towards climate-smart, three-dimensional protected areas for biodiversity conservation in the high seas

Abstract

Marine species are moving rapidly in response to warming, often in different directions and with variations dependent on location and depth. Given the current impetus to increase the area of protected ocean to 30%, conservation planning must include the 64% of the ocean beyond national jurisdictions, which in turn requires associated design challenges for conventional conservation to be addressed. Here we present a planning approach for the high seas that conserves biodiversity, minimizes exposure to climate change, retains species within reserve boundaries and reduces conflict with fishing. This is developed using data from across four depth domains, considering 12,932 vertebrate, invertebrate and algal species and three climate scenarios. The resultant climate-smart conservation areas cover 6% of the high seas and represent a low-regret option that provides a nucleus for developing a full network of high-seas marine reserves.

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Fig. 1: The degree of agreement between the climate-smart MPA networks for different planning domains and climate scenarios.
Fig. 2: Climate-smart networks in the high seas.
Fig. 3: Low-regret climate-smart networks in the high seas.

Data availability

The data used in this study (except the AquaMaps biodiversity and geomorphic features data) are available at Zenodo95 under the identifier https://doi.org/10.5281/zenodo.5912047. The AquaMaps77 data are freely available via www.aquamaps.org. The geomorphic features78 data are freely available via www.bluehabitats.org.

Code availability

All the scripts are available at Zenodo95 under the identifier https://doi.org/10.5281/zenodo.5912047.

References

  1. Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).

    CAS  Google Scholar 

  2. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Google Scholar 

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

    CAS  Google Scholar 

  4. Davies, T. E., Maxwell, S. M., Kaschner, K., Garilao, C. & Ban, N. C. Large marine protected areas represent biodiversity now and under climate change. Sci. Rep. 7, 9569 (2017).

    CAS  Google Scholar 

  5. Bates, A. E. et al. Climate resilience in marine protected areas and the ‘protection paradox’. Biol. Conserv. 236, 305–314 (2019).

    Google Scholar 

  6. Costello, M. J. & Ballantine, B. Biodiversity conservation should focus on no-take marine reserves: 94% of marine protected areas allow fishing. Trends Ecol. Evol. 30, 507–509 (2015).

    Google Scholar 

  7. Ballantine, B. Fifty years on: lessons from marine reserves in New Zealand and principles for a worldwide network. Biol. Conserv. 176, 297–307 (2014).

    Google Scholar 

  8. Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).

    Google Scholar 

  9. Jones, K. R., Watson, J. E. M., Possingham, H. P. & Klein, C. J. Incorporating climate change into spatial conservation prioritisation: a review. Biol. Conserv. 194, 121–130 (2016).

    Google Scholar 

  10. Grorud-Colvert, K. et al. The MPA Guide: a framework to achieve global goals for the ocean. Science 373, eabf0861 (2021).

    CAS  Google Scholar 

  11. McLeod, E. et al. Integrating climate and ocean change vulnerability into conservation planning. Coast. Manage. 40, 651–672 (2012).

    Google Scholar 

  12. Magris, R. A. et al. A blueprint for securing Brazil’s marine biodiversity and supporting the achievement of global conservation goals. Divers. Distrib. 27, 198–215 (2021).

    Google Scholar 

  13. Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Change 10, 576–581 (2020).

    CAS  Google Scholar 

  14. Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv. 5, eaay9969 (2019).

    Google Scholar 

  15. Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).

    CAS  Google Scholar 

  16. Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).

    CAS  Google Scholar 

  17. Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip inmarine species richness around the Equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).

    CAS  Google Scholar 

  18. Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).

    Google Scholar 

  19. Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).

    Google Scholar 

  20. Levin, N., Kark, S. & Danovaro, R. Adding the third dimension to marine conservation. Conserv. Lett. 11, e12408 (2018).

    Google Scholar 

  21. O’Leary, B. C. & Roberts, C. M. Ecological connectivity across ocean depths: implications for protected area design. Glob. Ecol. Conserv. 15, e00431 (2018).

    Google Scholar 

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

    Google Scholar 

  23. Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021); https://livereport.protectedplanet.net/

  24. Wright, G. et al. Marine spatial planning in areas beyond national jurisdiction. Mar. Policy 132, 103384 (2021).

    Google Scholar 

  25. Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020).

  26. Dunn, D. C. et al. The Convention on Biological Diversity’s ecologically or biologically significant areas: origins, development, and current status. Mar. Policy 49, 137–145 (2014).

    Google Scholar 

  27. Claudet, J., Loiseau, C., Sostres, M. & Zupan, M. Underprotected marine protected areas in a global biodiversity hotspot. One Earth 2, 380–384 (2020).

    Google Scholar 

  28. Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Change 8, 499–503 (2018).

    Google Scholar 

  29. Arafeh-Dalmau, N. et al. Incorporating climate velocity into the design of climate-smart networks of marine protected areas. Methods Ecol. Evol. 12, 1969–1983 (2021).

    Google Scholar 

  30. García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).

    Google Scholar 

  31. Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  33. Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).

    Google Scholar 

  34. Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).

    Google Scholar 

  35. Jones, K. R. et al. Area requirements to safeguard Earth’s marine species. One Earth 2, 188–196 (2020).

    Google Scholar 

  36. Ortuño Crespo, G. & Dunn, D. C. A review of the impacts of fisheries on open-ocean ecosystems. ICES J. Mar. Sci. 74, 2283–2297 (2017).

    Google Scholar 

  37. Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data 4, 170039 (2017).

    Google Scholar 

  38. Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0 (2021).

  39. Visalli, M. E. et al. Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction. Mar. Policy 122, 103927 (2020).

    Google Scholar 

  40. Dunn, D. C. et al. A strategy for the conservation of biodiversity on mid-ocean ridges from deep-sea mining. Sci. Adv. 4, eaar4313 (2018).

    Google Scholar 

  41. Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).

    Google Scholar 

  42. Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).

    CAS  Google Scholar 

  43. Venegas-Li, R., Levin, N., Possingham, H. & Kark, S. 3D spatial conservation prioritisation: accounting for depth in marine environments. Methods Ecol. Evol. 9, 773–784 (2018).

    Google Scholar 

  44. Menini, E. & Van Dover, C. L. An atlas of protected hydrothermal vents. Mar. Policy 108, 103654 (2019).

    Google Scholar 

  45. Crespo, G. O. et al. High-seas fish biodiversity is slipping through the governance net. Nat. Ecol. Evol. 3, 1273–1276 (2019).

    Google Scholar 

  46. Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).

    CAS  Google Scholar 

  47. Barton, A. D. et al. The biogeography of marine plankton traits. Ecol. Lett. 16, 522–534 (2013).

    Google Scholar 

  48. Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).

    Google Scholar 

  49. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).

    CAS  Google Scholar 

  50. Daigle, R. M. et al. Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect. Methods Ecol. Evol. 11, 570–579 (2020).

    Google Scholar 

  51. Fredston-Hermann, A., Gaines, S. D. & Halpern, B. S. Biogeographic constraints to marine conservation in a changing climate. Ann. N. Y. Acad. Sci. 1429, 5–17 (2018).

    Google Scholar 

  52. Cashion, T. et al. Shifting seas, shifting boundaries: dynamic marine protected area designs for a changing climate. PLoS ONE 15, e0241771 (2020).

    CAS  Google Scholar 

  53. Ortuño Crespo, G. et al. Beyond static spatial management: scientific and legal considerations for dynamic management in the high seas. Mar. Policy 122, 104102 (2020).

    Google Scholar 

  54. Levin, L. A., Amon, D. J. & Lily, H. Challenges to the sustainability of deep-seabed mining. Nat. Sustain. 3, 784–794 (2020).

    Google Scholar 

  55. Levin, L. A. et al. Climate change considerations are fundamental to management of deep-sea resource extraction. Glob. Change Biol. 26, 4664–4678 (2020).

    Google Scholar 

  56. Morato, T., Watson, R., Pitcher, T. J. & Pauly, D. Fishing down the deep. Fish Fish. 7, 24–34 (2006).

    Google Scholar 

  57. Rogers, A. D. & Gianni, M. Implementation of UNGA Resolutions 61/105 and 64/72 in the Management of Deep-Sea Fisheries on the High Seas (DIANE, 2011).

  58. Bailey, D. M., Collins, M. A., Gordon, J. D. M., Zuur, A. F. & Priede, I. G. Long-term changes in deep-water fish populations in the Northeast Atlantic: a deeper reaching effect of fisheries? Proc. R. Soc. B 276, 1965–1969 (2009).

    CAS  Google Scholar 

  59. NOAA National Geophysical Data Center ETOPO1 1 Arc-Minute Global Relief Model (NOAA National Centers for Environmental Information, 2009).

  60. O’Neill, B. C. et al. The roads ahead: narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).

    Google Scholar 

  61. Vrac, M., Stein, M. L., Hayhoe, K. & Liang, X.-Z. A general method for validating statistical downscaling methods under future climate change. Geophys. Res. 34, L18701 (2007).

    Google Scholar 

  62. Rogers, A. D. Environmental change in the deep ocean. Annu. Rev. Environ. Resour. 40, 1–38 (2015).

    Google Scholar 

  63. Sayre, R. G. et al. A three-dimensional mapping of the ocean based on environmental data. Oceanography 30, 90–103 (2017).

    Google Scholar 

  64. Schulzweida, U. CDO User Guide (Max Planck Institute for Meteorology, 2019).

  65. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  66. Mumby, P. J. et al. Reserve design for uncertain responses of coral reefs to climate change. Ecol. Lett. 14, 132–140 (2011).

    Google Scholar 

  67. Magris, R. A., Heron, S. F. & Pressey, R. L. Conservation planning for coral reefs accounting for climate warming disturbances. PLoS ONE 10, e0140828 (2015).

    Google Scholar 

  68. Chollett, I., Enríquez, S. & Mumby, P. J. Redefining thermal regimes to design reserves for coral reefs in the face of climate change. PLoS ONE 9, e110634 (2014).

    Google Scholar 

  69. Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).

    CAS  Google Scholar 

  70. García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).

    Google Scholar 

  71. Iwamura, T., Wilson, K. A., Venter, O. & Possingham, H. P. A climatic stability approach to prioritizing global conservation investments. PLoS ONE 5, e15103 (2010).

    CAS  Google Scholar 

  72. Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).

    Google Scholar 

  73. Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).

    Google Scholar 

  74. Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).

    Google Scholar 

  75. Ball, I. R., Possingham, H. P. & Watts, M. in Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (eds Moilanen, A. et al.) Ch. 14 (Oxford Univ. Press, 2009).

  76. Asaad, I., Lundquist, C. J., Erdmann, M. V. & Costello, M. J. Ecological criteria to identify areas for biodiversity conservation. Biol. Conserv. 213, 309–316 (2017).

    Google Scholar 

  77. Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species (2019).

  78. Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).

    Google Scholar 

  79. Froese, R. & Pauly, D. FishBase (2021).

  80. Palomares, M. L. D. & Pauly, D. SeaLifeBase (2021).

  81. Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. Proc. Natl Acad. Sci. USA 107, 9707–9711 (2010).

    CAS  Google Scholar 

  82. Rowden, A. A. et al. A test of the seamount oasis hypothesis: seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).

    Google Scholar 

  83. Devred, E., Sathyendranath, S. & Platt, T. Delineation of ecological provinces using ocean colour radiometry. Mar. Ecol. Prog. Ser. 346, 1–13 (2007).

    CAS  Google Scholar 

  84. Oliver, M. J. & Irwin, A. J. Objective global ocean biogeographic provinces. Geophys. Res. Lett. 35, L15601 (2008).

    Google Scholar 

  85. Costello, M. J. et al. Marine biogeographic realms and species endemicity. Nat. Commun. 8, 1057 (2017).

    Google Scholar 

  86. Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).

    Google Scholar 

  87. Global Open Oceans and Deep Seabed (GOODS)—Biogeographic Classification (UNESCO, 2009).

  88. Ban, N. C. & Klein, C. J. Spatial socioeconomic data as a cost in systematic marine conservation planning. Conserv. Lett. 2, 206–215 (2009).

    Google Scholar 

  89. Tai, T. C., Cashion, T., Lam, V. W. Y., Swartz, W. & Sumaila, U. R. Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Front. Mar. Sci. 4, 363 (2017).

    Google Scholar 

  90. Gurobi Optimizer Reference Manual (Gurobi Optimization, 2020).

  91. Hanson, J. O., Schuster, R., Strimas-Mackey, M. & Bennett, J. R. Optimality in prioritizing conservation projects. Methods Ecol. Evol. 10, 1655–1663 (2019).

    Google Scholar 

  92. IUCN Red List of Threatened Species (IUCN, 2020); https://www.iucnredlist.org/en

  93. Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0 (2020).

  94. McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med. 22, 276–282 (2012).

    Google Scholar 

  95. Brito-Morales, I. Towards climate-smart, 3-D protected areas for biodiversity conservation in the high seas (v2.0). Zenodo https://doi.org/10.5281/zenodo.5912047 (2022).

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Acknowledgements

I.B.-M. was supported by the Advanced Human Capital Program of the Chilean National Research and Development Agency (ANID Grant No. 72170231). C.J.K. was supported by an ARC Future Fellowship (no. FT200100314). J.D.E. was funded by Australian Research Council Discovery Project No. DP19010229. We thank K. Kaschner, C. Garilao and K. Kesner-Reyes for providing the AquaMaps marine biodiversity data.

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Contributions

I.B.-M., D.S.S. and A.J.R. conceived the research. J.D.E. generated the fishing cost layer. I.B.-M. analysed the data. I.B.-M. wrote the first draft with input from D.S.S., A.J.R., C.J.K. and D.C.D. I.B.-M., D.S.S., A.J.R., C.J.K., D.C.D., J.D.E., J.G.M., M.T.B., K.C.V.B., R.M.D. and H.P.P. contributed equally to the discussion of ideas and analyses, and all authors commented on the manuscript.

Corresponding author

Correspondence to Isaac Brito-Morales.

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The authors declare no competing interests.

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Nature Climate Change thanks Rafael Magris, Derek Tittensor and Qianshuo Zhao for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Map of monetary value of fishing and biodiversity in the high seas.

Opportunity cost of fishing (a, b, c, d) and species richness (number of species) with a probability of occurrence > 0.5 (d, e, f, g) in the high seas at four depth domains. Polygons represent Longhurst provinces for the epipelagic domain (a, e), Glasgow provinces for the mesopelagic (b, f) and bathyabyssopelagic domains (c, g), and the GOOD provinces for seafloor domain (d, h).

Extended Data Fig. 2 Geomorphic conservation features in the seafloor domain78.

For each map, green hexagons indicate the presence of each geomorphic feature in each planning unit. Polygons represent the GOODS provinces87.

Extended Data Fig. 3 Prioritised climate-smart networks in the high seas.

Prioritised networks for the high seas at three pelagic depth domains and the seafloor, under three IPCC Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). For each map, green hexagons represent selected planning units while light blue hexagons represent non-selected planning units. Polygons in each map represent Longhurst provinces for the epipelagic domain (a, b, c), Glasgow provinces for the mesopelagic (d, e, f) and bathyabyssopelagic (g, h, i) domains, and the GOODS provinces for seafloor domain (j, k, l).

Extended Data Fig. 4 The relationship between climate-smart networks with and without the cost layer.

Total Opportunity cost among the prioritised base scenario (that is, no cost) and the climate-smart prioritisation scenarios under three IPCC emission pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5).

Extended Data Fig. 5 Prioritised climate-smart networks in the high seas for the base scenario.

Prioritised networks for a base scenario (that is, no cost) for the high seas at three pelagic depth domains and the seafloor, under three IPCC Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). For each map, green hexagons represent selected planning units while light blue hexagons represent non-selected planning units. Polygons in each map represent Longhurst provinces for the epipelagic domain (a, b, c), Glasgow provinces for the mesopelagic (d, e, f) and bathyabyssopelagic (g, h, i) domains, and the GOODS provinces for seafloor domain (j, k, l).

Extended Data Fig. 6 Biodiversity representation for climate-smart networks in the high seas.

Average taxonomic group representation (%) in low-regret conservation areas for three pelagic depth domains and the seafloor (a), and throughout the water column for the pelagic domains and pelagic plus the seafloor domain under three IPCC Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5.

Extended Data Fig. 7 Climate velocity in the high seas.

Climate velocity (km decade−1) in the high seas for projected sea temperatures (2050–2100) at three pelagic depth domains and the seafloor, under three IPCC Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Polygons in each map represent Longhurst provinces for the epipelagic domain (a, b, c), Glasgow provinces for the mesopelagic (d, e, f) and bathyabyssopelagic (g, h, i) domains, and the GOODS provinces for the seafloor domain (j, k, l).

Extended Data Fig. 8 Relative Climate Exposure (RCE) index in the high seas.

RCE index (years) in the high seas for projected sea temperatures (2050–2100) at three pelagic depth domains and the seafloor, under three IPCC Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Polygons in each map represent Longhurst provinces for the epipelagic domain (a, b, c), Glasgow provinces for the mesopelagic (d, e, f) and bathyabyssopelagic (g, h, i) domains, and the GOODS provinces for the seafloor domain (j, k, l).

Extended Data Fig. 9 The degree of agreement between the climate-smart MPA networks for different sets of conservation targets.

The Kappa index for the relationship between each prioritised climate-smart network MPA for different area-based protection targets under four depth domains: Epipelagic, Mesopelagic, Bathyabyssopelagic and the Seafloor. The percentages represent the minimum and maximum targets of protection in each prioritisation analysis.

Extended Data Fig. 10 Process for setting climate-smart conservation targets.

Schematic representation for setting targets for conservation features in the climate-smart prioritisation planning approach.

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Brito-Morales, I., Schoeman, D.S., Everett, J.D. et al. Towards climate-smart, three-dimensional protected areas for biodiversity conservation in the high seas. Nat. Clim. Chang. 12, 402–407 (2022). https://doi.org/10.1038/s41558-022-01323-7

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