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Genomic evidence of past and future climate-linked loss in a migratory Arctic fish

Nature Climate Change volume 11, pages 158–165 (2021)Cite this article

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An Author Correction to this article was published on 17 March 2021

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Abstract

Despite widespread biodiversity losses, an understanding of how most taxa will respond to future climate change is lacking. Here we integrate genomics and environmental modelling to assess climate change responses in an ecologically and economically important Arctic species. Environmentally associated genomic diversity and machine learning are used to identify highly vulnerable populations of anadromous (migratory) Arctic charr, and we reconstruct estimates of effective population size spanning the twentieth century to identify past climate-associated declines. We uncover past region-wide declines in effective population size that correspond to decreases in temperature and community biomass in the Northwest Atlantic. We find vulnerable populations near the southern range limit, indicating northward shifts and a possible loss of commercially important life-history variation in response to climate change. The genomic approach used here to investigate climate change response identifies past and future declines that impact species persistence, ecosystem stability and food security in the Arctic.

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Fig. 1: Steep environmental gradients and population structure in Arctic charr.
Fig. 2: Environment explains spatial genomic variation in Arctic charr.
Fig. 3: Genomic vulnerability is highest in southern populations.
Fig. 4: Anadromous populations will be lost at southern locations.
Fig. 5: Past Arctic charr populations have declined in response to climate fluctuation.

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Data availability

Environmental, climate, community biomass, weight and abundance data were compiled from publicly available sources or other studies (https://doi.org/10.1098/rsos.170215 (refs. 37,39,40). Genotype data are available at: https://doi.org/10.5061/dryad.8sf7m0ckd.

Code availability

No custom scripts were used in these analyses.

Change history

References

  1. Serreze, M., Holland, M. & Stroeve, J. Perspectives on the Arctic’s shrinking sea-ice cover. Science 315, 1533–1536 (2007).

    Article  CAS  Google Scholar 

  2. Simpson, M. J. Global Climate Change impacts in the United States. J. Environ. Qual. 40, 279 (2011).

    Article  CAS  Google Scholar 

  3. Moon, T., Ahlstrøm, A., Goelzer, H., Lipscomb, W. & Nowicki, S. Rising oceans guaranteed: Arctic land ice loss and sea level rise. Curr. Clim. Change Rep. 4, 211–222 (2018).

    Article  Google Scholar 

  4. Shepherd, T. G. Effects of a warming arctic. Science 353, 989–990 (2016).

    Article  CAS  Google Scholar 

  5. Sévellec, F., Fedorov, A. V. & Liu, W. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nat. Clim. Change 7, 604–610 (2017).

    Article  Google Scholar 

  6. Casselman, J. M. Effects of temperature, global extremes, and climate change on year-class production of warmwater, coolwater, and coldwater fishes in the Great Lakes Basin. In Fisheries in a Changing Climate, American Fisheries Society Symposium 32 (ed. McGinn, N. A.) 39–60 (American Fisheries Society, 2002).

  7. Robillard, M. M. & Fox, M. G. Historical changes in abundance and community structure of warmwater piscivore communities associated with changes in water clarity, nutrients, and temperature. Can. J. Fish. Aquat. Sci. 63, 798–809 (2006).

    Article  CAS  Google Scholar 

  8. Alofs, K. M., Jackson, D. A. & Lester, N. P. Ontario freshwater fishes demonstrate differing range-boundary shifts in a warming climate. Divers. Distrib. 20, 123–136 (2014).

    Article  Google Scholar 

  9. Lynch, A. J. et al. Climate change effects on North American inland fish populations and assemblages. Fisheries 41, 346–361 (2016).

    Article  Google Scholar 

  10. Poesch, M. S., Chavarie, L., Chu, C., Pandit, S. N. & Tonn, W. Climate change impacts on freshwater fishes: a Canadian perspective. Fisheries 41, 385–391 (2016).

    Article  Google Scholar 

  11. Sgrò, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337 (2011).

    Article  Google Scholar 

  12. De Meester, L., Stoks, R. & Brans, K. I. Genetic adaptation as a biological buffer against climate change: potential and limitations. Integr. Zool. 13, 372–391 (2018).

    Article  Google Scholar 

  13. Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).

    Article  CAS  Google Scholar 

  14. Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).

    Article  Google Scholar 

  15. Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).

  16. Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).

    Article  Google Scholar 

  17. Cummins, D., Kennington, W. J., Rudin-Bitterli, T. & Mitchell, N. J. A genome-wide search for local adaptation in a terrestrial‐breeding frog reveals vulnerability to climate change. Glob. Change Biol. 25, 3151–3162 (2019).

    Article  Google Scholar 

  18. Rilov, G. et al. Adaptive marine conservation planning in the face of climate change: what can we learn from physiological, ecological and genetic studies? Glob. Ecol. Conserv. 17, e00566 (2019).

    Article  Google Scholar 

  19. Friedland, K. D. Ocean climate influences on critical Atlantic salmon (Salmo salar) life history events. Can. J. Fish. Aquat. Sci. 55, 119–130 (1998).

    Article  Google Scholar 

  20. Reist, J. D. et al. General effects of climate change on Arctic fishes and fish populations. Ambio 35, 370–380 (2006).

    Article  Google Scholar 

  21. Klemetsen, A. The most variable vertebrate on Earth. J. Ichthyol. 53, 781–791 (2013).

    Article  Google Scholar 

  22. Salisbury, S. J. et al. Genetic divergence among and within Arctic char (Salvelinus alpinus) populations inhabiting landlocked and sea-accessible sites in Labrador, Canada. Can. J. Fish. Aquat. Sci. 75, 1256–1269 (2018).

    Article  Google Scholar 

  23. Bernatchez, L., Rhydderch, J. G. & Kircheis, F. W. Microsatellite gene diversity analysis in landlocked Arctic char from Maine. Trans. Am. Fish. Soc. 131, 1106–1118 (2002).

    Article  CAS  Google Scholar 

  24. Kerr, R. A. A North Atlantic climate pacemaker for the centuries. Science 288, 1984–1985 (2000).

    Article  CAS  Google Scholar 

  25. Enfield, D. B., Mestas-Nuñez, A. M. & Trimble, P. J. The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett. 28, 2077–2080 (2001).

    Article  Google Scholar 

  26. Lehnherr, I. et al. The world’s largest High Arctic lake responds rapidly to climate warming. Nat. Commun. 9, 1290 (2018).

    Article  Google Scholar 

  27. Moore, J.-S., Chapman, J. M., Mazerolle, M. J., Harris, L. N. & Taylor, E. B. Premature alarm on the impacts of climate change on Arctic char in Lake Hazen. Nat. Commun. 9, 3985 (2018).

    Article  Google Scholar 

  28. Colella, J. P. et al. Conservation genomics in a changing Arctic. Trends Ecol. Evol. 35, 149–162 (2020).

    Article  Google Scholar 

  29. Nugent, C. M. et al. Design and characterization of an 87k SNP genotyping array for Arctic charr (Salvelinus alpinus). PLoS ONE 14, e0215008 (2019).

    Article  CAS  Google Scholar 

  30. Layton, K. K. S. et al. Resolving fine-scale population structure and fishery exploitation with sequenced microsatellites in a northern fish. Evol. Appl. 13, 1055–1068 (2020).

    Article  CAS  Google Scholar 

  31. Sloin, H. E. et al. Interactions between the circadian clock and TGF-β signaling pathway in zebrafish. PLoS ONE 13, e0199777 (2018).

    Article  Google Scholar 

  32. Almroth, B. C. et al. Warmer water temperature results in oxidative damage in an Antarctic fish, the bald notothen. J. Exp. Mar. Biol. Ecol. 468, 130–137 (2015).

    Article  Google Scholar 

  33. Sylvester, E. V. A. et al. Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America. Mol. Ecol. 27, 4026–4040 (2018).

    Article  Google Scholar 

  34. Frankham, R, Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge Univ. Press, 2002).

  35. Yannic, G. et al. Genetic diversity in caribou linked to past and future climate change. Nat. Clim. Change 4, 132–137 (2014).

    Article  Google Scholar 

  36. Hirase, S., Ozaki, H. & Iwasaki, W. Parallel selection on gene copy number variations through evolution of three-spined stickleback genomes. BMC Genomics 15, 735 (2014).

    Article  Google Scholar 

  37. Pedersen, E. J. et al. Signatures of the collapse and incipient recovery of an overexploited marine ecosystem. R. Soc. Open Sci. 4, 170215 (2017).

    Article  Google Scholar 

  38. Hollenbeck, C. M., Portnoy, D. S. & Gold, J. R. A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity 117, 207–216 (2016).

    Article  CAS  Google Scholar 

  39. Dempson, J. B. Evaluation of the Status of the Nain Stock Unit Arctic Charr Population in 1992. DFO Atlantic Fisheries Research Document 93/4 (Department of Fisheries and Oceans, 1993).

  40. Dempson, J. B. Trends in population characteristics of an exploited anadromous Arctic Charr, Salvelinus alpinus, stock in Northern Labrador. Nord. J Freshw. Res. 71, 197–216 (1995).

    Google Scholar 

  41. Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).

    Article  Google Scholar 

  42. Davis, M. B., Shaw, R. G. & Etterson, J. R. Evolutionary responses to changing climate. Ecology 86, 1704–1714 (2005).

    Article  Google Scholar 

  43. Nogués-Bravo, D. et al. Cracking the code of biodiversity responses to past climate change. Trends Ecol. Evol. 33, 765–776 (2018).

    Article  Google Scholar 

  44. Ørsted, M., Hoffmann, A. A., Sverrisdóttir, E., Nielsen, K. L. & Kristensen, T. N. Genomic variation predicts adaptive evolutionary responses better than population bottleneck history. PLoS Genet. 15, e1008205 (2019).

    Article  Google Scholar 

  45. Beatty, G. E., McEvoy, P. M., Sweeney, O. & Provan, J. Range‐edge effects promote clonal growth in peripheral populations of the one‐sided wintergreen Orthilia secunda. Divers. Distrib. 14, 546–555 (2008).

    Article  Google Scholar 

  46. Christensen, C., Jacobsen, M. W., Nygaard, R. & Hansen, M. M. Spatiotemporal genetic structure of anadromous Arctic char (Salvelinus alpinus) populations in a region experiencing pronounced climate change. Conserv. Genet. 19, 687–700 (2018).

    Article  Google Scholar 

  47. Etterson, J. R. & Shaw, R. G. Constraint to adaptive evolution in response to global warming. Science 294, 151–154 (2001).

    Article  CAS  Google Scholar 

  48. Jensen, J. W. Anadromous Arctic char, Salvelinus alpinus, penetrating southward on the Norwegian coast. Can. J. Fish. Aquat. Sci. 38, 247–249 (1981).

    Article  Google Scholar 

  49. Finstad, A. G. & Hein, C. L. Migrate or stay: terrestrial primary productivity and climate drive anadromy in Arctic char. Glob. Change Biol. 18, 2487–2497 (2012).

    Article  Google Scholar 

  50. Virkkala, R. & Lehikoinen, A. Patterns of climate-induced density shifts of species: poleward shifts faster in northern boreal birds than in southern birds. Glob. Change Biol. 20, 2995–3003 (2014).

    Article  Google Scholar 

  51. Lesica, P. & Crone, E. E. Arctic and boreal plant species decline at their southern range limits in the Rocky Mountains. Ecol. Lett. 20, 166–174 (2017).

    Article  Google Scholar 

  52. Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land use change on the global diversity of birds. PLoS Biol. 5, 1211–1219 (2007).

    Article  CAS  Google Scholar 

  53. Crozier, L. G. et al. Climate vulnerability assessment for Pacific salmon and steelhead in the California current large marine ecosystem. PLoS ONE 14, e0217711 (2019).

    Article  CAS  Google Scholar 

  54. deYoung, B. et al. Regime shifts in marine ecosystems: detection, prediction and management. Trends Ecol. Evol. 23, 402–409 (2008).

    Article  Google Scholar 

  55. Rocha, J., Yletyinen, J., Biggs, R., Blenckner, T. & Peterson, G. Marine regime shifts: drivers and impacts on ecosystems services. Phil. Trans. R. Soc. B 370, 20130273 (2015).

    Article  Google Scholar 

  56. Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).

    Article  Google Scholar 

  57. Berkes, F. Native subsistence fisheries: a synthesis of harvest studies in Canada. Arctic 43, 35–42 (1990).

    Article  Google Scholar 

  58. Stevenson, T. C., Davies, J., Huntington, H. P. & Sheard, W. An examination of trans-Arctic vessel routing in the central Arctic Ocean. Mar. Policy 100, 83–89 (2019).

    Article  Google Scholar 

  59. Funk, W. C., Forester, B. R., Converse, S. J., Darst, C. & Morey, S. Improving conservation policy with genomics: a guide to integrating adaptive potential into U.S. Endangered Species Act decisions for conservation practitioners and geneticists. Conserv. Genet. 20, 115–134 (2019).

    Article  Google Scholar 

  60. Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885 (2000).

    Article  CAS  Google Scholar 

  61. Lehnert, S. J. et al. Genomic signatures and correlates of widespread population declines in salmon. Nat. Commun. 10, 2996 (2019).

    Article  CAS  Google Scholar 

  62. Kess, T. et al. A migration-associated supergene reveals loss of biocomplexity in Atlantic cod. Sci. Adv. 5, eaav2461 (2019).

    Article  Google Scholar 

  63. Sgubin, G., Swingedouw, D., Drijfhout, S., Mary, Y. & Bennabi, A. Abrupt cooling over the North Atlantic in modern climate models. Nat. Commun. 8, 14375 (2017).

    Article  CAS  Google Scholar 

  64. Sittaro, F., Paquette, A., Messier, C. & Nock, C. A. Tree range expansion in eastern North America fails to keep pace with climate warming at northern range limits. Glob. Change Biol. 23, 3293–3301 (2017).

    Article  Google Scholar 

  65. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  66. Nugent, C. M., Easton, A. A., Norman, J. D., Ferguson, M. M. & Danzmann, R. G. A SNP based linkage map of the Arctic charr (Salvelinus alpinus) genome provides insights into the diploidization process after whole genome duplication. G3 7, 543–556 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  68. Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  Google Scholar 

  69. Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).

    Article  CAS  Google Scholar 

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

  71. Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers. Distrib. 20, 1–9 (2014).

    Article  Google Scholar 

  72. LaZerte, S. & Albers, S. weathercan: download and format weather data from Environment and Climate Change Canada. J. Open Source Softw. 3, 571 (2018).

    Article  Google Scholar 

  73. Chezik, K. A., Lester, N. P. & Venturelli, P. A. Fish growth and degree-days I: selecting a base temperature for a within-population study. Can. J. Fish. Aquat. Sci. 71, 47–55 (2014).

    Article  Google Scholar 

  74. Jonsson, N., Jonsson, B. & Hansen, L. P. Does climate during embryonic development influence parr growth and age of seaward migration in Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 62, 2502–2508 (2005).

    Article  Google Scholar 

  75. Skinner, L. A., Schulte, P. M., LaPatra, S. E., Balfry, S. K. & McKinley, R. S. Growth and performance of Atlantic salmon, Salmo salar L., following administration of a rhabdovirus DNA vaccine alone or concurrently with an oil-adjuvanted, polyvalent vaccine. J. Fish Dis. 31, 687–697 (2008).

    Article  CAS  Google Scholar 

  76. Delabbio, J. in Cold-Water Aquaculture in Atlantic Canada 2nd edn (ed. Boghen, A. D.) 85–106 (Canadian Institute for Research on Regional Development, 1995).

  77. Goudet, J. hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).

    Article  Google Scholar 

  78. Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).

    CAS  Google Scholar 

  79. Pante, E. & Simon-Bouhet, B. marmap: a package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051 (2013).

    Article  CAS  Google Scholar 

  80. Oksanen, J. et al. vegan: community ecology package. R package version 1.17-6 (2011).

  81. Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol. Ecol. 27, 2215–2233 (2018).

    Article  CAS  Google Scholar 

  82. Ratner, B. The correlation coefficient: its values range between +1/−1, or do they? J. Target Meas. Anal. Market. 17, 139–142 (2009).

    Article  Google Scholar 

  83. Luu, K., Bazin, E. & Blum, M. G. M. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).

    Article  CAS  Google Scholar 

  84. Salvelinus. NCBI https://www.ncbi.nlm.nih.gov/genome/86400 (2021).

  85. Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for gene ontology. R package version 2.28.0 (2016).

  86. Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).

    Article  Google Scholar 

  87. Stanley, R. R. E., Jeffery, N. W., Wringe, B. F., DiBacco, C. & Bradbury, I. R. genepopedit: a simple and flexible tool for manipulating multilocus molecular data in R. Mol. Ecol. Resour. 17, 12–18 (2017).

    Article  CAS  Google Scholar 

  88. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  Google Scholar 

  89. Chavarie, L. et al. Latitudinal variation in growth among Arctic charr in eastern North America: evidence for countergradient variation. Hydrobiologia 650, 161–177 (2010).

    Article  Google Scholar 

  90. Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).

    Article  CAS  Google Scholar 

  91. Tumino, G. et al. Population structure and genome-wide association analysis for frost tolerance in oat using continuous SNP array signal intensity ratios. Theor. Appl. Genet. 129, 1711–1724 (2016).

    Article  CAS  Google Scholar 

  92. Dempson, J. B., Shears, M., Furey, G. & Bloom, M. Resilience and stability of north Labrador Arctic charr, Salvelinus alpinus, subject to exploitation and environmental variability. Environ. Biol. Fish. 83, 57–67 (2008).

    Article  Google Scholar 

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Acknowledgements

We thank staff of the Newfoundland DFO Salmonids section, Parks Canada, the Nunatsiavut Government, the NunatuKavut Community Council, the Sivunivut Inuit Community Corporation, the Innu Nation, the Labrador Hunting and Fishing Association and fishers for their support, participation and tissue collections and the staff of the Aquatic Biotechnology Lab at the Bedford Institute of Oceanography for DNA extractions. This study was supported by the Ocean Frontier Institute, a Genomics Research and Development Initiative (GRDI) Grant, a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant and Strategic Project Grant to I.R.B., the Weston Family Award for research at the Torngat Mountains Base Camp and an Atlantic Canada Opportunities Agency and Department of Tourism, Culture, Industry and Innovation grant allocated to the Labrador Institute.

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Authors and Affiliations

  1. Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada

    K. K. S. Layton & P. V. R. Snelgrove

  2. Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador, Canada

    K. K. S. Layton, J. B. Dempson, T. Kess, S. J. Lehnert, S. J. Duffy, A. M. Messmer, R. R. E. Stanley, C. DiBacco & I. R. Bradbury

  3. University of Aberdeen, Aberdeen, UK

    K. K. S. Layton

  4. Dalhousie University, Halifax, Nova Scotia, Canada

    P. Bentzen, S. J. Salisbury & D. E. Ruzzante

  5. University of Guelph, Guelph, Ontario, Canada

    C. M. Nugent & M. M. Ferguson

  6. University of Victoria, Victoria, British Columbia, Canada

    J. S. Leong & B. F. Koop

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Contributions

K.K.S.L., P.V.R.S. and I.R.B. designed the study. K.K.S.L., T.K., S.J.L. and R.R.E.S. contributed to statistical analyses. P.V.R.S., J.B.D., P.B., S.J.D., A.M.M., C.M.N., M.M.F., J.S.L. and B.F.K. provided molecular data and metadata for the study. All authors discussed the findings. K.K.S.L. wrote the manuscript with contributions from all authors.

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Correspondence to K. K. S. Layton or I. R. Bradbury.

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Extended data

Extended Data Fig. 1 Absolute loadings for each SNP along the first canonical axis in an RDA.

Absolute loadings for each SNP along the first canonical axis in an RDA with SNPs in the top 95th percentile treated as significant. Environment-associated SNPs are highlighted in red and genes located near top SNPs are labeled (ASIC4 = acid-sensing ion channel 4-like, COL18A1= collagen alpha-1(XVIII) chain, ELOVL1= elongation of very long chain fatty acids protein 1, KCNH2= potassium voltage-gated channel subfamily H member 2-like, PITPNB= phosphatidylinositol transfer protein beta isoform, TGFBR1= TGF-beta receptor type-1, TGM2= protein-glutamine gamma-glutamyltransferase 2, TGOLN2= trans-Golgi network integral membrane protein 2).

Extended Data Fig. 2 Genomic vulnerability is strongly negatively correlated with nucleotide diversity.

Genomic vulnerability is strongly negatively correlated with nucleotide diversity at a all 16,431 SNPs in this study and b 822 environment-associated SNPs under four different emissions scenarios.

Extended Data Fig. 3 Effective population size (Ne) estimates with confidence intervals.

Effective population size (Ne) estimates, representing number of individuals, for 28 populations of Arctic Charr. 95% confidence intervals appear in black and population codes appear in Supplementary Table 1.

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Supplementary Figs. 1–5 and Tables 1–6.

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Layton, K.K.S., Snelgrove, P.V.R., Dempson, J.B. et al. Genomic evidence of past and future climate-linked loss in a migratory Arctic fish. Nat. Clim. Chang. 11, 158–165 (2021). https://doi.org/10.1038/s41558-020-00959-7

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