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Models of Arctic-alpine refugia highlight importance of climate and local topography

Abstract

Projected climatic warming calls for increased attention to the identification of suitable refugia for the preservation of biota and ecosystems in changing high-latitude environments. One such way is the development of models for drivers of refugia. Here, we investigate the distribution and species richness of Arctic-alpine vascular plant species’ refugia. The study is carried out in an environmentally variable area in N Europe, encompassing the northern boreal to the Arctic-alpine zone. We defined refugia as isolated 1 km × 1 km grid cells with multiple Arctic-alpine plant species occurrences outside their main distribution area and assessed the main environmental factors underlying their distribution and richness using cross-validated boosted regression tree modelling. In the modelling, we examined the effects of climatic, topographic, and geologic factors, and the connectivity of sites with refugia incrementally, i.e. first modelling climatic impact alone, then with separate additions of topographic, geologic and connectivity variables, concluding with a model including all predictor variables. The inclusion of slope and connectivity significantly improved model performance. Although climate has a central role in controlling the occurrence of refugia, topography provides important clues for recognizing heterogeneous locations that harbour refugia with suitable local thermal and moisture conditions. Results suggest considering refugia as, on the one hand, isolated pockets of suitable habitat, but on the other hand as potentially interconnected habitat networks. In general, our study demonstrates that the spatial patterns of refugia can be successfully modelled, but emphasizes a need for high-quality data sampled at resolutions reflecting significant environmental gradients.

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References

  1. Aalto J, Luoto M (2014) Integrating climate and local factors for geomorphological distribution models. Earth Surf Proc Land 39:1729–1740

    Article  Google Scholar 

  2. Aalto J, Le Roux PC, Luoto M (2014a) The meso-scale drivers of temperature extremes in high-latitude Fennoscandia. Clim Dyn 42:237–252

    Article  Google Scholar 

  3. Aalto J, Venäläinen A, Heikkinen RK, Luoto M (2014b) Potential for extreme loss in high-latitude Earth surface processes due to climate change. Geophys Res Lett 41:3914–3924

    Article  Google Scholar 

  4. Abbott RJ, Brochmann C (2003) History and evolution of the arctic flora: in the footsteps of Eric Hultén. Mol Ecol 12:299–313

    Article  PubMed  Google Scholar 

  5. Acia ACIA (2004) Impacts of a warming arctic: Arctic climate impact assessment. Cambridge University Press, Cambridge

    Google Scholar 

  6. Ackerly DD, Loarie SR, Cornwell WK, Weiss SB, Hamilton H, Branciforte R, Kraft NJB (2010) The geography of climate change: implications for conservation biogeography. Divers Distrib 16:476–487

    Article  Google Scholar 

  7. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232

    Article  Google Scholar 

  8. Alm T, Birks HH (1991) Late Weichselian flora and vegetation of Andøya, Northern Norway-macrofossil (seed and fruit) evidence from Nedre Æråsvatn. Nord J Bot 11:465–476

    Article  Google Scholar 

  9. Anderson MG, Ferree CE (2010) Conserving the stage: climate change and the geophysical underpinnings of species diversity. PLoS One 5:e11554

    Article  PubMed  PubMed Central  Google Scholar 

  10. Anderson P, Robert Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Article  Google Scholar 

  11. Ashcroft MB (2010) Identifying refugia from climate change. J Biogeogr 37:1407–1413

    Google Scholar 

  12. Ashcroft MB, Gollan JR (2013) Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: understanding factors that promote microrefugia. Agric For Meteorol 176:77–89

    Article  Google Scholar 

  13. Ashcroft MB, Gollan JR, Warton DI, Ramp D (2012) A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob Change Biol 18:1866–1879

    Article  Google Scholar 

  14. Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8

    Article  Google Scholar 

  15. Barnosky AD (2008) Climatic change, refugia, and biodiversity: where do we go from here? An editorial comment. Clim Change 86:29–32

    Article  Google Scholar 

  16. Bennett K, Provan J (2008) What do we mean by ‘refugia’? Quat Sci Rev 27:2449–2455

    Article  Google Scholar 

  17. Bennie J, Hill MO, Baxter R, Huntley B (2006) Influence of slope and aspect on long-term vegetation change in British chalk grasslands. J Ecol 94:355–368

    Article  Google Scholar 

  18. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  19. Birks H (1993) Is the hypothesis of survival on glacial nunataks necessary to explain the present-day distributions of Norwegian mountain plants? Phytocoenologia 23:399–426

    Article  Google Scholar 

  20. Birks H (1994) Plant macrofossils and the nunatak theory of per-glacial survival. Diss Bot 234:129–143

    Google Scholar 

  21. Birks H (1996) Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora. Ecography 19:332–340

    Article  Google Scholar 

  22. Birks HH (2008) The Late-Quaternary history of arctic and alpine plants. Plant Ecol Divers 1:135–146

    Article  Google Scholar 

  23. Birks HJB, Willis KJ (2008) Alpines, trees, and refugia in Europe. Plant Ecol Diver 1:147–160

    Article  Google Scholar 

  24. Birks HH, Giesecke T, Hewitt GM, Tzedakis PC, Bakke J, Birks HJ (2012) Comment on Glacial survival of boreal trees in northern Scandinavia. Science 338:742

    CAS  Article  PubMed  Google Scholar 

  25. Björnstad O (2014) ncf Spatial nonparametric covariance functions. R package version 1.1-5. h ttp. cran. r-project. org/web/packages/ncf/index. html. (3 Feb 2014)

  26. Bliss LC (1971) Arctic and alpine plant life cycles. Ann Rev Ecol Syst 2:405–438

    Article  Google Scholar 

  27. Bossard M, Feranec J, Otahel J (2000) CORINE land cover technical guide: addendum 2000. European Environment Agency Copenhagen. http://www.pedz.uni-mannheim.de/daten/edz-bn/eua/00/tech40add.pdf. Accessed 19 Nov 2014

  28. Braunisch V, Coppes J, Arlettaz R, Suchant R, Schmid H, Bollmann K (2013) Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36:971–983

    Article  Google Scholar 

  29. Brochmann C, Gabrielsen TM, Nordal I, Landvik JY, Elven R (2003) Glacial survival or tabula rasa? The history of North Atlantic biota revisited. Taxon 52:417–450

    Article  Google Scholar 

  30. Brown JH, Kodric-Brown A (1977) Turnover rates in insular biogeography: effect of immigration on extinction. Ecology 58:445–449

    Article  Google Scholar 

  31. Bush MB (1996) Amazonian conservation in a changing world. Biol Conserv 76:219–228

    Article  Google Scholar 

  32. Dahl E (1951) On the relation between summer temperature and the distribution of alpine vascular plants in the lowlands of Fennoscandia. Oikos 3:22–52

    Article  Google Scholar 

  33. De’Ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88:243–251

    Article  PubMed  Google Scholar 

  34. Dobrowski SZ (2011) A climatic basis for microrefugia: the influence of terrain on climate. Glob Change Biol 17:1022–1035

    Article  Google Scholar 

  35. Dubuis A, Giovanettina S, Pellissier L, Pottier J, Vittoz P, Guisan A (2013) Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables. J Veg Sci 24:593–606

    Article  Google Scholar 

  36. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813

    CAS  Article  PubMed  Google Scholar 

  37. Fickert T, Friend D, Grüninger F, Molnia B, Richter M (2007) Did debris-covered glaciers serve as Pleistocene refugia for plants? A new hypothesis derived from observations of recent plant growth on glacier surfaces. Arct Antarct Alp Res 39:245–257

    Article  Google Scholar 

  38. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

    Article  Google Scholar 

  39. Fridley JD (2009) Downscaling climate over complex terrain: high finescale (<1000 m) spatial variation of near-ground temperatures in a montane forested landscape (Great Smoky Mountains)*. J Appl Meteorol Climatol 48:1033–1049

    Article  Google Scholar 

  40. Gabrielsen T, Bachmann K, Jakobsen K, Brochmann C (1997) Glacial survival does not matter: rAPD phylogeography of Nordic Saxifraga oppositifolia. Mol Ecol 6:831–842

    CAS  Article  Google Scholar 

  41. Gavin DG, Fitzpatrick MC, Gugger PF, Heath KD, Rodríguez-Sánchez F, Dobrowski SZ, Hampe A, Hu FS, Ashcroft MB, Bartlein PJ (2014) Climate refugia: joint inference from fossil records, species distribution models and phylogeography. New Phytol 204:37–54

    Article  PubMed  Google Scholar 

  42. Graham CH, VanDerWal J, Phillips SJ, Moritz C, Williams SE (2010) Dynamic refugia and species persistence: tracking spatial shifts in habitat through time. Ecography 33:1062–1069

    Article  Google Scholar 

  43. Grytnes JA, Birks H, Peglar SM (1999) Plant species richness in Fennoscandia: evaluating the relative importance of climate and history. Nord J Bot 19:489–503

    Article  Google Scholar 

  44. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  45. Guisan A, Theurillat JP, Kienast F (1998) Predicting the potential distribution of plant species in an alpine environment. J Veg Sci 9:65–74

    Article  Google Scholar 

  46. Hampe A, Jump AS (2011) Climate relicts: past, present, future. Annu Rev Ecol Evol Syst 42:313–333

    Article  Google Scholar 

  47. Hampe A, Rodríguez-Sánchez F, Dobrowski S, Hu FS, Gavin DG (2013) Climate refugia: from the last glacial maximum to the twenty-first century. New Phytol 197:16–18

    Article  PubMed  Google Scholar 

  48. Hannah L, Flint L, Syphard AD, Moritz MA, Buckley LB, McCullough IM (2014) Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol Evol 29:390–397

    Article  PubMed  Google Scholar 

  49. Hanski I (1994) A practical model of metapopulation dynamics. J Anim Ecol :151–162

  50. Heikkinen RK, Marmion M, Luoto M (2012) Does the interpolation accuracy of species distribution models come at the expense of transferability? Ecography 35:276–288

    Article  Google Scholar 

  51. Hijmans RJ, Phillips S, Leathwick J, Elith J (2012) Dismo: species distribution modeling. R package version 0.7-17

  52. Hinzman LD, Bettez ND, Bolton WR, Chapin FS, Dyurgerov MB, Fastie CL, Griffith B, Hollister RD, Hope A, Huntington HP, Jensen AM, Jia GJ, Jorgenson T, Kane DL, Klein DR, Kofinas G, Lynch AH, Lloyd AH, McGuire AD, Nelson FE, Oechel WC, Osterkamp TE, Racine CH, Romanovsky VE, Stone RS, Stow DA, Sturm M, Tweedie CE, Vourlitis GL, Walker MD, Walker DA, Webber PJ, Welker JM, Winker KS, Yoshikawa K (2005) Evidence and implications of recent climate change in Northern Alaska and other Arctic regions. Clim Change 72:251–298

    Article  Google Scholar 

  53. Hughes L (2000) Biological consequences of global warming: is the signal already apparent? Trends Ecol Evol 15:56–61

    CAS  Article  PubMed  Google Scholar 

  54. Keppel G, Wardell-Johnson GW (2015) Refugial capacity defines holdouts, microrefugia and stepping-stones: a response to Hannah et al. Trends Ecol Evol 20:1–2

    Google Scholar 

  55. Keppel G, Van Niel KP, Wardell-Johnson GW, Yates CJ, Byrne M, Mucina L, Schut AG, Hopper SD, Franklin SE (2012) Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob Ecol Biogeogr 21:393–404

    Article  Google Scholar 

  56. Kohler J, Brandt O, Johansson M, Callaghan T (2006) A long-term Arctic snow depth record from Abisko, northern Sweden, 1913–2004. Polar Res 25:91–113

    Article  Google Scholar 

  57. Körner C (2005) The green cover of mountains in a changing environment. Global change and mountain regions. Springer

  58. Kurtto A, Lampinen R (1999) Atlas of the distribution of vascular plants in Finland: a digital view of the national floristic database. Acta Bot Fenn 162:67–74

    Google Scholar 

  59. le Roux PC, Aalto J, Luoto M (2013) Soil moisture’s underestimated role in climate change impact modelling in low-energy systems. Glob Chang Biol 19:2965–2975

    Article  PubMed  Google Scholar 

  60. Lindborg R, Eriksson O (2004) Historical landscape connectivity affects present plant species diversity. Ecology 85:1840–1845

    Article  Google Scholar 

  61. Luoto M, Heikkinen RK (2008) Disregarding topographical heterogeneity biases species turnover assessments based on bioclimatic models. Glob Change Biol 14:483–494

    Article  Google Scholar 

  62. Marchand F, Verlinden M, Kockelbergh F, Graae B, Beyens L, Nijs I (2006) Disentangling effects of an experimentally imposed extreme temperature event and naturally associated desiccation on Arctic tundra. Funct Ecol 20:917–928

    Article  Google Scholar 

  63. Mawdsley JR, O’Malley R, Ojima DS (2009) A review of climate-change adaptation strategies for wildlife management and biodiversity conservation. Conserv Biol 23:1080–1089

    Article  PubMed  Google Scholar 

  64. McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606

    Article  Google Scholar 

  65. Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J Biogeogr 36:1333–1345

    Article  Google Scholar 

  66. Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305:994–997

    CAS  Article  PubMed  Google Scholar 

  67. Moilanen A, Nieminen M (2002) Simple connectivity measures in spatial ecology. Ecology 83:1131–1145

    Article  Google Scholar 

  68. Moritz C, Agudo R (2013) The future of species under climate change: resilience or decline? Science 341:504–508

    CAS  Article  PubMed  Google Scholar 

  69. Mosblech NAS, Bush MB, van Woesik R (2011) On metapopulations and microrefugia: palaeoecological insights. J Biogeogr 38:419–429

    Article  Google Scholar 

  70. Noss RF (2001) Beyond Kyoto: forest management in a time of rapid climate change. Conserv Biol 15:578–590

    Article  Google Scholar 

  71. Oksanen L, Virtanen R (1995) Topographic, altitudinal and regional patterns in continental and suboceanic heath vegetation of northern Fennoscandia. Acta Bot Fenn 153:1–80

    Google Scholar 

  72. Olson D, DellaSala DA, Noss RF, Strittholt JR, Kass J, Koopman ME, Allnutt TF (2012) Climate change refugia for biodiversity in the Klamath-Siskiyou Ecoregion. Nat Areas J 32:65–74

    Article  Google Scholar 

  73. Pagel J, Schurr FM (2012) Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Glob Ecol Biogeogr 21:293–304

    Article  Google Scholar 

  74. Parducci L, Jorgensen T, Tollefsrud MM, Elverland E, Alm T, Fontana SL, Bennett KD, Haile J, Matetovici I, Suyama Y, Edwards ME, Andersen K, Rasmussen M, Boessenkool S, Coissac E, Brochmann C, Taberlet P, Houmark-Nielsen M, Larsen NK, Orlando L, Gilbert MT, Kjaer KH, Alsos IG, Willerslev E (2012) Glacial survival of boreal trees in northern Scandinavia. Science 335:1083–1086

    CAS  Article  PubMed  Google Scholar 

  75. Penna D, Borga M, Norbiato D, Dalla Fontana G (2009) Hillslope scale soil moisture variability in a steep alpine terrain. J Hydrol 364:311–327

    Article  Google Scholar 

  76. Pigott C, Walters SM (1954) On the interpretation of the discontinuous distributions shown by certain British species of open habitats. J Ecol 42:95–116

    Article  Google Scholar 

  77. Pimm SL (2009) Climate disruption and biodiversity. Curr Biol 19:R595–R601

    CAS  Article  PubMed  Google Scholar 

  78. Pirinen P, Simola H, Aalto J, Kaukoranta J, Karlsson P, Ruuhela R (2012) Climatological statistics of Finland 1981–2010. Finn Meteorol Inst Rep 2012:25

    Google Scholar 

  79. Porto TJ, Carnaval AC, da Rocha PLB (2013) Evaluating forest refugial models using species distribution models, model filling and inclusion: a case study with 14 Brazilian species. Divers Distrib 19:330–340

    Article  Google Scholar 

  80. Post E, Forchhammer MC, Bret-Harte MS, Callaghan TV, Christensen TR, Elberling B, Fox AD, Gilg O, Hik DS, Høye TT (2009) Ecological dynamics across the Arctic associated with recent climate change. Science 325:1355–1358

    CAS  Article  PubMed  Google Scholar 

  81. Press M, Potter J, Burke M, Callaghan T, Lee J (1998) Responses of a subarctic dwarf shrub heath community to simulated environmental change. J Ecol 86:315–327

    Article  Google Scholar 

  82. Prugh LR (2009) An evaluation of patch connectivity measures. Ecol Appl 19:1300–1310

    Article  PubMed  Google Scholar 

  83. Przybylak R (2002) Changes in seasonal and annual high-frequency air temperature variability in the Arctic from 1951 to 1990. Int J Climatol 22:1017–1032

    Article  Google Scholar 

  84. Raatikainen KM, Heikkinen RK, Luoto M (2008) Relative importance of habitat area, connectivity, management and local factors for vascular plants: spring ephemerals in boreal semi-natural grasslands. Biodivers Conserv 18:1067–1085

    Article  Google Scholar 

  85. Randin CF, Engler R, Normand S, Zappa M, Zimmermann NE, Pearman PB, Vittoz P, Thuiller W, Guisan A (2009) Climate change and plant distribution: local models predict high-elevation persistence. Glob Change Biol 15:1557–1569

    Article  Google Scholar 

  86. Rassi P, Alanen A, Kanerva T, Mannerkoski I (2001) The 2000 red list of Finnish species. Ministry of the Environment and Finnish Environment Institute, Helsinki

    Google Scholar 

  87. Reside AE, VanDerWal J, Phillips BL, Shoo LP, Rosauer DF, Anderson BJ, Welbergen JA, Moritz C, Ferrier S, Harwood TD (2013) Climate change refugia for terrestrial biodiversity. http://www.nccarf.edu.au/publications/climate-change-refugia-terrestrial-biodiversity. Accessed 20 Aug 2013

  88. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003) Fingerprints of global warming on wild animals and plants. Nature 421:57–60

    CAS  Article  PubMed  Google Scholar 

  89. Roux PC, Luoto M (2014) Earth surface processes drive the richness, composition and occurrence of plant species in an arctic–alpine environment. J Veg Sci 25:45–54

    Article  Google Scholar 

  90. Rull V (2009) Microrefugia. J Biogeogr 36:481–484

    Article  Google Scholar 

  91. Ryttäri T, Kettunen T, Alanen A (1997) Uhanalaiset kasvimme. Suomen Ympäristökeskus

  92. Scherrer D, Körner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38:406–416

    Article  Google Scholar 

  93. Shoo LP, Storlie C, Williams YM, Williams SE (2010) Potential for mountaintop boulder fields to buffer species against extreme heat stress under climate change. Int J Biometeorol 54:475–478

    Article  PubMed  Google Scholar 

  94. Shoo LP, Hoffmann AA, Garnett S, Pressey RL, Williams YM, Taylor M, Falconi L, Yates CJ, Scott JK, Alagador D (2013) Making decisions to conserve species under climate change. Clim Change 119:239–246

    Article  Google Scholar 

  95. Skov F, Svenning JC (2004) Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27:366–380

    Article  Google Scholar 

  96. Sormunen H, Virtanen R, Luoto M (2011) Inclusion of local environmental conditions alters high-latitude vegetation change predictions based on bioclimatic models. Polar Biol 34:883–897

    Article  Google Scholar 

  97. Stewart JR, Lister AM (2001) Cryptic northern refugia and the origins of the modern biota. Trends Ecol Evol 16:608–613

    Article  Google Scholar 

  98. Stewart JR, Lister AM, Barnes I, Dalen L (2010) Refugia revisited: individualistic responses of species in space and time. Proc Biol Sci 277:661–671

    Article  PubMed  Google Scholar 

  99. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    CAS  Article  PubMed  Google Scholar 

  100. Taberlet P, Cheddadi R (2002) Ecology. Quaternary refugia and persistence of biodiversity. Science 297:2009–2010

    CAS  Article  PubMed  Google Scholar 

  101. Tikkanen M (2005) Climate. In: Seppälä M (ed) The physical geography of Fennoscandia. Oxford University Press, Oxford

    Google Scholar 

  102. Vegas-Vilarrúbia T, Nogué S, Rull V (2012) Global warming, habitat shifts and potential refugia for biodiversity conservation in the neotropical Guayana Highlands. Biol Conserv 152:159–168

    Article  Google Scholar 

  103. Vorren TO, Vorren K-D, Aasheim O, Dahlgren KIT, Forwick M, Hassel K (2013) Palaeoenvironment in northern Norway between 22.2 and 14.5 cal. ka BP. Boreas 42:876–895

    Google Scholar 

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Acknowledgments

A. Niskanen was funded by the Doctoral school of Geosciences and Nordenskiöld samfundet. We acknowledge funding from the Academy of Finland (The Finnish Research Programme on Climate Change, Project Number 1140873). We would like to thank the Finnish Museum of Natural History as one of the main contributors of the vegetation data and Juha Aalto for helping in gathering the environmental data and data analysis.

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Correspondence to Annina Niskanen.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

List of the refugial species (species with more than two-thirds of their study area distribution occurring in the Arctic-alpine Scandes Mountains) used in this analysis. Observed refugia are defined as 1km2 grid cells that include five or more species from this list (PDF 21 kb)

Online Resource 2

Table showing variable coefficients for cells in the Scandes Mountains used to infer refugia, refugia cells, non-refugial cells, and all cells. Abbreviations for the variables are the same as in Table 1 (PDF 284 kb)

Online Resource 3

Maps showing the spatial patterns exhibited by the individual climatic, connectivity, topographic, and geologic explanatory variables used to model refugia distribution and refugial species richness. Abbreviations are the same as in Table 1 (PDF 153 kb)

Online Resource 4

Variable correlation matrix showing corresponding Spearman’s rank correlation p-values between the predictor variables used in the analysis (PDF 183 kb)

Online Resource 5

Predictive ability is further statistically significantly improved by integrating all the variables to make the full model (Fig. 3.) which correctly predicts 90% of the observed refugia in the area (PDF 172 kb)

Online Resource 6

Partial dependence plots of the response variables on all predictors in the full models (v) for refugia distribution (PDF 56 kb)

Online Resource 7

Partial dependence plots of the response variables on all predictors in the full models (v) for refugial species richness (PDF 95 kb)

Online Resource 8

Predicted refugia species richness across the whole study area using all models that showed improvement according to Spearman’s rank correlation: (a) climatic; (b) climatic and topographic; (c) climatic and geologic; (d) climatic and connectivity; and (e) climatic, topographic, geologic, and connectivity variables. Darker green shades show cells where model predictions indicate higher refugia richness; lighter shades specify cells where the model predicts low species richness. Black marks indicate the known refugia. The subset maps on the right show the refugia cluster and predictions in more detail (PDF 125 kb)

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Niskanen, A., Luoto, M., Väre, H. et al. Models of Arctic-alpine refugia highlight importance of climate and local topography. Polar Biol 40, 489–502 (2017). https://doi.org/10.1007/s00300-016-1973-3

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Keywords

  • Generalized boosted model
  • GBM
  • Spatial modelling
  • Species distribution models
  • Refugium
  • High-latitude environments